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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Linear gyrokinetic simulations of toroidal Alfvén eigenmodes in the Mega-Amp Spherical Tokamak

Linear gyrokinetic (GK) simulations using the Gyrokinetic Toroidal Code (GTC) have been performed to investigate Toroidicity-driven Alfvén Eigenmodes (TAEs) driven by the neutral beam injection (NBI) induced fast ions in the Mega-Amp Spherical Tokamak (MAST) to identify the non-perturbative and kinetic effects of thermal plasma. A specific TAE in MAST discharge 26887, with an on-axis NBI power of approximately 1.5 MW and plasma current around 800 kA, exhibited frequency chirping, and the tangential soft x-ray camera array resolved the radial mode structure peaked near |q|=1.5. Various excitation methods were used in the GTC linear simulations, illustrating this code's capability to realistically represent the mechanisms and behaviors of fast ion-driven TAEs in spherical tokamaks. The radial structures from these GK simulations closely match measurements and calculations performed using the NOVA ideal MHD code, though with the frequencies approximately 10 kHz lower, likely due to various kinetic and non-perturbative effects. The simulations measured the damping rates due to continuum damping, radiative damping, and ion Landau damping, revealing that ion Landau damping has the most significant contribution to the total damping rate of the TAE. A comparison of growth rates of TAEs excited by fast ion Maxwellian and slowing-down distributions shows that the TAEs excited by a fast ion anisotropic pitch distribution (as part of the slowing-down distributions) are more unstable compared to those excited by a Maxwellian distribution with an equivalent fast ion beta. This shows that the use of fast ion anisotropy alters the number of fast ions to be in shear Alfvén resonance, and hence, it can greatly affect the stability of TAEs. These tests can be performed with the GTC but impossible with ideal MHD simulations, highlighting the necessity of kinetic simulations such as the GTC for a precise prediction of the TAE stability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2025

Ship Motion Testing Needs Evaluation

The American Bureau of Shipping performed an evaluation of national maritime nuclear testing capabilities and determined there currently is an inability to test maritime nuclear power plants in a ship motion environment. A Ship Motion Test Facility (SMTF) is proposed to fill this gap. To inform design of this facility, this report documents an evaluation performed by a panel of technical experts that identifies key physical phenomena associated with maritime nuclear power plant operation that are important to safety and reliability and have a low state of knowledge. This evaluation concluded that all of the major test needs to understand a nuclear plant's behavior in a ship motion environment are for thermal-hydraulics related phenomena. Based on this finding, the evaluation panel concluded that the SMTF would not be required to use nuclear fuel to provide a heat source for the fluids. This would significantly decrease the cost, timeline, and complexity for this test facility to enable the advancement of maritime nuclear technology.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Battery Life Prediction Using Reduced-Order Physics Models and Machine Learning (CRADA Final Report)

Phase 1 (Original CRADA, plus no-cost extension modifications #1-3, 6/1/2017 to 3/13/2021): The Australian Department of Defence (AUDoD) is performing accelerated aging tests of Li-ion batteries to benchmark their reliability and degradation characteristics. Using its previously developed battery lifetime predictive model framework, the National Laboratory of the Rockies (NLR) will develop analytical models based the AUDoD data to predict lifetime of the multiple Li-ion battery chemistries under real-world use scenarios of interest to AUDoD. The NLR model is based on physical degradation mechanisms encountered by Li-ion batteries and has been previously validated. Phase 2 (CRADA modification #4, plus no-cost extension modification #5, 2/22/2021 to 3/30/2025): Train and support Australian Department of Defence personnel to use NLR software for model-based estimation of Li-ion battery lifetime using accelerated battery aging data collected by the Australian Department of Defence. Under separate DOE funding from 2019 to 2021, NLR enhanced its battery life-prediction software using machine learning algorithms to automate portions of the model-fitting process, requiring significantly less labor and expert judgment and also adding uncertainty quantification, increasing statistical rigor. Under Phase 2, NLR will customize NLR Software and provide it to AuDoD. NLR will enhance its NLR Model to capture aging modes of AuDoD's multi-cell modules, including cell-balancing effects. NLR will develop example single-cell and multi-cell models based on one AuDoD battery aging dataset. NLR will train AuDoD personnel on NLR Software. By the conclusion of the project, NLR will have provided AuDoD the training materials, a user manual and software needed to perform their own analysis of additional and/or future battery aging datasets.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Towards in-situ certification of additively manufactured parts: the vital roles of physics-based and data-driven models

Certifying additively manufactured (AM) parts in-situ at the completion of a build is an enticing prospect, as it can help reduce the high costs associated with post-build testing and evaluation. However, achieving this goal presents significant challenges that may keep it aspirational for the foreseeable future. Nonetheless, incremental progress can pave the way forward. A critical aspect of in-situ certification involves continuous quality checking due to the random nature of the AM process and the difficulties in detecting defects or anomalies once layers are built over. While real-time in-situ monitoring strategies assisted by machine learning (ML) play a pivotal role in auditing part quality, they must ideally be supported by real-time (or near real-time) adaptive process control enabled by ML-assisted decision-making. By analyzing in-situ monitoring data in real-time (or near real- time) to dynamically adjust manufacturing parameters, such intervention can ensure AM parts are built to meet stringent certification standards. This can be achieved virtually by using high-fidelity performance models for the physical testing and evaluation tasks. In this short editorial, we discuss the key contributions made by data-driven and physics-based models in providing intelligence to the monitoring and process control tasks underpinning in-situ certification and in the simulation of the build’s performance under test and service conditions. While our focus lies in metal AM, the concepts discussed here are also relevant to other AM processes.

: In-situ monitoring↗
DOE OSTI2025

Creation of a Weather Drivers Test Suite for Inclusion in ASHRAE Standard 140

Weather conditions are an important boundary condition for building performance simulation (BPS) calculations. For existing test cases in ASHRAE Standard 140 "Method of Test for Evaluating Building Performance Simulation Software" (ANSI/ASHRAE 2020), it was assumed that the software being tested could adequately read and interpret the weather data in the provided standard weather files. As differences between the programs have been reduced and as more programs have shifted to sub-hourly time steps this assumption has become more stretched. To address these concerns a new test suite testing a program's ability to read and interpret the data from a standard weather file was developed. The purpose of the test suite is to test the use of the typical data used from standard weather files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Speedup of UEDGE Parameter Scans Using Machine-Learning Optimized OpenMP Parallelization and a Continuation Solver

This article presents the OpenMP parallelization of the preconditioning Jacobian assembly and right‐hand side residual evaluation in UEDGE. A continuation algorithm, utilizing the internal NKSOL implicit Jacobian‐Free Newton‐Krylov solver to efficiently scan physical parameters, is also presented. The implemented parallelization reduces the computational time for a benchmark scan run on 32 threads by compared to the serial version when using trained random forest regression models to identify the optimal decomposition of the system of equations. Random forest regression models applied to the UEDGE time‐dependent and continuation solver algorithms did not yield meaningful improvement in computational performance. A benchmark DIII‐D gas injection rate scan in the 0.35–0.75 kA interval, performed on a test cluster using the parallelized code and continuation solver, produced 1066 steady‐state solutions with a 22 s average wall‐clock computational time per steady‐state solution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Strain-rate hardening enhances fatigue resistance of AlSi10Mg alloy at 350°C

The high cycle fatigue behavior of laser powder bed fusion processed AlSi10Mg has been investigated at 350 °C (T/T m ∼ 0.7). The alloy exhibited a fatigue strength of 30 MPa defined by runout after 10 7 cycles at a conventional loading frequency of 20 Hz, corresponding to a notable fatigue strength to ultimate tensile strength ratio of 0.73. The surprising fatigue resistance was attributed to the strain-rate hardening effect at high fatigue loading frequency relative to tensile loading rates at 350 °C. The strain-rate hardening effect was validated by performing ultrasonic fatigue tests (20 kHz loading frequency) with three orders of magnitude higher strain-rates than those at conventional loading frequency. The higher strain-rates in ultrasonic fatigue increased the magnitude of strain-rate hardening resulting in longer AlSi10Mg fatigue lives compared to fatigue at conventional frequency, thus confirming the strain-rate hardening effect. The fatigue crack initiation mechanism was strain-rate dependent. Post-mortem microstructural examination revealed intergranular cavitation inside clusters of fine equiaxed grains. The cavities interlinked with each other to initiate near-surface fatigue cracks at conventional frequency. Cavitation occurred to a lesser extent at the ultrasonic frequency. As a result, fatigue cracks initiated at pre-existing processing defects near the surface in ultrasonic fatigue samples. Finally, this investigation underscores the role of fine grain clusters in promoting high-temperature fatigue crack initiation and indicates a possible trade-off between printability via grain refinement and high-temperature fatigue resistance of additively manufactured alloys.

Additive manufacturing↗
DOE OSTI2025

Surrogate modeling and optimization of the leaching process in a rare earth elements recovery plant

Critical minerals (CMs) and Rare Earth Elements (REEs) play a vital role in crucial infrastructure technologies such as renewable energy generation and batteries. Recovering them from waste materials has recently been found to significantly reduce environmental impact and supply chain costs related to these materials. In this work, we investigate surrogate modeling techniques aimed to simplify the modeling, simulation, and optimization of the leaching processes involved in CM and REE recovery flowsheets. As there is currently a lack of systematic studies on this topic, we perform extensive computational testing to ascertain which surrogate models are easier to construct and offer high predictive accuracy. Further, our results suggest that sparse quadratic models balance predictive accuracy and computational efficiency. Training and using these surrogates for global optimization of the leaching process requires two orders of magnitude fewer measurements and is up to four orders of magnitude faster than optimizing the original simulation using equation-oriented optimization or derivative-free optimization.

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

Estimating the impact of tariff-driven behind-the-meter storage operation on distribution grid investments

Increasing growth of distributed solar photovoltaics (PV) and electric vehicles (EV) can strain local distribution networks and require costly upgrades. Distributed battery storage, often deployed alongside PV, can be used to mitigate those costs, depending on how batteries are operated. This study evaluates the potential deferral value of distributed battery storage across a range of tariff structures, focusing on the rate structures most commonly available to residential customers today and related variants. Deferrals are evaluated with a least-cost distribution grid expansion optimization model to identify requirements on line reconductoring, transformer upgrades, and voltage regulator installations under each tariff. Results show that TOU rates and net billing tariffs can yield meaningful deferral value, depending on specific tariff structure features. Under the best performing tariff structure tested, storage produced a median annualized deferral value of $7.18 per kW of storage capacity ( kW S ) across all feeders in the sample, though deferral values were considerably larger for feeders with peak loads that coincide with utility system peak, i.e., timing of TOU peak period. In contrast, under an unrestricted TOU design with no restrictions on grid charging or discharging, the median deferral value was $0/ kW S illustrating the critical importance of tariff structure details.

Rodriguez-Garcia, Luis↗
DOE OSTI2024

A New Simple-to-Configure Self-Perturbing Multivariable Extremum-Seeking Controller

This paper presents a new stochastic relay-based extremum-seeking controller (ESC) for multi-input-single-output (MISO) systems. The algorithm was developed with the goal of simplifying configuration to enable easier deployment to real-world problems. A solution is developed first for a static map and then adapted for a general class of dynamic systems. The number of configurable parameters is one per input channel for the static case and only one additional parameter is needed for the dynamic version. The problem of gradient identifiability is solved via the use of stochastic relay gains and a simple stability proof for the static case is presented. Simulation tests demonstrate the performance of the strategy for optimizing both static and dynamic systems.

Salsbury, Timothy [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2026

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled target data to align the learned feature representations between the source and target domains. We compare a range of different UDA techniques, finding that minimizing the maximum mean discrepancy (MMD) between source and target feature vectors yields the most consistent improvement to testing scores. For instance, using a custom transformer-based neural network, we achieved a testing accuracy of $0.904 \pm 0.022$ on an experimental LaBr test set after performing unsupervised feature alignment via MMD minimization, compared to $0.754 \pm 0.014$ before alignment. Overall, our results highlight the potential of using UDA to adapt a radioisotope classifier trained on synthetic data for real-world deployment.

Lalor, Peter W.↗
DOE OSTI2025

Pyomo: Accidentally outrunning the bear

Pyomo is an open-source optimization modeling software that has undergone significant evolution since its inception in 2008. Pyomo has evolved to enhance flexibility, solver integration, and community engagement. Modern collaborative tools for open-source software have facilitated the development of new Pyomo functionality and improved our development process through automated testing and performance-tracking pipelines. However, Pyomo faces challenges typical of research software, including resource limitations and knowledge retention. The Pyomo team’s commitment to better development practices and community engagement reflects a proactive approach to these issues. We describe Pyomo’s development journey, highlighting both successes and failures, in the hopes that other open-source research software packages may benefit from our experiences.

automation↗
DOE OSTI2026

Revisiting the validity of eddy viscosity models for predicting airflow over water waves

In this study, we revisit the validity of eddy viscosity models for predicting wave-induced airflow disturbances over ocean surface waves. We first derive a turbulence curvilinear model for the phase-averaged Navier–Stokes equations, extending the work of Cao, Deng & Shen (2020 J. Fluid Mech. 901, A27), by incorporating turbulence stress terms previously neglected in the linearised viscous curvilinear model. To verify our formulation, we perform a priori tests by numerically solving the model using mean wind and turbulence stress profiles from large-eddy simulations (LES) of airflow over waves across various wave ages. Results show that including turbulence stress terms improves wave-induced airflow predictions compared with the previous viscous curvilinear model. We further show that using a standard mixing-length eddy viscosity yields inaccurate predictions at certain wave ages, as it fails to capture wave-induced turbulence, which fundamentally differs from mean shear-driven turbulence. The LES data show that accurate representations of wave-induced stresses require a complex-valued eddy viscosity. The maximum magnitude of this eddy viscosity scales as ∼𝑢 𝜏 ⁢𝜁 𝑖𝑛𝑛𝑒𝑟 , where 𝑢 𝜏 is the friction velocity and 𝜁 𝑖𝑛𝑛𝑒𝑟 is the inner-layer thickness, the height at which the eddy-turnover time matches the wave advection time scale. This scaling aligns with the prediction by Belcher & Hunt (1993 J. Fluid Mech. 251, 109–148). Overall, the findings demonstrate that traditional eddy viscosity models are inadequate for capturing wave-induced turbulence. More sophisticated turbulence models are essential for the accurate prediction of airflow disturbances and form drag in wind–wave interaction models.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Advancing Sodium-Ion Battery Cathodes: A Low-Cost, Eco-Friendly Mechanofusion Route from TiO 2 Coating to Ti 4+ Doping

Layered oxide battery cathodes often require extra stabilization strategies, such as surface coating or doping, to mitigate side reactions and enhance longevity. Conventional methods such as aqueous deposition and atomic layer deposition are costly and environmentally unfriendly and even damage the original structure, especially for air-sensitive sodium-ion battery (SIB) cathodes. Herein, we introduce an all-dry mechanofusion technique that modifies hydroxide precursors with TiO 2 coating before sintering with a sodium source. Using advanced characterizations including X-ray diffraction, neutron diffraction, and solid-state nuclear magnetic resonance for structural insights, X-ray absorption spectroscopy to study metal valence states, and transmission X-ray microscopy for nanoscale visualization of nickel oxidation states, we verified that postsintering transforms TiO 2 surface coating into Ti doping, leading to improved Ni-oxidation homogeneity, modified charge compensation, and enhanced thermal stability. Electrochemical tests reveal superior performance in capacity retention, rate capability, and air stability for these modified cathodes, with pouch cells maintaining over 85% capacity after 650 cycles. This method presents a sustainable, cost-effective route for advanced SIB cathode development.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Lowering the Scaling of Self-Consistent Field Methods by Combining Tensor Hypercontraction and a Density Difference Ansatz

We present the tensor hypercontraction difference self-consistent field (SCF) method, an approach that reduces the formal computational scaling of traditional naive self-consistent field methods from 𝑂(𝑁 4 ) to 𝑂(𝑁 3 ) with system size 𝑁. The scaling reduction is achieved by developing a new technique for constructing the tensor hypercontraction decomposition based on the fundamental approximation made in density fitting. Combining this scheme with the difference self-consistent field methodology, we achieve a method that enables 𝑂(𝑁 3 ) scaling SCF calculations with only 𝑂(𝑁 2 ) storage requirements. In conclusion, our proof-of-concept numerical tests demonstrate robust performance with errors in total energies below 8 × 10 –4 E h and with sub 1 kcal/mol errors for relative energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Electrolyte Immersion Increases Photoconductivity in a Model Polymer Photocathode

Immersing polymer solar cells in aqueous electrolyte for photoelectrochemical (PEC) hydrogen production is likely to cause photophysical changes that could present both challenges and opportunities for engineering functional and durable devices. Herein we study the bulk heterojunction blend poly(4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b:4,5-b']dithiophene-2,6-diyl)-alt-(2-(((2-ethylhexyl)oxy)carbonyl)-3-fluorothieno[3,4-b]thiophene-4,6-diyl):poly(N,N'-di(2-octyldodecyl)naphthalene-1,8:4,5-bis(dicarboximide)-2,6-diyl)-alt-(2,2-bithiophene-5,5'-diyl) (PTB7-Th:N2200) excited-state dynamics in electrolyte from femtosecond to millisecond time scales using pump-probe microwave conductivity and absorption spectroscopy. While the blend swells very little, electrolyte exposure increases the microwave-frequency mobility and possibly the yield of photogenerated charges while also decreasing crystallinity. These results indicate an enhancement in key performance metrics, implying that any limitations on the performance of PEC test devices do not arise from active layer-electrolyte interactions. For the PTB7-Th:N2200 blend or similar photocathode systems, our results indicate that improving the interfacial kinetics and/or the carrier lifetime should be prioritized, not protecting the active layer from the electrolyte. Since this observation may not be universal to all polymer systems, future research should focus on identifying their limiting photophysical processes. electrolyte.

14 SOLAR ENERGY↗