Multi-physics Optimization Research Articles

Multiphysics Optimization of a High-Speed Permanent Magnet Motor Based on Subspace and Sequential Strategy

Multiphysics Optimization of a High-Speed Permanent Magnet Motor Based on Subspace and Sequential Strategy

Enhancing overall performance of thermophotovoltaics via deep reinforcement learning-based optimization

Thermophotovoltaic (TPV) systems can be used to harvest thermal energy for thermoelectric conversion with much improved efficiency and power density compared with traditional photovoltaic systems. As the key component, selective emitters (SEs) can re-emit tailored thermal radiation for better matching with the absorption band of TPV cells. However, current designs of the SEs heavily rely on empirical design templates, particularly the metal-insulator-metal (MIM) structure, and lack of considering the overall performance of TPV systems and optimization efficiency. Here, we utilized a deep reinforcement learning (DRL) method to perform a comprehensive design of a 2D square-pattern metamaterial SE, with simultaneous optimization of material selections and structural parameters. In the DRL method, only the database of refractory materials with gradient refraction indexes needs to be prepared in advance, and the whole design roadmap will automatically output the SE with optimal Figure-of-Merit (FoM) efficiently. The optimal SE is composed of a novel material combination of TiO2, Si, and W substrate, with its thickness and structure precisely optimized. Its emissivity spectra match well with the external quantum efficiency curve of the GaSb cell. Consequently, the overall performance of TPV is significantly enhanced with an output power density of 5.78 W/cm2, an energy conversion efficiency of 38.26%, and a corresponding FoM of 2.21, surpassing most existing designs. The underlying physics of optimal SE is explained by the coupling effect of multiple resonance modes. This work advances the practical application potential of TPV systems and paves the way for addressing other multi-physics optimization problems and metamaterial designs.

Exploring VAE-driven implicit parametric unit cells for multiscale topology optimization

The simulation and optimization of metamaterial, known for their engineered properties in various applications, encounter intricate challenges due to complex microstructure interactions, extensive design spaces, and substantial computational requirements. To address these challenges, our research introduces a novel data-driven framework utilizing deep generative modeling to enhance the design process of metamaterials.Focusing on composite metamaterial with double negative coefficients of thermal expansion and Poisson's ratio, we apply an Alternative Active Phase and Objective functions (AAPO) method for deciding the initial metamaterial dataset. To enhance dataset diversity, a distortion filter is applied, broadening the range of design possibilities. Subsequently, we utilize a Variational Autoencoder (VAE), integrated with a regressor, to train on this diversified database. This training effectively maps complex unit cell geometries to a coherent latent space, simultaneously correlating them with continuous material properties.Our approach demonstrates robustness in multi-phase and multi-physics optimization as well as efficiency in generating specialized databases of unit cells. This framework is pivotal in systematically designing unit cells and multiscale systems, specifically aiming for distinct thermo-mechanical behavior targets.To mitigate the computational demands encountered during multiple design meta-materials via gradient-based topology optimization, we have integrated high-performance methods and automatic differentiation. This integration marks a significant advancement in the data-driven design of metamaterials, offering substantial practical and theoretical benefits in the field.

Feature-Assisted Neural Network Surrogate-Based Multiphysics Optimization for Microwave Filters

Feature-Assisted Neural Network Surrogate-Based Multiphysics Optimization for Microwave Filters

Metaheuristic aided structural topology optimization method for heat sink design with low electromagnetic interference

This study investigates the application of the Metaheuristic Aided Structural Topology Optimization (MASTO) method as a novel approach to address the multiphysics design challenge of creating a heat sink with both high heat conductivity and minimal Electromagnetic Interference (EMI). A distinctive 2D layout with elongated fins is examined for electromagnetic traits, highlighting resonance-related EMI concerns. MASTO proves to be a valuable tool for navigating the complex design space, yielding thoughtfully optimized solutions that harmonize efficient heat dissipation with effective EMI control. By merging simulation findings with practical observations, this study underscores the potential of the MASTO method in achieving effective designs for intricate multiphysics optimization problems. Specifically, the method's capacity to address the complex interplay of heat transfer with convection and the suppression of electromagnetic emissions is showcased. Moreover, the study demonstrates the feasibility of translating these solutions into tangible outcomes through manufacturing processes.

Dynamic-constrained multi-material topology optimization under heat flux and thermal–mechanical loading

In practice, the structures are often simultaneously related to thermal and mechanical loads, which can lead to stability failure. Improving the stability of the structures is needed to keep them safe. This study aims to optimize vibration capacity in a thermal environment by using an extended solid isotropic material with penalization (SIMP). The effects of thermal and mechanical loadings are considered simultaneously with dynamic constraints as a first effort that includes heat fluxes around boundaries. The incorporation of heat flux boundary conditions is explored in the multi-physics optimization instead of uniform heating using multi-materials. The results show that the distribution of materials changes significantly when thermal properties are changed. The adjoint method is used to analyze the sensitivity of objective functions, and the method of moving asymptotes (MMA) is employed to update new design variables. The effectiveness and reliability of the proposed method are verified by some examples.

Development and Optimization of a Mechanically Robust Novel Rotor Topology for Very-High-Speed IPMSMs

Due to the trade-off between rotor mechanical strength and magnetic leakage, the high-speed capability of interior permanent magnet machines (IPMSM) is severely limited. This paper proposes a novel rotor topology capable of delivering 5 kW at 100,000 rpm using commercialized electrical steel laminations. By implementing the mechanical analysis in an iterative evolution process, two promising new topologies were developed. The first topology uses smooth compound curves (CC) to minimize the stress concentration and will be referred to as CC-type; the second topology is developed based on the CC-type with the aim to strengthen the structure further using the concept of a double-tied arch bridge (DAB) and referred as the DAB-type henceforward. After comparing the designs obtained from a detailed multiphysics optimization process, the DAB-type design was found to outperform the CC-type in all aspects, especially the mechanical robustness. This design was selected for experimental verification and a prototype was constructed. The mechanical and electromagnetic performances of the design were fully verified experimentally. The DAB-type prototype has achieved 2.31×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> rpm√kW, which is 1.5 times higher than other laminated rotor high-speed IPMSM designs found in the literature.

Optimal Sizing of Vanadium Flow Battery Stack

A number of effects impact on the performance a Vanadium Flow Battery (VFB) multicell stack, such as shunt currents, and hydraulic losses. The formers are caused by the different cell electric potentials which drive the vanadium charged species to move along the hydraulic flow paths inside the cells and stack, resulting in electric currents (dubbed “shunt”) and the related Joule losses [1]. The latter are due to the friction losses in the porous electrodes, in the stack hydraulic paths and in the external hydraulic piping. In addition, losses are also due to the hydraulic ancillary devices, to the cell electrochemical overpotentials and to self-discharge effects resulting from vanadium species crossover through the membranes [2]. Shunt-currents and hydraulic losses can be kept low with a proper hydraulic circuit design, even if they call for opposite choices. In fact, the trade-off between shunt current losses and pumping losses is the crucial issue in designing VRFB hydraulic circuits [3]. The evaluation of shunt current is quite difficult from experimental point of view [4]. Indeed, they can be computed numerically by solving an equivalent electrical circuit in which each cell was represented as an ideal voltage source E0,n in series with an internal resistance Ri,n , which represents the cell overpotential voltage drop, both parameters being SOC dependent. The electrolyte flow segments of the stack, i.e. manifolds, flow channels and felts, can be modelled as resistors [1]. In this work the authors provide a shunt current evaluation by calculating the electrical resistance of the n-th segment with length lk,n and cross section Ak,n as Rk,n, ± = lk,n /σk ± ,n Ak,n , where k stands for manifold (m) and cell flow channels and felts (c). In particular this work is focuses on the evaluation of Rc by means FEM analysis by varying the cell geometry. The final goal is a multiphysic optimization of a multicell VFB stack by reducing shunt currents losses and pressure loss, i.e. by optimizing the cell area and cell number. This work presents the preliminary results of such optimization analysis for VFBs stacks, performed at the Electrochemical Energy Storage and Conversion Laboratory (EESCoLab) at the University of Padua (Italy). In Fig. 1 is presented a preliminary evaluation of the electrical resistance of a flow channel geometry Rc of a large-scale cell, that is the starting point for the evaluation of the optimal size of the VFB stack for a desired power.

Performance Evaluation and Comparison of Three-Phase and Six-Phase Winding in Ultrahigh-Speed Machine for High-Power Application

This article investigates the influence of multiphase winding topologies in high-power ultra-high-speed machines (HP-UHSM) of 500 kr&#x002F;min. At this speed level, increasing the rotor&#x0027;s magnetic loading excites its critical bending resonances and leads to structural breakdown. On the other hand, increasing the stator&#x0027;s electric loading using the three-phase winding increases unwanted vibrations in a slotted stator and reduces the electromagnetic inter-action of the stator and rotor in a slotless stator. Consequently, the maximum output power level of UHSM (500 kr&#x002F;min or more) is limited to a few hundred watts only in the state-of-the-art. To over-come such a critical limitation, this article proposes a new design methodology for HP-UHSM, where the rotor&#x0027;s bending resonances and centrifugal stresses are restricted by limiting the maximum aspect ratio (<i>L&#x002F;D</i>), and an optimal multiphase winding is adopted in the slotless stator to increase the power level by effective electric loading. Also, a Multiphysics optimization is utilized to obtain the optimum magnetic loading and electric loading, where the bending resonance and other system limits are defined using multi-disciplinary design constraints. It is observed that the multiphase winding provides an added degree of freedom to increase the power level of UHSM without exciting the rotor&#x0027;s bending resonances and structural breakdown. Using the proposed method, a multiphase 2 kW 500 kr&#x002F;min HP-UHSM has been designed for the safety-critical AMEBA system and compared its Multiphysics performance with the three-phase machine having the same volume. Finally, exten-sive experiments are performed on both prototypes to validate the effectiveness of the proposed method. It is shown that the multi-phase HP-UHSM has no critical bending resonance below the 500 kr&#x002F;min, and it has 16.3&#x0025; higher output power with 1.18&#x0025; higher efficiency and 28.6&#x0025; lower back-EMF than the three-phase design.

Concurrent Shape Optimization of the Part and Scanning Path for Powder Bed Fusion Additive Manufacturing

This paper investigates the concurrent path planning optimization and the built part structural optimization for powder bed fusion additive manufacturing processes. The state of the art studies rely on existing patterns for trajectories for a fixed built shape. The shape is often optimized for its mechanical performance but rarely in a combined way with its path planning building process. In this work, a two-dimensional model (in the layer plane) of the process is proposed under a steady state assumption. Then a systematic path optimization approach, free from a priori restrictions and previously developed in [M. Boissier, G. Allaire, and C. Tournier [Struct. Multidiscip. Optim., 61 (2020), pp. 2437–2466], is coupled to a structural optimization tool, both of them based on shape optimization theory. This multiphysics optimization leads to innovative and promising results. First, they confirm that it is essential to take into account the part shape in the scanning path optimization. Second, they also give hints to some design recipes: the material and the source parameters must be related to the thickness of the bars that compose the structure. Indeed, the thickness of a bar is a key ingredient which determines the type of path pattern to scan it: straight line, Omega-pattern, and Wave-pattern.

Convection Heat Transfer Coefficient Regression Models Construction for Fast High-Speed Motor Thermal Analysis

This article proposes a computationally efficient thermal study method that substitutes computational fluid dynamics (CFDs) with a finite-element analysis (FEA) involving minimal loss of accuracy and omission of the increased computational load. The suggested technique contributes toward the realization of multiphysics optimization of high-speed electrical machines that effectively includes critical thermal evacuation phenomena. The strategy is regression-based and succeeds in constructing convection coefficient regression models that typically involve many variables and small training datasets. The proposed scheme is verified in high-loss density and high-speed outer rotor surface-mounted permanent magnet (SMPM) motor geometries that an optimization algorithm typically encounters during execution. The reference target specifications correspond to 20 kW output power, 15k r/min rotating speed, and 2 kHz electrical frequency.

Multi-physics design of a new battery packaging for electric vehicles utilizing multifunctional composites

A multi-physics optimization framework is presented to design a new battery packaging for electric vehicles (EV). This battery packaging utilizes two types of multifunctional composites: structural battery composites (SBC) and microvascular composites (MVC). SBC has profound potential in harvesting electrical energy, and MVC shows promising capability in providing thermal regulation for battery packs. The proposed optimization scheme is aimed to maximize the driving range of EVs while making sure that the battery pack during operation would not overheat and also shows promising mechanical integrity. To reduce the computational burden of this multi-physics problem, a new reduced-order thermal/hydraulic model for transient analysis of MVC is proposed in this study as a part of the optimization framework. The thickness of constituents in the battery cell, carbon fiber volume fraction of the electrodes, number of microvascular composite panels for thermal regulations, and fiber directions in the battery cell plies are considered as design parameters. A parametric study is performed to evaluate the effect of each one of these design parameters on the driving range of an EV as well as overheating and structural integrity of battery packaging. The optimized battery packaging design obtained from the suggested optimization framework shows about a 23% increase in the driving range of Tesla model S.

Multi-physics field simulation and parametric optimization of a medium-power inductively coupled plasma torch

Multi-physics field simulation and parametric optimization of a medium-power inductively coupled plasma torch

Multiphysics Optimization for First Wall Design Enhancement in Water-Cooled Breeding Blankets

The commercial feasibility of the first fusion power plant generation adopting D-T plasma is strongly dependent upon the self-sustainability in terms of tritium fuelling. Within such a kind of reactor, the component selected to house the tritium breeding reactions is the breeding blanket, which is further assigned to heat power removal and radiation shielding functions. As a consequence of both its role and position, the breeding blanket is heavily exposed to both surface and volumetric heat loads and, hence, its design requires a typical multiphysics approach, from the neutronics to the thermo-mechanics. During last years, a great deal of effort has been put in the optimization of the breeding blanket design, with the aim of maximizing the tritium breeding and heat removal performances without undermining its structural integrity. In this paper, a derivative-free optimization method named “Complex method” is applied for the design optimization of the European DEMO Water-Cooled Lithium Lead breeding blanket concept. To this purpose, a potential performances-based objective function, focusing on the maximization of the tritium breeding, is defined and a multiphysics numerical model of the blanket is developed in order to solve the coupled thermo-mechanical problem, while the optimization algorithm leads the design towards a minimum optimum point compliant with the prescribed requirements. Once the optimized design is obtained, its nuclear and thermo-structural performances are assessed by means of specific neutron transport and multiphysics simulations, respectively. Finally, the structural integrity is verified by means of the application of the RCC-MRx design criteria. • A tool for First Wall optimization in water-cooled breeding blankets is developed. • The optimization is performed using the gradient-free method named “Complex method”. • An objective function aiming at tritium breeding maximization is defined. • A FE model is used to check the compliance with the thermo-structural requirements. • The optimized design is finally assessed by proper, more detailed numerical analyses.

Multiphysics Optimization of Thermal Management Designs for Power Electronics Employing Impingement Cooling and Stereolithographic Printing

Meeting the stringent performance requirements for power electronic converters in electric vehicles requires an integrated approach for optimizing the inherently coupled electrical and thermal performances of converter systems. This article presents a multidisciplinary thermal management design methodology that utilizes genetic algorithms (GAs) to generate topologically optimized geometries for liquid-cooled heat sinks. These GA-generated heat sinks are based on impingement cooling principles and leverage the flexibility of stereolithographic manufacturing techniques. The proposed optimization methodology incorporates the interdependence between the thermal and electrical aspects of the system, and it is capable of targeting performance metrics in either or both domains. This optimization process is demonstrated for a 6.6-kW integrated power module design employing bare-die silicon carbide devices on an FR4-based printed circuit board with embedded ceramic elements. Experimentally validated electrothermal multiphysics simulations of the GA-optimized heat sinks targeting various performance metrics show successful optimization of targeted metrics relative to the initial seed design. The results demonstrate the importance of the multidisciplinary design approach and the effectiveness of the GA-based optimization methodology.

Advanced Parallel Space-Mapping-Based Multiphysics Optimization for High-Power Microwave Filters

Space mapping is a recognized surrogate-based optimization method to accelerate the electromagnetic (EM) design. In this article, for the first time, space mapping is elevated from solving the problem of EM optimization to solving the problem of multiphysics optimization for high power microwave filters. Multiphysics analysis, which involves the EM domain with other physics domains, is increasingly important for high-performance microwave components to obtain an accurate system design. To speed up the multiphysics design, a space-mapping-based surrogate model including a coarse model and two mapping functions is proposed in this article. We propose to use EM single physics responses as the coarse model to provide good approximations to fine model multiphysics responses. To avoid repetitive EM simulations during the surrogate model training and optimization process, the coarse model is developed using an artificial neural network (ANN). Frequency mapping and explicit input mapping are further performed to develop the proposed surrogate model. Multiple EM and multiphysics training samples are evaluated in parallel to develop the surrogate model. A trust-region algorithm, tailored to the space-mapping-based multiphysics optimization technique, is proposed to improve the convergence. By exploiting the knowledge of the coarse model established by relatively inexpensive EM data, the proposed technique can provide a larger and more efficient optimization update in each optimization iteration, consequently obtaining optimal solutions faster than the existing multiphysics optimization without space mapping. Two examples of multiphysics optimization of high-power microwave filters are used to validate the proposed technique.

Parallel Multiphysics Optimization for Microwave Devices Exploiting Neural Network Surrogate

This letter proposes a new surrogate-based multiphysics optimization technique for microwave devices incorporating artificial neural networks (ANNs) and trust-region algorithm. In the proposed technique, at each optimization iteration, we build an accurate and efficient ANN surrogate model using multiple multiphysics training samples around the optimized solution from the previous iteration. A parallel data generation technique is exploited to accelerate the optimization process. To improve the convergence of the proposed technique, we use a trust-region algorithm to recalculate the ANN surrogate model range at each optimization iteration. By using the proposed technique, the values of design parameters have a large and effective update toward the optimal solution at each iteration, and the optimization can converge in fewer iterations. Therefore, we can achieve the optimal solution faster than existing multiphysics optimization methods. A waveguide filter using piezo actuator is used as an example to demonstrate the validity of our technique.

A novel multi-physics field optimization method for GaN HEMT circuit design

A device modeling and topology design method suitable for high-power-density and high-temperature applicationare important to develop with the development of devices. A novel multi-physics analysis model based on GaN high electron mobility transistor (HEMT) is proposed in this paper. The coupled electromagnetic and electrothermal model for GaN HEMT is beneficial to simulate a device’s external characteristic waveform affected by different stray parameters in the surrounding. In addition, the proposed method can accurately reflect the switching loss and transient switching process of GaN HEMT.

Wide Speed Range Noise and Vibration Mitigation in Switched Reluctance Machines With Stator Pole Bridges

Noise, vibration, and harshness (NVH) issue in switched reluctance machines (SRMs), originating from their doubly salient structure and unique principle of operation, is addressed in this work by proposing a structural design modification in the stator, which increases stiffness to mass ratio of the structure. A 24-slot 16-pole (24s/16p) SRM designed with the aim of automotive application is studied here for the NVH optimization, at different target operating points, using stator pole bridges. Stator pole bridges link consecutive stator teeth to provide additional stiffness to the stator structure. Average torque reduction due to flux shorting in stator pole bridges is tackled by proposing a low-permeability material, with considerable stiffness, which has not yet seen its' application in SRM NVH domain. Multiphysics aspects of stator pole bridge design encompassing electromagnetic radial force, mechanical stress, steady-state temperature distribution, and acoustic noise analyses are presented in this article. Possible manufacturing issues are considered during the design phase and appropriate measures are implemented to facilitate easier construction of two 100-kW prototypes. The final design with stator pole bridges and a baseline design without any stator pole bridges are prototyped, after rigorous multiphysics optimization, for extensive testing. Experimental results verify simulation outputs and report a maximum noise reduction of 12.52 dBA in the stator pole bridge model compared to the baseline SRM.

Heat Sink Design for WBG Power Modules Based on Fourier Series and Evolutionary Multi-Objective Multi-Physics Optimization

Optimal heat dissipation in power modules can significantly increase their power density. Removing the generated heat is critical for capturing the benefits of advanced semiconductor materials and improving the reliability of the device operation. This study proposes a design optimization method for liquid-cooled heat sinks that use a Fourier analysis--based tool and an evolutionary optimization algorithm to optimize the heat sink geometry for specified objectives. The optimized heat sink geometry was compared with state-of-the-art solutions in the literature based on finite element analysis of different designs. The proposed methodology can develop complex geometries that outperform conventional heat sink geometries. Optimized heat sink design from the proposed method was fabricated and tested in an experimental setup under representative operating conditions. The experimental setup was also modeled in the finite element model that was used for the proposed heat sink optimization method. The experimental results show that developed finite element models can predict the thermal and flow performance of the complex design with high fidelity, and the results validate the proposed design approach.