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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Separation of Radionuclides from a Rare Earth-Containing Solution by Zeolite Adsorption

The increasing industrial demand for rare earths requires new or alternative sources to be found. Within this context, there have been studies validating the technical feasibility of coal and coal byproducts as alternative sources for rare earth elements. Nonetheless, radioactive materials, such as thorium and uranium, are frequently seen in the rare earths’ mineralization, and causes environmental and health concerns. Consequently, there exists an urgent need to remove these radionuclides in order to produce high purity rare earths to diversify the supply chain, as well as maintain an environmentally-favorable extraction process for the surroundings. In this study, an experimental design was generated to examine the effect of zeolite particle size, feed solution pH, zeolite amount, and contact time of solid and aqueous phases on the removal of thorium and uranium from the solution. The best separation performance was achieved using 2.50 g of 12-µm zeolite sample at a pH value of 3 with a contact time of 2 h. Under these conditions, the adsorption recovery of rare earths, thorium, and uranium into the solid phase was found to be 20.43 wt%, 99.20 wt%, and 89.60 wt%, respectively. The Freundlich adsorption isotherm was determined to be the best-fit model, and the adsorption mechanism of rare earths and thorium was identified as multilayer physisorption. Further, the separation efficiency was assessed using the response surface methodology based on the development of a statistically significant model.

58 GEOSCIENCES↗
DOE OSTI2023

How do magnetized HED flows transition to collimated plasma jets? (Final Report)

We present results from a pulsed-power experiment designed to generate accretion-like flows in the laboratory that may be used to study the dynamics of astrophysical accretion-driven outflows. To emulate systems like Young Stellar Object (YSO) outflows, we create radially converging plasma flows in a short disk that transition into much longer bipolar outflows. Our experiment uses a ~1 MA pulsed-power driver to generate plasma from an array of thin aluminum wires mounted to a 3D-printed current path that controls the angular momentum and magnetic field topology. In this paper, we show that experimental data scale favorably to observed properties of YSO jets. This set of experiments tests the simplest version of our possible plasma dynamics, where outflows are generated without rotation or axial magnetic field present. The temperature, velocity and density of the disk and outflows are characterized using interferometry, gated optical and ultraviolet imaging, and Thomson scattering diagnostics. We discuss the Reynolds number, magnetic Reynolds number and Mach numbers calculated in three different stages of the experiment, and compare our results to 3D numerical simulations in the PERSEUS magnetohydrodynamics code. This report is a summary of the work done by the graduate students who were supported by the project.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Programa de Diseno, Fabricacion y Pruebas del Sistema de Desalinizacion por Olas del NREL: Preprint (Spanish Translation)

To de-risk the U.S. Department of Energy's Waves to Water Prize, the National Renewable Energy Laboratory (NREL) developed a modular, wave powered desalination system. The prize was open to wave energy converter (WEC) designs that generate electricity or WECs that desalinate water mechanically. This added installation risks due to the variance in competitor devices, and the aggressive installation timeline. To reduce these risks NREL developed a wave energy converter (WEC) that the installation team could use to practice installation techniques prior to the competitors arriving to ensure all steps had been considered prior to the event. This was achieved by developing a WEC with a modular power-take-off (PTO). The modular PTO can be configured in one configuration to drive an electric generator that sends electricity to a pier. The electricity that is generated is converted, and stored, so that it can be used to power an electric pump that feeds water to a Reverse Osmosis (RO) desalination unit. In the other configuration the generator is replaced with a pump and seawater is pumped to the RO system on the pier without any electrons being generated. This WEC is formally known as the Hydraulic and Electric Reverse Osmosis (HERO) WEC. For the English version of this report, see NREL/CP-5700-86623 (https://www.nrel.gov/docs/fy24osti/86623.pdf).

laboratory testing↗
DOE OSTI2022

Advanced modeling and simulation of research reactors using dynamic mode decomposition

Full text of publication follows. Due to the ever-increasing safety requirements, the current trend of nuclear reactor analysis is shifting towards high-fidelity multi-physics models, which have a very high computational cost and modelling complexity. As the cost of even a single model run makes it impossible to analyse the behaviour and performance of these models on large-scale commercial plants, it has become even more significant to provide suitable benchmarks to validate and test them extensively. In this sense, research reactors offer a promising solution for the initial validation of high-fidelity models, as they are significantly smaller than commercial reactors and their characteristics are well known. In particular, the reactors of the TRIGA family have been used to assess and validate models and methods for Generation-IV designs, as they have some similar features (such as the dominance of natural convection as cooling mechanism and the difficulties in performing sub-channel analysis using standard codes). Still, the computational requirements of high-fidelity models make them unsuitable for real-time analysis, even following their assessment on research reactors. In this sense, Model Order Reduction (MOR) techniques give an additional strategy to reduce the computational cost of high-fidelity models (whilst preserving sufficient accuracy). In particular, this work focuses on Dynamic Mode Decomposition (DMD), a non-intrusive MOR technique that aims at representing models with explicit temporal dynamics by extracting the time-varying characteristics and the governing structures based only on a set of available data, thus without needing any underlying knowledge of the governing equations. In addition, DMD also computes a low-dimensional surrogate of the dynamic matrix of the system, making it suited for stability analysis and real-time evaluations. This work focuses on the application and validation of the DMD method on the Computational Fluid-Dynamics (CFD) model TRIGA Mark II reactor, also discussing in detail the potentiality of this algorithm as an advanced modelling tool for nuclear reactor analysis. (author)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Towards Automated Generation of Chiplet-Based Systems

The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application- Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a high-level frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frameworks), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This talk will discuss ongoing work on the SODA synthesizer to enable no-human-in-the-loop generation and design space exploration of the chiplets for highly specialized artificial intelligence accelerators. Connecting these highly specialized chiplets to general-purpose cores or programmable accelerators will allow to quickly deploy autonomous systems for scientific discovery.

Limaye, Ankur M.↗
DOE OSTI2025

Reactors Section Presentation - INL, HPS Society 2025

The ongoing Nuclear Renaissance includes many next generation reactor designs and testing, this means translates to increased demand for Health Physicists. These opportunities are not limited to just reactors but the entire fuel cycle from mining and extraction from existing tailings, R&D support efforts at new start-up companies, to shielding modeling and design, to Recycling and Reprocessing, and eventually Decommissioning.

Health Physics↗
DOE OSTI2026

Hadron-Argon Cross-Section Measurements at Protodune

The Deep Underground Neutrino Experiment (DUNE) is a next-generation experiment designed to measure neutrino oscillations with unprecedented precision, specifically seeking to identify CP violation in the lepton sector. DUNE utilizes the Liquid Argon Time Projection Chamber (LArTPC) as its core detecting technology, offering high-resolution imaging to reconstruct neutrino interactions on argon. A primary challenge in this reconstruction is the modeling of hadronic final-state interactions (FSI), where hadrons produced at the initial interaction vertex undergo further scattering before escaping the nucleus. In this thesis, I utilize the ProtoDUNE-SP detector and its 1~GeV/$c$ hadron beam at the CERN Neutrino Platform to present the first measurements of total inelastic $\pi^+$-argon and proton-argon cross sections in an energy regime critical to DUNE. I detail the development and validation of the ``slicing method'', which utilizes a kiloton-scale LArTPC to extract hadron-argon cross sections. These measurements provide an indispensable benchmark for informing DUNE's FSI modeling, and our results show no significant tension with current model predictions. Additionally, I study the impact of FSI modeling on DUNE’s sensitivity to oscillation parameters. This study demonstrates that variations in FSI models may be degenerate with oscillation features, highlighting the necessity of using direct experimental data to refine interaction models. Finally, I provide an outlook on related studies that will further contribute to this effort, supporting the ambitious physics goals of upcoming neutrino oscillation programs including DUNE.

Yin-Rui, Liu [U. Chicago (main)]↗
DOE OSTI2019

Evaluating next-generation intensity–duration–frequency curves for design flood estimates in the snow-dominated western United States

Civil infrastructure such as culverts and bridges are commonly designed using precipitation-based intensity-duration-frequency (PREC-IDF) curves, which assume that the occurrence of precipitation is in the form of rainfall and immediately available for the rainfall-runoff process. In snow-dominated regions, where most winter precipitation occurs as snow that melts during spring to early summer, the use of standard PREC-IDF curves may lead to substantial underestimation of design floods and high failure risk of infrastructure. In this context, we developed next-generation IDF (NG-IDF) curves that characterize the actual water reaching the land surface (i.e., rainfall plus snowmelt) to enhance standard infrastructure design in snow-dominated regions. This study evaluates the performance of NG-IDF curves coupled with U.S. Department of Agriculture Technical Release 55 hydrologic model in estimating design floods for 246 snowy locations in different hydroclimate regimes of the western United States. Design flood estimates from a well-validated continuous simulation using a physics-based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), were used as the performance benchmark. Compared with the benchmark estimates, the standard PREC-IDF curves led to substantial errors in design flood estimates while the NG-IDF curves significantly reduced these errors. For example, the averaged error in the 50-year design flood estimates over the 246 locations was reduced from 31% with the use of PREC-IDF curves to 12% with the use of NG-IDF curves. Despite the different model structures, the single-event NG-IDF approach versus the continuous simulation DHSVM did not exhibit statistically significant differences in 91% of the 246 locations for the 50-year design flood estimates. We find this indicates a satisfactory performance of NG-IDF curves to estimate design flow under the conditions tested in the snow-dominated western United States. This article also presents technical suggestions and the limitations of infrastructure design using NG-IDF curves for regulatory agencies and practicing engineers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The impact of market design and clean energy incentives on strategic generation investments and resource adequacy in low-carbon electricity markets

Well-designed electricity markets play a crucial role in maintaining reliable electric power systems, which are critical in modern society. Here, this study examines the impact of different electricity market designs and clean energy incentive schemes on supporting renewable energy integration and achieving clean energy goals. To this end, we utilize a game-theoretical generation expansion planning model where generation companies make investment and retirement decisions to maximize their expected profit. The model is structured as an equilibrium problem with equilibrium constraints (EPEC) and solved using a diagonalization approach combined with progressive hedging. We analyze three types of electricity market designs: an energy-only market, a capacity market, and a clean energy market, and consider a wide range of market parameters resulting in 14 total scenarios. Wind and solar capacity comprise the majority of new investments in all considered scenarios, but the resultant system planning reserve margin (PRM) can differ significantly depending on market parameters. We also find that profit-driven investments lead to lower PRMs than a traditional system cost minimization approach. These individual scenario results further demonstrate how different market designs and clean energy incentive schemes may influence investor decision-making and impact resource adequacy throughout the clean energy transition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Giant energy-storage density with ultrahigh efficiency in lead-free relaxors via high-entropy design

Next-generation advanced high/pulsed power capacitors rely heavily on dielectric ceramics with high energy storage performance. However, thus far, the huge challenge of realizing ultrahigh recoverable energy storage density (W rec ) accompanied by ultrahigh efficiency (η) still existed and has become a key bottleneck restricting the development of dielectric materials in cutting-edge energy storage applications. Here, we propose a high-entropy strategy to design “local polymorphic distortion” including rhombohedral-orthorhombic-tetragonal-cubic multiphase nanoclusters and random oxygen octahedral tilt, resulting in ultrasmall polar nanoregions, an enhanced breakdown electric field, and delayed polarization saturation. A giant W rec ~10.06 Jcm –3 is realized in lead-free relaxor ferroelectrics, especially with an ultrahigh η ~90.8%, showing breakthrough progress in the comprehensive energy storage performance for lead-free bulk ceramics. This work opens up an effective avenue to design dielectric materials with ultrahigh comprehensive energy storage performance to meet the demanding requirements of advanced energy storage applications.

25 ENERGY STORAGE↗
DOE OSTI2024

Designs from Local Random Quantum Circuits with SU ( d ) Symmetry

The generation of k -designs (pseudorandom distributions that emulate the Haar measure up to k moments) with local quantum circuit ensembles is a problem of fundamental importance in quantum information and physics. Despite the extensive understanding of this problem for ordinary random circuits, the crucial situations in which symmetries or conservation laws are in play are known to pose fundamental challenges and remain little understood. Here, we construct explicit local unitary ensembles that can achieve high-order unitary k -designs under transversal continuous symmetry, in the particularly important SU ( d ) case. Specifically, we define the convolutional quantum alternating (CQA) group generated by 4-local SU ( d ) -symmetric Hamiltonians as well as associated 4-local SU ( d ) -symmetric random unitary circuit ensembles and prove that they form and converge to SU ( d ) -symmetric k -designs, respectively, for all k < n ( n − 3 ) / 2 , with n being the number of qudits. A key technique that we employ to obtain the results is the Okounkov-Vershik approach to S n representation theory. To study the convergence time of the CQA ensemble, we develop a numerical method using the Young orthogonal form and the S n branching rule. We provide strong evidence for a subconstant spectral gap and certain convergence time scales of various important circuit architectures, which contrast with the symmetry-free case. We also provide comprehensive explanations of the difficulties and limitations in rigorously analyzing the convergence time using methods that have been effective for cases without symmetries, including Knabe’s local gap threshold and Nachtergaele’s martingale methods. This suggests that a novel approach is likely necessary for understanding the convergence time of SU ( d ) -symmetric local random circuits. Published by the American Physical Society 2024

Li, Zimu (ORCID:0000000314736492)↗
DOE OSTI2022

Advanced Lightweight HiE PMDD Generator for WTGs

Preliminary designs of advanced lightweight permanent magnet direct drive generators for offshore and “tall wind” onshore applications utilizing funding from DOE/EERE/WETO were developed by WEG, RTX, NREL and supporting partners. This work significantly reduces tower head mass, increases efficiency, reduces the rare earth content of the generator, and leads to additional turbine system benefits that improve competitiveness, such as the use of taller towers onshore and less expensive foundations (“hull”) for floating offshore. The end result is an approximately 10% reduction of LCOE in both cases, which could significantly improve competitiveness and support the EERE/DOE goal of promoting US manufacturing.

17 WIND ENERGY↗
DOE OSTI2022

Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing

Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield and thereby brightness of a dim monomeric RFP (mRojoA, quantum yield = 0.02) by optimizing chromophore packing with aliphatic residues, which we hypothesized would reduce torsional motions causing non-radiative decay. Experimental characterization of the top 10 designed sequences yielded mSandy1 (λ em = 609 nm, quantum yield = 0.26), a variant with equivalent brightness to mCherry, a widely used RFP. We next used directed evolution to further increase brightness, resulting in mSandy2 (λ em = 606 nm, quantum yield = 0.35), the brightest Discosoma sp. derived monomeric RFP with an emission maximum above 600 nm reported to date. Crystallographic analysis of mSandy2 showed that the chromophore p-hydroxybenzylidene moiety is sandwiched between the side chains of Leu63 and Ile197, a structural motif that has not previously been observed in RFPs, and confirms that aliphatic packing leads to chromophore rigidification. Our results demonstrate that computational protein design can be used to generate bright monomeric RFPs, which can serve as templates for the evolution of novel far-red fluorescent proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Enhanced Second and Third Harmonic Generations in Metasurfaces Enabled through Point Group Design and Accidental Bound States in the Continuum

Here, we investigate enhanced second‑ and third‑harmonic generation (SHG and THG) in GaAs metasurfaces by combining electromagnetic point‑group designs with accidental bound states in the continuum (BICs). Using group theory, we predict the existence, suppression, and polarization selection rules of SHG and THG for metasurfaces belonging to the D 2h and C 2v point groups. In D 2h metasurfaces, inversion symmetry suppresses SHG while enabling polarization‑selective THG, which is strongly enhanced near accidental BICs with ultrahigh Q‑factors approaching 107. By breaking inversion symmetry to form C 2v metasurfaces through the introduction of nanogaps, SHG is now allowed and is dramatically enhanced near accidental BICs. Importantly, accidental BICs preserve polarization selection rules while providing a robust parameter range for high‑Q resonances, in contrast to quasi‑BICs induced by symmetry breaking. Our results demonstrate that the combination of point‑group theory and accidental‑BIC engineering offers a predictive and robust route toward highly efficient nonlinear metasurfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Harnessing structural stochasticity in the computational discovery and design of microstructures

This paper presents a deep generative model-based design methodology for tailoring the structural stochasticity of microstructures. Although numerous methods have been established for designing deterministic (periodic) or stochastic microstructures, a systematic design approach that allows the unified treatment of both deterministic and stochastic microstructure design domains has yet to be created. The proposed methodology resolves this issue by learning a unified feature space that embodies diverse structural patterns with continuously varying stochasticity levels. A highly diverse microstructure database is established to incorporate various types of deterministic and stochastic microstructure patterns. A property-aware deep generative model is proposed to learn a unified feature space of the structural characteristics, as well as the relationship between structure features and properties of interest. Autoencoder (AE), Variational Autoencoder (VAE), and Adversarial Autoencoder (AAE) are compared to understand their relative merits in the property-aware learning of the unified feature space. Microstructural designs with tailorable stochasticity and properties are obtained by searching the unified feature space. Multiple design cases are presented to demonstrate the capability of designing microstructures for structural stochasticity and properties. Furthermore, the proposed method is employed to create stochastically graded structures, which manipulate the mechanical behaviors by varying the local stochasticity of the structure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

$\mathrm{IH}$-$\mathrm{GAN}$: A conditional generative model for implicit surface-based inverse design of cellular structures

Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically optimized structures, particularly those represented as discrete density maps. However, the optimization of such cellular structures is challenging due to the multiscale design problem. Past work addressing this problem generally either only optimizes the volume fraction of single-type unit cells but ignoring the effects of unit cell geometry on properties, or considers the geometry–property relation but builds this relation via heuristics. In contrast, we propose a simple yet more principled way to accurately model the property to geometry mapping using a conditional deep generative model, named Inverse Homogenization Generative Adversarial Network (IH-GAN). It learns the conditional distribution of unit cell geometries given properties and can realize the one-to-many mapping from properties to geometries. Here we further reduce the complexity of IH-GAN by using the implicit function parameterization to represent unit cell geometries. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy (R 2 -scores between target properties and properties of generated unit cells >98%) and 2) improve the optimized structural performance over the conventional variable-density single-type structure. In the minimum compliance example, our IH-GAN generated structure achieves a 79.7% reduction in concentrated stress and an extra 3.03% reduction in displacement. In the target deformation examples, our IH-GAN generated structure reduces the target matching error by 86.4% and 79.6% for two test cases, respectively. We also demonstrated that the connectivity issue for multi-type unit cells can be solved by transition layer blending.

42 ENGINEERING↗