Design Parameter Space Research Articles

Parametric Study on Lateral–Directional Stability of Hypersonic Waverider

Lateral–directional stability is a critical issue for the design of any kind of aircraft. For the hypersonic waverider, the nonaxisymmetric, flat, and slender geometric feature makes it susceptible to the problem of lateral–directional instability. Therefore, the influence of geometric feature on the lateral–directional static and dynamic stability of hypersonic waverider is studied so as to enable the designer to consider this problem in the preliminary aircraft design stage. A series of power-law waveriders is generated at a given design space of the leading-edge parameters. Then, the variation of the static and dynamic derivatives with the design parameter is obtained by use of a surrogate model. It is found that increasing the dihedral angle of the lower surface can improve both the lateral and directional static stability of the total waverider. Furthermore, the dynamic instability mechanisms are analyzed in detail based on the approximate formulas derived from the linearized small-disturbance equations of motion. Especially, a concept of the Dutch-roll dynamic derivative is proposed, which plays an important part in the damping of the Dutch roll. Finally, it is found that increasing the dihedral angle can also improve the damping of the dynamic modes, including the roll, spiral, and Dutch roll.

Thermal Analysis of a MEMS-Based Self-Adaptive Microfluidic Cooling Device.

This study presents a thermal analysis of a temperature-driven microfluidic cell through a nonlinear self-adaptive micro valve that provides the mechanisms for the system to maintain a given critical temperature in an efficient way. For the description of the dynamics of the microfluidic cell, a system of two ordinary differential equations subjected to a nonlinear boundary condition, which describes the behavior of the valve, is proposed. The solution of the model, for determined conditions, shows the strong nonlinearity between the overall thermal resistance of the device and the heat flux dissipated due to the action of the thermostatic valve, obtaining a variable thermal resistance from 1.6 × 10−5 to 2.0 × 10−4 Km2/W. In addition, a stability analysis of the temperature-driven microfluidic cell is presented. The stability of the device is essential for its proper functioning and thus, to prevent its oscillating behavior. Therefore, this work focuses on assessing the range of design parameters of the self-adaptive micro valve to produce a stable behavior for the entire system. The stability analysis was performed by studying the linear perturbation around the stationary solution, with the model solved for various heat flows, flow rates, and critical temperatures. Finally, a map of the design parameters space, which specifies the region with asymptotic stability, was found. In this map, the critical temperature (temperature at which the valve initiates the buckling) plays and important role.

Nonlinear energy sink to enhance the landing gear shimmy performance

The shimmy phenomenon is a significant concern in aircraft landing gear dynamics. The prediction of the shimmy instability is an essential issue in landing gear design to develop a passive or active suppression method. This work investigates the application of the nonlinear energy sink (NES) concept to mitigate the effects of shimmy in landing gears. The NES concept has been used in recent research on mechanical vibrations. It comprises a passive target energy transfer method that refers to a one-way energy transfer from a primary to a nonlinear subsystem. The landing gear model is based on torsional displacement coupled with the tyre classical elastic string analogy model. The NES device connects to the wheel shaft, and it comprises a mass, a linear damper, and a pure cubic spring. The numerical integration in time was used to assess the shimmy onset speed and the post-shimmy limit cycle oscillations. A parametric analysis of the landing gear nonlinear dynamics without the NES is presented. The design space of possible NES parameters is given, obeying design constraints, and inspected to assess adequate NES designs. The best NES samples are included in the landing gear dynamics to study their influence. Results have shown that the NES can adequately expand the operational speed range for no shimmy and lead to lower LCO amplitudes in the post-shimmy for a reasonable range of speeds. The NES concept’s successful employment for the landing gear dynamics suggests an enormous potential form of passive shimmy control.

On Local Activity and Edge of Chaos in a NaMLab Memristor.

Local activity is the capability of a system to amplify infinitesimal fluctuations in energy. Complex phenomena, including the generation of action potentials in neuronal axon membranes, may never emerge in an open system unless some of its constitutive elements operate in a locally active regime. As a result, the recent discovery of solid-state volatile memory devices, which, biased through appropriate DC sources, may enter a local activity domain, and, most importantly, the associated stable yet excitable sub-domain, referred to as edge of chaos, which is where the seed of complexity is actually planted, is of great appeal to the neuromorphic engineering community. This paper applies fundamentals from the theory of local activity to an accurate model of a niobium oxide volatile resistance switching memory to derive the conditions necessary to bias the device in the local activity regime. This allows to partition the entire design parameter space into three domains, where the threshold switch is locally passive (LP), locally active but unstable, and both locally active and stable, respectively. The final part of the article is devoted to point out the extent by which the response of the volatile memristor to quasi-static excitations may differ from its dynamics under DC stress. Reporting experimental measurements, which validate the theoretical predictions, this work clearly demonstrates how invaluable is non-linear system theory for the acquirement of a comprehensive picture of the dynamics of highly non-linear devices, which is an essential prerequisite for a conscious and systematic approach to the design of robust neuromorphic electronics. Given that, as recently proved, the potassium and sodium ion channels in biological axon membranes are locally active memristors, the physical realization of novel artificial neural networks, capable to reproduce the functionalities of the human brain more closely than state-of-the-art purely CMOS hardware architectures, should not leave aside the adoption of resistance switching memories, which, under the appropriate provision of energy, are capable to amplify the small signal, such as the niobium dioxide micro-scale device from NaMLab, chosen as object of theoretical and experimental study in this work.

Machine learning-based surrogate model for accelerating simulation-driven optimisation of hydropower Kaplan turbine

In this work, a data-driven technique is proposed for efficient design exploration and optimisation of the Kaplan turbine. To avoid the curse of dimensionality, the proposed approach commences with the extraction of latent features of a parametric design space, which form a lower-dimensional subspace accumulating maximum geometric variability of designs. Afterwards, this subspace is exploited for the construction of a Gaussian Process-based surrogate model using an adaptive training strategy to infer the relative-tangential velocities at the leading and trailing edges of the turbine. The training strategy is structured on a high-fidelity sampling approach to ensure a notable prediction accuracy with a few training samples. After training, the surrogate model is integrated with an optimiser to explore the subspace for an optimal design and to determine the sensitivity of design parameters. The results showed that the optimal design generated with the proposed method increases the efficiency of the initial design from 56.98% to 90.73% at a significantly low computational cost. Finally, the convergence performance is verified with different experimentation and its accuracy to extract latent features and to predict the relative-tangential velocity is demonstrated via a comparative study in which different state-of-the-art approaches are compared with the proposed approach.

Untuned broadband spiral micro-coils achieve sensitive multi-nuclear NMR TX/RX from microfluidic samples

The low frequency plateau in the frequency response of an untuned micro-resonator permits broadband radio-frequency reception, albeit at the expense of optimal signal-to-noise ratio for a particular nucleus. In this contribution we determine useful figures of merit for broadband micro-coils, and thereby explore the parametric design space towards acceptable simultaneous excitation and reception of a microfluidic sample over a wide frequency band ranging from 13C to 1H, i.e., 125–500 MHz in an 11.74 T magnet. The detector achieves 37% of the performance of a comparably sized, tuned and matched resonator, and a linewidth of 17 ppb using standard magnet shims. The use of broadband detectors circumvents numerous difficulties introduced by multi-resonant RF detector circuits, including sample loading effects on matching, channel isolation, and field distortion.

FEECA: Design Space Exploration for Low-Latency and Energy-Efficient Capsule Network Accelerators

In the past few years, Capsule Networks (CapsNets) have taken the spotlight compared to traditional convolutional neural networks (CNNs) for image classification. Unlike CNNs, CapsNets have the ability to learn the spatial relationship between features of the images. However, their complexity grows because of their heterogeneous capsule structure and the dynamic routing , which is an iterative algorithm to dynamically learn the coupling coefficients of two consecutive capsule layers. This necessitates specialized hardware accelerators for CapsNets. Moreover, a high-performance and energy-efficient design of CapsNet accelerators requires exploration of different design decisions (such as the size and configuration of the processing array and the structure of the processing elements). Toward this, we make the following key contributions: 1) FEECA , a novel methodology to explore the design space of the (micro)architectural parameters of a CapsNet hardware accelerator and 2) CapsAcc , the first specialized RTL-level hardware architecture to perform CapsNets inference with high performance and high energy efficiency. Our CapsAcc achieves significant performance improvement, compared to an optimized GPU implementation, due to its efficient implementation of key activation functions, such as squash and softmax , and an efficient data reuse for the dynamic routing. The FEECA methodology employs the Non-dominated Sorting Genetic Algorithm (NSGA-II) to explore the Pareto-optimal points with respect to area, performance, and energy consumption. This requires analytical modeling of the number of clock cycles required to perform each operation of the CapsNet inference and the memory accesses to enable a fast yet accurate design space exploration. We synthesized the complete accelerator architecture in a 45-nm CMOS technology using Synopsys design tools and evaluated it for the MNIST benchmark (as done by the original CapsNet paper from Google Brain’s team) and for a more complex data set, the German Traffic Sign Recognition Benchmark (GTSRB).

A neural architecture search based framework for liquid state machine design

Liquid state machines (LSMs), also known as the recurrent version of spiking neural networks, have garnered significant research interest owing to their high computational power, biological plausibility, simple structure, and low training complexity. By exploring the design space in network architectures and parameters, recent works have demonstrated the great potential for improving the accuracy of LSM models with low complexity. However, these works are based on manually defined network architectures or predefined parameters, which may ignore the potential optimization of the architectures and parameters of LSMs. In this study, we propose a neural architecture search-based framework to explore the architecture and parameter design space for the automatic dataset-oriented LSM models. To manage the exponentially increasing design space, we adopt a three-step search for LSMs, including dynamic multiple-liquid architecture search in multiple layers, variations in the number of neurons in each liquid, and parameter search such as percentage connectivity and excitatory neuron ratio within each liquid. In addition, we propose the use of a simulated annealing algorithm to implement three-step heuristic search. Two datasets, including the image dataset of NMNIST and speech dataset of FSDD, were used to test the effectiveness of the proposed framework. Simulation results demonstrated that our framework can produce the dataset-oriented optimal LSM models with high accuracy and low complexity. The best classification accuracy on the two datasets with only 1000 spiking neurons was observed to be 92.5% and 84%. Meanwhile, the network connections of discovered optimal multiple-liquid LSM models for the two datasets, on average, were reduced by 56.3% and 60.2% separately compared with a single LSM. Furthermore, the total number of neurons in the optimal multiple-liquid LSM models on the two datasets was reduced by 20% with an accuracy loss of only 0.5%.

Optimization-based Component Sizing Method for Electrified Heavy-Duty Powertrain Concepts

The demand for battery-powered, exhaust emission-free mobile machinery is increasing. However, the high energy requirements of mobile machines result in large, heavy and expensive battery storage systems. Driven by continuously decreasing but still fairly high battery costs battery costs, the efficiency of the drive system is becoming the focus of development activities, even in an industry where robustness, service lifetime, productivity and power density have been the driving factors up to now. Interactions between topology, component sizing, and control strategy require a system-level approach for the development of drive systems. In this paper, a novel optimization-based sizing method for electrified heavy-duty powertrain concepts is presented and applied to subsystems of a wheeled excavator. Scalable component cost and efficiency models are used in a system simulation to calculate objective function values for a multi-objective optimization. The result of the optimization, a trade-off between energy consumption and costs provides the OEM with a decision support in the development process. The modular structure of the approach allows a subsequent extension of the design parameter space as well as the detailing of the models used.

Applicability of Field Plate in Double Channel GaN HEMT for Radio-Frequency and Power-Electronic Applications

In the present communication, for the first time, applicability of Field Plate (FP) for Double Channel (DC) AlGaN/GaNHEMT is demonstrated. Impact of design space parameters such as field plate length (LFP) and Silicon Nitride thickness (tSiN) on breakdown voltage of DC HEMT is investigated and benchmarked with Single Channel (SC) HEMT. The investigation is carried out using ATLAS Technological Computer Aided Design (TCAD) Simulation tool, which is an efficient method in terms of time and cost to analyze and understand DC HEMT prior to the fabrication. The simulation shows new findings that breakdown voltage of DC device exhibited a large deviation with that of SC device. The breakdown voltage deviation is well corroborated through electric field, impact ionization and off-state leakage analysis. The electric field analysis has shown optimal value of LFP and tSiN for DC HEMT. It is also observed that tSiN for DC-HEMT should be lower than that of SC-HEMT. In this paper, the off-state leakage, electron-depletion and impact ionization are the first-hand observations for benchmarking DC device with SC device. Furthermore, it is found that DC device yield higher cut-off frequency and device linearity than that of SC device, which is highly desirable for Radio-Frequency (RF) and linear amplifiers. Thus, the observation presented in this paper assist device technologist to enhance the performance of DC device further and foundries to optimize the FP for DC device.

Micro-coil probes for magnetic intracortical neural stimulation: Trade-offs in materials and design.

Neural probes for intracortical neuromodulation in the brain have advanced with the developments in micro- and nanofabrication technologies. Most of these technologies for the intracortical stimulation have relied on the direct electrical stimulation via electrodes or arrays of electrodes. Generating electric fields using time-varying magnetic fields is a more recent neuromodulation technique that has proven to be more specifically effective for the intracortical stimulation. Additionally, current-actuated coils require no conductive contact with tissues and enable precise tailoring of magnetic fields, which are unaffected by the non-magnetic nature of the biological tissue and encapsulation layers. The material and design parameter space for such micro-coil fabrication can be optimized and tailored to deliver the ideal performance depending on the parameters needed for operation. In this work, we review the key requirements for implantable microcoils including the probe structure and material properties and discuss their characteristics and related challenges for the applications in intracortical neuromodulation.

Workspace analysis in the design parameter space of a 2-DOF spherical parallel mechanism for a prescribed workspace: Application to the otologic surgery

During Otologic surgery, and more broadly during microsurgery, the surgeon encounters several difficulties due to the confined spaces and micro-manipulations. The purpose of the paper is to design a robot with a prescribed regular workspace shape to handle an endoscope to assist the Otologic surgery. A spherical parallel mechanism with two degrees of freedom is analysed in its design parameter space. This mechanism is composed of three legs (2USP-U) to connect the base to a moving platform connected to a double parallelogram to create a remote center of motion (RCM). Its kinematic properties, i.e. the singularity locus and the number of direct kinematic solutions, are investigated. For some design parameters, non-singular assembly modes changing trajectories may exist and have to be investigated inside the prescribed regular workspace shape. Two sets of design parameters are presented with their advantages and disadvantages.

Modeling and experimental investigation of the dynamics of a spherical Transforming Roving Rolling Explorer (TRREx) prototype

This paper examines a biologically inspired, transforming mars rover concept known as the Transforming Roving Rolling Explorer (TRREx). The rover is designed to operate in two modes: the ‘roving’ and ‘rolling’ modes. The roving mode is similar to many traditional wheeled rover designs and is useful for exploring relatively flat terrains. The rolling mode is intended to enable the rover to travel down steep slopes, such as those found in ravines and crater walls. In the rolling mode, the vehicle folds itself into a sphere and propels itself by shifting its center of mass. It does this by opening and closing the eight panels which form the surface of the sphere. The evaluation of this rolling mode is the primary focus of this paper. To that end, a spherical prototype TRREx has been designed, constructed, and tested. In addition, a mathematical model of the vehicle’s rolling mode dynamics has been created. The performance of the physical prototype has been compared to simulation results and the qualitative agreement between the experimental and simulated results indicate that the mathematical model created for the vehicle’s rolling mode dynamics captures the essential behavior of the vehicle. The mathematical simulation was then used to explore the TRREx parameter design space in order to analyze the effect of changing various system parameters. A path tracking simulation is also included as a case study to highlight the ability of a TRREx vehicle to traverse a desired path.

Gaussian Process-Driven, Nested Experimental Co-Design: Theoretical Framework and Application to an Airborne Wind Energy System

Abstract This paper presents and experimentally evaluates a nested combined plant and controller optimization (co-design) strategy that is applicable to complex systems that require extensive simulations or experiments to evaluate performance. The proposed implementation leverages principles from Gaussian process (GP) modeling to simultaneously characterize performance and uncertainty over the design space within each loop of the co-design framework. Specifically, the outer loop uses a GP model and batch Bayesian optimization to generate a batch of candidate plant designs. The inner loop utilizes recursive GP modeling and a statistically driven adaptation procedure to optimize control parameters for each candidate plant design in real-time, during each experiment. The characterizations of uncertainty made available through the GP models are used to reduce both the plant and control parameter design space as the process proceeds, and the optimization process is terminated once sufficient design space reduction has been achieved. The process is validated in this work on a lab-scale experimental platform for characterizing the flight dynamics and control of an airborne wind energy (AWE) system. The proposed co-design process converges to a design space that is less than 8% of the original design space and results in more than a 50% increase in performance.

Evolutionary design of optimal surface topographies for biomaterials

Natural evolution tackles optimization by producing many genetic variants and exposing these variants to selective pressure, resulting in the survival of the fittest. We use high throughput screening of large libraries of materials with differing surface topographies to probe the interactions of implantable device coatings with cells and tissues. However, the vast size of possible parameter design space precludes a brute force approach to screening all topographical possibilities. Here, we took inspiration from Nature to optimize materials surface topographies using evolutionary algorithms. We show that successive cycles of material design, production, fitness assessment, selection, and mutation results in optimization of biomaterials designs. Starting from a small selection of topographically designed surfaces that upregulate expression of an osteogenic marker, we used genetic crossover and random mutagenesis to generate new generations of topographies.

Optimization of Dynamic and Buckling Behavior of Thin-Walled Composite Cylinder, Supported by Nature-Inspired Agorithms.

The paper presents the optimization of stacking sequence (the lamination angles in subsequent composite layers) of the composite cylinder in order to simultaneously maximize the values of the first natural frequency and the first buckling force . The optimization problem involves either two objective functions or one which combines both problems using a coefficient whose optimal value is also being searched for. The main idea of the paper is the application of two neural network metamodels which substitute very time- and resource-consuming Finite Element (FE) calculations. The metamodels are created separately through a novel iterative procedure, using examples obtained through Finite Element Method (FEM). The metamodels, once ready, are able to assess the values of and instantly and thus enable the application of nature-inspired Genetic Algorithm (GA) minimization with reasonable calculation times. Obviously, the maxima of and may be located in different points of the design parameters (i.e., lamination angles) space, the considered optimization task is to find a solution for which both and simultaneously reach values as close to their maxima as possible. All the investigated optimization examples are repeated several times and basic statistical analysis of the results is presented.

Trained Harmonic Balance Method for Parametrically Excited Jeffcott Rotor Analysis

Abstract Being able to identify instability regions is an important task for the designers of rotating machines. It allows discarding, since the early design stages, those configurations which may lead to catastrophic failures. Instability can be induced by different occurrences such as an unbalanced disk, torsional, and axial forces on the shaft or periodic variation of system parameters known as “parametric excitation.” In this paper, the stability of a Jeffcott rotor, parametrically excited by the time-varying stiffness of the rolling bearings, is studied. The harmonic balance method (HBM) is here applied as an approximate procedure to obtain the well-known “transition curves (TCs)” which separate the stable from the unstable regions of the design parameter space. One major challenge in the HBM application is identifying an adequate harmonic support (i.e., number of harmonics in the Fourier formulation), necessary to produce trustworthy results. A procedure to overcome this issue is here proposed and termed “trained HBM” (THBM). The results obtained by THBM are compared to those computed by Floquet theory, here used as a reference. The THBM proves to be able to produce reliable TCs in a timely manner, compatible with the design process.

Adjoint-based optimization to minimize scattered pressure fields

Acoustic and elastic metamaterials have shown potential for many different applications in wave manipulation and structural isolation. Minimization of a scattered pressure field has been identified as an important use for such materials, and a wide variety of designs have been proposed for this purpose. Several of these designs are difficult to manufacture (e.g., pentamode designs), and many others involve numerous tunable parameters and a large design parameter space (e.g., distributions of resonators). This presents a challenge for global search-based optimization and raises the need for gradient-based methods. In this work, adjoint-based, PDE-constrained optimization is used to improve the feasibility of minimizing a scattered pressure field via an optimal set of channels in an annulus surrounding a rigid scatterer. Two different optimization strategies are investigated: material identification, where the physical properties of each channel are treated as unknown, and force identification, where the magnitude and phase of the reflected field is treated as unknown at various patches on the annulus. Results are presented for both a single plane wave direction and multiple plane wave directions, as well as for both a single frequency and multiple frequencies. [S.N.L. is managed and operated by NTESS under DOE NNSA Contract No. DE-NA0003525.]

Optimal design of experiments to identify latent behavioral types

Bayesian optimal experiments that maximize the information gained from collected data are critical to efficiently identify behavioral models. We extend a seminal method for designing Bayesian optimal experiments by introducing two computational improvements that make the procedure tractable: (1) a search algorithm from artificial intelligence that efficiently explores the space of possible design parameters, and (2) a sampling procedure which evaluates each design parameter combination more efficiently. We apply our procedure to a game of imperfect information to evaluate and quantify the computational improvements. We then collect data across five different experimental designs to compare the ability of the optimal experimental design to discriminate among competing behavioral models against the experimental designs chosen by a "wisdom of experts" prediction experiment. We find that data from the experiment suggested by the optimal design approach requires significantly less data to distinguish behavioral models (i.e., test hypotheses) than data from the experiment suggested by experts. Substantively, we find that reinforcement learning best explains human decision-making in the imperfect information game and that behavior is not adequately described by the Bayesian Nash equilibrium. Our procedure is general and computationally efficient and can be applied to dynamically optimize online experiments.

Spectral singularities and asymmetric light scattering in PT-symmetric 2D nanoantenna arrays.

The intriguing physics of non-Hermitian systems satisfying parity-time (PT) symmetry has spurred a surge of both theoretical and experimental research in interleaved gain-loss systems for novel photonic devices. In this work, we investigate vertically stacked GaInP PT-symmetric nanodisk resonators arranged in two-dimensional periodic lattice using full-wave numerical simulations and scattering matrix theory. The proposed dielectric metasurface supports lasing spectral singularities with asymmetric reflection and highly anisotropic far-field scattering patterns. It offers a much broader design parameter space to control wavelength, scattering direction, and efficiency of optical emission when compared to the predominantly one-dimentional (1D) or quasi-1D structures studied so far. The proposed system with Q-factor >105 serves as a powerful platform for enhanced light-matter interaction by enabling extensive control of asymmetric light scattering, amplification, and unprecedented localization of electromagnetic fields.