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Hydrodynamic approaches that treat granular materials as a continuum via the homogenization of discrete flow properties have become viable options for efficient predictions of bulk flow behaviours. However, simplified boundary conditions in computational fluid dynamics are often adopted, which have difficulty in describing the complex stick–slip phenomenon at the boundaries. This paper extends the lattice Boltzmann method for granular flow simulations by incorporating a novel frictional boundary condition. The wall slip velocity is first calculated based on the shear rate limited by the Coulomb friction, followed by the reconstruction of unknown density distribution functions through a modified bounce-back scheme. Validation is performed against a unique plane Couette flow configuration, and the analytical solutions for the flow velocity profile and the wall slip velocity, as functions of the friction coefficient, are reproduced by the numerical model. The transition between no-slip and partial-slip regimes is captured well, but the convergence rate drops from second order to first order when slip occurs. The rheological parameters and the basal friction coefficient are calibrated further against the discrete element simulation of a square granular column collapsing over a horizontal bottom plane. It is found that the calibrated continuum model can predict other granular column collapses with different initial aspect ratios and slope inclination angles, including the basal slip and the complex internal flow structures, without any further adjustments to the model parameters. This highlights the generalization ability of the numerical model, which has a wide range of application in granular flow predictions and controls.
Powered exoskeletons need actuators that are lightweight, compact, and efficient while allowing for accurate torque control. To satisfy these requirements, researchers have proposed using series elastic actuators (SEAs). SEAs use a spring in series with rotary or linear actuators. The spring compliance, in conjunction with an appropriate control scheme, improves torque control, efficiency, output impedance, and disturbance rejection. However, springs add weight to the actuator and complexity to the control, which may have negative effects on the performance of the powered exoskeleton. Therefore, there is an unmet need for new SEA designs that are lighter and more efficient than available systems, as well as for control strategies that push the performance of SEA-based exoskeletons without requiring complex modeling and tuning. This article presents the design, development, and testing of a novel SEA with high force density for powered exoskeletons, as well as the use of a two degree-of-freedom (2DOF) PID system to improve output impedance and disturbance rejection. Benchtop testing results show reduced output impedance and damping values when using a 2DOF PID controller as compared to a 1DOF PID controller. Human experiments with three able-bodied subjects (N = 3) show improved torque tracking with reduced root-mean-square error by 45.2% and reduced peak error by 49.8% when using a 2DOF PID controller. Furthermore, EMG data shows a reduction in peak EMG value when using the exoskeleton in assistive mode compared to the exoskeleton operating in transparent mode.
We use experiments to study the evolution of bubble clusters in a swarm of freely rising, deformable bubbles. A new machine learning-aided algorithm allows us to identify and track bubbles in clusters and measure the cluster lifetimes. The results indicate that contamination in the carrier liquid can enhance the formation of bubble clusters and prolong the cluster lifetimes. The mean bubble rise velocities conditioned on the bubble cluster size are also explored, and we find a positive correlation between the cluster size and the rise speed of the bubbles in the cluster, with clustered bubbles rising up to $20\,\%$ faster than unclustered bubbles.
Introduced in 2020, the notion of living artefacts encompasses biodesign outcomes that maintain the vitality of organisms such as fungi, algae, bacteria, and plants in the use of everyday artefacts, enabling new functions, interactions, and expressions within our daily lives. This paper situates living artefacts at the intersection of the sustainability discourse and more-than-human ontologies, illuminating the unprecedented opportunities that living artefacts present for regenerative ecologies. These ecologies are characterized by a fundamental inclination toward mutualism, creativity, and coevolution. In regenerative ecologies, the human-nature relationship transcends the binary distinction and it manifests as a single autopoietic system in which the constituent members collaboratively engage in the creation, transformation, and evolution of shared habitats. The paper outlines five pillars, supplemented by guiding questions and two illustrative cases, to aid designers in unlocking, articulating, and critically evaluating the potential of living artefacts for regenerative ecologies.
This paper considers the initial stage of radiatively driven convection, when the perturbations from a quiescent but time-dependent background state are small. Radiation intensity is assumed to decay exponentially away from the surface, and we consider parameter regimes in which the depth of the water is greater than the decay scale of $e$ of the radiation intensity. Both time-independent and time-periodic radiation are considered. In both cases, the background temperature profile of the water column is time-dependent. A linear analysis of the system is performed based on these time-dependent profiles. We find that the perturbations grow in time according to $\exp [(\sigma (t) t)]$, where $\sigma (t)$ is a time-dependent growth rate. An appropriately defined Reynolds number is the primary dimensionless number characterising the system, determining the wavelength, vertical structure and growth rate of the perturbations. Simulations using a Boussinesq model (the Stratified Ocean Model with Adaptive Refinement) confirm the linear analysis.
Recent numerical studies have shown that forced, statistically isotropic turbulence develops a ‘thermal-equilibrium’ spectrum, $\mathcal {E}(k) \propto k^2$, at large scales. This behaviour presents a puzzle, as it appears to imply the growth of a non-zero Saffman integral, which would require the longitudinal velocity correlation function, $\chi (r)$, to satisfy $\chi (r\to \infty )\propto r^{-3}$. As is well known, the Saffman integral is an invariant of decaying turbulence, precisely because non-local interactions (i.e. interactions via exchange of pressure waves) are too weak to generate such correlations. Subject to certain restrictions on the nature of the forcing, we argue that the same should be true for forced turbulence. We show that long-range correlations and a $k^2$ spectrum arise as a result of the turbulent diffusion of linear momentum, and extend only up to a maximum scale that grows slowly with time. This picture has a number of interesting consequences. First, if the forcing generates eddies with significant linear momentum (as in so-called Saffman turbulence), a thermal spectrum is not reached – instead, a shallower spectrum develops. Secondly, the energy of turbulence that is forced for a while and then allowed to decay obeys Saffman's decay laws for a period that is much longer than the duration of the forcing stage.
(S)-α-Ethyl-2-oxo-1-pyrrolidineacetamide (trade name levetiracetam), a derivative of piracetam, is used clinically as an add-on treatment for partial-onset seizures. In this study, we report the solid-state structure of a new drug co-crystal produced from levetiracetam and 3,5-dinitrosalicylic acid through cooling crystallization. This compound was further characterized by infrared spectroscopy, powder X-ray diffraction, and single-crystal X-ray diffraction. The new co-crystals show a 1:1 stoichiometry and crystallize in the monoclinic system, space group P21, with cell parameters: a = 9.7709(3) Å, b = 6.2202(2) Å, c = 14.7280(4) Å, α = 90°, β = 96.0340(10)°, γ = 90°, V = 890.16(5) Å3, and Z = 2. It is identified that hydrogen bonds are the main interactions between levetiracetam and 3,5-dinitrosalicylic acid, and the contribution of each hydrogen bond in maintaining the stability of the crystal structure was also quantified using Hirshfeld surface analysis.
A multi-agent deep reinforcement learning (DRL)-based model is presented in this study to reconstruct flow fields from noisy data. A combination of reinforcement learning with pixel-wise rewards, physical constraints represented by the momentum equation and the pressure Poisson equation, and the known boundary conditions is used to build a physics-constrained deep reinforcement learning (PCDRL) model that can be trained without the target training data. In the PCDRL model, each agent corresponds to a point in the flow field and learns an optimal strategy for choosing pre-defined actions. The proposed model is efficient considering the visualisation of the action map and the interpretation of the model operation. The performance of the model is tested by using direct numerical simulation-based synthetic noisy data and experimental data obtained by particle image velocimetry. Qualitative and quantitative results show that the model can reconstruct the flow fields and reproduce the statistics and the spectral content with commendable accuracy. Furthermore, the dominant coherent structures of the flow fields can be recovered by the flow fields obtained from the model when they are analysed using proper orthogonal decomposition and dynamic mode decomposition. This study demonstrates that the combination of DRL-based models and the known physics of the flow fields can potentially help solve complex flow reconstruction problems, which can result in a remarkable reduction in the experimental and computational costs.
We calculate the hydrodynamic force on a small spherical, unsteady squirmer moving with a time-dependent velocity in a fluid at rest, taking into account convective and unsteady fluid inertia effects in perturbation theory. Our results generalise those of Lovalenti & Brady (J. Fluid Mech., vol. 256, 1993, pp. 561–605) from passive to active spherical particles. We find that convective inertia changes the history contribution to the hydrodynamic force, as it does for passive particles. We determine how the hydrodynamic force depends on the swimming gait of the unsteady squirmer. Since swimming breaks the spherical symmetry of the problem, the force is not determined completely by the outer solution of the asymptotic matching problem, as it is for passive spheres. There are additional contributions due to the inhomogeneous solution of the inner problem. We also compute the disturbance flow, illustrating convective and unsteady effects when the particle experiences a sudden start followed by a sudden stop.
By combining the technique of energy selective surface and frequency selective rasorber, an energy selective rasorber is proposed, which performs selective energy protection in the low communication frequency band (0.8–2 GHz) and wave-absorbing property in the high-frequency band (6–18 GHz). The design consists of two layers, of which the bottom one contains a lumped diode structure for energy selection function in the transmission band, while together with the top layer, they perform a wideband wave absorbing function. The simulated and measured results agree well with each other, and both show good absorption in 6–18 GHz and energy-selective property around 1.86 GHz. That is, when the incident power changes from −30 to 14 dBm, the reflection coefficient changes from below −22 dB to above −2 dB, while the transmission coefficient changes from above −3 dB to below −17 dB.
This paper presents a low-profile six-beam antenna implemented by a compact two-layer 6 × 6 beamforming network (BFN) and a 6 × 2 slot antenna in substrate integrated waveguide (SIW) technology. The main components of the proposed 6 × 6 BFN are 3 × 3 multi-aperture couplers, interlayer hybrid couplers, and several phase shifters which are embedded on two microwave substrates. The proposed antenna has been designed, simulated, and fabricated for the frequency range of 28–32 GHz. The size of this antenna is 82 × 31.8 × 0.787 mm3, which can be a suitable choice for 5G applications due to its compact dimensions compared to similar works. Prototype testing shows that the proposed structure presents a stable beamforming performance both in simulation and measurement with good agreement. The antenna generates six radiation beams in directions ±9°, ±30°, and ±54° with good return losses and isolations.
With the rapid development of communication technology, the researches of multi-band filtering circuits have become more and more important. Multimode resonator (MMR) is one of the vital methods to provide multi-resonant modes within a single design. In this paper, a dual-band ultra-wideband bandpass filter (UWB-BPF) using stepped impedance stub-loaded resonators (SI-SLR) is presented. The main advantage of using SI-SLR is to have better performance with multimode behavior and more parameters to control resonant modes. SI-SLR combines the advantages of SIR and SLR structures, which gives a compact, high-performance multiband filter. The proposed filter design has compact size, sharp and flat response with low insertion loss (IL), low return loss (RL), and high band-to-band rejection. The filter is designed for UWB communication in wireless body area networks and fabricated on Arlon substrate with relative permittivity ${\varepsilon_{\textrm{r}}} = 3.25$, thickness $0.8\;{\textrm{mm}}$. The resulted dual-bands are centered at $4{\textrm{ GHz}}$ and $8.3{\textrm{ GHz}}$ with fractional bandwidths $37{\textrm{% }}$ and $48{\textrm{%}}$. The simulation was carried out using CST Microwave Studio. The filter provides good passband performances, with IL 0.49 dB and 0.31 dB at the center frequency of lower and higher bands, respectively. The band-to-band 40 dB rejection is realized by adding circular spiral at the input/output of the filter.
Learn to design and improve state-of-the-art aerodynamic ground testing facilities in this comprehensive reference book, with particular focus on high-enthalpy shock tunnels. Including the latest advances in detonation-driven high-enthalpy shock tunnels, readers will discover how to extend test time with brand new concepts and duplicate real hypersonic flight test conditions. Through a systematic approach, the book describes technologies for a variety of different drivers in hypersonic and high-enthalpy shock tunnels. The fundamental theories for hypersonic and high-enthalpy shock tunnels are described step-by-step, with examples throughout, providing an accessible introduction. Built on years of real-world experience, this book examines in detail the advantages and challenges of improving test flow qualities, including increasing total pressure and enthalpy, model scale amplification and test-time extending for different types of shock tunnel drivers. This is an ideal companion handbook for aerospace engineers as well as graduate students.
This accessible and self-contained guide provides a comprehensive introduction to the popular programming language Python, with a focus on applications in chemistry and chemical physics. Ideally suited to students and researchers of chemistry learning to employ Python for problem-solving in their research, this fast-paced primer first builds a solid foundation in the programming language before progressing to advanced concepts and applications in chemistry. The required syntax and data structures are established, and then applied to solve problems computationally. Popular numerical packages are described in detail, including NumPy, SciPy, Matplotlib, SymPy, and pandas. End of chapter problems are included throughout, with worked solutions available within the book. Additional resources, datasets, and Jupyter Notebooks are provided on a companion website, allowing readers to reinforce their understanding and gain confidence applying their knowledge through a hands-on approach.
Virtual reality (VR) is a powerful technology that promises to transform our lives. This balanced and interdisciplinary text blends the key components from computer graphics, perceptual psychology, human physiology, behavioral science, media studies, human-computer interaction, optical engineering, and sensing and filtering, showing how each contributes to engineering perceptual illusions. Steven LaValle draws on his unique experience as a teacher, researcher, and early founder of Oculus VR, to demonstrate how the best practices and insights from industry are built on fundamental computer science principles. Topics include media history, geometric modeling, optical systems, displays, eyes, ears, low-level perception, neuroscience of vision, graphical rendering, tracking systems, interaction mechanisms, audio, evaluating VR systems, and mitigating side effects. Students, researchers, and developers will gain a clear understanding of timeless foundations and new applications, enabling them to make innovative contributions to this growing field as scientists, engineers, business developers, and content makers.
Edited by
Jong Chul Ye, Korea Advanced Institute of Science and Technology (KAIST),Yonina C. Eldar, Weizmann Institute of Science, Israel,Michael Unser, École Polytechnique Fédérale de Lausanne
A simple and compact three-way planar power divider, which avoids the floating common node of the isolation resistors, is presented. The proposed structure exhibits a wideband operation (measured frequency range of 1.6–3.3 GHz and bandwidth of 69.4%) with good return loss and isolation characteristics. Transmission line theory is used for the mathematical analysis and extraction of design equations, followed by simulations and experimental measurements that confirm the predicted results. The proposed divider achieves an equal power split (∼32%, −4.9 ± 0.4 dB insertion loss) between the input and each output port. The measured return loss is better than −10 dB at all ports, and the measured maximum isolation is close to −30 dB. The proposed design exhibits a fully planar structure, thus eliminating the need for a floating common node for the isolation resistors. Additionally, its structure is much simpler (i.e., no coupled lines, crossovers, or lumped capacitors are required) than other designs, achieves wideband operation, and provides design simplicity, flexibility, and easy implementation. Despite its simple noncomplicated structure, the proposed three-way planar divider achieves similar (or in some cases, better) performance and size than other more complicated structures. Furthermore, it can be expanded to an n-way structure.
Edited by
Jong Chul Ye, Korea Advanced Institute of Science and Technology (KAIST),Yonina C. Eldar, Weizmann Institute of Science, Israel,Michael Unser, École Polytechnique Fédérale de Lausanne
We provide a short, self-contained introduction to deep neural networks that is aimed at mathematically inclined readers. We promote the use of a vect--matrix formalism that is well suited to the compositional structure of these networks and that facilitates the derivation/description of the backpropagation algorithm. We present a detailed analysis of supervised learning for the two most common scenarios, (i) multivariate regression and (ii) classification, which rely on the minimization of least squares and cross-entropy criteria, respectively.
Edited by
Jong Chul Ye, Korea Advanced Institute of Science and Technology (KAIST),Yonina C. Eldar, Weizmann Institute of Science, Israel,Michael Unser, École Polytechnique Fédérale de Lausanne
Since the groundbreaking performance improvement by AlexNet at the ImageNet challenge, deep learning has provided significant gains over classical approaches in various fields of data science including imaging reconstruction. The availability of large-scale training datasets and advances in neural network research have resulted in the unprecedented success of deep learning in various applications. Nonetheless, the success of deep learning appears very mysterious. The basic building blocks of deep neural networks are convolution, pooling, and nonlinearity, which are primitive tools of mathematics. Interestingly, the cascaded connection of these primitive tools results in superior performance over traditional approaches. To understand this mystery, one can go back to the basic ideas of the classical approaches to understand the similarities and differences from modern deep-neural-network methods. In this chapter, we explain the limitations of the classical machine learning approaches, and provide a review of mathematical foundations to understand why deep neural networks have successfully overcome their limitations.
Edited by
Jong Chul Ye, Korea Advanced Institute of Science and Technology (KAIST),Yonina C. Eldar, Weizmann Institute of Science, Israel,Michael Unser, École Polytechnique Fédérale de Lausanne
Inspired by the success of deep learning in computer vision tasks, deep learning approaches for various MRI problems have been extensively studied in recent years. Early deep learning studies for MRI reconstruction and enhancement were mostly based on image-domain learning. However, because the MR signal is acquired in the k-space domain, researchers have demonstrated that deep neural networks can be directly designed in k-space to utilize the physics of MR acquisition. In this chapter, the recent trend of k-space deep learning for MRI reconstruction and artifact removal are reviewed. First, scan-specific k-space learning, which is inspired by parallel MRI, is covered. Then we provide an overview of data-driven k-space learning. Subsequently, unsupervised learning for MRI reconstruction and motion artifact removal are discussed.