3.1. Linearisation and global stability analysis

We start with the compressible Navier–Stokes equations written as

(3.1) \begin{equation} \frac {\partial \boldsymbol{q}}{\partial t}= \mathcal{F} (\boldsymbol{q}), \end{equation}

where $\boldsymbol{q}$ is a state vector of flow variables $[\rho ,\rho u_{x},\rho u_{y},\rho u_{z},\rho E]^{\rm T}$ and $\mathcal{F}$ is the nonlinear Navier–Stokes operator. We linearise about the mean flow rather than a steady fixed point due to the superior ability of the linear operator obtained from the mean flow to model the vortex-shedding frequency and corresponding flow structures observed in data (Barkley Reference Barkley2006; Colonius & Towne Reference Colonius and Towne2025). The equations are linearised using a Reynolds decomposition, giving

(3.2) \begin{equation} \frac {\partial \boldsymbol{q}'}{\partial t}-\unicode{x1D63C}\boldsymbol{q}'=\unicode{x1D63D}\boldsymbol{f}(\boldsymbol{\bar {q}},\boldsymbol{q}'), \end{equation}

where $\bar {\boldsymbol{q}}$ and $\boldsymbol{q}'$ represent the mean and perturbation state vectors, respectively, $\unicode{x1D63C} = {\partial \mathcal{F} (\boldsymbol{\bar {q}})}/{\partial \boldsymbol{q}}$ is the linearised Navier–Stokes operator, $\boldsymbol{f}$ comprises the remaining nonlinear terms and any exogenous forcing, and $\unicode{x1D63D}$ is an input matrix that can be used to restrict the form of $\boldsymbol{f}$ . For convenience, we omit $(\cdot )^{\prime }$ for perturbation from this point on. Our approach for constructing $\unicode{x1D63C}$ is detailed in $\S$ 6.

Figure 4.

Eigenspectrum: (a) eigenspectrum, the dotted circle shows the dominant wake eigenmode at the vortex-shedding frequency $St_{\alpha }\approx 0.17$ ; (b) the corresponding streamwise velocity eigenmode; and (c) cross-streamwise velocity eigenmode.

Resolvent-based estimation and control are nominally applicable and robust only for globally stable systems (Schmid & Sipp Reference Schmid and Sipp2016; Martini et al. Reference Martini, Cavalieri, Jordan, Towne and Lesshafft2020, Reference Martini, Jung, Cavalieri, Jordan and Towne2022). Thus, we first conduct a global stability analysis to ensure that our linear operator is stable. Figure 4(a) shows the spectrum of the linearised Navier–Stokes operator $\unicode{x1D63C}$ , represented in terms of the complex frequency $\omega = i \lambda$ , where $\lambda$ is the eigenvalue of $\unicode{x1D63C}$ . The imaginary part $\omega _i$ is negative for all eigenvalues, indicating that the flow around the airfoil is globally stable. The least-damped eigenvalue appears at the frequency $St_{\alpha } \approx 0.169$ , which matches the vortex-shedding frequency observed in the PSD in figure 3. The corresponding eigenmode, shown in figures 4(b) and 4(c), exhibits the expected characteristics of vortex shedding (Noack et al. Reference Noack, Afanasiew, Morzynski, Tadmor and Thiele2003).

3.2. Resolvent analysis

Resolvent analysis is central to our estimation and control methods. Therefore, we compute the leading resolvent modes for the airfoil flow as a preliminary step to validate our implementation and to gain insights into the flow physics and the appropriate sensor and actuator placements. After transforming the linear system (3.2) into the frequency domain and solving for the state, we obtain

(3.3) \begin{equation} \boldsymbol{\hat{q}} = \unicode{x1D64D} \unicode{x1D63D} \boldsymbol{\hat{f}}, \end{equation}

where the resolvent operator is defined as $\unicode{x1D64D} = (-i \omega \unicode{x1D644} - \unicode{x1D63C})^{-1}$ . The notation ( $\hat{\cdot }$ ) indicates a quantity in the frequency domain throughout this paper.

Resolvent analysis seeks input and output modes that maximise the resolvent gain

(3.4) \begin{equation} \sigma ^{2} = \frac {\| \hat{\boldsymbol{y}} \|^2}{\| \hat{\boldsymbol{f}} \|^2}, \end{equation}

where $\hat{\boldsymbol{y}} = \unicode{x1D63E}\hat{\boldsymbol{q}}$ is an output of interest extracted from the state by the output matrix $\unicode{x1D63E}$ . The norm is induced by the inner product $\langle \boldsymbol{q}_{1},\boldsymbol{q}_{2}\rangle = \boldsymbol{q}_{1}^{*} \unicode{x1D652} \boldsymbol{q}_{2}$ , where $\unicode{x1D652}$ is a weight matrix used to set a desired norm and $(\cdot )^{*}$ indicates the conjugate transpose. We use the Chu compressible energy norm (Chu Reference Chu1965), and set $\unicode{x1D63D}$ and $\unicode{x1D63E}$ to the identity matrix for the preliminary resolvent analysis in this section (non-identity values will be used later for estimation and control).

After defining the weighted resolvent operator

(3.5) \begin{equation} {\skew2\tilde {\unicode{x1D64D}}}= \unicode{x1D652}^{\,\frac {1}{2}} \unicode{x1D63E}\unicode{x1D64D} \unicode{x1D63D} \unicode{x1D652}^{\,-\frac {1}{2}}, \end{equation}

the resolvent gains and modes are obtained from the SVD

(3.6) \begin{equation} {\skew2\tilde {\unicode{x1D64D}}}=\tilde {\boldsymbol{U}} \Sigma \tilde {\boldsymbol{V}}^{*}. \end{equation}

The resolvent gains are contained within the diagonal matrix $\Sigma = \rm diag[\sigma _{1},\sigma _{2},\ldots,\sigma _{\textit{n}}]$ . The forcing and response modes that maximise (3.4) are recovered as $\boldsymbol{V}=\unicode{x1D652}^{\,-{1}/{2}}\tilde {\boldsymbol{V}}$ and $\boldsymbol{U}=\unicode{x1D652}^{\,-{1}/{2}}\tilde {\boldsymbol{U}}$ , respectively (Towne et al. Reference Towne, Schmidt and Colonius2018). We compute the resolvent modes by transforming the SVD into an equivalent eigenvalue problem, which is solved using an Arnoldi iteration in which the action of $\tilde {\unicode{x1D64D}}$ is obtained by computing its LU decomposition (Jeun, Nichols & Jovanović Reference Jeun, Nichols and Jovanović2016; Schmidt et al. Reference Schmidt, Towne, Rigas, Colonius and Brès2018).

In figure 5, the peak of the leading resolvent gain is observed at the vortex-shedding frequency $St_{\alpha } \approx 0.169$ . The streamwise and transverse velocity components of the optimal forcing and response modes are shown in figure 5(b–e). The optimal forcing mode is primarily located upstream and above the airfoil, while the optimal response mode is clearly observed downstream in the wake, as expected for a convective flow. The optimal response mode is similar to the dominant eigenmode.

Figure 5.

Resolvent gains, optimal forcing and response modes: (a) leading and second optimal gains; (b) optimal forcing mode of $u_{x}$ ; (c) optimal forcing mode of $ u_{y}$ ; (d) optimal response mode of $u_{x}$ ; and (e) optimal response mode of $u_{y}$ .

Figure 6.

Block diagram representation of the linear system.

4.1. System set-up

As a generalisation of (3.2), we consider the linear time-invariant system

(4.1a) \begin{align} \frac {{\rm d}\boldsymbol{q}}{{\rm d}t}(t)&= \unicode{x1D63C}\boldsymbol{q}(t)+\unicode{x1D63D}_{f}\boldsymbol{f}(t)+\unicode{x1D63D}_{a}\boldsymbol{a}(t), \end{align}

(4.1b) \begin{align} \boldsymbol{y}(t)&= \unicode{x1D63E}_{y}\boldsymbol{q}(t)+\boldsymbol{n}(t), \end{align}

(4.1c) \begin{align} \boldsymbol{z}(t)&= \unicode{x1D63E}_{z}\boldsymbol{q}(t). \end{align}

The system matrix $\unicode{x1D63C} \in \mathbb{C}^{n \times n}$ is the linearised compressible Navier–Stokes operator, $\boldsymbol{q} \in \mathbb{C}^{n}$ is the full state of flow and $n$ is the total size of the state. The forcing $\boldsymbol{f} \in \mathbb{C}^{n_{f}}$ represents the nonlinear terms from the Navier–Stokes equations and any exogenous forcing. The forcing matrix $\unicode{x1D63D}_{f} \in \mathbb{C}^{n \times n_f}$ restricts the form of the forcing $\boldsymbol{f}$ , e.g. localises it to a certain spatial region. The actuation signal $\boldsymbol{a} \in \mathbb{C}^{n_{a}}$ , which will be the output of the controller, is mapped onto the system by the actuation matrix $\unicode{x1D63D}_{a} \in \mathbb{C}^{n \times n_a}$ , where $n_a$ is the total number of actuators. The sensor measurement $\boldsymbol{y} \in \mathbb{C}^{n_y}$ is extracted from the state by the measurement matrix $\unicode{x1D63E}_{y} \in \mathbb{C}^{n_y \times n}$ , which defines the sensor locations and types, and $n_y$ is the total number of sensors. The sensor measurements are corrupted by the sensor noise $\boldsymbol{n} \in \mathbb{C}^{n_{y}}$ . The target $\boldsymbol{z} \in \mathbb{C}^{n_z}$ , i.e. the quantity that we wish to estimate and control, is extracted from the state by the target matrix $\boldsymbol{{C}}_{z} \in \mathbb{C}^{n_z \times n}$ , where $n_z$ is the total number of targets. Analogous to other optimal estimation and control methods, the forcing and noise are described by correlations that are assumed to be known.

Following Martini et al. (Reference Martini, Jung, Cavalieri, Jordan and Towne2022), we split the system (4.1) into two parts: the so-called forcing system that is driven by the forcing $\boldsymbol{f}(t)$ and corrupted by the sensor noise $\boldsymbol{n}(t)$ ,

(4.2a) \begin{align} \frac {{\rm d}\boldsymbol{q}_{f}}{{\rm d}t}(t)&= \unicode{x1D63C}\boldsymbol{q}_{f}(t)+\unicode{x1D63D}_{f}\boldsymbol{f}(t), \end{align}

(4.2b) \begin{align} \boldsymbol{y}_{f}(t)&= \unicode{x1D63E}_{y}\boldsymbol{q}_{f}(t)+\boldsymbol{n}(t), \end{align}

(4.2c) \begin{align} \boldsymbol{z}_{f}(t)&= \unicode{x1D63E}_{z}\boldsymbol{q}_{f}(t), \end{align}

and the actuation system that is driven only by the actuation signal $\boldsymbol{a}(t)$ ,

(4.3a) \begin{align} \frac {{\rm d}\boldsymbol{q}_{a}}{{\rm d}t}(t)&=\unicode{x1D63C}\boldsymbol{q}_{a}(t)+\unicode{x1D63D}_{a}\boldsymbol{a}(t), \end{align}

(4.3b) \begin{align} \boldsymbol{y}_{a}(t)&=\unicode{x1D63E}_{y}\boldsymbol{q}_{a}(t), \end{align}

(4.3c) \begin{align} \boldsymbol{z}_{a}(t)&=\unicode{x1D63E}_{z}\boldsymbol{q}_{a}(t). \end{align}

The state, sensor measurements and targets for the full system are recovered as

(4.4) \begin{equation} \boldsymbol{q}=\boldsymbol{q}_{f}+\boldsymbol{q}_{a}, \qquad \boldsymbol{y}=\boldsymbol{y}_{f}+\boldsymbol{y}_{a} \quad \textrm {and} \quad \boldsymbol{z}=\boldsymbol{z}_{f}+\boldsymbol{z}_{a}. \end{equation}

By applying the Fourier transform to (4.2) and (4.3), and using the resolvent operator defined in (3.3), we obtain the frequency-domain representations of sensor measurements and targets of the forcing and actuation systems,

(4.5a) \begin{align} \boldsymbol{\hat{y}}_{f}&= \unicode{x1D64D}_{yf}\boldsymbol{\hat{f}}+\boldsymbol{\hat{n}}, \end{align}

(4.5b) \begin{align} \boldsymbol{\hat{z}}_{f}&= \unicode{x1D64D}_{zf}\boldsymbol{\hat{f}}, \end{align}

(4.5c) \begin{align} \boldsymbol{\hat{y}}_{a}&= \unicode{x1D64D}_{ya}\boldsymbol{\hat{a}}, \end{align}

(4.5d) \begin{align} \boldsymbol{\hat{z}}_{a}&= \unicode{x1D64D}_{za}\boldsymbol{\hat{a}}. \end{align}

Here, $\unicode{x1D64D}_{yf}=\unicode{x1D63E}_{y}\unicode{x1D64D}\unicode{x1D63D}_{f}$ , $\unicode{x1D64D}_{zf}=\unicode{x1D63E}_{z}\unicode{x1D64D}\unicode{x1D63D}_{f}$ , $\unicode{x1D64D}_{ya}=\unicode{x1D63E}_{y}\unicode{x1D64D}\unicode{x1D63D}_{a}$ and $\unicode{x1D64D}_{za}=\unicode{x1D63E}_{z}\unicode{x1D64D}\unicode{x1D63D}_{a}$ are modified resolvent operators (sometimes called input–output operators) that will appear in the resolvent-based estimation and control kernels.

4.2. Resolvent-based estimation

The resolvent-based estimates of the target $\tilde {\boldsymbol{z}}$ are obtained via a convolution between the sensor measurements and a kernel. Here, we define three distinct resolvent-based estimation kernels: non-causal, truncated non-causal and causal kernels.

First, we define a non-causal estimator

(4.6) \begin{equation} \tilde {\boldsymbol{z}}_{nc}(t)=\int _{-\infty }^{\infty } \unicode{x1D64F}_{nc}(t-\tau ) \ \boldsymbol{y}(\tau ){\rm d}\tau , \end{equation}

where $\unicode{x1D64F}_{nc} \in \mathbb{C}^{n_z \times n_y}$ is a non-causal estimation kernel. The optimal estimation kernel is obtained by minimising a cost function defined as the time-integrated expected value of the estimation error,

(4.7) \begin{equation} {J}_{nc}=\int _{-\infty }^{\infty } \mathbb{E} \big\{ \boldsymbol{e}(t)^{\dagger } \boldsymbol{e}(t)\big\}{\rm d}t, \end{equation}

where the estimation error $ \boldsymbol{e}(t) = \tilde {\boldsymbol{z}}(t) - \boldsymbol{z}(t)$ is the difference between the estimated and true target, $(\cdot )^{\dagger }$ denotes the adjoint operator using a suitable inner product, and $\mathbb{E} \{ \cdot \}$ is the expectation operator. The cost function $ J_{nc}$ is minimised by setting its derivative with respect to $ \unicode{x1D64F}_{nc}$ to zero, yielding the optimal non-causal estimation kernel (Martini et al. Reference Martini, Cavalieri, Jordan, Towne and Lesshafft2020)

(4.8) \begin{equation} {\skew5\hat{\unicode{x1D64F}}}_{nc}(\omega )= \unicode{x1D64D}_{zf} {\skew5\hat{\unicode{x1D641}}} \unicode{x1D64D}_{yf}^{\dagger } (\unicode{x1D64D}_{yf} {\skew5\hat{\unicode{x1D641}}} \unicode{x1D64D}_{yf}^{\dagger } + {\skew5\hat{\unicode{x1D649}}})^{-1}, \end{equation}

where ${\skew5\hat{\unicode{x1D641}}} = \mathbb{E} \{ \hat{\boldsymbol{f}} \hat{\boldsymbol{f}}^{\dagger } \}$ and ${\skew5\hat{\unicode{x1D649}}} = \mathbb{E} \{ \hat{\boldsymbol{n}} \hat{\boldsymbol{n}}^{\dagger } \}$ are the cross-spectral densities (CSDs) of the forcing and sensor noise, respectively. The time-domain estimation kernel $\unicode{x1D64F}_{nc}$ is obtained by inverse Fourier transforming the frequency domain kernel ${\skew2\hat{\unicode{x1D64F}}}_{nc}$ . In general, this kernel will be non-causal, i.e. $\unicode{x1D64F}_{nc}(\tau )$ will not be strictly zero for $\tau \lt 0$ , therefore requiring future sensor data, $\boldsymbol{y}(\tau \gt 0)$ , which is unavailable for real-time applications, to evaluate (4.6).

Second, we define a truncated non-causal estimation kernel as a simple baseline approach to address the aforementioned lack of causality. This is done by simply truncating the integral in (4.6),

(4.9) \begin{equation} \tilde {\boldsymbol{z}}_{tnc}(t)=\int _{-\infty }^{0} \unicode{x1D64F}_{tnc}(t-\tau )\boldsymbol{y}(\tau ){\rm d}\tau , \end{equation}

which is equivalent to truncating the non-causal part of the kernel,

(4.10) \begin{eqnarray} \unicode{x1D64F}_{tnc}(\tau )= \begin{cases} \unicode{x1D64F}_{nc}(\tau ),& \tau \geqslant 0,\\ 0, & \tau \lt 0. \end{cases} \end{eqnarray}

The downside of this approach is that the ex post facto truncation of the optimal non-causal kernel ruins its optimality.

Third, we define an optimal causal resolvent-based estimation kernel by enforcing causality using the Wiener–Hopf formalism (Martinelli Reference Martinelli2009; Martini et al. Reference Martini, Jung, Cavalieri, Jordan and Towne2022). The causal estimator,

(4.11) \begin{equation} \tilde {\boldsymbol{z}}_{c}(t)=\int _{-\infty }^{0} \unicode{x1D64F}_{c}(t-\tau )\boldsymbol{y}(\tau ){\rm d}\tau , \end{equation}

is defined in terms of the causal estimation kernel $\unicode{x1D64F}_{c} \in \mathbb{C}^{n_{z} \times n_{y}}$ . To find the optimal kernel under the constraint of causality, we modify the cost function (4.7) to read

(4.12) \begin{equation} {J}_{c}=\int _{-\infty }^{\infty } \mathbb{E}\big\{ e(t)^{\dagger } e(t)\big\} + \big(\boldsymbol{\mathsf{\Lambda }}_{-}(t) \unicode{x1D64F}_{c} (t) + \boldsymbol{\mathsf{\Lambda }}_{-}^{\dagger }(t) \unicode{x1D64F}_{c}^{\dagger } (t)\big){\rm d}t, \end{equation}

where $\boldsymbol{\mathsf{\Lambda }}$ is a Lagrange multiplier that is used to force the causal kernel to be zero for the non-causal part ( $\tau \lt 0$ ). The ( $+$ ) and ( $-$ ) subscripts indicate that the non-causal ( $\tau \lt 0$ ) and causal ( $\tau \gt 0$ ) parts, respectively, of a matrix or function are set to zero using a Wiener–Hopf factorisation. An introduction to the Wiener–Hopf method is described in Appendix B. Similar to the derivation of (4.8), we minimise the causal cost function (4.12) by setting its derivative with respect to $\unicode{x1D64F}_{c}$ to zero. In doing so, we encounter the Wiener–Hopf problem (B4) with ${\skew5\hat{\unicode{x1D642}}}=\unicode{x1D64D}_{zf}{\skew5\hat{\unicode{x1D641}}}\unicode{x1D64D}_{yf}^{\dagger }$ and ${\skew5\hat{\unicode{x1D643}}}=\unicode{x1D64D}_{yf}{\skew5\hat{\unicode{x1D641}}}\unicode{x1D64D}_{yf}^{\dagger } + {\skew5\hat{\unicode{x1D649}}}$ . Using the solution of this Wiener–Hopf problem given by (B8), the causal estimation kernel (Martini et al. Reference Martini, Jung, Cavalieri, Jordan and Towne2022) is

(4.13) \begin{eqnarray} {\skew2\hat{\unicode{x1D64F}}}_{c}(\omega ) &=& \left [ \unicode{x1D64D}_{zf} {\skew5\hat{\unicode{x1D641}}} \unicode{x1D64D}_{yf}^{\dagger } \left(\unicode{x1D64D}_{yf} {\skew5\hat{\unicode{x1D641}}} \unicode{x1D64D}_{yf}^{\dagger } + {\skew5\hat{\unicode{x1D649}}}\right)^{-1}_{-} \right ]_{+} \big(\unicode{x1D64D}_{yf} {\skew5\hat{\unicode{x1D641}}} \unicode{x1D64D}_{yf}^{\dagger } + {\skew5\hat{\unicode{x1D649}}}\kern2.2pt \big)^{-1}_{+}. \end{eqnarray}

4.3. Resolvent-based control

Analogous to the estimation problem, the actuation signal $\boldsymbol{a}(t)$ that will be used to control the flow is obtained via a convolution between the sensor measurements and a control kernel (also referred to as control law in some studies). Again, we consider non-causal, truncated non-causal and causal variations of the resolvent-based control kernels (Martini et al. Reference Martini, Jung, Cavalieri, Jordan and Towne2022).

The non-causal controller takes the form

(4.14) \begin{equation} \boldsymbol{a}_{nc}(t)=\int _{-\infty }^{\infty } \boldsymbol{\mathsf{\Gamma }}_{nc}(t-\tau )\boldsymbol{y}_{f}(\tau ){\rm d}\tau , \end{equation}

where $\boldsymbol{\mathsf{\Gamma }}_{nc} \in \mathbb{C}^{n_a \times n_y}$ is the non-causal control kernel. The optimal kernel is obtained by minimising the cost function

(4.15) \begin{equation} {J}_{nc}=\int _{-\infty }^{\infty } \mathbb{E}\big\{\boldsymbol{z}(t)^{\dagger } \boldsymbol{z}(t) + \boldsymbol{a}(t)^{\dagger } \unicode{x1D64B} \boldsymbol{a}(t)\big\}{\rm d}t, \end{equation}

where $\unicode{x1D64B}$ is a weight matrix that penalises the actuation effort. Minimising this cost function yields the optimal non-causal control kernel

(4.16) \begin{eqnarray} {\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}}_{nc}(\omega ) &=& \big(\unicode{x1D64D}_{za}^{\dagger }\unicode{x1D64D}_{za} + {\hat{\unicode{x1D64B}}}\big)^{-1} \big(\!-\unicode{x1D64D}_{za}^{\dagger }\big) \unicode{x1D64D}_{zf} {\skew5\hat{\unicode{x1D641}}} \unicode{x1D64D}_{yf}^{\dagger } \big(\unicode{x1D64D}_{yf} {\skew5\hat{\unicode{x1D641}}} \unicode{x1D64D}_{yf}^{\dagger } + {\skew5\hat{\unicode{x1D649}}}\big)^{-1}. \end{eqnarray}

The truncated non-causal control kernel is defined as

(4.17) \begin{eqnarray} \boldsymbol{\mathsf{\Gamma }}_{tnc}(\tau )= \bigg\{\kern-5pt\begin{array}{ll}\boldsymbol{\mathsf{\Gamma }}_{nc}(\tau ),& \tau \geqslant 0,\\ 0, & \tau \lt 0, \end{array}\end{eqnarray}

from which the actuation signal is computed as

(4.18) \begin{equation} \boldsymbol{a}_{tnc}(t)=\int _{-\infty }^{0} \boldsymbol{\mathsf{\Gamma }}_{tnc}(t-\tau )\boldsymbol{y}_{f}(\tau ){\rm d}\tau . \end{equation}

As before, truncating the optimal non-causal kernel yields a non-optimal causal kernel.

Finally, the optimal causal controller is

(4.19) \begin{equation} \boldsymbol{a}_{c}(t)=\int _{-\infty }^{0} \boldsymbol{\mathsf{\Gamma }}_{c}(t-\tau )\boldsymbol{y}_{f}(\tau ){\rm d}\tau , \end{equation}

and the optimal causal control kernel is obtained by using Lagrange multipliers to enforce causality, expressed by the cost function

(4.20) \begin{equation} {J}_{c}=\int _{-\infty }^{\infty } \mathbb{E}\big\{\boldsymbol{z}(t)^{\dagger } \boldsymbol{z}(t) + \boldsymbol{a}(t)^{\dagger } \unicode{x1D64B} \boldsymbol{a}(t) + (\boldsymbol{\mathsf{\Lambda }}_{-}(t) \boldsymbol{\mathsf{\Gamma }}_{c} (t) + \boldsymbol{\mathsf{\Lambda }}_{-}^{\dagger }(t) \boldsymbol{\mathsf{\Gamma }}_{c}^{\dagger } (t)) \big\}{\rm d}t. \end{equation}

Minimising (4.20) leads to the Wiener–Hopf problem (B5) with ${\skew5\hat{\unicode{x1D646}}}=\unicode{x1D64D}_{za}^{\dagger }\unicode{x1D64D}_{za} + {\hat{\unicode{x1D64B}}}$ and ${\hat{\unicode{x1D647}}}=-\unicode{x1D64D}_{za}^{\dagger }$ . Using the solution of this Wiener–Hopf problem given in (B9), the optimal causal resolvent-based control kernel is

(4.21) \begin{eqnarray} {\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}}_{c}(\omega )&=& \big(\unicode{x1D64D}_{za}^{\dagger }\unicode{x1D64D}_{za}+{\hat{\unicode{x1D64B}}}\big)_{+}^{-1}\big[\big(\unicode{x1D64D}_{za}^{\dagger }\unicode{x1D64D}_{za}+{\hat{\unicode{x1D64B}}}\big)_{-}^{-1}\nonumber \big(\kern-2pt-\kern-1pt\unicode{x1D64D}_{za}^{\dagger }\big)\unicode{x1D64D}_{zf}{\skew5\hat{\unicode{x1D641}}}\unicode{x1D64D}_{yf}^{\dagger }\\ &&\big(\unicode{x1D64D}_{yf}{\skew5\hat{\unicode{x1D641}}}\unicode{x1D64D}_{yf}^{\dagger }+ {\skew5\hat{\unicode{x1D649}}}\big)_{-}^{-1}\big]_{+}\big(\unicode{x1D64D}_{yf}{\skew5\hat{\unicode{x1D641}}}\unicode{x1D64D}_{yf}^{\dagger }+ {\skew5\hat{\unicode{x1D649}}}\big)_{+}^{-1}. \end{eqnarray}

In (4.14), (4.18) and (4.19), the actuation signal is obtained as a convolution between the control kernel and the sensor measurement $\boldsymbol{y}_{f}$ for the forcing system (4.2), i.e. the control kernels were derived using a measurement excluding the response of the system (4.3) to actuation. In practice, only the complete measurement $\boldsymbol{y}=\boldsymbol{y}_f + \boldsymbol{y}_a$ is available. Considering the full (combined) linear system (4.1), the final closed-loop controller takes the form

(4.22) \begin{equation} \boldsymbol{a}(t)=\int _{-\infty }^{0} \boldsymbol{\mathsf{\Gamma }}_{cl}(t-\tau )\boldsymbol{y}(\tau ){\rm d}\tau , \end{equation}

where the final closed-loop kernel is

(4.23) \begin{eqnarray} {\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}}_{cl} &=& \big(\unicode{x1D644} + {\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}} \unicode{x1D64D}_{ya}\big)^{-1} {\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}}, \end{eqnarray}

with $\hat{\boldsymbol{\mathsf{\Gamma }}}$ replaced by ${\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}}_{tnc}$ or ${\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}}_{c}$ for truncated non-causal and optimal causal resolvent-based control, respectively.

5.1. Operator-based approach

The resolvent operator $\unicode{x1D64D}$ is defined in terms of an inverse, the cost of which scales poorly with the problem dimension $n$ and becomes computationally expensive for large systems. To circumvent this, we employ a time-stepping approach that avoids the inverse operation and instead constructs the necessary modified resolvent operators that appear in the estimation and control kernels by solving linear equations in the time domain (Martini et al. Reference Martini, Cavalieri, Jordan, Towne and Lesshafft2020, Reference Martini, Jung, Cavalieri, Jordan and Towne2022; Farghadan, Martini & Towne Reference Farghadan, Martini and Towne2023). For both cases, the cost of this approach scales linearly with the problem dimension, avoiding the need to reduce the system via a priori model reduction and the associated loss of accuracy. Individual modified resolvent operators such as $\unicode{x1D64D}_{za}$ and $\unicode{x1D64D}_{ya}$ can be obtained using a single-stage run of the direct linear equations. Products of modified resolvent operators (and the forcing CSD) such as $\unicode{x1D64D}_{zf}{\skew5\hat{\unicode{x1D641}}}\unicode{x1D64D}_{yf}^{\dagger }$ and $\unicode{x1D64D}_{yf} {\skew5\hat{\unicode{x1D641}}}\unicode{x1D64D}_{yf}^{\dagger }$ can be obtained with greater efficiently using two-stage runs of both the adjoint and direct linear equations.

5.1.1. Single-stage run

The operators $\unicode{x1D64D}_{za}$ and $\unicode{x1D64D}_{ya}$ appearing in (4.21) and (4.23), respectively, can be constructed by computing a series of impulse responses of the actuation system (4.3),

(5.1a) \begin{align} \frac {{\rm d}\boldsymbol{q}_{a,k}}{{\rm d}t}(t) &= \unicode{x1D63C}\boldsymbol{q}_{a,k}(t)+\unicode{x1D63D}_{a,k}\delta (t), \end{align}

(5.1b) \begin{align} \boldsymbol{y}_{a,k}(t)&= \unicode{x1D63E}_{y}\boldsymbol{q}_{a,k}(t), \end{align}

(5.1c) \begin{align} \boldsymbol{z}_{a,k}(t)&= \unicode{x1D63E}_{z}\boldsymbol{q}_{a,k}(t). \end{align}

Here, $\boldsymbol{y}_{a,k} \in \mathbb{C}^{n_{y}}$ and $\boldsymbol{z}_{a,k} \in \mathbb{C}^{n_{z}}$ are the sensor and target measurement of the direct system forced by an impulse $\delta (t)$ located at the $k=1, 2, 3,\dots$ actuator, as encoded by the $k=1, 2, 3,\dots$ column of the actuation matrix, $\unicode{x1D63D}_{a,k}$ . By collecting these data for each actuator $k=1,\dots ,n_a$ and taking a Fourier transform, we obtain

(5.2a) \begin{align} {\; \;\hat{\kern-4pt\unicode{x1D654}}}_{a} &= \begin{bmatrix} \hat{\boldsymbol{y}}_{a,1}&\hat{\boldsymbol{y}}_{a,2} & \dots & \hat{\boldsymbol{y}}_{a,n_{a}} \end{bmatrix} = \unicode{x1D64D}_{ya}, \end{align}

(5.2b) \begin{align} {\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{a} &= \begin{bmatrix} \hat{\boldsymbol{z}}_{a,1}&\hat{\boldsymbol{z}}_{a,2}& \dots & \hat{\boldsymbol{z}}_{a,n_{a}} \end{bmatrix} = \unicode{x1D64D}_{za}, \end{align}

with ${\; \hat{\kern-4pt\unicode{x1D654}}}_{a} \in \mathbb{C}^{n_{y} \times n_{a}}$ and ${ \; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{a} \in \mathbb{C}^{n_{z} \times n_{a}}$ . That is, the Fourier transform of each measurement $\boldsymbol{y}_{a,k}$ and $\boldsymbol{z}_{a,k}$ yields a column of the modified resolvent operators $\unicode{x1D64D}_{ya}$ and $\unicode{x1D64D}_{za}$ , respectively.

5.1.2. Two-stage run

While single-stage runs could be used to construct all of the modified resolvent operators in the estimation and control kernels, certain products thereof can be constructed more efficiently using pairs of adjoint and direct runs. The procedure begins with solving the adjoint system

(5.3a) \begin{align} -\frac {{\rm d}\boldsymbol{q}_{f,i}}{{\rm d}t}(t) &= \unicode{x1D63C}^{\dagger }\boldsymbol{q}_{f,i}(t)+\unicode{x1D63E}_{y,i}^{\dagger }\delta (t), \end{align}

(5.3b) \begin{align} \boldsymbol{s}_{i}(t)&= \unicode{x1D63D}_{f}^{\dagger }\boldsymbol{q}_{f,i}(t), \end{align}

where $\unicode{x1D63C}^{\dagger }$ is the adjoint linearised Navier–Stokes operator and the subscript $i$ indicates the sensor defined by the $i$ th row of the sensor measurement matrix $\unicode{x1D63E}_y$ . The output $\boldsymbol{s}_{i}$ of the adjoint run is used as a forcing in a corresponding direct run of the forcing system (4.2),

(5.4a) \begin{align} \frac {{\rm d}\boldsymbol{q}_{f,i}}{{\rm d}t}(t) &= \unicode{x1D63C}\boldsymbol{q}_{f,i}(t)+\unicode{x1D63D}_{f}\boldsymbol{s}_{i}(t), \end{align}

(5.4b) \begin{align} \boldsymbol{y}_{f,i}(t) &= \unicode{x1D63E}_{y}\boldsymbol{q}_{f,i}(t) +\boldsymbol{n}_{i}(t), \end{align}

(5.4c) \begin{align} \boldsymbol{z}_{f,i}(t) &= \unicode{x1D63E}_{z}\boldsymbol{q}_{f,i}(t). \end{align}

As in the single-stage run, by collecting each of the final sensor and target measurements, and taking a Fourier transform, we obtain

(5.5a) \begin{align} {\; \;\hat{\kern-4pt\unicode{x1D654}}}_{f} &= \begin{bmatrix} \hat{\boldsymbol{y}}_{1}&\hat{\boldsymbol{y}}_{2} & \dots & \hat{\boldsymbol{y}}_{n_{y}} \end{bmatrix} = \unicode{x1D64D}_{yf}\unicode{x1D64D}_{yf}^{\dagger }, \end{align}

(5.5b) \begin{align} {\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{f} &= \begin{bmatrix} \hat{\boldsymbol{z}}_{1}&\,\hat{\boldsymbol{z}}_{2}& \dots &\, \hat{\boldsymbol{z}}_{n_{y}} \end{bmatrix} = \unicode{x1D64D}_{zf} \unicode{x1D64D}_{yf}^{\dagger }, \end{align}

with ${\; \hat{\kern-4pt\unicode{x1D654}}}_{f} \in \mathbb{C}^{n_{y} \times n_{y}}$ and ${\; \hat{\kern-3.5pt\unicode{x1D655}}}_{f} \in \mathbb{C}^{n_{z} \times n_{y}}$ . To account for the nonlinearity of the flow, the coloured forcing statistics, $\hat{\unicode{x1D641}}$ , can be incorporated into (5.5) during adjoint and direct simulations, resulting in $\unicode{x1D64D}_{yf} {\skew5\hat{\unicode{x1D641}}} \unicode{x1D64D}_{yf}^{\dagger }$ and $\unicode{x1D64D}_{zf} {\skew5\hat{\unicode{x1D641}}} \unicode{x1D64D}_{yf}^{\dagger }$ (Jung Reference Jung2024).

Finally, (5.2) and (5.5) are used to write the estimation kernels in (4.8) and (4.13), and control kernels in (4.16) and (4.21) in terms of ${\; \hat{\kern-4pt\unicode{x1D654}}}_{a}$ , ${\; \hat{\kern-3.5pt\unicode{x1D655}}}_{a}$ , ${\; \hat{\kern-4pt\unicode{x1D654}}}_{f}$ and ${\; \hat{\kern-3.5pt\unicode{x1D655}}}_{f}$ . The final operator-based estimation and control kernels are

(5.6a) \begin{align} {\skew2\hat{\unicode{x1D64F}}}_{nc,O} &= {\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{f} \big( {\; \;\hat{\kern-4pt\unicode{x1D654}}}_{f} + {\skew5\hat{\unicode{x1D649}}}\big)^{-1}, \end{align}

(5.6b) \begin{align} {\skew2\hat{\unicode{x1D64F}}}_{c,O} &= \big[{\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{f} \big( {\; \;\hat{\kern-4pt\unicode{x1D654}}}_{f} + {\skew5\hat{\unicode{x1D649}}} \big)^{-1}_{-}\big]_{+} \big({\; \;\hat{\kern-4pt\unicode{x1D654}}}_{f}+ {\skew5\hat{\unicode{x1D649}}}\big)_{+}^{-1}, \end{align}

and

(5.7a) \begin{align} {\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}}_{nc,O} &= \big(\hat{\boldsymbol{Z}}_{a}^{\dagger } \hat{\boldsymbol{Z}}_{a} +\hat{\boldsymbol{P}}\big)^{-1} \big({-}\hat{\boldsymbol{Z}}_{a}^{\dagger }\big) \hat{\boldsymbol{Z}}_{f} \big(\hat{\boldsymbol{Y}}_{f}+ {\skew5\hat{\unicode{x1D649}}}\big)^{-1}, \end{align}

(5.7b) \begin{align} {\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}}_{c,O} &= \big({\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{a}^{\dagger } {\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{a}+{\hat{\unicode{x1D64B}}}\big)_{+}^{-1}\big[\big({\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{a}^{\dagger } {\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{a}+{\hat{\unicode{x1D64B}}}\big)_{-}^{-1} \big({-}{\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{a}^{\dagger }\big){\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{f}\big({\; \;\hat{\kern-4pt\unicode{x1D654}}}_{f}+ {\skew5\hat{\unicode{x1D649}}}\big)_{-}^{-1}\big]_{+}\big({\; \;\hat{\kern-4pt\unicode{x1D654}}}_{f}+ {\skew5\hat{\unicode{x1D649}}}\big)_{+}^{-1}. \\[6pt] \nonumber \end{align}

5.2. Data-driven approach

When the linearised Navier–Stokes operator is unavailable, a data-driven approach (Martini et al. Reference Martini, Jung, Cavalieri, Jordan and Towne2022) can be employed to build the required modified resolvent operators using CSDs computed from data (Towne et al. Reference Towne, Schmidt and Colonius2018, Reference Towne, Lozano-Durán and Yang2020). This approach extends the applicability of resolvent-based estimation and control to experimental settings and circumvents the need for adjoint solvers. Additionally, when the CSD is computed from the dataset of a nonlinear system, the resulting kernels automatically include the forcing CSD $\hat{\unicode{x1D641}}$ , which statistically accounts for the nonlinearity of the flow, thereby improving the estimation and control performance for the nonlinear system.

The nonlinear terms in the Navier–Stokes equations act as a forcing of the resolvent operator (McKeon & Sharma Reference McKeon and Sharma2010) and their influence is crucial in complex dynamic systems (Amaral et al. Reference Amaral, Cavalieri, Martini, Jordan and Towne2021). To explicitly address the nonlinear terms, we split the forcing vector $\boldsymbol{f}$ into two components: the external forcing $\boldsymbol{f}_{\textit{ext}}$ and the nonlinear terms $\boldsymbol{f}_{nl}$ . This distinction helps us better understand their impact when building the CSDs from the data. The overall forcing term in (4.1) can be split as

(5.8) \begin{equation} \unicode{x1D63D}_{f}\boldsymbol{f} \\ = \begin{bmatrix} \unicode{x1D63D}_{\textit{ext}} & \unicode{x1D63D}_{nl} \end{bmatrix} \begin{bmatrix} \boldsymbol{f}_{\textit{ext}} \\ \boldsymbol{f}_{nl} \end{bmatrix}, \end{equation}

where $\unicode{x1D63D}_{\textit{ext}} \in \mathbb{C}^{n \times n_{\textit{ext}}}$ and $\unicode{x1D63D}_{nl} \in \mathbb{C}^{n \times n}$ . Typically, the region subject to external forcing is smaller than the overall domain, such that $n_{\textit{ext}}\lt n$ . In a linear system, the nonlinear terms $\boldsymbol{f}_{nl}$ are not included, allowing us to analyse the linear dynamics of the flow, and to build linear estimators and controllers. However, our ultimate goal is to manipulate the unsteady fluctuations inherent in the actual flow, which necessitates considering nonlinearity. For the nonlinear system, we collect data from the systems without and with external forcing to better capture the behaviour of the systems influenced by external forcing. The forcing system (4.2) without and with the external forcing can be expressed as

(5.9) \begin{align} \frac {{\rm d}\boldsymbol{q}}{{\rm d}t}(t) & = \unicode{x1D63C}\boldsymbol{q}(t)+\unicode{x1D63D}_{nl}\boldsymbol{f}_{nl}(t),\end{align}

(5.10) \begin{align} \frac {{\rm d}\boldsymbol{q}_{e}}{{\rm d}t}(t)& = \unicode{x1D63C}\boldsymbol{q}_{e}(t)+\unicode{x1D63D}_{\textit{ext}}\boldsymbol{f}_{\textit{ext}}+\unicode{x1D63D}_{nl}\boldsymbol{f}_{nl,e}(t).\end{align}

The subscript $e$ indicates the flow quantity that contains the development of nonlinearity, which was impacted by the external forcing. The $\boldsymbol{f}_{nl,e}$ term is evolved by the external forcing in time and space, so in the nonlinear system, the nonlinear effect can not be neglected. Equation (5.9) is the DNS or LES system without any source term. We assume external and nonlinear forcings are uncorrelated. Then, we obtain

(5.11) \begin{align} \begin{bmatrix} \hat{\boldsymbol{y}}_{f,nl}\\ \hat{\boldsymbol{z}}_{f,nl} \end{bmatrix} & = \begin{bmatrix} \unicode{x1D64D}_{yf,nl} \\ \unicode{x1D64D}_{zf,nl} \end{bmatrix} \hat{\boldsymbol{f}}_{nl} + \begin{bmatrix} \hat{\boldsymbol{n}} \\ 0 \end{bmatrix},\end{align}

(5.12) \begin{align} \begin{bmatrix} \hat{\boldsymbol{y}}_{f,ext,nl}\\ \hat{\boldsymbol{z}}_{f,ext,nl} \end{bmatrix} & = \begin{bmatrix} \unicode{x1D64D}_{yf,ext} & \unicode{x1D64D}_{yf,nl} \\ \unicode{x1D64D}_{zf,ext} & \unicode{x1D64D}_{zf,nl} \end{bmatrix} \begin{bmatrix} \hat{\boldsymbol{f}}_{\textit{ext}} \\ \hat{\boldsymbol{f}}_{nl,e} \end{bmatrix} + \begin{bmatrix} \hat{\boldsymbol{n}} \\ 0 \end{bmatrix} . \end{align}

Computing the cross-spectral density of $ [ \hat{\boldsymbol{y}} \quad \hat{\boldsymbol{z}} ]^{T}$ from (5.11) and (5.12) gives

(5.13) \begin{align} \begin{bmatrix} \unicode{x1D64E}_{yy} \\ \unicode{x1D64E}_{zy} \end{bmatrix} \triangleq \begin{bmatrix} \unicode{x1D64E}_{yy,f,nl} \\ \unicode{x1D64E}_{zy,f,nl} \end{bmatrix} & = \begin{bmatrix} \unicode{x1D64D}_{yf,nl} {\skew5\hat{\unicode{x1D641}}}_{nl} \unicode{x1D64D}_{yf,nl}^{\dagger } + {\skew5\hat{\unicode{x1D649}}} \\ \unicode{x1D64D}_{zf,nl} {\skew5\hat{\unicode{x1D641}}}_{nl} \unicode{x1D64D}_{yf,nl}^{\dagger } \end{bmatrix},\end{align}

(5.14) \begin{align} \begin{bmatrix} \unicode{x1D64E}_{yy,e} \\ \unicode{x1D64E}_{zy,e} \end{bmatrix} \triangleq \begin{bmatrix} \unicode{x1D64E}_{yy,f,ext,nl} \\ \unicode{x1D64E}_{zy,f,ext,nl} \end{bmatrix} & = \begin{bmatrix} \unicode{x1D64D}_{yf,nl} {\skew5\hat{\unicode{x1D641}}}_{nl,r} \unicode{x1D64D}_{yf,nl}^{\dagger } + \unicode{x1D64D}_{yf,ext} {\skew5\hat{\unicode{x1D641}}}_{\textit{ext}}\unicode{x1D64D}_{yf,ext}^{\dagger } + {\skew5\hat{\unicode{x1D649}}} \\ \unicode{x1D64D}_{zf,nl} {\skew5\hat{\unicode{x1D641}}}_{nl,r} \unicode{x1D64D}_{yf,nl}^{\dagger } + \unicode{x1D64D}_{zf,ext} {\skew5\hat{\unicode{x1D641}}}_{\textit{ext}}\unicode{x1D64D}_{yf,ext}^{\dagger } \end{bmatrix},\end{align}

with $\unicode{x1D64E}_{yy}=\mathbb{E} \{ \hat{\boldsymbol{y}} \hat{\boldsymbol{y}}^{\dagger } \}$ and $\unicode{x1D64E}_{zy}=\mathbb{E} \{ \hat{\boldsymbol{z}} \hat{\boldsymbol{y}}^{\dagger } \}$ . Since the right-hand sides of (5.13) and (5.14) contain the terms needed to build the estimation and control kernels (4.8), (4.13), (4.16) and (4.21), this shows that the CSDs on the left-hand side can be used in their place. Note that the CSDs inherently contain statistical information about the nonlinearity of the flow within the forcing CSD matrix.

The data-driven non-causal and causal estimation kernels in (4.8) and (4.13) are computed using the CSDs from (5.13), yielding

(5.15a) \begin{align} {\skew2\hat{\unicode{x1D64F}}}_{nc,D} &= \unicode{x1D64E}_{zy} \big( \unicode{x1D64E}_{yy} + {\skew5\hat{\unicode{x1D649}}}\big)^{-1}, \end{align}

(5.15b) \begin{align} {\skew2\hat{\unicode{x1D64F}}}_{c,D} &= \big[\unicode{x1D64E}_{zy} \big( \unicode{x1D64E}_{yy} + {\skew5\hat{\unicode{x1D649}}}\big)^{-1}_{-}\big]_{+} \big(\unicode{x1D64E}_{yy} + {\skew5\hat{\unicode{x1D649}}}\big)_{+}^{-1}. \end{align}

The control kernels require $\unicode{x1D64D}_{ya}$ and $\unicode{x1D64D}_{za}$ , which do not include the forcing CSD matrix and can be obtained by imposing an impulse forcing at the actuator location in the nonlinear system,

(5.16) \begin{eqnarray} \frac {{\rm d}\boldsymbol{q}_{a,k}}{{\rm d}t}(t) &=& \unicode{x1D63C}\boldsymbol{q}_{a,k}(t)+\unicode{x1D63D}_{a,k}\delta (t)+\unicode{x1D63D}_{nl}\boldsymbol{f}_{nl,k}(t). \end{eqnarray}

The Fourier-transformed sensor and target reading from the nonlinear system forced by impulses at the actuator locations are subtracted by the same quantities, $\hat{\boldsymbol{y}}_{f,nl}$ and $\hat{\boldsymbol{z}}_{f,nl}$ , from the nonlinear system without the actuators. Then, we obtain

(5.17) \begin{equation} \begin{bmatrix} {\; \;\hat{\kern-4pt\unicode{x1D654}}}_{s}\\ {\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{s} \end{bmatrix} = \begin{bmatrix} \hat{\boldsymbol{y}}_{a}-\hat{\boldsymbol{y}}_{f,nl}\\ \hat{\boldsymbol{z}}_{a}-\hat{\boldsymbol{z}}_{f,nl} \end{bmatrix} \approx \begin{bmatrix} \unicode{x1D64D}_{ya} \\ \unicode{x1D64D}_{za} \end{bmatrix} , \end{equation}

where the subscript $s$ denotes the Fourier-transformed readings computed through subtraction using the data-driven approach. The resulting CSDs are

(5.18) \begin{equation} \begin{bmatrix} \unicode{x1D64E}_{yy,s} \\ \unicode{x1D64E}_{zz,s} \end{bmatrix} = \begin{bmatrix} \unicode{x1D64D}_{ya} \unicode{x1D64D}_{ya}^{\dagger } \\ \unicode{x1D64D}_{za} \unicode{x1D64D}_{za}^{\dagger } \end{bmatrix} \approx \begin{bmatrix} \unicode{x1D654}_{a} \unicode{x1D654}_{a}^{\dagger } \\ \unicode{x1D655}_{a} \unicode{x1D655}_{a}^{\dagger } \end{bmatrix}. \end{equation}

Using this result, we can finally obtain the data-driven control kernels,

(5.19a) \begin{align} {\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}}_{nc,D} &= \big(\unicode{x1D64E}_{zz,s}^{\dagger } +{\hat{\unicode{x1D64B}}}\big)^{-1} \big({-}{\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{s}^{\dagger }\big) \unicode{x1D64E}_{zy}\big(\unicode{x1D64E}_{yy}+{\skew5\hat{\unicode{x1D649}}}\big)^{-1}, \end{align}

(5.19b) \begin{align} {\skew3\hat{\boldsymbol{\mathsf{\Gamma }}}}_{c,D}&= \big(\unicode{x1D64E}_{zz,s}^{\dagger } +{\hat{\unicode{x1D64B}}}\big)_{+}^{-1}\big[\big(\unicode{x1D64E}_{zz,s}^{\dagger } +{\hat{\unicode{x1D64B}}}\big)_{-}^{-1} \big({-}{\; \;\hat{\kern-3.5pt\unicode{x1D655}}}_{s}^{\dagger }\big)\unicode{x1D64E}_{zy}\big(\unicode{x1D64E}_{yy}+{\skew5\hat{\unicode{x1D649}}}\big)_{-}^{-1}\big]_{+}\big(\unicode{x1D64E}_{yy}+{\skew5\hat{\unicode{x1D649}}}\big)_{+}^{-1}. \end{align}

To apply these kernels to the system that is excited by the external forcing, $\unicode{x1D64E}_{yy}$ and $\unicode{x1D64E}_{zy}$ can be replaced with $\unicode{x1D64E}_{yy,r}$ and $\unicode{x1D64E}_{zy,r}$ .

6.1. Linearisation

The operator-based approach in $\S$ 5 requires access to the linear operator $\unicode{x1D63C}$ or the actions of both the linear and adjoint operators. We use a matrix-forming approach in which the linear operator is directly computed and stored within the nonlinear solver. This approach accounts for the numerical schemes and boundary conditions employed in the nonlinear simulations, and ensures that the linear operator is readily available at run-time. Extracting the linear operator from large-scale, compressible CFD solvers is not a trivial task and several approaches have been suggested in the literature (Nielsen & Kleb Reference Nielsen and Kleb2006; Fosas, Sipp & Schmid Reference Fosas, Sipp and Schmid2012; Cook et al. Reference Cook, Thome, Brock, Nichols and Candler2018; Bhagwat Reference Bhagwat2020). The approach we adopt most closely mirrors that of Nielsen & Kleb (Reference Nielsen and Kleb2006) and Cook et al. (Reference Cook, Thome, Brock, Nichols and Candler2018).

The CharLES solver uses a control-volume-based finite volume method. A straightforward way to extract the linear operator numerically is to use a finite-difference approximation of the Jacobian, computing the linear operator one column at a time. For example, the $ j{\text{th}}$ column of the linear operator can be extracted using a second-order approximation as

(6.1) \begin{equation} \unicode{x1D63C}(:,j) = \frac {\boldsymbol{\mathcal{F}}\left(\boldsymbol{\bar {q}}+\epsilon \boldsymbol{e}_j\right) - \boldsymbol{\mathcal{F}}\left(\boldsymbol{\bar {q}}-\epsilon {\boldsymbol{e}}_j\right)}{2\epsilon } \,, \end{equation}

where $ j$ refers to the $j$ th degree of freedom and $ \boldsymbol{{\mathcal{F}}}$ represents the right-hand side of the CFD code (the discretised nonlinear compressible Navier–Stokes operator). However, this approach is computationally expensive, as the number of the global right-hand-side evaluations ( $\boldsymbol{{\mathcal{F}}}$ ) required to form the operator equals the problem dimension $ n$ .

Instead, we use an approach adopted by Nielsen & Kleb (Reference Nielsen and Kleb2006) and more recently applied by Cook et al. (Reference Cook, Thome, Brock, Nichols and Candler2018) that relies on perturbing multiple degrees of freedom (DOFs) simultaneously. The key insight is that perturbing an element of the state vector $ \boldsymbol{q}$ affects only a small number of nearby control volumes, known as its computational stencil. This stencil is determined by the numerics used in the CharLES solver (Brès et al. Reference Brès, Ham, Nichols and Lele2017). Thus, it is feasible to perturb multiple elements of the state vector at once without the perturbations interfering with each other. This approach allows us to compute multiple columns of $ \unicode{x1D63C}$ simultaneously, significantly reducing the computational cost. This is accomplished by replacing unit vector $\boldsymbol{e}_j$ in (6.1) with $\tilde {\boldsymbol{e}}_{k}$ , which represents the multiple degrees of freedom perturbed simultaneously. Using this approach, the number of right-hand-side evaluations $\boldsymbol{\mathcal{F}}(\boldsymbol{q})$ scales with the extent of the computational stencil, not with the size of the problem. In the spatial discretisation in CharLES, the flux at a face depends on the adjoining control volumes and their immediate face neighbours, as illustrated in figure 8(a).

To optimise the process of building the $\tilde {\boldsymbol{e}}_{k}$ vectors, we sort the computational grid into lists of non-overlapping degrees of freedom on a single processor and then broadcast this information to all other processors. The linearised compressible Navier–Stokes operator $\unicode{x1D63C}$ is extracted and saved at the initial step, and the routine is performed only once before the time-stepping or nonlinear runs (DNS or LES). Typically, the number of right-hand-side evaluations required for standard hexahedral/quadrilateral grids is approximately 100 and 300 for two-dimensional (2-D) and three-dimensional (3-D) problems, respectively. The small parameter $ \epsilon$ in (6.1) is empirically chosen to minimise the error in the numerical derivatives. We have found $ \epsilon = \epsilon _0 ||\boldsymbol{q}||$ with $ \epsilon _0 = 10^{-6}$ to be an effective and robust choice. The $\|\boldsymbol{q}\|$ is computed separately for each quantity (density, velocities and energy).

6.2. Efficient implementation of the estimation and control tools

To effectively store and use the linear operator for large-scale numerical linear algebra computations such as matrix-vector products, we parallelise our implementation using the open-source linear algebra package PETSc (Balay et al. Reference Balay2019). PETSc leverages the underlying domain decomposition used by the CFD solver to partition the computational grid. Once the matrices such as $\unicode{x1D63C}$ , $\unicode{x1D63E}_{y}$ , $\unicode{x1D63E}_{z}$ , $\unicode{x1D63D}_{a}$ and $\unicode{x1D63D}_{f}$ are constructed, PETSc is used to advance the linear dynamics for the single- or two-stage runs described in § 5.1. To advance these equations in time, we use the TVD-RK3 scheme (Gottlieb & Shu Reference Gottlieb and Shu1998), the same scheme used by the nonlinear solver.

In the two-stage run described in $\S$ 5.1.2, the time-series data of $\boldsymbol{s}_{i}(t)$ from (5.3b ) must be stored to serve as the forcing term $\unicode{x1D63D}_f \boldsymbol{s}_{i}(t)$ in (5.4a ), as shown schematically in figure 8(b). However, storing all snapshots from the initial step of the adjoint run to the time $T$ where the direct run begins becomes prohibitively expensive over long time horizons. We use checkpointing, as shown in figure 8(c), to address this issue by only storing snapshots at particular intervals during the adjoint run in (5.3a ). After completing the first full adjoint run, the direct run is advanced in chunks. The adjoint run is then rerun between the last two checkpoints, using only the stored snapshots within that interval, before conducting the direct run through the same interval. This approach reduces memory usage, which is particularly beneficial for large-scale problems. For example, if advancing $N_t$ time steps in the adjoint run, the storage requirement can be reduced from $O(N_t)$ to $O(W_t + N_t/W_t)$ , where $W_t$ is the length of the interval between two checkpoints and $N_t/W_t$ roughly indicates the number of checkpoints. The minimum memory requirement is achieved when $W_t \approx \sqrt {N_t}$ . Thus, checkpointing reduces the memory required from $O(N_t)$ to $O(2\sqrt {N_t})$ .

Constructing the data-driven estimation and control kernels (5.15) and (5.19) requires the computation of CSDs, as described in $\S$ 5.2. Typically, CSDs are computed using Fast Fourier transforms (FFTs), which require simultaneous access to many snapshots of the state (to be precise, the number of desired frequencies $n_{\textit{freq}}$ ). However, this approach quickly becomes infeasible when each snapshot is large, i.e. when the state dimension $n$ is large. To reduce data size and memory usage, we employ streaming discrete Fourier transforms (DFTs), as proposed by Schmidt & Towne (Reference Schmidt and Towne2019) within the context of a streaming algorithm for spectral proper orthogonal decomposition (SPOD) and further used by Farghadan et al. (Reference Farghadan, Martini and Towne2023) within a scalable time-stepping algorithm for resolvent analysis.

The streaming algorithm requires access to only one instantaneous snapshot of the state at a time, avoiding the need to store the entire time series. This is achieved by using the definition of the discrete Fourier transform (DFT), which yields results equivalent to the FFT. Each snapshot contributes to the summation of the Fourier modes as

(6.2) \begin{equation} \hat{\boldsymbol{f}}_{k}^{l} = \sum _{j=1}^{n_{\textit{freq}}} \boldsymbol{f}_{\!\!j}^{l} p_{jk}, \end{equation}

where $ p_{jk} = e^{(k-1)(j-1)(-i2\pi /n_{\textit{freq}})}$ , with $k$ representing the $k$ th frequency, $j$ the $j$ th snapshot and $l$ the $l$ th block of data. The full time series data are divided into multiple blocks, each windowed with a 50 % overlap. Each snapshot is multiplied by the complex scalar $p_{jk}$ and added to the summation of the Fourier modes. Our implementation is integrated with FFTW (Frigo & Johnson Reference Frigo and Johnson2005) and stores the DFT matrix $\mathbb{C}^{n_{\textit{freq}} \times n_{\textit{freq}}}$ during the initialisation step of the CFD solver.

6.3. Extracting nonlinear terms

Extracting the nonlinear terms ( $\boldsymbol{f}_{nl}$ in (5.8)) of Navier–Stokes equations is useful to investigate the nonlinear interactions. The nonlinear terms are extracted within the application developed for the resolvent-based estimation and control tool. The principle is described here.

The nonlinear Navier–Stokes operator $\mathcal{F}$ can be expressed as

(6.3) \begin{equation} \mathcal{F}(\boldsymbol{q}) = \mathcal{F}(\boldsymbol{\bar {q}}) + \frac {\partial \mathcal{F}\!\left(\boldsymbol{\bar {q}}\right)}{\partial \boldsymbol{q}}\boldsymbol{q'} + nl (\boldsymbol{q'}), \end{equation}

where $nl(\boldsymbol{q'})$ represents all remaining nonlinear terms after linearisation. The forcing vector that accounts for the nonlinear terms can be derived as follows:

(6.4) \begin{equation} \boldsymbol{f}_{nl}(\boldsymbol{q'}) = \mathcal{F}\!\left(\bar {\boldsymbol{q}}\right) + nl(\boldsymbol{q'}) = \underbrace {\mathcal{F}\!\left(\boldsymbol{q}\right)}_{\text{from DNS}} - \underbrace {\unicode{x1D63C}\boldsymbol{q'}}_{\text{from linear run}}. \end{equation}

We run the nonlinear simulation (DNS or LES) in time and, within the same loop, compute the term $\unicode{x1D63C}\boldsymbol{q}'$ to subtract from $\mathcal{F}(\boldsymbol{q})$ . The resulting nonlinear term $\boldsymbol{f}_{nl}$ is saved in the solver. Computing the CSDs of the nonlinear term requires significant memory due to the large $n_{nl}$ . Therefore, to efficiently compute $\hat{\unicode{x1D641}}$ , we use a streaming Fourier transform in (6.2), which does not require to save all the snapshot $\boldsymbol{f}_{nl}$ .

7.1. Linear system

Figure 9 shows an instantaneous snapshot of the streamwise velocity fluctuation for the linear system driven by an external disturbance in the upstream region. The dominant wake mode, closely resembling the least stable eigenmode and the optimal resolvent response mode, is clearly visible.

7.1.1. Building estimation kernels with white-noise forcing

We begin by demonstrating how to construct the estimation kernel using the operator-based approach described in $\S$ 5.1. A sensor ( $y_1$ ) is placed on the suction surface of the airfoil at $x/L_c = 0.5$ , and four targets ( $z_1, z_2, z_3, z_4$ ) are positioned in the wake aligned with the trailing edge at $x/L_c = [1.2, 2.0, 3.0, 4.0]$ and $y/L_c = -0.11$ , as illustrated in figure 9. In figure 10, we show an example of the two-stage run used to build the operator-based kernel between sensor $ y_1$ and targets $ z_1$ or $ z_2$ . An impulsive forcing is applied to the adjoint system at the sensor location at time zero, as shown in figure 10(a). This forcing has Gaussian temporal and spatial support, with $\sigma _{t} = 12.5$ and $\sigma _{x} = \sigma _{y} = 0.02$ , and the sensors and targets have the same spatial support. The input matrix $\unicode{x1D63D}_{f}$ is defined such that the external forcing $\boldsymbol{f}_{\textit{ext}}$ is defined only in the prescribed upstream region. The forcing CSD matrix is assumed to be white noise, i.e. $ \unicode{x1D641}_{\textit{ext}} = \unicode{x1D644}$ , implying that the forcing is uncorrelated in space and time. The sensor noise CSD is $ {\skew5\hat{\unicode{x1D649}}} = \epsilon I$ , with $\epsilon$ equal to $0.1$ of the maximum value of $ {\; \;\hat{\kern-4pt\unicode{x1D654}}}$ . While sensor noise amplitude can affect the smoothness of the estimated data, it does not significantly impact its amplitude or phase.

Figure 9.

Estimation set-up for the linear system, showing the locations of the upstream forcing (white box), sensors (red circle) and targets (blue circle). The contours show an instantaneous snapshot of the streamwise velocity fluctuation.

Figure 10.

Operator-based estimation approach: (a) snapshot of the adjoint run at a specific time instant, including a zoom-in of the impulsive forcing applied at time zero; (b) snapshot of the direct run forced by the $\unicode{x1D63D}_{f}$ readings from the adjoint run; (c) sensor and target readings $y_1$ $[{x}/L_c = 0.5]$ , $z_1$ $[{x}/L_c = 1.2]$ and $z_2$ $[{x}/L_c = 2.0]$ from the direct run in panel (b); (d) non-causal estimation kernels constructed using the readings from panel (c).

The readings $\boldsymbol{s}_{i}$ from the adjoint run are then used to force the direct run, leading to the response shown in figure 10(b). The corresponding sensor and target measurements are recorded in $\unicode{x1D654}$ and $\unicode{x1D655}$ , as defined in (5.5) and shown in figure 10(c). $\unicode{x1D654}$ is symmetric about $\tau U_{\infty } / L_{c}=0$ as it is autocorrelation. The perturbation grows as it travels downstream, so $Z_{y_{1} \to z_{2}}$ is generally greater than $Z_{y_{1} \to z_{1}}$ . To achieve convergence in the adjoint and direct runs, we simulate the two-stage run over a time span $ t U_{\infty }/L_{c} \in [-36, 36]$ with a time step twice that of the DNS.

The Fourier-transformed data, $\; \;\hat{\kern-4pt\unicode{x1D654}}$ and $\; \;\hat{\kern-3.5pt\unicode{x1D655}}$ , are equivalent to $\unicode{x1D64D}_{yf}\unicode{x1D64D}_{yf}^{\dagger }$ and $\unicode{x1D64D}_{zf}\unicode{x1D64D}_{yf}^{\dagger }$ , as shown in (5.5). Finally, the Fourier-transformed data are used to form the resolvent-based kernels using (5.6). Figure 10(d) illustrates the estimation kernel $\unicode{x1D64F}_{nc}$ in (4.8) computed from $\unicode{x1D654}$ and $\unicode{x1D655}$ . The peaks of $T_{y_{1} \to z_1}$ and $T_{y_1 \to z_2}$ can be explained as the travel time from the sensor location (where the impulse forcing is imposed) to the target. A second dominant peak is observed for $T_{y_1 \to z_1}$ and $T_{y_1 \to z_2}$ on the left side of the main peaks, which may be the result of acoustic waves, which are faster than the hydrodynamic waves. For the estimation kernels, $\tau U_{\infty } / L_{c}\gt 0$ and $\tau U_{\infty } / L_{c}\lt 0$ represent past and future times, respectively. Both kernels shown in figure 10(d) have non-zero amplitude mainly for $\tau U_{\infty }/ L_{c}\gt 0$ , i.e. they are nearly causal. If a significant non-causal part is present, truncating it will degrade the performance of the estimator; optimality, under the constraint of causality, can be restored using the Wiener–Hopf decomposition. This impact is more significant for the nonlinear system, so we will discuss it in greater detail in that section.

7.1.2. Estimation results for the linear system

We present the causal resolvent-based estimation result only using a single sensor, comparing the true streamwise velocity fluctuation $u_{x}'$ with the estimated value over time. Additionally, we estimate the fluctuations of both the streamwise $u_{x}'$ and cross-streamwise $u_{y}'$ velocity components in an extended region of the targets using a small number of sensors. To quantify the accuracy of the estimates, we calculate the estimation error

(7.2) \begin{equation} \textit {E} = \frac {\Sigma _{i} \int (\boldsymbol{\tilde {z}_{i}}(t)-\boldsymbol{z}_{i}(t))^{2}\, {\rm d}t}{\Sigma _{i} \int (\boldsymbol{z}_{i}(t))^{2}\, {\rm d}t}, \end{equation}

where $\tilde {\boldsymbol{z}}_{i}$ and $\boldsymbol{z}_{i}$ represent the estimated and true values for the $i$ th target, respectively. In computing the estimation error, we assume the system is ergodic, allowing the ensemble average to be replaced by the time average.

Figure 11.

Causal estimation using an operator-based approach for the linear system at the targets: (a) $z_{1}$ ; (b) $z_{2}$ ; (c) $z_{3}$ ; and (d) $z_{4}$ at positions [ $x/L_{c} = 1.2, 2.0, 3.0, 4.0$ ], as shown in figure 9. The estimation error (7.2) is reported for each case.

Since the estimator was designed for this linear system, the causal estimates are theoretically optimal. Figure 11 shows examples of the true and estimated target readings as a function of time. The result indicates that the target near the trailing edge, positioned in a more complex flow region, is estimated less accurately. In contrast, the other three targets ( $z_2, z_3$ and $z_4$ ) show better estimation with an error of $0.07{-}0.08$ . Overall, the frequency and amplitude of the fluctuations are well estimated.

Figure 12.

Estimation error for the linear system as a function of the target location $x/L_{c}$ and $y/L_{c} = -0.11$ .

Next, we explore the impact of the sensor location on the estimation accuracy. Figure 12 shows the estimation error as a function of the target location $ x/L_{c}$ (aligned with the trailing edge) for six different sensor locations on the airfoil surface. For targets near the trailing edge ( $x/L_{c}\lt 1.5$ ), the front sensors on the suction side ( $ y_{1}$ ) and pressure side ( $ y_{6}$ ) produce inaccurate estimates. In contrast, the rear sensors ( $ y_{3}$ and $ y_{4}$ ) and the middle sensor $ y_{2}$ , which is the location used to demonstrate building the kernels in the previous section, result in better estimation accuracy. The suction-side sensors $ y_{2}$ and $ y_{3}$ accurately capture the flow dynamics near the trailing edge generated by the separation bubble over the airfoil. While $y_{4}$ faces challenges in capturing flow information from the bottom of the airfoil, it can still estimate the downstream targets ( $x/L_{c}\gt 2$ ) as effectively as the sensors $y_{2}$ and $y_{3}$ . These observations are consistent with the leading resolvent (response) mode shown in figure 5; qualitatively, lower errors are observed for sensors located in regions where the mode has a meaningful footprint.

Finally, we estimate the state in an extended region of the flow rather than at individual, discrete targets, allowing us to evaluate the estimation accuracy in different regions. The estimation kernel in this context takes the form

(7.3) \begin{equation} \begin{bmatrix} \hat{\boldsymbol{z}}_{1}\\ \hat{\boldsymbol{z}}_{2}\\ \vdots \\ \hat{\boldsymbol{z}}_{n_{z}}\\ \end{bmatrix} = \begin{bmatrix} \hat{\boldsymbol{T}}_{z_{1}y_{1}}&\hat{\boldsymbol{T}}_{z_{1}y_{2}}&\ldots &\hat{\boldsymbol{T}}_{z_{1}n_{y}}\\ \hat{\boldsymbol{T}}_{z_{2}y_{1}}&\hat{\boldsymbol{T}}_{z_{2}y_{2}}&\ldots &\hat{\boldsymbol{T}}_{z_{2}n_{y}}\\ \vdots &\vdots &\ddots &\vdots &\\ \hat{\boldsymbol{T}}_{z_{n_{z}}y_{1}}&\hat{\boldsymbol{T}}_{z_{n_{z}}y_{2}}&\ldots &\hat{\boldsymbol{T}}_{z_{n_{z}}y_{n_{y}}}\\ \end{bmatrix} \begin{bmatrix} \hat{\boldsymbol{y}}_{1}\\ \hat{\boldsymbol{y}}_{2}\\ \vdots \\ \hat{\boldsymbol{y}}_{n_{y}}\\ \end{bmatrix} ,\end{equation}

where $ n_{y}$ and $ n_{z}$ represent the number of sensors and targets, respectively. Based on the optimal results from figure 12, we use just one sensor located on the suction side, $ y_{3}$ . Figure 13 shows snapshots of the estimates in the extended target regions. The three time steps $ t_{1}, t_{2}, t_{3}$ are selected to represent different phases of the vortex shedding. Figure 14 shows the estimation error as a function of position within the extended target region. As expected, the error increases as the target moves downstream or laterally away from the wake.

7.2. Nonlinear system

In the previous section, we confirmed that the resolvent-based kernels, derived from the resolvent operator, provide accurate estimates for the linear system. We now shift our focus to the actual (nonlinear) system, where it is crucial to statistically account for the nonlinear terms using coloured forcing. We do so by using the data-driven approach described in $\S$ 5.2, which is equivalent to the operator-based method with the appropriate forcing CSD $\hat{\unicode{x1D641}}$ . As discussed earlier, we consider both clean and noisy free stream conditions for the nonlinear system, as illustrated in figures 15(a) and 15(b). The flow is simulated using DNS with the same numerical set-up described in § 2. Since the estimator is defined in terms of perturbations to the mean, the mean is removed from the sensor readings before convolution with the estimation kernels.

Figure 13.

Estimation of streamwise velocity fluctuation for the extended target region at three different time steps for the linear system using the sensor $ y_{3}$ , as shown in figure 12.

Figure 14.

Estimation error in the extended target regions for the linear system using the sensor $ y_{3}$ .

Figure 15.

Instantaneous snapshot of the streamwise velocity $u_{x}$ for (a) the clean and (b) the noisy DNS cases. The symbols show the sensor and target locations. The noisy free stream is generated by a random forcing within the region $x/L_{c} \in [-2,-1]$ and $y/L_{c} \in [-0.5,0.5]$ .

7.2.1. Nonlinear response to the external forcing

The nonlinear system subject to external forcing can be expressed as

(7.4) \begin{equation} \frac {\partial \boldsymbol{q}}{\partial t}= \mathcal{F} (\boldsymbol{q}) + \unicode{x1D63D}_{f,ext} \boldsymbol{f}_{\textit{ext}}. \end{equation}

We choose the external forcing $\boldsymbol{f}_{\textit{ext}} (x,t)$ to be white noise in space and time, generated using random vectors with each entry uniformly distributed over $[-1, 1]\times W$ , where $W$ controls the variance of the random vector, adjusting the noise level of the free stream. The CSD matrix for this forcing is $\hat{\unicode{x1D641}}_{\textit{ext}} = W^{2} \unicode{x1D644}$ . In contrast, the linear system models the nonlinear terms as white noise (an identity matrix; McKeon & Sharma Reference McKeon and Sharma2010).

Figure 16.

Instantaneous snapshots of the streamwise velocity $u_x$ for varying free stream noise intensities, as determined by the forcing amplitude $W$ : (a) $W=0$ (clean); (b) $W = 0.1$ ; (c) $W = 0.2$ ; (d) $W = 0.5$ ; (e) $W = 1$ ; and (f) $W = 3$ . The blue dot in panel (a) indicates the location for which the PSD is analysed in figure 17.

Figure 17.

Power spectral density of the streamwise velocity $u_{x}$ for the nonlinear system in terms of the noise level $W$ at the point $[x/L_{c},y/L_{c}]=[2.11,-0.11]$ in figure 16.

To help us select an appropriate forcing amplitude $W$ , we analyse the PSD of the state for varying noise levels, focusing on the impact on the vortex shedding. The snapshots in figure 16 demonstrate that while vortex shedding persists at all noise levels, the spatial periodic pattern in the streamwise direction is disrupted. Figure 17 makes this point quantitative by showing the PSD of the state at the location indicated in figure 16(a). The results confirm that the vortex-shedding frequencies ( $St_{\alpha }\approx 0.17 \times n$ with $n=1,2,3$ ) are suppressed for $W \geqslant1$ , indicating a strong nonlinear modification to the linear dynamics. Further insights are provided in figure 18, which compares the mean streamwise velocity $\bar {u}_x$ for the clean ( $W=0$ ) and noisy ( $W=1$ ) free stream cases. The noticeable modification to the mean flow further highlights the nonlinearity induced by the noisy free stream; in particular, the increased randomness of the vortex shedding eliminates the spatial oscillation in the clean mean flow. Based on these observations, we select $W = 1$ as the noise level for our estimation and control studies, using the modified mean flow depicted in figure 18(b) under the influence of the noisy free stream.

Figure 18.

Mean streamwise velocity $\bar {u}_x$ fields for (a) the clean ( $W=0$ ) and (b) the noisy free stream ( $W=1$ ) cases.

7.2.3. Single-sensor estimation results for the nonlinear system

We assess the estimation accuracy for the nonlinear system for a single sensor as a function of its location on the surface of the airfoil. Figures 20(b) (clean free stream) and 20(c) (noisy free stream) show the averaged estimation errors for four sets of targets (A, B, C and D) shown in figure 20(a). The errors are approximately an order of magnitude lower for the clear free stream than for the noisy free stream. Imposing external forcing leads to higher-energy fluctuations in the wake, as illustrated in figure 35(c), which increases the impact of nonlinearity and deteriorates the estimation accuracy as the target is moved downstream (estimation error: $D \gt C \gt B \gt A$ in figure 20 b). In contrast, the error for the clean free stream is non-monotonic with downstream distance: it is lowest for the nearest set of targets, increases and then decreases again. This latter decrease in error is likely due to the increasingly low-rank behaviour of the wake with increasing downstream distance for the clean inflow case.

Figure 19.

Estimation kernels with a coloured forcing statistics: non-causal (black solid line) and causal (blue dashed line) kernels between (a) $y_{1}$ [ $x/L_{c} = 0.5$ ] and $z_{1}$ [ $x/L_{c} = 1.2$ ], (b) $z_{2}$ [ $x/L_{c} = 2.0$ ], (c) $z_{3}$ [ $x/L_{c} = 3.0$ ], and (d) $z_{4}$ [ $x/L_{c} = 4.0$ ].

Figure 20.

Streamline of the flow is shown along with four sets of targets: A ( $x/L_c = 1.2$ ), B ( $x/L_c = 1.5$ ), C ( $x/L_c = 2.0$ ), and D ( $x/L_c = 2.5$ ). Averaged estimation errors are reported for these four lines (A, B, C and D), which are based on sensor locations on the airfoil surfaces. Panel (b) illustrates the clean system, while panel (c) depicts the noisy system.

The separation bubble impacts the sensors differently for the clean and noisy free stream cases. For the clean free stream results in figure 20(b), sensors positioned within the recirculation region $0.6 \lt x/L_{c} \lt 1$ (Marquet et al. Reference Marquet, Leontini, Zhao and Thompson2022) show reduced accuracy. However, this effect is not as evident for the noisy free stream case in figure 20(c). This suggests that, whereas the separation bubble shields the sensors in the clean free stream case, incoming fluctuations from the noisy free stream instantaneously penetrate the bubble and later contribute to the wake dynamics, providing useful information to the sensors. Among the sensors on the suction surface for the noisy free stream in figure 20(c), those positioned before the separation bubble ( $0.2\lt x/L_{c}\lt 0.5$ ) and near the trailing edge ( $0.8\lt x/L_{c}\lt 1.0$ ) most effectively predict the wake dynamics. Notably, rear sensors on the pressure surface also show high estimation accuracy.

7.2.4. Multi-sensor estimation results for the nonlinear system

Next, we present estimation results for the nonlinear system with clean and noisy free stream conditions using multiple sensors. In Appendix D, we empirically evaluate six candidate sensor configurations guided by the single-sensor results from the previous section. Ultimately, we selected candidate 6, as defined in table 3. Since we are interested in vortex shedding, we report estimation results for both components of velocity that would be needed to compute vorticity.

Figures 21 and 22 present the causal resolvent estimation of $u_{x}^{\prime}$ (panels a,c,e,g) and $u_{y}^{\prime}$ (panels b,d,f,h) comparing with other methods and the true target reading (from DNS) for the clean and noisy free stream inflows at the target points ( $x/L_{c} = 1.2, 2.0, 3.0, 4.0$ ) aligned with the trailing edge, shown in the top of each figure. The clean DNS system is well estimated using the causal resolvent-based approaches. The Kalman filter captures the dominant frequency’s high-energy parts effectively, but lacks spatial and temporal detail due to treating the nonlinear terms as white noise. The target near trailing edge, where nonlinearity is strongest in figure 35 in Appendix C, is poorly estimated using the Kalman filter, shown in figure 22(a), while the poor estimates obtained using the TNC approach are due to the presence of substantial amplitude of the non-causal kernels in the non-causal part $\tau U_{\infty } L_{c}\lt 0$ ). However, the truncated non-causal kernels include the impact of the coloured forcing statistics, making them effective when the kernels are mostly naturally causal for the further downstream target locations, e.g. in the case of figures 22(e) and 22(g). Comparing left and right panels in figures 21 and 22, we observe that the cross-stream velocity is estimated more accurately than the streamwise velocity.

Figure 21.

Estimation of (a,c,e,g) $u_{x}^{\prime}$ and (b,d,f, h) $u_{y}^{\prime}$ for the nonlinear system with a clean free stream for four targets. Lines: (black solid) true target from the DNS; (green dashed) Kalman filter estimates; (magenta dashed) truncated non-causal estimates; (blue solid) resolvent-based causal estimates. The target locations are: (a,b) [ $z_{1}=x/L_{c}, y/L_{c}$ ] = [1.2, –0.11]; (c,d) [2.0, –0.11]; (e,f) [3.0, –0.11]; and (g,h) [4.0, –0.11], as shown in the top figure and figure 15. The estimation errors for the resolvent-based method are noted in the top-right corner of each panel.

Figure 22.

Estimation of (a,c,e,g) $u_{x}^{\prime}$ and (b,d,f,h) $u_{y}^{\prime}$ for the nonlinear system with the noisy free stream ( $W=1$ ). The black (solid) line shows the true DNS, while the other lines represent different methods: green (dashed) for Kalman filter; magenta (dashed) for truncated non-causal estimation; and blue (solid) for causal estimation (our method). The target locations are: (a,b) at [ $z_{1}=x/L_{c}, y/L_{c}$ ] = [1.2, –0.11]; (c,d) at [2.0, –0.11]; (e,f) at [3.0, –0.11]; and (g,h) at [4.0, –0.11], as shown in the top figure and figure 15. The estimation errors for the causal method are noted in the top right corner of each panel.

Figure 23.

Estimation errors for nonlinear systems: (a) $u_{x}^{\prime}$ and (b) $u_{y}^{\prime}$ for clean free stream; (c) $u_{x}^{\prime}$ and (d) $u_{y}'$ for noisy free stream. Blue lines represent causal resolvent-based estimation, while magenta and green lines denote truncated non-causal estimation using coloured forcing and a Kalman filter (white noise), respectively.

Figure 23 provides a more quantitative assessment of the estimation performance by showing the estimation error for each method as a function of the target position for both velocity components in the clean and noisy free stream cases. Notably, the causal resolvent-based approach estimates both velocity components in the clean system with high accuracy. In the noisy system, the estimation accuracy decreases as the distance between the sensor and the target increases downstream. The causal resolvent-based approach enhances estimation accuracy near the trailing edge, where the lack of causality of the optimal non-causal estimator and the effects of nonlinearity are most significant. The causal resolvent-based estimator consistently outperforms the Kalman filter, highlighting the value of incorporating the space–time forcing statistics.

Using the same four shear stress sensors, we estimate the velocity fluctuations $u_{x}^{\prime}$ and $u_{y}^{\prime}$ for an extended region of the wake in both clean and noisy nonlinear systems. The sensors are positioned near the trailing edge, allowing us to estimate the region behind $x/L_{c} \gt 0.8$ . We present two snapshots of the estimation, selected to represent different phases of the vortex shedding. For the clean system, our estimation results for $u_{x}'$ , as shown in figure 24, are highly accurate. In the noisy nonlinear system, however, the estimation accuracy decreases due to perturbations that disrupt the vortex structure. Despite the challenges posed by chaotic fluctuations within the wake, the causal resolvent-based approach effectively estimates the wake flow, as demonstrated in figure 25.

Figure 24.

Estimation of the streamwise velocity fluctuation $u_{x}^{\prime}$ in an extended wake region for the nonlinear system with a clean free stream at two times: (a,b) DNS results, and (c,d) results from causal resolvent-based estimation.

Figure 25.

Estimation of the streamwise velocity fluctuation $u_{x}^{\prime}$ in an extended wake region for the nonlinear system with a noisy free stream at two times: (a,b) DNS results, and (c,d) results from causal resolvent-based estimation.

8.1. Control set-up

Our overall control architecture is shown schematically in figure 26. Some closed-loop controllers used to suppress wake fluctuations rely on impractically located sensors, e.g. behind the body in the wake itself. Our resolvent-based controller avoids this issue by implicitly using the resolvent-based estimator to predict the evolution of disturbances using shear-stress sensors on the airfoil surface. Our actuators mimic unsteady blowing and suction at discrete positions on the surface of the airfoil, modelled as compact source terms (again with Gaussian spatial support) in the momentum equations. As shown earlier in figure 16, the control signal is computed and applied online during the simulation.

Figure 26.

Control scheme for resolvent-based control of the flow around a NACA0012 airfoil. Red markers indicate shear-stress sensors, while contoured circles represent actuators in the form of momentum sources with Gaussian spatial support on the airfoil surface. The green circle marks the target location. For the control of the nonlinear system, we use a second nested controller (controller B) designed for the modified mean flow produced by the original controller (A). The insert highlights the grid resolution around the rear actuators.

In choosing the placement of our sensors and actuators, we account for the combined amplifier and oscillator characteristics of this flow (Schmid & Sipp Reference Schmid and Sipp2016); the linear operator is globally stable, such that upstream disturbances are convectively amplified as they travel downstream, but it also contains a single lightly damped mode that generates oscillator-like vortex shedding. The sensor and actuator near the leading edge are designed to disrupt the separation bubble and suppress the global vortex-shedding behaviour (Broglia et al. Reference Broglia, Choi, Houston, Pasquale and Zanchetta2018; Déda et al. Reference Déda, Wolf and Dawson2023). The sensors and actuators near the trailing edge are designed to mitigate free stream fluctuations before they are amplified in the wake. We position the front sensors and actuators near the leading edge similarly to prior works (Colonius & Williams Reference Colonius and Williams2011; Broglia et al. Reference Broglia, Choi, Houston, Pasquale and Zanchetta2018; Yeh & Taira Reference Yeh and Taira2019; Asztalos, Dawson & Williams Reference Asztalos, Dawson and Williams2021), while the positions of the rear sets are motivated by our estimation work.

For the nonlinear problem, the actuation modifies the mean flow via triadic interactions, even if the actuation itself is zero-mean. This presents a challenge since the controller is derived based on linearisation about the original (uncontrolled) mean flow and is designed to minimise fluctuations to that mean. However, our primary objective remains to minimise the flow unsteadiness at the targets, i.e. the velocity fluctuations about the modified (controlled) mean flow. To address this challenge, we use an iterative approach similar to that of Leclercq et al. (Reference Leclercq, Demourant, Poussot-Vassal and Sipp2019). After applying the controller based on the original (uncontrolled) mean flow (labelled as controller A in figure 26), we design a second controller (controller B) based on the new (controlled) mean flow and wrap it around the system that is still under the influence of controller A. The combined influence of the two controllers can be thought of as a single new nested controller that takes in the sensor measurements and drives the actuators. This iterative process can be repeated any number of times, but we achieve satisfactory results with just two nested controllers.

Following our previous work (Martini et al. Reference Martini, Jung, Cavalieri, Jordan and Towne2022), we quantify the controller performance via the metric

(8.1) \begin{equation} \varepsilon _{{con}} = 1- \frac {\sum _{i} \int (\boldsymbol{z}_{i,{con}}(t))^{2}\, {\rm d}t}{\sum _{i} \int (\boldsymbol{z}_{i,{uncon}}(t))^{2}\, {\rm d}t}, \end{equation}

which measures the reduction in fluctuation energy at the targets compared with the uncontrolled flow.

8.2. Control of the linear system

We first consider the linear system obtained by linearising the Navier–Stokes equations about the uncontrolled mean flow subject to external forcing, as in $\S$ 7.1. For the linear system, we use only controller A and just two actuators ( $a_{3}$ and $a_{4}$ ). The resolvent-based control kernels are obtained using the operator-based approach described in § 5.1. The direct and adjoint runs are conducted over the interval $t U_{\infty }/L_{c}\in [-48, 48]$ .

Figure 27.

Control kernels with the sensor positioned near the trailing edge ( $y_{3}$ ) and the target $z$ located at $x/L_{c}=1.5$ in the (a) time and (b) frequency domains. The black line represents the non-causal control kernel, the magenta line shows the truncated non-causal kernel, and the blue line depicts the causal control kernel computed using the Wiener–Hopf method.

Figure 27 shows the non-causal and causal control kernels between the $y_3$ sensor and $a_3$ actuator for a target $z$ at $x/L_{c}=1.5$ in the time and frequency domains. In figure 27(a), due to the close proximity of the sensor and actuator, the non-causal control kernel contains relatively large values in the non-causal part $\tau U_{\infty } L_{c}\lt 0$ . This issue is moderated using the Wiener–Hopf method, similar to the estimation kernels. For the causal kernel, the current measurement significantly impacts the actuation signal (Morra et al. Reference Morra, Sasaki, Hanifi, Cavalieri and Henningson2020; Martini et al. Reference Martini, Jung, Cavalieri, Jordan and Towne2022). Figure 27(b) presents the control kernels in the frequency domain. The truncated non-causal control kernel (magenta line) shows a considerable loss in magnitude, while the causal control kernel significantly amplifies the sensor measurement at the vortex-shedding frequency $St_{\alpha } = 0.17$ . As expected, increasing the actuation cost $\unicode{x1D64B}$ reduces the relative magnitude of the control kernel, leading to a smaller actuation force. We set $\unicode{x1D64B}=\epsilon I$ with $\epsilon = 10^{-1}$ of the maximum value of $\unicode{x1D64D}_{za}$ .

Figure 28.

Time series of velocity fluctuations for the uncontrolled and controlled linear systems: (a) $u_{x}^{\prime}$ ; (b) $u_{y}^{\prime}$ . Lines: uncontrolled (black line); truncated non-causal control (magenta dashed line); and causal control (blue line).

Figure 29.

Control performance for the linear system: (a) PSD of the streamwise velocity fluctuations $u_{x}^{\prime}$ for the controlled (blue) and uncontrolled (black) cases, with the magenta line representing the truncated non-causal control approach; (b) turbulent kinetic energy (TKE) integrated over the wake region.

Figure 28 presents the time-series data for the controlled and uncontrolled streamwise and cross-streamwise velocity fluctuations at the target. While the truncated non-causal controller achieves modest improvements, the causal resolvent-based control significantly reduces both velocity components. The power spectral density (PSD) of the uncontrolled and controlled streamwise velocity is shown in figure 29(a). The causal resolvent-based controller effectively suppresses the dominant vortex-shedding frequency, while the truncated non-causal controller fails to accomplish this. Using the causal approach, the two actuators at the trailing edge reduce the turbulent kinetic energy of the velocity fluctuations at the target, as measured by (8.1), by 85 %. In contrast, the truncated non-causal approach achieves only a 27 % reduction. In terms of root-mean-square (r.m.s.) velocities, the causal controller achieves a 62 % decrease, compared with approximately 14 % for the truncated non-causal controller.

Figure 30.

Snapshots of the streamwise and cross-streamwise velocity fluctuation fields for the linear system: (a,b) uncontrolled; (c,d) truncated non-causal (TNC) control; (e,f) causal resolvent-based control.

While the controller is designed to minimise the velocity fluctuations at the target, it also does so over an extended region of the wake. Figure 30 presents instantaneous snapshots of $u_{x}^{\prime}$ and $u_{y}^{\prime}$ for the controlled and uncontrolled systems. The wake modes excited by the upstream-generated external disturbance are effectively suppressed by the actuation at the trailing edge, as illustrated in figures 30(e) and 30(f). To make this reduction quantitative, the turbulent kinetic energy (TKE) integrated over the wake region ( $x/L_{c}=[1.1,5], y/L_{c}=[-1,1]$ ) is shown as a function of time in figure 29(b).

8.3. Control of the nonlinear system

The ultimate goal of this work is to reduce the wake fluctuations in the nonlinear system (DNS) using the optimal linear resolvent-based controller. As discussed earlier, we use a second controller (controller B) to account for the impact of the first controller on the mean flow. After turning on the first controller, we wait until the flow achieves a new statistical steady state before turning on the second controller. The actuator outputs are determined by resolvent-based control kernels, with an additional constant forcing applied to the two front actuators to destroy the separation bubble. The steady forcing is not derived from the resolvent model, but is introduced to improve flow receptivity and enhance control effectiveness (Radespiel et al. Reference Radespiel, Burnazzi, Casper and Scholz2016; Eggert & Rumsey Reference Eggert and Rumsey2017; Puri et al. Reference Puri, Laufer, Müller-Vahl, Greenblatt and Frankel2018). The data-driven approach is used to build the resolvent-based kernels to account for the coloured nonlinear forcing. The operator approach is used to compute $\unicode{x1D64D}_{ya}$ and $\unicode{x1D64D}_{za}$ , required in (5.19). Alternatively, these terms can be obtained using the data-driven approach by running a series of impulse response simulations, as discussed in $\S$ 5.2. We show results only for the noisy free stream, as our controller entirely stabilises the flow for the clean free stream case, resulting in a steady flow maintained by a steady actuation signal.

Figure 31 shows the control kernels for two sensor–actuator pairs in the time and frequency domains. The sensor, actuator and target combination of figures 31(a) and 31(c) are equivalent to those considered for the linear system in figure 27. Since our sensor and actuator locations are positions close to each other, the peaks of the non-causal kernels are near $ \tau U_{\infty } /L_{c}=0$ . A notable difference between the kernels for the linear and nonlinear systems (which are different because of the coloured nonlinear forcing) is that the vortex-shedding frequency is considerably less prevalent in the nonlinear case, presumably due to the disruption of the vortex shedding by the noisy free stream. Accordingly, the control kernels will amplify a wide range of frequencies in the sensor readings, producing broadband actuation signals.

Figure 31.

Control kernels for the nonlinear system: (a,b) kernels in the time domain; (c,d) kernels in the frequency domain. Specifically, panels (a) and (c) correspond to $y_{3}$ and $a_{3}$ , and panels (b) and (d) correspond to $y_{4}$ and $a_{4}$ , as shown in figure 26. The green dashed line in panels ( $\textit {c}$ ) and ( $\textit {d}$ ) indicates the vortex-shedding frequency.

Figure 32.

PSD of the streamwise velocity fluctuation $u_{x}^{\prime}$ at the target $z$ located at $[x/L_c, y/L_c] = [1.5, -0.11]$ . The black solid line represents the uncontrolled flow; the blue line shows the controlled flow using Controller A; the cyan line depicts the controlled flow using both controllers (Controller A + Controller B).

Figure 32 shows the PSD of the streamwise velocity at the target for the uncontrolled and controlled flows. Both the single and nested controllers significantly reduce the energy of velocity fluctuations for $St_{\alpha }\lt 1$ . The control performance, measured by the metric in (8.1) representing the reduction in velocity fluctuation energy, reaches approximately 94 % with Controller A. By incorporating a second controller (Controller A + B), the performance improves further, achieving 98 % reduction in fluctuation energy at the target. The reduction in r.m.s. velocity is 78 % using Controller A, and 90 % using both Controllers A and B. As shown in figure 32, this improvement further mitigates target fluctuations at higher frequencies ( $0.4 \lt St_{\alpha } \lt 0.7$ ), which originate further downstream, as we can verify in figure 33.

Figure 33.

Velocity ( $u_{x}$ ) and vorticity ( $\omega$ ) fields for the system with noisy free stream inflow. (a,b) Uncontrolled flows; (c,d) controlled flows using Controller A; (e,f) controlled flows using both controllers (Controller A + Controller B).

Figure 33 presents both uncontrolled and controlled snapshots of the streamwise velocity and vorticity. As shown in figures 33(a) and 33(b), chaotic vortex shedding is prominent in the wake of the uncontrolled flow. However, this can be significantly mitigated by Controller A. Introducing Controller B further suppresses the oscillating flow downstream ( $x/L_{c} \gt 2$ ). In the controlled flow, the mean flow changed, which is essential as the wake fluctuations from vortex shedding originate from the separated flow.

Finally, figure 34 demonstrates the impact of the controllers on the aerodynamic coefficients. The time-averaged lift coefficient ( $\bar {C}_{L}$ ) is improved by 143 % with the use of Controllers A and B, while the time-averaged drag coefficient ( $\bar {C}_{D}$ ) remains largely unchanged. The fluctuations in both coefficients are effectively suppressed by the controllers. Although improving the aerodynamic coefficients was not the primary objective, mitigating wake fluctuations improved the lift coefficient as a welcome byproduct.

Figure 34.

Lift and drag coefficients for the uncontrolled and controlled flow over time.