2.1. Introduction

The NLSE model (Poli et al. Reference Poli, Bottino, Maj, Palermo and Weber2021) describes the dynamics of an isolated GAM packet via the complex wavefunction $\psi (r,t)$, where its real part

(2.1)\begin{equation} \mbox{Re} [\psi(r,t)] \equiv E_r(r,t), \end{equation}

represents the axisymmetric component of the GAM radial electric field $E_r(r,t)$. The function $\psi$ obeys the cubic NLSE given by

(2.2)\begin{equation} {\rm i}\frac{\partial \psi}{\partial t}={\mathcal{F}}\psi-\frac{\partial}{\partial r} \left(\frac{{\mathcal{G}}}{2}\frac{\partial\psi}{\partial r}\right)-\alpha_\text{NL} |\psi|^2\psi, \end{equation}

where the first two terms on the right-hand side characterize the linear GAM dispersion, while the last term introduces the contribution from nonlinear self-interactions. Their respective strengths are determined by the parameters ${\mathcal {F}}$, ${\mathcal {G}}$ and $\alpha _\text {NL}$, which in most sections of this study will be assumed to be real values and independent of the radial coordinate $r$, which is equivalent to assuming no damping and a uniform plasma background, respectively. Damping in the NLSE will be considered later in this paper in §§ 5.2 and 5.5, and thus is discussed separately there.

The values of the parameters ${\mathcal {F}}$ and ${\mathcal {G}}$ are obtained from the analytical GK result for the linear GAM dispersion relation, which according to Smolyakov et al. (Reference Smolyakov, Bashir, Efimov, Yagi and Miyato2016) is given by

(2.3)\begin{equation} \omega(k_r) = \omega_0\sqrt{1 + \tfrac{1}{2} k_r^2 \rho_i^2 D(\tau_e)}, \end{equation}

and holds only when $k_r^2 \rho _i^2 \ll 1$. Here, $\omega _0$ is the dispersionless GAM frequency given by (2.5) below, $k_r$ is the radial wavevector of the GAM spectrum, $\rho _i$ is the ion Larmor radius and $D(\tau _e)$ is a coefficient characterizing the strength of the dispersive corrections, which depends on the electron-to-ion temperature ratio $\tau _e = T_e/T_i$

(2.4)\begin{equation} D(\tau_e) = \frac{3}{4} - \frac{\dfrac{13}{4} + 3\tau_e + \tau_e^2}{\dfrac{7}{4} + \tau_e} + \frac{\dfrac{747}{32} + \dfrac{481}{32}\tau_e + \dfrac{35}{8}\tau_e^2 + \dfrac{1}{2}\tau_e^3}{\left(\dfrac{7}{4} + \tau_e\right)^2}. \end{equation}

For $\omega _0$ the expression derived in Sugama & Watanabe (Reference Sugama and Watanabe2006) is utilized and damping is neglected

(2.5)\begin{equation} \omega_0^2 = \left[ 1 + \frac{2(23 + 16\tau_e + 4\tau_e^2)}{q_s^2(7 +4\tau_e)^2}\right] \left(\frac{7}{4} + \tau_e\right) \frac{v^2_{Ti}}{R_0^2}, \end{equation}

where $v_{Ti} = \sqrt {2T_i/m_i}$ is the thermal ion velocity, $m_i$ the ion mass, $R_0$ the major tokamak radius and $q_s$ the safety factor. In order to obtain expressions for the parameters ${\mathcal {F}}$ and ${\mathcal {G}}$, the square root in the dispersion relation (2.3) is expanded using the approximation $k_r^2 \rho _i^2 \ll 1$ that was already assumed to hold during the derivation of the dispersion relation

(2.6)\begin{gather} \omega \approx \omega_0 + \tfrac{1}{4} k_r^2 \rho_i^2 \omega_0 D =: {\mathcal{F}} + \tfrac{1}{2}{\mathcal{G}} k_r^2, \end{gather}

(2.7)\begin{gather}{\mathcal{F}} := \omega_0, \end{gather}

(2.8)\begin{gather}{\mathcal{G}} := \tfrac{1}{2}\omega_0 \rho_i^2 D. \end{gather}

Here, a definition is denoted by ‘$:=$’. For the strength $\alpha _\text {NL}$ of the nonlinear self-interaction term there currently exists no analytical expression. As a consequence in this study the values for $\alpha _\text {NL}$ are obtained through comparisons with GK GAM simulations, which were generated with the particle-in-cell code ORB5 (Bottino & Sonnendrücker Reference Bottino and Sonnendrücker2015; Lanti et al. Reference Lanti, Ohana, Tronko, Hayward-Schneider, Bottino, McMillan, Mishchenko, Scheinberg, Biancalani and Angelino2020). The results of these comparisons are presented in Appendix A and show that the parameter is positive, $\alpha _\text {NL} > 0$, increases with $\tau _e$ and depends approximately inversely on the ion Larmor radius, $\alpha _\text {NL} \propto 1/\rho _i$. Furthermore, $\alpha _\text {NL}$ increases when approaching the centre of the plasma cross-section $r=0$. No significant impact of the safety factor $q_s$ on $\alpha _\text {NL}$ was found.

2.2. Dynamics

This section gives a short introduction to the terms in the NLSE (2.2), that will be relevant for the MI. The first term on the right-hand side, ${\mathcal {F}} \psi$, is responsible for the coherent oscillation of the GAM at the dispersionless frequency ${\mathcal {F}} = \omega _0$. The corresponding dynamics can be split off of the wavefunction $\psi$ through the following transformation:

(2.9)\begin{equation} \psi(r,t) = \hat{\psi}(r,t) e^{-{\rm i} {\mathcal{F}} t}, \end{equation}

where $\hat {\psi }$ is the envelope of the GAM packet. This ansatz reduces the NLSE to the usually reported form

(2.10)\begin{equation} {\rm i}\frac{\partial{\hat{\psi}}}{\partial{t}}={-}\frac{{\mathcal{G}}}{2}\frac{\partial{^2\hat{\psi}}}{\partial{r^2}}-\alpha_\text{NL} |\hat{\psi}|^2\hat{\psi}. \end{equation}

The second term on the right-hand side of (2.2), $-{\mathcal {G}}/2\partial _r^2 \psi$ (for ${\mathcal {G}} \neq {\mathcal {G}}(r)$), gives the dispersive properties to the GAM dynamics. The nature of the dispersion is determined by the sign of the parameter ${\mathcal {G}}$, which, as can be deduced from (2.4)–(2.8), depends solely on the value of the electron-to-ion temperature ratio $\tau _e$, with $\tau _e \approx 5.45$ marking the boundary between positive and negative values of ${\mathcal {G}}$ (Nguyen, Garbet & Smolyakov Reference Nguyen, Garbet and Smolyakov2008). The corresponding regimes are labelled as follows:

(2.11)\begin{equation} \left.\begin{gathered} \text{Anomalous Dispersion} \quad {\mathcal{G}} > 0 \quad \tau_e \lesssim 5.45, \\ \text{No Dispersion} \quad {\mathcal{G}} = 0 \quad \tau_e \approx 5.45, \\ \text{Normal Dispersion} \quad {\mathcal{G}} < 0 \quad \tau_e \gtrsim 5.45. \end{gathered}\right\} \end{equation}

The full dependency of ${\mathcal {G}}$ on $\tau _e$ is depicted in figure 1. The different kinds of dynamics corresponding to the three dispersion regimes are illustrated through NLSE simulations (without the nonlinear term) with an initial Gaussian profile in figure 2. The dispersive term broadens the width of packets as time progresses and alters the oscillation frequency of the Gaussian flanks compared with the maximum at $r = 0.5$ (a.u.), resulting in a curvature of the phase front in $(r, t)$-space. In the case of normal dispersion, the flanks oscillate faster than the packet centre, leading to convex curvature, and vice versa for anomalous dispersion.

Figure 1.

Dependency of the dispersion coefficient ${\mathcal {G}}$ on the electron-to-ion temperature ratio $\tau _e$ as defined in (2.4)–(2.8). The parameters were chosen as specified in table 1, with ion cyclotron frequency $\omega _{ci} \approx 1.82\times 10^8\,({\textrm {rad}}/{\textrm {s}}^{-1})$ and ion Larmor radius $\rho _i/ a_\textrm {min} = 4.08\times 10^{-4}$.

Figure 2.

NLSE simulations illustrating the isolated impact of the dispersive term on the dynamics. The figure shows the real part $\mbox {Re}[\psi ]$ of the wavefunction (which for the GAM corresponds to the radial electric field $E_r$) normalized to the maximum amplitude $a_0$ of the Gaussian initial condition. The nonlinear term is disregarded ($\alpha _\textrm {NL} = 0$). The selected values of ${\mathcal {F}}$ and ${\mathcal {G}}$ are chosen such that their relative orders of magnitude match the GAM simulations considered in the later sections. It can be observed that the coefficient ${\mathcal {F}}$ is responsible for the oscillation of $\mbox {Re}[\psi ]$, while the dispersive term introduces a curvature in $(r,t)$-space as well as an increase of the Gaussian width as time progresses.

The nonlinear term introduces a shift that lowers the frequency (due to $\alpha _\textrm {NL} > 0$ for GAMs) proportionally to the packet amplitude squared at the location $r$, which for ${\mathcal {G}} = 0$ amounts to

(2.12)\begin{align} \Delta \omega_\text{NL}(r,t) ={-} \alpha_\text{NL} |\psi|^2(r,t). \end{align}

This shift is illustrated in figure 3. The interaction between the nonlinear phase shift and the dispersive term creates two distinct regimes called self-defocusing regime (when ${\mathcal {G}} / \alpha _\textrm {NL} < 0$, i.e. for GAMs for normal dispersion, i.e. $\tau _e \gtrsim 5.45$) and self-focusing regime (for ${\mathcal {G}} / \alpha _\textrm {NL} > 0$, i.e. vice versa) (Scott Reference Scott2006). Since the self-focusing regime is deeply connected with the formation of MI, it will be explained in further detail in the next section.

Figure 3.

NLSE simulations illustrating the nonlinear frequency shift (in the dispersionless regime, ${\mathcal {G}} = 0$) for a Gaussian initial condition. Similarly to figure 2 the real part $\mbox {Re}[\psi ]$ of the wavefunction is illustrated, which corresponds to the GAM radial electric field $E_r$. Comparing the upper simulation (without nonlinearity, $\alpha _\textrm {NL} = 0$) with the lower one, it is evident to see that the frequency shift as described by (2.12) is most pronounced at the centre of the Gaussian at $r=0.5$, since there the amplitude reaches its highest value.

3.1. Introduction and behaviour

The MI is usually analysed for the case of a plane wave $A_0(t)$ with amplitude $a_0$ (which can be considered to be a packet with very large width, $k_r\rightarrow 0$) superimposed with a radially periodic perturbation $A_1$ with a wavevector $k_\textrm {pert}$

(3.1)\begin{gather} \psi(r, t) = [A_0(t) + A_1(r,t)] \exp({- {\rm i} \omega_0 t}), \end{gather}

(3.2)\begin{gather}A_1(r,t=0) = a_1 \cos(k_\text{pert} r),\quad a_1 \ll a_0. \end{gather}

When the perturbation wavevector $k_\textrm {pert}$ lies in the unstable range, which will be specified in § 3.2, due to the previously mentioned self-focusing effect the sinusoidal perturbation will grow exponentially (as long as the perturbation amplitude $a_1$ is small compared with the plane wave amplitude $a_0$) at the expense of the plane wave ($k_r = 0$) component of the wave, which in the following will be called the background wave. More details on the growth process are given in Appendix B. When the perturbation has grown so large that it dominates the shape of the wave $\psi$ it saturates and acquires a large nonlinear phase shift (as mentioned in § 2.2) compared to the background, leading to strongly incoherent phase fronts. In this saturation phase the radial spectrum of the wave contains many high-$k_r$ components. When the nonlinear phase shift is large enough that the perturbation has skipped an entire oscillation compared with the background, the wave front reconnects and the perturbation decreases again. Finally, the initial condition is restored and the MI process can start anew, leading to a cyclic behaviour called Akhmediev breathers (Akhmediev & Korneev Reference Akhmediev and Korneev1986). A single Akhmediev breather cycle together with the corresponding radial spectrum is shown in figure 4.

Figure 4.

Single cycle of an Akhmediev breather where the initial perturbation wavevector was chosen to be $k_r = 10$ (a.u.). The upper figure illustrates $\mbox {Re}[\psi ]/a_0$, which for the GAM corresponds to the radial electric field $E_r$. As indicated by the colour bar only the positive values are drawn in the figure to emphasize the decoherent phase front at $t=4$ (a.u.). The bottom figure shows the evolution of the corresponding radial spectrum (i.e. the absolute value of the Fourier transform $|\mathfrak {F}[\mbox {Re}[\psi ]]|$). The perturbation grows exponentially until $t \approx 2.5$, after which the growth slows down and at $t\approx 4$ the perturbation reaches its maximum value (which can be seen in the spectrum as well as in real space).

3.2. Conditions for instability

The conditions for instability will be briefly summarized in the following (see e.g. Remoissenet Reference Remoissenet1996). By assuming that the time evolution of the perturbation $A_1(r,t)$ will be of the form

(3.3)\begin{equation} A_1(r,t) = \frac{a_1}{2} \exp({{\rm i} (k_\text{pert} r - \omega_\text{pert} t)}) + \frac{a_1^*}{2} \exp({-{\rm i} (k_\text{pert} r - \omega_\text{pert} t)}), \end{equation}

one finds that $\omega _\textrm {pert}$ fulfils the dispersion relation

(3.4)\begin{equation} \omega_\text{pert}^2 = \left(k_\text{pert}^2 - 4 \frac{\alpha_\text{NL}}{{\mathcal{G}}}a_0^2\right) \frac{{\mathcal{G}}^2 k_\text{pert}^2}{4}. \end{equation}

It is immediate to see that exponential growth occurs (i.e. $\omega _\textrm {pert}$ is imaginary) when firstly

(3.5)\begin{equation} \frac{\alpha_\text{NL}}{{\mathcal{G}}} > 0,\quad (\Leftrightarrow\text{NLSE is self-focusing)} ,\end{equation}

which due to $\alpha _\textrm {NL} > 0$ for GAMs is equivalent to requiring anomalous dispersion (${\mathcal {G}} > 0$, $\tau _e \lesssim 5.45$ as described in § 2.2), and secondly when the perturbation wavevector is within the range

(3.6)\begin{equation} |k_\text{pert}| < |k_\text{lim}| = 2 a_0 \sqrt{\frac{\alpha_\text{NL}}{{\mathcal{G}}}} = \sqrt{2}k_\text{max}, \end{equation}

where $k_\textrm {lim}$ marks the boundary between stable and unstable perturbation wavevectors. The perturbation wavevector $k^2_\textrm {pert}$ provides the frequency mismatch of the sideband wave with respect to the background (pump) wave. The corresponding growth rate, which will be labelled $\gamma _\textrm {MI} := |\mbox {Im}\, \omega _\textrm {pert}|$ in the following, reaches its maximum value at the wavevector $|k_\textrm {max}| := a_0 \sqrt {2 \alpha _\textrm {NL}/{\mathcal {G}}}$. From (3.4) one finds that

(3.7)\begin{gather} \gamma_\text{MI}(k_\text{pert}) = \left|\mbox{Im}\left[\frac{{\mathcal{G}} k_\text{pert}}{2} \sqrt{k_\text{pert}^2 - 4 \frac{\alpha_\text{NL}}{{\mathcal{G}}} a_0^2}\right]\right|, \end{gather}

(3.8)\begin{gather}\gamma_\text{MI}(k_\text{pert}=k_\text{max}) = \alpha_\text{NL} a_0^2. \end{gather}

The full dependency of $\gamma _\textrm {MI}$ on the wavevector of the initial perturbation $k_\textrm {pert}$ is illustrated in figure 5.

Figure 5.

Dependency of the MI growth rate $\gamma _\textrm {MI} := |\mbox {Im}\,\omega _\textrm {pert}|$ on the perturbation wavenumber $k_\textrm {pert}$, as given by (3.7). It is assumed that the first condition, given by (3.5), is satisfied, i.e. dispersion is anomalous.

4.1. Split-step NLSE solver

The split-step solver (SSS) code was written by G.P. Agrawal (Agrawal Reference Agrawal2019) in MatLab and was created to solve the NLSE (2.10), which describes the dynamics of the envelope, in the context of optical fibres where it is reported. The code was modified to incorporate the originally missing oscillation term by utilizing the transformation introduced in (2.9) and adapted to make its input and output consistent with ORB5 simulations.

The split-step method states that, if the chosen numerical time step $\Delta t$ is small enough, the nonlinear and dispersive contributions to the NLSE dynamics (of the wave envelope $\hat {\psi }$) act (mostly) independently from each other (Faou Reference Faou2012; Agrawal Reference Agrawal2019). It follows that the solution can be approximated by alternatingly solving the two equations

(4.1)\begin{gather} {\rm i} \frac{\partial \hat{\psi}}{\partial t} ={-} \alpha_\text{NL} |\hat{\psi}|^2 \hat{\psi} \quad \text{(isolated nonlinear dynamics)} , \end{gather}

(4.2)\begin{gather}{\rm i} \frac{\partial \hat{\psi}}{\partial t} ={-} \frac{{\mathcal{G}}}{2} \frac{\partial^2 \hat{\psi}}{\partial r^2},\quad \text{(isolated dispersive dynamics)} , \end{gather}

for each time step. This approach has the significant advantage that the equations for the isolated nonlinear and isolated dispersive dynamics can each be immediately solved from an initial condition $\hat {\psi }(r,t=0)=\hat {\psi }_0$ using the following analytic solutions:

(4.3)\begin{gather} \hat{\psi}_\text{NL}(r,t) = \exp({\rm i} \alpha_\text{NL}|\hat{\psi}_0|^2 t) \hat{\psi}_0 =: \varphi_\text{NL}^t[\hat{\psi}_0], \end{gather}

(4.4)\begin{gather}\hat{\psi}_{D}(r, t) = \mathfrak{F}^{{-}1}\left\lbrace \exp\left(- {\rm i} t \frac{{\mathcal{G}}}{2} k^2\right) \mathfrak{F}\lbrace\hat{\psi}_0 \rbrace \right\rbrace =: \varphi_{D}^t [\hat{\psi}_0], \end{gather}

where $\mathfrak {F}\lbrace \cdot \rbrace$ is the spatial Fourier transform which is calculated using the fast-Fourier-transform algorithm, $\mathfrak {F}^{-1}\lbrace \cdot \rbrace$ denotes the inverse Fourier transform and $\varphi _\textrm {NL}^t[\cdot ]$ and $\varphi _{D}^t[\cdot ]$ denote the exact flows (i.e. the temporal evolution starting from an initial condition $\hat \psi _0(r)$) for the isolated nonlinear and isolated dispersive equation, respectively.

The flow $\varphi _\textrm {NLSE}^t$ associated with the complete dynamics of the NLSE is then approximated as follows:

(4.5)\begin{equation} \hat{\psi}(r, t=n\Delta t) = \varphi_\text{NLSE}^{n \Delta t} [\hat{\psi}_0] \approx (\varphi_{D}^{\Delta t} \circ \varphi_\text{NL}^{\Delta t})^n [\hat{\psi}_0], \end{equation}

also known as the Lie splitting method, where ‘$\circ$’ denotes the concatenation of flows, $(\varphi _{D}^{\Delta t} \circ \varphi _\textrm {NL}^{\Delta t}) [\hat {\psi }_0] = \varphi _{D}^{\Delta t} [\varphi _\textrm {NL}^{\Delta t} [\hat {\psi }_0]]$.

The SSS implements a refinement of the split-step method with higher accuracy, called the symmetrized split-step Fourier method or Strang splitting scheme. In this form, the nonlinear dynamics is split into two parts of duration $\Delta t/2$, with the dispersive (and thus smoothing) evolution acting in between (Faou Reference Faou2012; Agrawal Reference Agrawal2019).

4.2. ORB5

The GK code ORB5 (Bottino & Sonnendrücker Reference Bottino and Sonnendrücker2015; Lanti et al. Reference Lanti, Ohana, Tronko, Hayward-Schneider, Bottino, McMillan, Mishchenko, Scheinberg, Biancalani and Angelino2020) is a global particle-in-cell code that simulates the plasma dynamics inside a tokamak for processes occurring on a time scale slower than that of the ion gyromotion. It can be executed with or without nonlinear plasma interactions.

The GK theory reduces the six-dimensional kinetic theory by one dimension by averaging over the gyromotion to obtain a five-dimensional problem describing the dynamics of the guiding centre distribution function $f_s$ of each species $s$. The set of GK equations describing the dynamics of $f_s$ can be constructed in different ways. The model implemented in ORB5 is derived from a GK Lagrangian describing the particle motion in a magnetic field (Brizard & Hahm Reference Brizard and Hahm2007). The time-symmetric Hamiltonian within the Lagrangian conserves energy automatically, leading to a model which is particularly useful for numerical simulations (Bottino & Sonnendrücker Reference Bottino and Sonnendrücker2015). ORB5 provides numerical results that exhibit strong agreement with other GK codes (Lanti et al. Reference Lanti, Ohana, Tronko, Hayward-Schneider, Bottino, McMillan, Mishchenko, Scheinberg, Biancalani and Angelino2020). Its recent application to the dynamics of isolated GAMs is documented in Palermo et al. (Reference Palermo, Biancalani, Angioni, Zonca and Bottino2016), Biancalani et al. (Reference Biancalani, Palermo, Angioni, Bottino and Zonca2016), Palermo et al. (Reference Palermo, Poli, Bottino, Biancalani, Conway and Scott2017), Palermo, Poli & Bottino (Reference Palermo, Poli and Bottino2020), Poli et al. (Reference Poli, Palermo, Bottino, Maj and Weber2020) and Palermo et al. (Reference Palermo, Conway, Poli and Roach2023). The interested reader is referred to these references for more details about the numerical model.

In order to create GAMs in the ORB5 simulations, an electric field $E_r$ is initialized in ORB5 via a perturbation $\delta n_i$ of the gyrocentre density of the only ion species included in the simulations. This perturbation leads through the GK Poisson equation to a potential perturbation (or equivalently to a polarization density) that ensures quasineutrality. The relevant relation between density perturbation and electric field for a (0,0) perturbation can be expressed as

(4.6)\begin{align} \delta n_i(r) \propto \frac{1}{r} \frac{\partial{}}{\partial{r}}(r E_r(r)). \end{align}

As a result, the GAM is essentially ‘dropped into’ the simulations and the formation process is not considered.

5.1. General comparison between NLSE and GK simulations

A first comparison between NLSE and GK simulations of the general GAM dynamics (without modulation of the initial condition and thus without MI) was made using the parameters in table 1. Specifically, the initial GAM electric field amplitude $a_0 = 2.5\times 10^{-4}$ (a.u.), sound Larmor radius $\rho _s/a_\textrm {min} = 2/375$ and ion Larmor radius $\rho _i/a_\textrm {min} \approx 4.355\times 10^{-3}$ (where $\rho _i$ and $\rho _s$ are determined as described in (A1) and (A2)) were chosen as a starting point. The dispersion coefficient ${\mathcal {G}}$ is obtained according to (2.8) and $\alpha _\textrm {NL}$ is chosen as described in Appendix A (with $r=r_0=0.6a_\textrm {tok}$). Figure 7 presents the GK and NLSE simulation results.

Figure 7.

The GAM radial electric field $E_r$ in an ORB5 GK (colour contours) and NLSE (red levels) simulation of an initially unmodulated GAM. While the frequency of the oscillation matches well at the packet centre and further outward (for $r \geq 0.6 a_\textrm {min}$), differences increase when moving to smaller values of $r$. Since the plasma parameters were chosen to be constant across the radial coordinate $r$, these discrepancies can be attributed to an influence of the tokamak geometry on the nonlinear parameter $\alpha _\textrm {NL}$, which is analysed in Appendix A.

The comparison in figure 7 shows that, despite the uniform plasma background and thus (according to the equations introduced in § 2.1) constant values of ${\mathcal {F}}$ and ${\mathcal {G}}$, the frequency of the nonlinear GAM is radially varying in the GK simulations. This major discrepancy between the NLSE and GK simulation results may be explained by a radial dependence of $\alpha _\textrm {NL}$ that stems from the geometry of the tokamak. This is not included in the NLSE used in this paper since, as mentioned in § 2.1, the NLSE model for GAMs has not yet been derived from analytic theory. The dependency is analysed numerically in more detail in Appendix A and will be considered in future work.

Next, an initial condition with a modulated envelope is considered. From the parameters $a_0 = 2.5\times 10^{-4}$ (a.u.), $\alpha _\textrm {NL}$ and ${\mathcal {G}}$ one obtains the range of unstable perturbation wavevectors $k_\textrm {pert}$ through (3.6) as

(5.2)\begin{equation} |k_\text{pert}| < |k_\text{lim}| \approx 14.6 \frac{2{\rm \pi}}{a_\text{min}} = \sqrt{2}\cdot k_\text{max} \approx \sqrt{2}\cdot 10.3\frac{2{\rm \pi}}{a_\text{min}}. \end{equation}

It follows that the wave is unstable to MI when the wavelength of the perturbation $\lambda _\textrm {pert} = 2{\rm \pi} /k_\textrm {pert}$ is larger than approximately 1/15th of the minor tokamak radius, and the maximum growth rate from (3.8) of $\gamma _\textrm {MI} = 3.05\times 10^{-5}\omega _{ci}$ is achieved when the perturbation wavelength is approximately $a_\textrm {min}/10$.

A comparison of an NLSE and GK simulation with $k_\textrm {pert} = 10 ({2{\rm \pi} }/{a_\textrm {min}}) \approx k_\textrm {max}$ and ${a_1 = 0.1}$ is shown in figure 8. While again significant differences between the NLSE and GK simulations can be observed, the results illustrate that the MI does occur in GK GAM simulations. This is apparent since the maxima of the initial condition start to grow as time progresses and the phase skipping of the maxima (as described in § 3.1) compared with the background is observed e.g. for $t\approx 1.5 \times 10^5/\omega _{ci}$ at $r = 0.5 a_\textrm {min}$ and for $t\approx 2.2 \times 10^5/\omega _{ci}$ at $r = 0.6 a_\textrm {min}$ in figure 8(b). The observed radial difference in growth rate can be explained by the radial dependency of the nonlinear parameter $\alpha _\textrm {NL}$, as from (3.4) it follows that $\gamma _\textrm {MI}$ increases monotonically with $\alpha _\textrm {NL}$, which gets larger for smaller values of $r$ (as determined in Appendix A). Another discrepancy is the merging of two maxima at $r=0.8 a_\textrm {min}$ and $r=0.9 a_\textrm {min}$ in the GK simulation, which stay separated for the NLSE. This is again explained by $\alpha _\textrm {NL}$ decreasing with $r$ since, according to (3.6), the wavevector with the highest growth rate depends as $k_\textrm {max} \propto \sqrt {\alpha _\textrm {NL}}$. As a consequence at $r\approx 0.85 a_\textrm {min}$ smaller wavevectors (i.e. larger structures) will grow faster, thus favouring the merging of structures. Generally, one finds that across the whole radial space the growth rate and maximum amplitude in the GK simulation are significantly smaller compared with the NLSE simulation and theoretical predictions, as can be noticed comparing the colour bars in figure 8. This observation is further discussed in §§ 5.3 and 5.4.

Figure 8.

Comparison of a NLSE and a GK simulation where the envelopes are modulated sinusoidally with the perturbation wavevector $k_\textrm {pert} = 10 ({2{\rm \pi} }/{a_\textrm {min}}) \approx k_\textrm {max}$. The figures depict the GAM radial electric field $E_r$, which in the NLSE model is the real part of the wavefunction $\mbox {Re}[\psi ] = E_r$. While the growth of the modulation is observable in both simulations, the growth rate appears to be significantly lower in the GK result compared with the NLSE simulation. One can find further discrepancies, e.g. the two individual maxima that form in the NLSE simulation at $r=0.8 a_\textrm {min}$ and $r=0.9 a_\textrm {min}$ are seemingly merged together in the GK simulation. Panels show the (a) NLSE simulation result and (b) the GK simulation result.

To study the aforementioned differences in further detail the radial spectra of the results are compared, as depicted in figure 9. The spectrum of the NLSE simulation contains much larger wavevectors $k_r$ than the GK simulation spectrum, most notably in the nonlinear saturation phase at $t\approx 1.1\times 10^5/\omega _{ci}$ (see figure 8a). Together with the lower amplitude and growth rate, these findings indicate that a damping mechanism acting preferentially on higher wavevectors is present in the GK simulations, which is not contained in the NLSE solver. As a side note it is remarked that the NLSE spectrum extends up to wavevectors larger than $30 ({2{\rm \pi} }/{a_\textrm {min}})$, where the assumption $k_r^2 \rho _i^2 \ll 1$ posed in § 2.1 is marginally or no longer satisfied, as e.g. $k_r^2\rho _i^2 = 0.2$ corresponds to $k_r \approx 16 ({2{\rm \pi} }/{a_\textrm {min}})$ for the current ion Larmor radius $\rho _i/a_\textrm {min} \approx 4.355\times 10^{-3}$. One can also notice in figure 9 that higher spectral components develop later in ORB5 simulations as compared with the NLSE solution. This is due to the fact that the instability develops on a slower scale in GK simulations, in particular at larger $r$.

Figure 9.

Comparison of the radial spectrum of the GK and NLSE simulations reported in figure 8. The figures show the absolute value of the radial Fourier transform of the GAM radial electric field, $|\mathfrak {F}[E_r]|$. It is apparent that the GK spectrum is in general more restrained to small wavevectors compared with the NLSE spectrum. This is especially noticeable in the saturation phase at $t\approx 1.1 \times 10^5/\omega _{ci}$, which as described in § 3 is (according to the NLSE predictions) associated with a spectrum that contains high wavevector components. Panels show the (a) NLSE radial spectrum and (b) the GK radial spectrum.

5.2. Damping term

This section gives a short introduction to the damping term derived in Qiu et al. (Reference Qiu, Chen and Zonca2009), which, as mentioned in § 5, can be applied to the radial GAM spectrum when electrons are considered to be adiabatic, $1/q_s^2 \ll k_r^2\rho _i^2 \ll 1$ and $\frac {1}{2}\tau _e k_r^2\rho _i^2 \ll 1$. The condition $1/q_s^2 \ll k_r^2\rho _i^2$ is always satisfied for the chosen safety factor $q_s = 15$ and the wavevectors $k_\textrm {pert}$ of the initial perturbation. Furthermore, due to $\tau _e = 3$ the remaining requirements can be merged as follows:

(5.3)\begin{equation} k_r^2\rho_i^2 < \frac{\tau_e}{2} k_r^2\rho_i^2 = \frac{3}{2} k_r^2\rho_i^2 \ll 1. \end{equation}

Taking $0.3\ll 1$ as the boundary value for the validity of this assumption, one finds that, for the largest ion Larmor radius used in this paper, $\rho _i/ a_\textrm {min} = 5.025\times 10^{-3}$, the applicability of the damping term is limited to wavevectors $k_r \lesssim 14.2 ({2{\rm \pi} }/{a_\textrm {min}})$. The term is given by the following expression:

(5.4)\begin{align} \gamma_\text{Qiu} & ={-} \frac{|\omega_b|}{\sqrt{2}b} \exp\left\lbrace -\sigma\frac{\omega_b}{\omega_\text{dt}} \right\rbrace \left[1 + b \frac{v_{Ti}^2}{\omega_b^2R_0^2}\left(\frac{31}{16}+\frac{9}{4}\tau_e + \tau_e^2\right)\right. \nonumber\\ & \quad \left. -\,b\frac{v_{Ti}^4}{\omega_b^4R_0^4}\left(\frac{747}{32} + \frac{481}{32}\tau_e+ \frac{35}{8}\tau_e^2+\frac{1}{2}\tau_e^3\right) - 2\frac{v_{Ti}^4}{\omega_b^4R_0^4q_s^2} \left(\frac{23}{8} + 2 \tau_e + \frac{1}{2}\tau_e^2\right)\right] \nonumber\\ & \quad \times\left\lbrace 1 + \frac{1}{24}\omega_b\omega_\text{dt}^2 \left(-\sigma\frac{4}{\omega_\text{dt}^3} + \frac{\omega_b}{\omega_\text{dt}^4}\right) + \sigma \frac{\omega_\text{dt}}{\omega_b}\tau_e + \left(\tau_e^2 + \frac{5}{4} \tau_e + 1\right) \frac{\omega_\text{dt}^2}{\omega_b} - 2b \right\rbrace, \end{align}

where the components are defined as

(5.5)\begin{gather} b = k_r^2 \rho_i^2 / 2, \end{gather}

(5.6)\begin{gather}v_{Ti} = \sqrt{2T_i/m_i}, \end{gather}

(5.7)\begin{gather} \omega_b = \sqrt{\frac{7}{4} + \tau_e} \frac{v_{Ti}}{R_0} \left\lbrace 1 - \frac{b}{2}\left(\frac{31}{16} + \frac{9}{4}\tau_e + \tau_e^2\right)\left(\frac{7}{4} + \tau_e\right)^{{-}1} \right. \nonumber\\ +\,\frac{b}{2}\left(\frac{747}{32} + \frac{481}{32}\tau_e + \frac{35}{8}\tau_e^2 + \frac{1}{2}\tau_e^3\right)\left(\frac{7}{4} + \tau_e\right)^{{-}2} \nonumber\\ \left. +\, \frac{1}{2q_s^2} \left(\frac{23}{8} + 2\tau_e + \frac{1}{2}\tau_e^2\right) \left(\frac{7}{4}\tau_e\right)^{{-}2}\right\rbrace, \end{gather}

(5.8)\begin{gather} \omega_\text{dt} = \frac{v_{Ti}}{R_0} k_r \rho_i, \end{gather}

(5.9)\begin{gather}\omega_\text{tt} = \frac{v_{Ti}}{R_0 q_s}, \end{gather}

(5.10)\begin{gather}\sigma = \mathrm{sgn}\left[\frac{\omega_\text{b}}{\omega_\text{dt}}\right]. \end{gather}

This damping term describes the collisionless Landau damping of the GAM and can be applied to larger wavevectors $k_r$ than e.g. the term derived by Sugama & Watanabe (Reference Sugama and Watanabe2006), which is achieved by including higher-order harmonics of the ion transit resonances in the derivation. The resulting dependency is depicted in figure 10 for the parameters from table 1 (notably with $\rho _s/a_\textrm {min} = 2/375$, $\rho _i/a_\textrm {min} = 4.355\times 10^{-3}$). It is apparent that the strength is negligible for wavevectors $k_\textrm {r} < 5({2{\rm \pi} }/{a_\textrm {min}})$, but increases rapidly at $k\approx 10 ({2{\rm \pi} }/{a_\textrm {min}})$.

Figure 10.

Damping term for the parameters given in table 1, with $\rho _s/a_\textrm {min} = 2/375$, $\rho _i/a_\textrm {min} = 4.355\times 10^{-3}$. The red line illustrates the point $\frac {3}{2} k_r^2 \rho _i^2 = 0.3$ beyond which the expression may not be applicable anymore. It can be observed that the slope of the damping expression changes roughly where the line is located, which can be an indicator that after this point the behaviour is unphysical. It is remarked that the high end of the perturbation wavevectors unstable to MI, i.e. for $k_\textrm {pert} = 14 ({2{\rm \pi} }/{a_\textrm {min}})$, is very close to the damping applicability limit at $k_r \approx 16 ({2{\rm \pi} }/{a_\textrm {min}})$.

5.3. Role of the perturbation wavelength

This section analyses the influence of the perturbation wavelength $\lambda _\textrm {pert} = 2{\rm \pi} /k_\textrm {pert}$ chosen in the initial condition of the GAM radial electric field $E_r$ on MI growth (as predicted by (3.7), see figure 5). Additionally, the damping mechanism introduced in the previous section is taken into account. Similarly to the previous section, the parameters from § 5.1 are used and consequently all perturbation wavevectors $k_\textrm {pert} < 14.6 ({2{\rm \pi} }/{a_\textrm {min}}) = k_\textrm {lim}$ should be susceptible to MI. Thus, simulations with modulation wavevectors in the range $4-14 ({2{\rm \pi} }/{a_\textrm {min}})$ are analysed in this section.

In order to obtain a quantitative measure of the growth (or damping) $\gamma$ of the sinusoidal perturbation, the radial Fourier transforms of the GK simulation results, $\mathfrak {F}[E_r](k_r, t)$, are calculated using the fast-Fourier-transform algorithm. The Fourier coefficient corresponding to the perturbation wavevector $k_\textrm {pert}$, $\mathfrak {F}[E_r](k_r = k_\textrm {pert}, t)$, is then extracted and an exponential fit is applied to the region of exponential MI growth or exponential decay. Due to the fact that the growth rate depends on $\alpha _\textrm {NL}$, which in turn depends on the radial position $r$, the Fourier coefficient of the whole packet would return a complex mixture of the different growth stages at the different radial positions. As a consequence, before the Fourier transform is performed, a mask is applied such that only the simulation data around each maximum of the wavefront are included. This scheme is illustrated for the example of $k_\textrm {pert} = 8 ({2{\rm \pi} }/{a_\textrm {min}})$ in figure 11.

Figure 11.

Scheme for determining the growth rate $\gamma$ of the perturbation in the GK GAM simulations. The upper picture depicts the radial electric field of the case with $k_\textrm {pert} = 8 ({2{\rm \pi} }/{a_\textrm {min}})$. The bottom picture depicts the absolute value of the Fourier coefficient at $|\mathfrak {F}[E_r](k_r = 8 ({2{\rm \pi} }/{a_\textrm {min}}),t)|$ and the envelope of the coefficient. The exponential fit is applied only to the region where the growth rate is highest, as for the first oscillation cycles the GAM electric field is experiencing an initial transient where higher GAM harmonics that were excited by the initial ‘drop-in’ are still fading away, and for higher values of $t$ the assumption that the perturbation amplitude $a_1$ is small compared with the plateau amplitude $a_0$ is not fulfilled anymore. (a) The GAM radial electric field $E_r$ from a GK simulation with perturbation wavevector $k_\textrm {pert} = 8 ({2{\rm \pi} }/{a_\textrm {min}})$. The red lines show the time window where the fit was applied, the black lines indicate the radial region that was included in the calculation of the Fourier coefficient. (b) Time evolution of the absolute value of the Fourier coefficient of the perturbation wavevector $k_\textrm {pert} = 8 ({2{\rm \pi} }/{a_\textrm {min}})$. The red lines indicate the time window where the fit was applied.

The aforementioned scheme, illustrated in figure 11, is applied to all simulations, with respective wavevectors $k_\textrm {pert} = 4, 5, \ldots, 14 ({2{\rm \pi} }/{a_\textrm {min}})$. In figure 12, the MI growth rates obtained from the GK simulation data employing the method described above are compared with the theoretical results of § 3 (see figure 5 and (3.7)). The theoretical MI predictions, which do not contain damping, clearly overestimate the GK MI growth rate $\gamma$ for high perturbation wavevectors $k_\textrm {pert} > 6 ({2{\rm \pi} }/{a_\textrm {min}})$. As can be seen in figure 12(a), even when the nominal damping rate following (5.4) is included the growth rate of the MI is overpredicted as compared with the GK simulations. However, after adjusting the strength of the damping amplitude by multiplying it with a factor of 2.5, the simulation results show good agreement with the theoretic predictions (figure 12b). Not only does this adjustment improve the matching of the threshold between stable and unstable perturbations at $k_\textrm {pert} \approx 11 ({2{\rm \pi} }/{a_\textrm {min}})$, but it furthermore fits better to the values of the growth rates for the wavevectors in the domain $6 ({2{\rm \pi} }/{a_\textrm {min}}) \leq k_\textrm {pert} \leq 11 ({2{\rm \pi} }/{a_\textrm {min}})$. We remark that this correction factor is also in line with the benchmark in Biancalani et al. (Reference Biancalani, Bottino, Ehrlacher, Grandgirard, Merlo, Novikau, Qiu, Sonnendrücker, Garbet and Görler2017) (see figure 4(b) in this reference), where a similar difference between numeric simulations and theoretical prediction has been observed.

Figure 12.

The GAM perturbation growth and damping rates $\gamma$ in GK simulations where the initial condition was modulated with different perturbation wavevectors $k_\textrm {pert}$. The GK results were obtained via the method illustrated in figure 11. The left figure shows the comparison of the simulation results with the theoretical predictions for the analytic MI growth rate $\gamma _\textrm {MI}$ (3.4), and the damping $\gamma _\textrm {Qiu}$ (5.4). The theoretical predictions are found to overestimate the growth rate significantly, as seen in figure 12(a). The right figure establishes that, when the damping term is amplified by a factor of 2.5, data for the wavevectors in the region $6 ({2{\rm \pi} }/{a_\textrm {min}}) \leq k_\textrm {pert} \leq 11 ({2{\rm \pi} }/{a_\textrm {min}})$ more closely match the theoretical predictions. (a) Unmodified damping according to $\gamma _\textrm {Qiu}$. (b) Damping multiplied by a factor of 2.5.

In contrast to the well-matching growth rates at medium wavevectors, the matching becomes poorer at larger and smaller $k_\textrm {pert}$, as is apparent in figure 12(b). For wavevectors $k_\textrm {pert} > 11 ({2{\rm \pi} }/{a_\textrm {min}})$, the MI is damped in GK simulations and its time behaviour is more prone to fitting errors due to the initial transient process. Additionally, these values are close to the applicability limit $\frac {3}{2} k_r^2\rho _i^2 \ll 1$ of the damping term. On the other hand, the growth rates obtained from the GK simulations at the lower end, with $k_\textrm {pert} < 6 ({2{\rm \pi} }/{a_\textrm {min}})$, are too high compared with theory. Further investigations regarding this discrepancy are needed.

5.4. Role of the ion Larmor radius

The last section established that the small scales (high wavevectors) are more strongly affected by damping and that the analytical term from Qiu et al. (Reference Qiu, Chen and Zonca2009), when adjusted by a factor 2.5, gives a good approximation to the observations. This section tests this hypothesis further by analysing the impact of a change of the ion Larmor radius $\rho _i$ on the damping scale, where the values $\rho _{i1}/a_\textrm {min} = 3.842\times 10^{-3}$, $\rho _{i2}/a_\textrm {min} = 4.355\times 10^{-3}$ (same value as in the previous sections) and $\rho _{i3}/a_\textrm {min} = 5.025\times 10^{-3}$ were chosen for the comparison. The changes of $\rho _i$ are achieved by adjusting the ion thermal velocity $v_{Ti}$, which in the simulations is determined according to (A1) and (A2). The analytic damping term $\gamma _\textrm {Qiu}$ predicts that, for smaller Larmor radii, smaller scales i.e. higher wavevectors $k_\textrm {pert}$ should be less damped.

A change of $\rho _i$ (via $v_{Ti}$) influences both ${\mathcal {G}}$ and $\alpha _\textrm {NL}$, meaning that the range of unstable wavevectors $k_\textrm {pert} < k_\textrm {lim} = 2 a_0 \sqrt {\alpha _\textrm {NL}/{\mathcal {G}}}$ from (3.6) may change. To decorrelate the change of $\rho _i$ from the change of the unstable MI wavevectors, the parameter $a_0$ is selected depending on $\rho _i$ to ensure that the range of the unstable MI wavevectors stays the same. Precisely, due to ${\mathcal {G}} \propto \rho _i^3$ (see (2.8) with $v_{Ti} \propto \rho _i$) and $\alpha _\textrm {NL} \propto 1/\rho _i$ (see Appendix A) one finds from (3.6) that in order to keep the unstable range constant $a_0$ needs to be adjusted as

(5.11)\begin{align} k_\text{lim} = 2 a_0 \sqrt{\frac{\alpha_\text{NL}}{{\mathcal{G}}}} \propto \sqrt{\frac{1}{\rho_i^4}} a_0 = \text{const.} \quad \Rightarrow \quad a_0 \propto \rho_i^2. \end{align}

In order to obtain the same $k_\textrm {lim} = 14.5 ({2{\rm \pi} }/{a_\textrm {min}})$ as in the previous sections the corresponding amplitudes are $a_{01} \approx 1.99\times 10^{-4}$ (a.u.), $a_{02} \approx 2.56\times 10^{-4}$ (a.u.) and $a_{03} \approx 3.40\times 10^{-4}$ (a.u.) for $\rho _{i1}, \rho _{i2}$ and $\rho _{i3}$, respectively. The simulations were analysed with the scheme introduced in the last section with the corresponding growth and damping rates presented in figure 13.

Figure 13.

The GAM MI growth and damping rates in GK simulations with different perturbation wavevectors $k_\textrm {pert}$ and ion Larmor radii $\rho _i$ ($\rho _{i1}/a_\textrm {min} = 3.842\times 10^{-3}$, $\rho _{i2}/a_\textrm {min} = 4.355\times 10^{-3}$ and $\rho _{i3}/a_\textrm {min} = 5.025\times 10^{-3}$) compared with the theoretical predictions from the analytic MI growth rate $\gamma _\textrm {MI}$, (3.4) and the analytic GAM damping $\gamma _\textrm {Qiu}$, (5.4). The difference in the maxima of the growth rates stems mainly from the different packet background amplitudes that were chosen according to (5.11).

The results confirm the findings of the previous section that the adjustment by a factor of 2.5 of the amplitude of the damping term does reproduce the observed damping rate. Additionally, the theoretical predictions for the change of growth rates from $\gamma _\textrm {MI}$ due to the changing amplitude is in good agreement with the simulation results. However, similarly to figure 12(b) the damping rate is overestimated in the region where the simulations are damped and the growth rate results for $k_\textrm {pert} < 6 ({2{\rm \pi} }/{a_\textrm {min}})$ are again higher than the theoretical prediction.

5.5. NLSE simulations including damping

We now include the findings regarding the damping term of the previous two sections in the NLSE solver and compare a damped NLSE simulation with a GK simulation. The damping given by (5.4) is amplified by a factor 2.5 and included in the numerical solver at the point where the dispersive term is applied, i.e. (4.4) in § 4.1. However, as shown in figure 9(a), during the MI saturation phase large wavevectors ($k_r > 20 ({2{\rm \pi} }/{a_\textrm {min}})$) appear in the spectrum which, due to the condition $({\tau _e}/{2}) k_r^2 \rho _i^2 \ll 1$ from § 5.2, lie outside of the applicability range of $\gamma _\textrm {Qiu}$. From the GK simulations, see figure 9(b), it is clear that these high wavevectors should be strongly suppressed, which was achieved by applying $\gamma = -4\times 10^{-4}\omega _{ci}$ to all wavevectors fulfilling $({\tau _e}/{2}) k_r^2 \rho _i^2 \geq 0.3$.

The (undamped) NLSE simulation presented in figure 8(a) is now repeated with the inclusion of the above described damping scheme. The damped result is reported in figure 14 together with the corresponding spectrum. A clear improvement in regards to multiple aspects can be discerned when comparing damped and undamped NLSE simulations with the related GK result from figure 8(b). First of all, the point in time at which the MI saturation phase appears in the simulations matches much better, with $t^\textrm {NLSE damp.}_\textrm {sat.} \approx 1.8\times 10^5\,1/\omega _i$ and $t^\textrm {GK}_\textrm {sat.} \approx 2.2\times 10^5\,1/\omega _i$ (at the packet centre $r = r_0 = 0.6 a_\textrm {tok}$), compared with the undamped NLSE simulation from figure 8(a) with $t^\textrm {NLSE undamp.}_\textrm {sat.} \approx 1.1\times 10^5\,1/\omega _i$. Furthermore, the maximal amplitude at the centre $r=r_0$ is in better agreement, with $E^\textrm {NLSE damp.}_\textrm {max} = 1.31 a_0$ and $E^\textrm {GK}_\textrm {max} \approx 1.26 a_0$, compared with $E^\textrm {NLSE undamp.}_\textrm {max} = 2.43 a_0$. The growth rate is $\gamma ^\textrm {NLSE damp.} \approx 1.48\times 10^{-5} \omega _{ci}$, which is very similar to the value obtained for the GK simulation with $\gamma ^\textrm {GK} \approx 1.41\times 10^{-5} \omega _{ci}$. However, a spectral comparison between figures 14 and 9(b) illustrates significant differences, notably that the GK spectrum is much more complex, with more interactions of the different wavevectors. This may in part stem from the radial dependence of the value of $\alpha _\textrm {NL}$, which leads to incoherent phase fronts in the GK simulations and is not included in the NLSE simulation (see also discussion in § 5.1), on the other hand, it may also be a consequence of the generally reduced complexity of the NLSE model.

Figure 14.

Repetition of the NLSE simulation reported in figure 8(a), where now the damping scheme described at the beginning of this section is included. The figure shows the time evolution of the GAM radial electric field $E_r$ (left) as well as the corresponding radial spectrum (right), i.e. the absolute value of the radial Fourier transform of the GAM radial electric field, $|\mathfrak {F}[E_r]|$.

5.6. Self-focusing of Gaussian packets

This section illustrates the self-focusing effect of the NLSE on an unmodulated initial condition where for the envelope a Gaussian packet is chosen. As detailed in § 2.2, self-focusing is observed for anomalous dispersion, i.e. ${\mathcal {G}} > 0$ and $\tau _e \lesssim 5.45$. The resulting comparison between a GK, an undamped NLSE and a damped NLSE (as specified in § 5.2) simulation is presented in figure 15.

Figure 15.

Comparison of the evolution of the GAM radial electric field $E_r$ with an unperturbed Gaussian initial condition according to the undamped NLSE, damped NLSE and GK theory. The plasma background parameters of the simulations are $\tau _e = 2$, $q_s = 11$, $\rho _i/a_\textrm {min} = 3.784\times 10^{-3}$, the initial condition is given by (5.1) with $a_0 = 3\times 10^{-4}$ (a.u.), $a_1 = 0$, $\textit {w}_0 = 0.1 a_\textrm {min}$ and $p = 1$. (a) The NLSE simulation result. (b) The GK simulation result. (c) The damped NLSE simulation result.

One can observe that, in all three simulations, the Gaussian packet experiences self-focusing, resulting in an increase of the maximal amplitude and a phase skip of the Gaussian centre compared with the packet edges. It is apparent that this behaviour is very similar to the MI, while it in contrast does not require an initial modulation of the envelope. We should note at this point that, differently from the standard scenario of the MI presented in § 3.1, the background wave is not a plane wave but a packet with a finite spectrum. Hence, it contains also higher spectral components, which can act as a seed for MI. Indeed, a spectral analysis, which for the sake of brevity is not reported here, reveals exponential growth of the wavevectors $k_r = 3 ({2{\rm \pi} }/{a_\textrm {min}})$, $4 ({2{\rm \pi} }/{a_\textrm {min}})$ and $5 ({2{\rm \pi} }/{a_\textrm {min}})$. This observation suggests an interpretation of this behaviour as an MI, whereas the observed unstable wavevectors are not necessarily the ones with the strongest growth rates but also depend on the initial conditions of the packet. We should add that the generation of higher spectral components is an inherent part of the nonlinear dynamics of the GAM, which might contribute to the observed spectral behaviour on top of the MI.

Similarly to § 5.2 it is observed that, with damping included in the NLSE model, the maximum amplitude of the GK simulation is more closely reproduced. Furthermore one finds better agreement for the width and steepness of the focused packet, i.e. in the GK and damped NLSE simulations at $t \approx 1.1\times 10^5\,1/\omega _i$ compared with the undamped NLSE at $t \approx 0.8\times 10^5\,1/\omega _i$. The radial asymmetry that develops in the GK simulation is not found in the NLSE simulations and can be explained by the radial dependence of $\alpha _\textrm {NL}$ that was introduced in § 5.1 and is explored further in Appendix A.

5.7. Breather simulations

In this section the phenomenon of the Akhmediev breather is studied in GK simulations. Akhmediev breathers (ABs) (Akhmediev & Korneev Reference Akhmediev and Korneev1986) are special types of MI solutions to the NLSE which predict that after the saturation phase, the MI initial condition is restored, as illustrated in figure 4, and new MI growth should be observable. This phenomenon is hard to observe in GK simulations as e.g. the dependency of $\alpha _\textrm {NL}$ breaks the packet apart and hinders the return to the initial condition after the first saturation phase. As a consequence, we concentrate on the region $r> 0.6 a_\textrm {min}$, where the results from Appendix A predict only small changes of $\alpha _\textrm {NL}$. A GK simulation with a breather solution is depicted in figure 16.

Figure 16.

Gyrokinetic simulation of a GAM which shows the breather behaviour of the MI. The upper figure shows the radial electric field $E_r$ with its radial shape, the bottom figure shows the value of $E_r$ along the lines shown in the upper figure, which follow the maxima. The red curve shows two MI saturation phases, the first at $t\approx 1.7\times 10^5\,1/\omega _{ci}$ and the second one at $t\approx 3.3\times 10^5\,1/\omega _{ci}$. The black curve shows the start of a second MI growth phase at the end of the simulation. The parameters are chosen as specified in table 1, with $\rho _{i3}/a_\textrm {min} = 5.025\times 10^{-3}$, $a_0 = 3.4\times 10^{-4}$ and $k_\textrm {pert} = 8 ({2{\rm \pi} }/{a_\textrm {min}})$.

The maximum at $r_2\approx 0.87 a_\textrm {min}$ experiences two saturation phases, the first one at $t\approx 1.5\times 10^5 \omega _{ci}^{-1}$ and the second at $t\approx 3.3\times 10^5 \omega _{ci}^{-1}$, thus demonstrating the AB phenomenon for a GAM packet in a GK simulation. Comparable behaviour happens for the maximum at $r\approx 0.71$, however, the growth is slower and not as pronounced. The impact of the damping mechanism and the differences in the nonlinear coefficient $\alpha _\textrm {NL}$ are again present in the simulations: for example, the second saturation phase at $t\approx 3.3\times 10^5 \omega _{ci}^{-1}$ of the maximum centred around $r_2$ is observed to have a maximum amplitude of $1.44 a_0$, which is significantly lower than the value of $1.74 a_0$ found in the first saturation phase, whereas an undamped AB would under ideal conditions yield the same maximum value in both saturation phases.

While more complex patterns of the AB are possible under ideal conditions, such as the appearance of growth phases of other unstable wavevectors in between the saturation phases of the initial perturbation wavevector $k_\textrm {pert}$, see e.g. Copie, Randoux & Suret (Reference Copie, Randoux and Suret2020), the deviations from a pure NLSE-like behaviour due to the mechanisms introduced in the previous sections make the possibility of such observations highly unlikely.