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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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208 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Electromagnetic Modeling of Lattice Structures in Additively Manufactured Electric Machines

Recent advancements in metal additive manufacturing (AM) show great potential to revolutionize the design and manufacturing of electromagnetic components used in the field of electrical engineering. Lattice structures directly printed by AM processes typically offer better structural performance with reduced weight, such as high stiffness, surface area, elongation, energy absorption, and porosity, than the solid counterpart. This paper aims to study the electromagnetic modeling of lattice structures used in additively manufactured magnetic cores or windings for electric machines. Three dimensional (3D) electromagnetic finite element (FE) analysis with high performance computing (HPC) shows the highest fidelity in predicting the electromagnetic performance of designs with lattice structures by preserving complex geometry details. FE-based homogenization methods have also been explored to potentially speed up concept design. A case study based on an additively manufactured axial-flux permanent magnet machine with a Hilbert pattern stator validates the discussed electromagnetic modeling approaches.

Chung, Shi-Uk [ANSYS, Inc.]↗
DOE OSTI2025

Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem

Three-dimensional target identification using scattering techniques requires high accuracy solutions and very fast computations for real-time predictions in some critical applications. We first train a deep neural operator (DeepONet) to solve wave propagation problems described by the Helmholtz equation in a domain without scatterers but at different wavenumbers and with a complex absorbing boundary condition. We then design two classes of fast meta-solvers by combining DeepONet with either relaxation methods, such as Jacobi and Gauss-Seidel, or with Krylov methods, such as GMRES and BiCGStab, using the trunk basis of DeepONet as a coarse-scale preconditioner. We leverage the spectral bias of neural networks to account for the lower part of the spectrum in the error distribution while the upper part is handled inexpensively using relaxation methods or fine-scale preconditioners. The meta-solvers are then applied to solve scattering problems with different shape of scatterers, at no extra training cost. We first demonstrate that the resulting meta-solvers are shape-agnostic, fast, and robust, whereas the standard standalone solvers may even fail to converge without the DeepONet. We then apply both classes of meta-solvers to scattering from a submarine, a complex three-dimensional problem. We achieve very fast solutions, especially with the DeepONet-Krylov methods, which require orders of magnitude fewer iterations than any of the standalone solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

One-shot learning for solution operators of partial differential equations

Learning and solving governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in many areas of science and engineering. Traditional numerical methods can be computationally expensive for complex systems and require complete governing equations. Existing data-driven machine learning methods require large datasets to learn a surrogate solution operator, which could be impractical. Here, we propose a solution operator learning method that requires only one PDE solution, i.e., one-shot learning, along with suitable initial and boundary conditions. Leveraging the locality of derivatives, we define a local solution operator in small local domains, train it using a neural network, and use it to predict solutions of new input functions via mesh-based fixed-point iteration or meshfree neural-network based approaches. We test our method on various PDEs, complex geometries, and a practical spatial infection spread application, demonstrating its effectiveness and generalization capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Physics-Informed Active Learning With Simultaneous Weak-Form Latent Space Dynamics Identification

The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. Here, to enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared with the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5%–10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1%–7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1779x speed-up.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Surface Heat Load Analysis Due to Plasma Thermal Radiation on Equatorial Port 09

This paper reports calculated surface heat load owing to plasma thermal radiation, which will be applied to the surfaces of Equatorial Port 09 (EP09) diagnostic shield modules (DSMs) for thermal analysis during normal operation. The calculation assumes that the thermal energy will deposit uniformly in all directions from the plasma to the adjacent components and environment (i.e. gray body radiation). Assuming the temperature for the plasma surface, calculated view factors are used to determine the allocation of thermal energy to each component. The analysis provides an initial assessment of the thermal radiation heat load on the DSMs to be checked in structural/thermal analyses to verify significance. In conclusion, the designs of the EP09 components are still in the early preliminary phase, and changes are expected in the future.

ANSYS↗
DOE OSTI2020

Estimation, Minimization, and Validation of Commutation Loop Inductance for a 135-kW SiC EV Traction Inverter

With growing interests in low inductance SiC based power module packaging, it is vital to focus on system level design aspects to facilitate easy integration of the modules and reap system level benefits. To effectively utilize the low inductance modules, busbar and interconnects should also be designed with low stray inductances. A wholistic investigation of the flux path and flux cancellations in the module-busbar assembly which as differentially coupled series inductors is thus mandatory for a system level design. This work presents a busbar design which can be adopted to effectively integrate the CREE’s low inductance 1.2 kV/1.7 kV SiC power modules. The paper also proposes a novel measurement technique to measure the inductance of the modulebusbar assembly as a whole rather than deducing it from individual components. The inductance of the overall commutation loop of the inverter which encompasses the SiC power module, interconnects and PCB busbar have been estimated using finite element analysis (FEA). Furthermore, insights gained from FEA provided the guidelines to decide on the placement of the decoupling capacitors in the busbar to minimize the overall commutation loop inductance from 12.8 nH to 7.4 nH which resulted in significant reduction in the device voltage overshoot. The simulation results have been validated through measurements using an impedance analyzer with less than 5% difference between extracted loop inductance from FEA and measurements. The bus bar design study and the measurement technique discussed in this paper, can be easily extended to other power module packages. Finally, the 135 kW inverter has been compared to a similar highpower inverter utilizing a laminated busbar to highlight the performance of the former.

42 ENGINEERING↗
DOE OSTI2022

COMPASS-U Global Heat Balance Calculations

COMPASS-U is a medium size, high magnetic field experimental tokamak (R = 0.9 m, B t = 5 T, and I p = 2 MA), built at the Institute of Plasma Physics, Czech Academy of Sciences (IPP-CAS). This global heat balance calculation for COMPASS-U was done at Princeton Plasma Physics Laboratory (PPPL). Based on our previous experience of building global thermal model for National Spherical Torus Experiment-Upgrade (NSTX-U) at PPPL, this 2-D global thermal model geometry represents a typical cross section of COMPASS-U machine. The model includes thermal radiation, conduction, and convection among components and also between the machine and outer environment. Helium gas heating/cooling was modeled with fluid element and surface element. This model was used to calculate component temperatures and heat distribution during heat up, cryogenic cool down, normal operation, and fault operation scenarios. Definition of 14 main thermal scenarios was provided by IPP-CAS. First nine are normal operation scenarios. Scenarios #10–#14 are fault scenarios. Results of thermal scenario #5 will be given and discussed in this article, including peak temperatures, temperature ratcheting, energy distribution, and required cooling power.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Augmenting the Structural Dynamics Workflow to Reduce Development Times

The success of future systems development at Los Alamos National Laboratory (LANL) depends, in part, on the ability to make informed decisions quickly. This is particularly true when asked to perform structural-mechanical analyses across many design alternatives. To help reduce development time, techniques in reduced-order finite-element modeling (FEM), pretest predictions, and FEM-test correlation and updating were demonstrated in the fiscal year (FY) 23 Technology and Demonstration Project (TED): Augmenting the Structural Dynamics Workflow to Reduce Development Times. This report expands on the TED project final report, LA-UR-34022.

42 ENGINEERING↗
DOE OSTI2024

Taylor-Couette Flow Modeling (Rev.1)

The Taylor-Couette Flow is a form of viscous flow in which fluid flows between two cylindrical walls with a prescribed rotation. A diagram is shown on the right (Figure 1) where Ω is the rotational velocity, H is the distance between the two cylinders, R is the radius the outer cylinder, r is the radius of the inner cylinder and L is the length of the cylinders. When the rotating inner cylinder reaches a critical velocity, the flow of the liquid becomes unstable and creates vortices in between the layers. In this experiment, the Taylor-Couette cylinders were modeled so that the vortices would be created after reaching a critical velocity. After the initial experiment, the velocity of the inner cylinder was decreased and increased to look at how the results changed.

42 ENGINEERING↗
DOE OSTI2024

Robust Dissimilar Metal Friction Welded Spool for Enhanced Capability for Steam Power Components (Final Program Report)

This project successfully demonstrated the feasibility and advantages of producing durable, friction-welded dissimilar-metal spools, both with and without transition pieces, as well as the use of advanced oxidation-protective coatings for steam power applications. The work encompassed the optimization and full-scale production of NFA tubing, overcoming manufacturing and processing challenges to achieve enhanced strength, creep, and fatigue properties validated by extensive microstructural characterization. Optimized welding techniques were developed and refined for joining dissimilar materials, resulting in defect-free welds optimized for both low- and high-pressure boiler circuits. Systematic evaluation of welding parameters, coupled with heat treatments, ensured reliable performance and minimized weld defects such as banding and cracking. Comprehensive mechanical and non-destructive testing, including tensile, bend, fatigue, and CT scanning, confirmed the superior integrity and durability of the friction-welded joints under representative service conditions. To address oxidation-related degradation, a range of protective alloy coatings—such as IN625, APMT, Duplex 2507, and FeCrAlY—were selected and evaluated for their ability to counteract oxide notch formation at dissimilar metal interfaces. These coatings demonstrated improved oxidation resistance and long-term stability under thermal cycling, thereby extending weld service life. Thermal cyclic testing and long-term studies captured the phase stability and diffusion behavior of coated and uncoated configurations. Validated models for low-cycle fatigue (LCF), creep, and thermal mechanical fatigue (TMF) were developed to predict component life and failure modes. These models confirmed that friction-welded configurations exhibited significantly enhanced performance compared to conventional gas-tungsten arc welding (GTAW), with up to 7× improvement in creep resistance and at least 2× enhancement in fatigue properties. The introduction of a tailored transition piece further reduced thermally induced strains, enhancing fatigue life. The results demonstrate a robust pathway for retrofitting existing steam fleet components and upgrading future equipment to deliver substantially improved cold-start cycling behavior and reduction of unplanned outages due to premature weld failures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗