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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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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Local Markers for Crystalline Topology

Over the last few years, crystalline topology has been used in photonic crystals to realize edge- and corner-localized states that enhance light-matter interactions for potential device applications. However, the band-theoretic approaches currently used to classify bulk topological crystalline phases cannot predict the existence, localization, or spectral isolation of any resulting boundary-localized modes. While interfaces between materials in different crystalline phases must have topological states at some energy, these states need not appear within the band gap, and thus may not be useful for applications. Here, we derive a class of local markers for identifying material topology due to crystalline symmetries, as well as a corresponding measure of topological protection. As our real-space-based approach is inherently local, it immediately reveals the existence and robustness of topological boundary-localized states, yielding a predictive framework for designing topological crystalline heterostructures. In conclusion, beyond enabling the optimization of device geometries, we anticipate that our framework will also provide a route forward to deriving local markers for other classes of topology that are reliant upon spatial symmetries.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

A Multi-Objective Bayesian Optimization Approach Using the Weighted Tchebycheff Method

Abstract Bayesian optimization (BO) is a low-cost global optimization tool for expensive black-box objective functions, where we learn from prior evaluated designs, update a posterior surrogate Gaussian process model, and select new designs for future evaluation using an acquisition function. This research focuses upon developing a BO model with multiple black-box objective functions. In the standard multi-objective (MO) optimization problem, the weighted Tchebycheff method is efficiently used to find both convex and non-convex Pareto frontiers. This approach requires knowledge of utopia values before we start optimization. However, in the BO framework, since the functions are expensive to evaluate, it is very expensive to obtain the utopia values as a prior knowledge. Therefore, in this paper, we develop a MO-BO framework where we calibrate with multiple linear regression (MLR) models to estimate the utopia value for each objective as a function of design input variables; the models are updated iteratively with sampled training data from the proposed MO-BO. These iteratively estimated mean utopia values are used to formulate the weighted Tchebycheff MO acquisition function. The proposed approach is implemented in two numerical test examples and one engineering design problem of optimizing thin tube geometries under constant loading of temperature and pressure, with minimizing the risk of creep-fatigue failure and design cost, along with risk-based and manufacturing constraints. Finally, the model accuracy with frequentist, Bayesian and without MLR-based calibration are compared to true Pareto solutions.

Engineering↗
DOE OSTI2022

Thermal Optimization of a Silicon Carbide, Half-Bridge Power Module

This project describes the modeling process to design the packaging and heat exchanger for a half-bridge wide-bandgap (WBG) power semiconductor module. The module uses two silicon carbide, metal-oxide-semiconductor field-effect transistor (MOSFET) devices per switch position that are soldered to an aluminum nitride, direct-bond copper (DBC) substrate. A baseplate cooling configuration (e.g., no thermal grease) is used along with a water-ethylene glycol, jet-impingement-style heat exchanger. The heat exchanger was designed to be fabricated using prototyping equipment from the National Renewable Energy Laboratory, complies with automotive standards (for minimal channel sizes, flow rates, and coolant), and considers reliability aspects (i.e., erosion/corrosion). Device-scale computational fluid dynamics (CFD) is used first to design the slot jet impingement cooling configuration and compute the effective heat transfer coefficient (HTC) of the concept. The computed HTCs are then used as boundary conditions for a finite element study to optimize the package geometry (e.g., device layout and baseplate thickness) to minimize thermal resistance and minimize temperature variation between the module's four devices. Finally, a fluid manifold is designed to generate the slot jets and cool the devices. Module-scale CFD predicts a relatively low junction-to-fluid thermal resistance of 16.7 mm2 K/W, a 1.4 degrees C temperature variation between devices, and a total pressure drop of 5,860 Pa (0.85 psi) for the design. The thermal resistance of the module design is about 67% lower than the 2015 BMW i3 power electronics/modules thermal resistance.

DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Inverse design for waveguide dispersion with a differentiable mode solver

Inverse design of optical components based on adjoint sensitivity analysis has the potential to address the most challenging photonic engineering problems. However, existing inverse design tools based on finite-difference-time-domain (FDTD) models are poorly suited for optimizing waveguide modes for adiabatic transformation or perturbative coupling, which lies at the heart of many important photonic devices. Among these, dispersion engineering of optical waveguides is especially challenging in ultrafast and nonlinear optical applications involving broad optical bandwidths and frequency-dependent anisotropic dielectric material response. In this work, we develop gradient back-propagation through a general-purpose electromagnetic eigenmode solver and use it to demonstrate waveguide dispersion optimization for second harmonic generation with maximized phase-matching bandwidth. This optimization of three design parameters converges in eight steps, reducing the computational cost of optimization by ∼100x compared to exhaustive search and identifying new designs for broadband optical frequency doubling of laser sources in the 1.3–1.4 µm wavelength range. Furthermore, we demonstrate that the computational cost of gradient back-propagation is independent of the number of parameters, as required for optimization of complex geometries. This technique enables practical inverse design for a broad range of previously intractable photonic devices.

Gray, Dodd (ORCID:000000030469599X)↗
DOE OSTI2022

DEMONSTRATION OF TWICE REDUCED LORENTZ FORCE DETUNING IN SRF CAVITY BY COPPER COLD SPRAYING

Superconducting RF (SRF) cavities usually are made of thin-wall high RRR Niobium that is susceptible to Lorentz Force Detuning (LFD) ? cavity deformation phenomena caused by high magnitude RF fields. This type of deformation can be mitigated by using an additional copper layer deposited on the outer surface of the cavity. In this paper, we present both modeling results and experimental data of high gradient test of an SRF cavity at cryogenic temperatures. It was demonstrated that LFD can be significantly reduced (factor of two) by the copper cold spray reinforcement without sacrificing cavity flexibility for tuning. We also present a finite-element model that allows us to confirm our experimental results and optimize the cavity geometry for LFD reduction with incorporated coupled RF, structural and thermal modules.

Kostin, R.↗
DOE OSTI2020

Optimization of Archimedes Screw for Use in Hydroelectric Projects

In 2017, the Department of Energy’s (DOE) Water Power Technologies Office (WPTO) made federal funding available to several awardees to investigate and provide innovative technological solutions to reduce capital costs of installed hydroelectric facilities at non-powered dam’s (NPD’s) across the United States, while maintaining a high level of efficiency. Awardees were to develop specific technologies that showed promise of reducing installed capital cost (ICC) by at least 20% over a given baseline and maintain a water-to-wire efficiency of at least 80% at head levels of 50 feet or less and flows of 1000 cubic feet per second (cfs) or less. Canyon Hydro (Canyon) was selected as an awardee for this funding and worked to refine the implementation of the Archimedes screw turbine for use at these site conditions. Canyon and its project partners analyzed several dam inventory datasets to determine the most common head and flow range of current NPD’s in order to best select design conditions for a turbine that would perform successfully at common heads and flows. Computational Fluid Dynamic (CFD) analyses were conducted after site conditions had been selected to optimize the turbine geometry for efficiency. Through this effort we were able to estimate water-to-wire efficiency over a range of flows. Equipment design focused on concepts of modularity and pre-fabricated construction. These methodologies were used where possible to reduce costly on-site construction and shorten deployment timelines. Cost modeling was conducted with input from industry experts in general construction, pre-fabricated concrete construction, and composite fabrication. As a hydroelectric turbine manufacturer of 35 years, Canyon drew upon its own knowledge base in steel fabrication, water-to-wire component specification and ancillary systems to develop the remaining cost matrix for the project. As a result of these efforts, Canyon was able to present findings that indicated a modular Archimedes screw turbine system could be designed that reduced ICC by 23.9% and maintain a maximum water-to-wire efficiency of 82.3%, meeting the goals of the project.

13 HYDRO ENERGY↗
DOE OSTI2023

Survey of AC-LGADs for future 4D trackers with a proton beam

We will present the first beam test results with centimeter-scale AC-LGAD strip sensors, using the Fermilab Test Beam Facility, and a study of the performance of AC-LGAD sensors as a function of their thickness. Sensors of this type are envisioned for applications that require large-area precision 4D tracking coverage with economical channel counts, including timing layers for the Electron Ion Collider (EIC), and space-based particle experiments. Long strip sensors with sparse readout offer better cost and performance for applications where channel count or electrical power density is a constraint. Thanks to the excellent signal to noise ratio in AC-LGADs, sparse readout can be exploited without significant degradation of spatial or time resolution, which is demonstrated in our studies. A survey of sensor designs is presented, with the aim of optimizing the electrode geometry for spatial resolution and timing performance. We will present our studies of the sensor geometry optimizatio n to maintain the desirable sensor performance characteristics with increasingly larger electrodes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Evaluation of Additively Manufactured Monolithic SiC and SiC Ceramic Matrix Composites for CSP- Fabrication and Testing of Receiver Design Feature Specimens in a Simulating Lab Test via Laser Heating

Critical to the creation of a multi-component SiC receiver design concept capable of meeting the SETO CST cost goal objectives of <$150/kWth is the optimal utilization of material for solar absorption and heat transfer to transport fluid. This is to maximize the solar-thermal efficiency in the techno-economic analysis of derived use case. As a first step in preparation of sub-component design for on-sun test evaluations we use different proposed SiC absorber element in a customized laser heat flux test to assess an effective heat transfer coefficient characteristic of the design. The test results are analyzed to extract the effective heat transfer coefficient, thus enable optimization of the geometry of receiver components for heat transfer, efficiency attributes and the thermo-structural management of the components. High intensity CO2 laser is used in combination with air flow through the test specimen to assess the effective heat transfer coefficient under heat flux conditions corresponding to high solar concentrations in the range of 1000-2000 suns.

14 SOLAR ENERGY↗
DOE OSTI2021

A High Gain Lens-Coupled On-Chip Antenna Module for Miniature-Sized Millimeter-Wave Wireless Transceivers

This paper presents high gain and compact Transmit/Receive (TX/RX) integrated antennas in a standard BiCMOS 130nm technology for millimeter-scale millimeter-wave (mm-wave) applications, including high data rate radios and high resolution radars. The proposed TX/RX antenna module utilizes an integrated dipole antenna for the receiver and a slot antenna for the transmitter, placed orthogonally. The achieved gain and radiation efficiency are 5.7dBi and 41.3% for the slot antenna, respectively, and 6dBi and 39% for the dipole antenna. The link budget is improved by 16dB by optimization on the geometry aswell as application of a high resistivity hemispheric silicon dielectric lens.

Engineering↗
DOE OSTI2022

First survey of centimeter-scale AC-LGAD strip sensors with a 120 GeV proton beam

We present the first beam test results with centimeter-scale AC-LGAD strip sensors, using the Fermilab Test Beam Facility and sensors manufactured by the Brookhaven National Laboratory. Sensors of this type are envisioned for applications that require large-area precision 4D tracking coverage with economical channel counts, including timing layers for the Electron Ion Collider (EIC), and space-based particle experiments. A survey of sensor designs is presented, with the aim of optimizing the electrode geometry for spatial resolution and timing performance. Several design considerations are discussed towards maintaining desirable signal characteristics with increasingly larger electrodes. The resolutions obtained with several prototypes are presented, reaching simultaneous 18 micron and 32 ps resolutions from strips of 1 cm length and 500 micron pitch. With only slight modifications, these sensors would be ideal candidates for a 4D timing layer at the EIC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Photonuclear Production of Radioxenon for Air Samples

Radioxenon plays an important role in ensuring compliance with the Comprehensive Nuclear-Test-Ban Treaty due to its ability to be transported through the atmosphere, as well as the fact that the half-lives of its isotopes provide a window long enough to be detected but not long enough that it could become background radiation. This allows it to be correlated with specific events that indicate the testing of nuclear weapons. Idaho National Laboratory supports this mission by providing spiked air samples for monitoring stations to enable instrument calibration and ensure measurement accuracy. We tested a photonuclear method of producing one of the main isotopes of radioxenon, Xe-135. We also tested the separation of Xe-135 from the parent isotope Xe-136 using the method of kinematic recoil. Aluminum coils were placed in quartz ampoules filled with enriched Xe-136 and were irradiated by a bremsstrahlung beam with an endpoint energy of 21 MeV. The results showed that we were not only successful in producing Xe-135 but also that the Xe-135 was deposited into the coil allowing it to be removed from the Xe-136 gas. The coil and the Xe-135 can then be chemically separated. At the time of counting the coil, it was calculated that there was about 18 Bq of activity from Xe-135 remaining in the coil. Future work includes determining the optimal material and geometry of the catcher to maximize the amount of Xe-135 captured.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

A Conjugated Oligoelectrolyte Exhibiting Room Temperature Spin-Correlated Radical Pair Character for Biological Sensing

We report a water-soluble conjugated oligoelectrolyte (COE) composed of carbazole-benzophenone, COE-CbzBP, that exhibits photogenerated spin-correlated radical pair (SCRP) behavior sensitive to static electric fields from DNA but not from lipid bilayers. The SCRP forms from a thermally activated, spin-polarized state enabled by partial π-conjugation disruption at the donor–acceptor (carbazole-benzophenone) nitrogen–carbon (N–C) junction, which facilitates a twisted intramolecular charge-transfer (TICT) geometry. This state minimizes the singlet–triplet energy gap (ΔE ST = 0.12 eV), radical–pair exchange coupling (J RP ∼ ΔE ST /2), and charge separation free energy (ΔG CS ) in both DNA (−0.19 eV) and lipid bilayers (−0.55 eV). Room-temperature continuous-wave electron paramagnetic resonance (CW-EPR) reveals a photogenerated spin-polarized singlet for COE-CbzBP that splits upon DNA association, consistent with modulation of J RP and hyperfine coupling (A x ), presumably via electric field-spin coupling. No spin-polarized signal was observed under dark, cryogenic conditions, or in liposomes, but was quenched by the spin trap 4-POBN. Transient absorption and spectroelectrochemistry confirmed magnetic-field sensitive long-lived excited-state absorption features attributed to charge-separated states 3 [Cbz •+ -BP •– ]*, which were lengthened by DNA, and quenched in lipid bilayers and 4-POBN. Quantum chemical simulations show that planar geometries (lipid-like) increase ΔE ST by 0.31 eV compared to TICT-optimized structures. This geometry-dependent modulation explains the absence of SCRP signatures in rigid environments, underscoring the importance of TICT states, minimized ΔE ST , and favorable ΔG CS for achieving room-temperature SCRP generation. These findings establish design principles for TICT-enabled molecules exhibiting qubit-like behavior that operate under ambient and biologically relevant conditions, with direct implications for quantum information science (QIS).

Aromatic compounds↗
DOE OSTI2025

A geometric framework for momentum-based optimizers for low-rank training

Low-rank pre-training and fine-tuning have recently emerged as promising techniques for reducing the computational and storage costs of large neural networks. Training low-rank parameterizations typically relies on conventional optimizers such as heavy ball momentum methods or Adam. In this work, we identify and analyze potential difficulties that these training methods encounter when used to train low-rank parameterizations of weights. In particular, we show that classical momentum methods can struggle to converge to a local optimum due to the geometry of the underlying optimization landscape. To address this, we introduce novel training strategies derived from dynamical low-rank approximation, which explicitly account for the underlying geometric structure. Our approach leverages and combines tools from dynamical low-rank approximation and momentum-based optimization to design optimizers that respect the intrinsic geometry of the parameter space. We validate our methods through numerical experiments, demonstrating faster convergence, and stronger validation metrics at given parameter budgets.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗
DOE OSTI2021

Gradient-Informed Design Optimization of Select Nuclear Systems

In this work, we present a gradient-informed design optimization of nuclear reactor core components based on neutronics objectives with both continuous and discrete materials. The main argument in favor of using gradient-informed design optimization is that it scales well with increasing dimensionality of the design space. First, a challenge problem with 121 free parameters is solved with a gradient-informed method and then with a genetic algorithm. Then, a challenge problem to optimize the flux profile of a simplified assembly with eight axial zones is solved. Both challenge problems are solved using directly calculated derivatives from Tools for Sensitivity and Uncertainty Analysis Methodology Implementation (TSUNAMI) in the SCALE package. Furthermore, we demonstrate how a discrete optimization problem—selection of materials for 121 voxels—can be lifted into a continuous problem with mixed materials. In the continuous space, adjoint-based gradients are well-defined, and gradient descent is applicable. Then, a forcing function is introduced that with the selection of an appropriately sized hyperparameter can be used to guide the optimized continuous solution back into a discrete solution. This paper presents an account of the challenges that were faced when applying a gradient-informed optimization algorithm using a Monte Carlo calculation to estimate the gradient information and compares a gradient descent optimization method to a genetic algorithm optimization of the same geometry. Overall, this work demonstrates the potential use of adjoint-based gradient calculations in design optimization of nuclear systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Comparison of tungsten versus molybdenum for double shell capsules using machine learning design optimization

Double shell targets are an alternative ignition platform for inertial confinement fusion. One design consideration for double shell targets is the choice of inner shell material to help trap radiation emitted by the hot fuel to aid ignition. Materials such as molybdenum and tungsten are of interest for the inner shell layer of the targets. While molybdenum has a lower density that could inhibit instability growth and allow for radiography and code benchmarking, tungsten has a higher density that could provide more compression and confinement. These tradeoffs have been explored using optimized designs for each material. Our previous work [Vazirani et al., “Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules,” Phys. Plasmas 28, 122709 (2021); Vazirani et al., “Coupling multi-fidelity xRAGE with machine learning for graded inner shell design optimization in double shell capsules,” Phys. Plasmas 30, 062704 (2023); and Vazirani et al., “Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design,” Stat. Anal. Data Min. 17, e11698 (2024)] resulted in a multi-fidelity Bayesian optimization framework to find yield-optimized double shell target geometries. By leveraging simulations of varying fidelities (one-dimensional and two-dimensional) to inform one another, the multi-fidelity optimization was able to optimize a design in the highest fidelity with significantly fewer simulations than would be used in a systematic parameter scan. In this work, we apply the multi-fidelity Bayesian optimization to explore the optimized designs of double shell targets with molybdenum and tungsten inner shells as well as the physics producing the high performing implosions. A physics exploration of all the simulations used in this study shows trends in designs that contribute to high yields, ion temperatures, and fuel areal densities. Comparison of molybdenum and tungsten simulations shows that they can produce similar implosion conditions with different geometries, which would be important to study in experiments. Graded density layers produce varying performances with the two materials but continue to be of interest for future studies along with studies of doped inner shell materials and applied surface roughness.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗