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Transformer Leakage Inductance Design Methodology
The leakage inductance exhibited by a transformer depends on its winding geometry, which generally involves the selection of several key design parameters in addition to the winding structure and the interleaving configuration. With few resources explaining the effects of these design choices on the observed leakage inductance, numerous trial-and-error iterations become necessary to realize the desired leakage inductance. This paper explores more than a hundred winding geometries feasible in a 2-winding transformer comprising the same magnetic core, number of turns, and wire gauge, and finds the leakage inductance for each unique design using 2-D finite element method (FEM) simulations in association with the semi-analytical double- 2-D model. These leakage inductances are plotted and further analyzed to understand the effects of different design parameters on the effective leakage inductance. The results presented herein and the conclusions drawn from this research can serve as a valuable resource for future design practitioners from both industry and academia.
New Hybrid Model for Evaluating the Frequency-Dependent Leakage Inductance of a Variable Inductance Transformer (VIT)
Skin and proximity effects can cause a significant drop in the effective leakage inductance of a transformer when the operating frequency is increased. Although the magnetic image method-based double-2-D model can calculate the lowfrequency leakage inductance with sufficient accuracy, it is inherently a frequency-independent model. While Dowell’s 1-D model uses frequency-dependent relations to account for both skin and proximity effects, its accuracy is severely affected by the assumed winding geometry. In this paper, a hybrid model is proposed that uses superposition to combine a modified Dowell’s model with the double-2-D model. The proposed model is investigated on a variable inductance transformer (VIT)—a partially-filled transformer whose leakage inductance can be varied by moving one of the windings mechanically. The frequency-dependent leakage inductances of the VIT evaluated using the hybrid model are in excellent agreement with the corresponding finite element method (FEM) simulated and experimentally measured values, thereby validating the proposed hybrid model.
An Integrated Simulation of Multiple-Pass U-10Mo Alloy Hot Rolling and Static Recrystallization
To achieve a desired microstructure and minimize the thickness variation in rolled foils, researchers must understand the effects of foil fabrication process variables on microstructure evolution. We developed an integrated simulation of deformation and recrystallization that employs the finite element method (FEM) and the kinetic Monte Carlo (KMC) Potts model, respectively, to investigate microstructure evolution during multiple-pass hot rolling and heat treatment in polycrystalline U-10Mo fuel. Scanning electron microscopy and electron backscatter diffraction images of microstructures were directly used as input in FEM calculation of deformation, and the calculated strains were used to determine the driving force of nucleation and growth of recrystallized grains in the Potts model. Grain structures predicted by the Potts model were used to update the grain structure and material properties for FEM. Simulation alternated between FEM and the Potts model to simulate grain structure evolution during multiple rolling and heat treatments. The initial model parameters were determined by benchmarking the recrystallization kinetics against experimental data. Then, the model was applied to predict the grain structure evolution. Results showed that our model can capture the coupling between deformation and recrystallization and can quantitatively reproduce the observed U-10Mo recrystallization and grain growth kinetics. The simulation results demonstrated that the developed model can predict U-10Mo grain structures as a function of initial microstructure and foil fabrication parameters.
Measurement of amplification and absorption of a THz quantum-cascade metasurface free-space amplifier
An active amplifying metasurface based on a quantum-cascade gain material at 2.7 THz is studied. The metasurface is first evaluated as the active component of an external cavity laser with excellent beam quality and frequency tunability from 2.55–2.8 THz. Amplification and absorption of the metasurface alone are then separately measured at a single frequency using a probe signal from a CO 2 -pumped gas laser operating at 2.743 THz. The metasurface reflectance vs bias is measured and compared with expectations from non-equilibrium Green’s function simulations of the quantum-cascade gain material and FEM simulations of the metasurface reflectance. A peak amplification on the order of 0.3 dB is measured. Design strategies are discussed for increasing single-reflection amplification (upward of 10 dB) and reducing power dissipation. Further increased amplification could be achieved by cascading multiple metasurfaces.
Microstructure-process relationships in monolithic U-10Mo fuel foil single-pass rolling: A parametric simulation study
In this work, a previously validated coupling of Kinetic Monte Carlo (KMC) Potts Model and finite element method (FEM) simulations was implemented to investigate the effects of microstructural features in as-cast and homogenized monolithic U-10Mo foils on the emergent microstructure after rolling and reheating. Parameters that could potentially affect recrystallization behavior of the rolled U-10Mo foils were considered: grain size distribution, uranium carbide (UC) size distribution, UC volume fraction, spatial distribution of UC, and rolling reduction magnitude. Grain structure and the magnitude of rolling reduction have the strongest influence on recrystallization kinetics and the fabricated grain size distribution. The UC distribution had only a weak effect on the recrystallization kinetics and final microstructures. While particle-stimulated nucleation (PSN) occurred in simulation more frequently as grain size increased, its incidence did not appear to considerably affect the recrystallization kinetics or grain size distribution.
High Throughput Computational Framework of Materials Properties for Extreme Environments
This project aims to establish a framework capable of efficiently predicting the properties of structural materials for service in harsh environments over a wide range of temperatures and over long periods of time. The approach is to develop and integrate high throughput first-principles calculations in combination with machine learning (ML) methods, perform high throughput CALPHAD (calculations of phase diagrams) modeling, and carry out finite element method (FEM) simulations. Relevant to high temperature service in fossil power system, nickel-based superalloys such as Inconel 740 and Haynes 282 as well as the associated (Ni-Cr-Co)-Al-C-Fe-Mn-Mo-Nb-Si-Ti system, were investigated. The present framework was built on the concept of phase-based property data, in which properties of individual phases are modeled as a function of internal and external independent variables. This project established an open-source infrastructure with the following capabilities: (1) High throughput implementation of first-principles calculations at finite temperatures and variable compositions using both accurate phonon calculations and the efficient Debye model for thermodynamic properties, elastic constants, diffusion coefficients, vacancy formation, stacking and twin faults, and dislocation mobility; i.e., using the developed code DFTTK; (2) Machine learning capabilities to predict the above properties so that the number of first-principles calculations can be significantly reduced; e.g., using the developed code SIPFENN; (3) High throughput CALPHAD modeling of the above properties as a function of temperature and composition using our unique capability based on ESPEI and PyCalphad; (4) New capabilities to predict the stress-strain behavior of individual phases; and (5) New models for tensile strength prediction in common FEM software with the crystal plasticity finite element simulations (CPFEM).
Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene
Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. Here, in this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEM-based training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields.
Novel processing route for design and manufacturing of metal toughened nanoceramics: Al–Al 2 O 3 nanocermets
Monolithic alumina is a highly brittle ceramic with a very low fracture toughness, making it an unreliable material for mechanical and structural applications. Ceramic-metal composites (cermets) are the subject of intensive interest to obtain materials which can address the intrinsic brittleness of ceramics without compromising their high strength and light weight. In the present investigation, aluminium-alumina (Al–Al 2 O 3 ) nanocermets were developed. The nanocermets possessed the toughness of Al phase and the strength of reinforced nano Al 2 O 3 particles. Here, the nanocermets green bodies were prepared such that upon sintering each adjacent Al 2 O 3 grain was conformally coated with a continuous Al film. Mechanical milling via a combination of cryo-milling and ball-milling was used to prepare Al–Al 2 O 3 nano-powder mixture. Spark plasma sintering was used to consolidate the green nanocermets. Results show that specimens prepared from 35 h milled powders exhibited superior mechanical properties as compared to other nanocermets, with an improvement in microhardness, elastic modulus, compressive strength, and flexural strength by 88%, 78%, 69% and 40% with respect to sintered pure Al. The indentation toughness of 35 h milled nanocermet showed an increment of 12.5% with respect to sintered pure Al 2 O 3 . Characterization techniques such as XRD, SEM, HR-TEM, X-ray tomography were employed to investigate the phase evolution and structural morphology, while FEM simulations were performed for understanding the crack propagation mechanisms in nanocermets.
Evaluation of adsorption and mechanical strength of 13X zeolite mixtures with phyllosilicate binders using molecular dynamics simulation and positron annihilation spectroscopy
There is growing interest in developing zeolites with novel internal structures that have optimal adsorptive capacity and high mechanical strength, while offering advantages, such as being light weight. We integrate computational and experimental methods to explore the effect of binder/zeolite types, and weight percentages on the mechanical strength of 13X zeolite and adsorption capacities of N 2 , H 2 O, and CO 2 for additive manufacturing (AM) applications with the goal of maximizing both adsorption and strength. Zeolite 13X mixtures and phyllosilicate binders (either bentonite or kaolin) are combined using molecular dynamics (MD) simulations to create structures with various binder/zeolite weight percentages. Further, adsorption capabilities and mechanical strength are assessed using the grand canonical Monte Carlo (GCMC) and ReaxFF modules, respectively. Our modeling shows that an optimized zeolite/binder ratio for N 2 adsorption is around 15 wt% for kaolin and roughly 10 wt% for bentonite. The resulting parameters can be applied to facilitate macro-scale computational fluid dynamics (CFD) and finite element method (FEM) simulations of an AM zeolite structure. We also performed Positron Annihilation Lifetime Spectroscopy (PALS) measurements on zeolite samples to explore the effect of changes in the internal volume. The results show an inverse relationship between the free volume and the solid loading and adding a binder changes the morphology of the zeolite-binder compound and decreases open-volume area significantly.
Generalizing synthetic data-trained acoustic predictive models to real-world measurements
Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.
A numerical and experimental approach to compare the effect of sample thickness in small in-situ SEM and large ex-situ tensile testing in Alloy 709
ASTM standards for tensile tests define specific sample size requirements regardless of grain size of the material. However, sample size requirements for testing should be considered in conjunction with the number of grains within its reduced cross-sectional area. This is particularly important for in-situ Scanning Electron Microscope (SEM) tensile tests, as they have to be conducted on smaller samples. In this study, a comprehensive experimental and numerical evaluation of the effect of specimen thickness (and the resulting number of grains within the cross-section) were conducted using in-situ SEM tensile test (on submillimeter thick samples) and ex-situ tensile tests (on samples of 0.68–5.9 mm thickness) and the results are compared with FEM simulations outcome. All tests were conducted at room temperature and the results are correlated to the number of grains within the thickness of specimens. The tensile test results indicated that even though the 0.2% proof stress and the tensile strength do not vary for the current range of thicknesses (with number of grains in the cross-section varying from 13 to 118), a difference in necking mechanism exists. Post tensile strength, thinner samples undergo more shear failure and diagonal localized necking whereas thicker samples experience more diffuse necking indicated by decreased area of shear failure at the edges and increased area of dimpled tensile failure at the center of the sample. Further, FEM results complement the experimental findings by showing the formation of conjugated localized shear bands on the upper and lower surface in the 0.68 mm thick sample and shear bands combining to form singular shear bands in thicker samples. These results also confirm the validity of the in-situ SEM tensile tests conducted on thinner samples as long as the required minimum number of grains exist within the cross-section.
A combined experimental and numerical approach that eliminates the non-uniqueness associated with the Johnson-Cook parameters obtained using inverse methods
Abstract Johnson-Cook constitutive model is a commonly used material model for machining simulations. The model includes five parameters that capture the initial yield stress, strain-hardening, strain-rate hardening, and thermal softening behavior of the material. These parameters are difficult to determine using experiments since the conditions observed during machining (such as high strain-rates of the order of $$10^5$$ 10 5 /sec - $$10^6$$ 10 6 /sec) are challenging to recreate in the laboratory. To address this problem, several researchers have recently proposed inverse approaches where a combination of experiments and analytical models are used to predict the Johnson-Cook parameters. The errors between the measured cutting forces, chip thicknesses and temperatures and those predicted by analytical models are minimized and the parameters are determined. In this work, it is shown that only two of the five Johnson-Cook parameters can be determined uniquely using inverse approaches. Two different algorithms, namely, Adaptive Memory Programming for Global Optimization (AMPGO) and Particle Swarm Optimization (PSO), are used for this purpose. The extended Oxley’s model is used as the analytical tool for optimization. For determining a parameter’s value, a large range for each parameter is provided as an input to the algorithms. The algorithms converge to several different sets of values for the five Johnson-Cook parameters when all the five parameters are considered as unknown in the optimization algorithm. All of these sets, however, yield the same chip shape and cutting forces in FEM simulations. Further analyses show that only the strain-rate and thermal softening parameters can be determined uniquely and the three parameters present in the strain-hardening term of the Johnson-Cook model cannot be determined uniquely using the inverse method. A combined experimental and numerical approach is proposed to eliminate this determine all parameters uniquely.
Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures
Additive Manufacturing (AM) of porous polymeric materials, such as foams, recently became a topic of intensive research due their unique combination of low density, impressive mechanical properties, and stress dissipation capabilities. Conventional methods for fabricating foams rely on complex and stochastic processes, making it challenging to achieve precise architectural control of structured porosity. In contrast, AM provides access to a wide range of printable materials, where precise spatial control over structured porosity can be modulated during the fabrication process enabling the production of foam replacement structures (FRS). Current approaches for designing FRS are based on intuitive understanding of their properties or an extensive number of finite element method (FEM) simulations. These approaches, however, are computationally expensive and time consuming. As such, in this work, we present a novel methodology for determining the mechanical compression response of direct ink write (DIW) 3D printed FRS using a simple cross-sectional image. By obtaining measurement data for a relatively small number of samples, an artificial neural network (ANN) was trained, and a computer vision algorithm was used to make inferences about foam compression characteristics from a single cross-sectional image. Finally, a genetic algorithm (GA) was used to solve the inverse design problem, generating the AM printing parameters that an engineer should use to achieve a desired compression response from a DIW printed FRS. The methods developed herein present an avenue for entirely autonomous design and analysis of additively manufactured structures using artificial intelligence.
Effects of pillar design on the thermal performance of vacuum-insulated glazing
Vacuum-insulated glazing (VIG) with a low-emittance coating has a great market potential as an effective transparent insulator. The thermal insulating performance of VIG is determined by its design, including material selection and configuration of different components. Thermal conductance of the vacuum gap as a transport bottleneck is one of the primary factors controlling the thermal transport across VIG. In particular, because support pillars provide the main thermal transport channels across the vacuum gap, increasing the pillar thermal resistance is a key strategy for creating effective thermal insulation while maintaining the vacuum space. Here, the effects of various pillar design parameters, such as thermal conductivity, geometry, and arrangement, on the VIG thermal performance were comprehensively investigated via the finite element method. In addition, analytical models for thermal transport were examined and thermal conductance across the VIG unit was experimentally measured for validation. The pillar design parameters, especially the height, shape, spacing, and arrangement of the pillars, showed significant effects on the thermal performance of VIG. This research also shows that the smaller contact area for horizontal pillars can effectively decrease the heat loss by more than 30%. Because current VIG analytical equations of thermal performance are only applicable to cylindrical pillars, an analytical equation that can better describe the thermal performance of rectangular parallelepiped pillars is presented, along with a discussion about the mechanism of thermal transfer for different pillar shapes. Thermo-mechanical analyses based on 3D FEM simulations can provide valuable insights into the effect of various design parameters on the overall performance the VIG, allowing for the development of an optimal VIG design.
Hybrid model to evaluate the frequency-dependent leakage inductance of partially-filled transformers
The leakage inductance of a transformer designed for a power electronic converter can drop significantly as the switching frequency is increased due to skin and proximity effects. Although the magnetic image method-based double-2-D model can predict the low-frequency leakage inductance of a partially-filled transformer with sufficient accuracy, it is inherently a frequency-independent model. While Dowell’s 1-D model uses frequency-dependent relations to account for both skin and proximity effects, its accuracy is severely affected by the assumed winding geometry. In this paper, a semianalytical hybrid model is proposed that uses superposition to combine a modified Dowell’s model with the double-2-D model to predict the true leakage inductance of partially-filled transformers at any given frequency. All three conductor types—round, foil, and litz wire—are modeled and analyzed. The quasi-2-D model is further investigated on a variable inductance transformer (VIT) whose winding geometry can be modified mechanically to vary its leakage inductance. With less than 5 % error throughout, the semi-analytically evaluated leakage inductances are in excellent agreement with the finite element method (FEM) simulated and experimentally measured leakage inductances.
AnisONet: A deep neural operator-based anisotropic permeability upscaler from pore to Darcy scale
Directional permeability variations, which govern directional fluid flow in porous media with anisotropy, are important to accurately predict flow behavior, reactive transport, and fluid–solid interactions for various processes such as enhanced geothermal systems, energy storage devices, and biological systems. However, the intricate architecture of porous media makes it difficult to predict directional permeabilities. In this work, we present a novel machine learning (ML) framework, AnisONet, built upon an integration of a convolutional neural network, Swin transformer, and the deep operator network architecture, designed to predict anisotropic permeability and upscale predictions to larger spatial domains. First, AnisONet was evaluated with three classes of two-dimensional (2D) porous media, including synthetic circular and elliptical grains and natural sandstone grains from micro-computed tomography images. A lattice Boltzmann model (LBM) was used to calculate directional permeabilities at every 10° angle, producing 19 data points per image of porous media. AnisONet is then trained to predict permeability as a function of rotation angle. AnisONet showed strong predictive capability of directional permeability. Second, we tested our model for five upscaling cases with a large image size in the finite-element method (FEM) for 2D Darcy flow with various permeability tensor construction methods. Overall, upscaled permeability tensors in FEM simulations produce a reasonably good match with LBM results, highlighting the importance of selecting appropriate tensor formation strategies for accurate permeability upscaling. AnisONet, as a directional permeability estimator, could be further developed for more complex geometries, with the potential to develop a foundational ML model for various applications in porous media.
Genetic Algorithm-Based Commutation Angle Control for Torque Ripple Mitigation in Switched Reluctance Motor Drives
This work addresses the application of the Genetic Algorithm (GA) technique to optimize the commutation angles of a 2 kW 8/6 switched reluctance machine (SRM). The primary goal is to reduce the well-known drawback of SRMs: the torque ripple. Firstly, the machine was modeled in Matlab /Simulink ® using lookup tables obtained via finite element method (FEM) simulations. Subsequently, the model was used to perform the GA routine aiming to find the optimal phase commutation angles that minimize the torque ripple factor. Notably, the torque performance of the SRM was significantly affected by the commutation angles during the search for the optimal solution. Afterwards, the GA results for four different operation points were verified experimentally through a developed drive platform with digital signal processor-based (DSP) control and an asymmetric bridge converter. As showed by the experiments, the proposed approach was suitable to reduce the torque ripple by more than 50% for one of the evaluated operating points. Furthermore, it was confirmed that the torque ripple mitigation led to acoustic noise improvement.