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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nuclear Materials Packaging, Transportation, and Systems Analysis Group Software Quality Assurance Plan: ANSYS Mechanical Finite Element Analysis Software Version 2023R1

ANSYS Inc. develops and markets engineering simulation software and services used in the aerospace, automotive, manufacturing, electronics, biomedical, energy, defense, and many other industries. ANSYS is dedicated to engineering simulation and is the world’s leading software provider. ANSYS was founded in 1970 and is headquartered in Canonsburg, Pennsylvania. ANSYS provides an engineering analysis tool combining structural, thermal, computational fluid dynamics, acoustic, and electromagnetic simulation capabilities. ANSYS has two main programs, which use the same solvers: (1) Mechanical APDL (ANSYS Design Parametric Language), a Fortran-based coding platform, and (2) ANSYS Workbench, which uses a graphical user interface to aid in finite element analysis implementation. This plan covers both APDL and Workbench. The ANSYS computer program is a large-scale, multipurpose finite element program that can be used to solve several classes of engineering analyses. The analysis capabilities of ANSYS include the ability to solve static and dynamic structural analyses, steady-state and transient heat transfer problems, mode-frequency and buckling eigenvalue problems, static or time-varying magnetic analyses, and various types of field and coupled-field applications. The program contains many special features that allow nonlinearities or secondary effects such as plasticity, large strain, hyperelasticity, creep, swelling, large deflections, contact, stress stiffening, temperature dependency, material anisotropy, and radiation to be included in the solution. As ANSYS has been developed, other special capabilities such as substructuring, submodeling, random vibration, kinetostatics, kinetodynamics, free convection fluid analysis, acoustics, magnetics, piezoelectrics, coupled-field analysis, and design optimization have been added to the program. These capabilities contribute further to making ANSYS a multipurpose analysis tool for varied engineering disciplines. The ANSYS program has been in commercial use for over 50 years and has been used extensively in the aerospace, automotive, construction, electronic, energy services, manufacturing, nuclear, plastics, oil, and steel industries. Additionally, many consulting firms and hundreds of universities have used ANSYS for analysis, research, and educational purposes. ANSYS is recognized worldwide as one of the most widely used and capable programs of its type. Ansys design analysis software is the first created within a quality system with ISO 9001 certification, the internationally accepted quality standard. Product development, testing, maintenance and support processes also meet the United States Nuclear Regulatory Commission's quality requirements, as they have for nearly four decades. The Quality Assurance Service Agreement is suitable for the customers working in the nuclear industry who need to meet specific federal regulations including 10CRF50 Appendix B and provisions of 10CFR21. ANSYS has retained its original International Organization for Standardization (ISO) 9001 accreditation certificate since1995-05-04, It’s current certificate is valid until 2027-05-29.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Blueprint for DOE Quantum Supercomputing: Ensuring U.S. Leadership in the Quantum Decade

Quantum computing stands at the threshold of a transformative decade, where the field will evolve from small-scale demonstrations toward practical scientific computing at scale. This Blueprint identifies fault-tolerant quantum computers (FTQCs) as a viable, scalable, and broadly applicable path to achieving “quantum scientific utility,” defined as solving scientifically valuable problems beyond the reach of conventional, classical computers. This capability is expected to show scientific demonstrations in the late 2020s and to mature in the early-to-mid 2030s. This Blueprint outlines a strategy to prepare the U.S. Department of Energy (DOE) for FTQCs and their integration into the U.S. national scientific computing infrastructure. Its purpose is to identify the steps, milestones, and research directions necessary for DOE to enable initial deployment of FTQCs in 2028 as a scientific tool for the nation and mature this capability into the 2030s. DOE has a long history of supporting quantum information science and technology, contributing significantly to research advancements, training a quantum-ready workforce, and providing access to early small-scale quantum hardware. Given recent demonstrations of logical operations on error-corrected logical qubits and the advancement of commercial hardware roadmaps, DOE should begin preparations for large-scale, fault-tolerant quantum computing deployment for DOE science missions. This Blueprint proposes that DOE focus on (1) deploying first-generation scientifically relevant quantum computers with at least 100 logical qubits and performing at least 10,000 to 100,000 hard logical operations in scientifically relevant calculations; (2) developing essential FTQC programming competencies, system software, and facility readiness; and (3) investing in cutting edge focused R&D that fosters breakthroughs in scientific applications, algorithms, and logical architectures needed to accelerate the advent of scientific utility. This effort will position DOE to transition to larger systems: production-scale quantum computers that comprise 1,000 to 10,000 logical qubits, perform 1 to 10 billion hard logical operations, and execute scientifically useful computations at scale. Achieving these goals will require DOE facilities to evolve with urgency to support scientific campaigns that integrate quantum and classical computing resources into efficient workflows, novel software and firmware environments for compiling and routing quantum programs on FTQC machines, and suitable infrastructure for quantum hardware. It will also require further development and optimization of scientific applications from the fields of materials science, quantum chemistry, and high-energy and nuclear physics. The Blueprint calls for transformative R&D and collective action to accelerate the advent of scientific quantum utility and bring it within reach by 2028.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The NREL Sensor Laboratory Detection of Hydrogen Emissions

The development of a functional hydrogen detection system is a multifaceted process that integrates hardware, deployments strategies, and analytics which can be supported by the NREL Sensor Laboratory: 1. Support of the design, validation and optimization of sensing prototypes; 2. Guide optimized sensing element development, including control electronics; 3. Laboratory testing to validate/optimize metrological performance (measurement range, detection limit, etc.); 4. Provide test sites for field deployments representative of real-world scenarios with controlled hydrogen releases; 5. Develop sensor placement and operation guidance; 6. Provide guidance on electronics to accommodate facility integration; 7. Electrical safety designs to allow for operation within restricted zones; 8. Integration into facility monitoring and control systems; 9. Guide incorporation of cyber security elements to protect facilities from malicious attacks; 10. Modeling and application of advanced analytics to detect and quantify emissions; 11. Higher Order dispersion models to guide sensor placement for reliable detection; 12. Advanced analytics for improved metrological performances, and to inform inverse modeling; 13. Market support and commercialization (national and international markets); 14. Commercial deployments in H2@SCALE markets (e.g., HUBs and other large-scale hydrogen markets); and 15. Leverage off international collaborations/partnerships (e.g., NREL is on the advisory board for the European initiative "pre-Normative Research on Hydrogen Releases Assessment"-NHyRA).

08 HYDROGEN↗
DOE OSTI2026

Optimized cryogenic setup for microwave loss characterization of superconducting coaxial cables

As superconducting quantum systems scale up to enable applications like large-scale quantum computing, challenges such as fabrication yield, wiring complexity, and microwave crosstalk drive the need for high-fidelity, low-loss, modular interconnects. In recent experiments, the performance of these interconnects is often limited by cable loss, a factor that becomes even more critical as we aim for higher fidelity operations and connect increasingly coherent modules. In this talk, we present our cryogenic microwave loss characterization setup, carefully designed to minimize losses external to the coaxial cables under test. We also share results for several commercially-available superconducting cables measured with this setup and briefly discuss our ongoing efforts to develop custom coaxial cables capable of achieving state-of-the-art performance.

Vallières, André [Northwestern U.]↗
DOE OSTI2026

Optimized cryogenic setup for microwave loss characterization of superconducting coaxial cables

As superconducting quantum systems scale up to enable applications like large-scale quantum computing, challenges such as fabrication yield, wiring complexity, and microwave crosstalk drive the need for high-fidelity, low-loss, modular interconnects. In recent experiments, the performance of these interconnects is often limited by cable loss, a factor that becomes even more critical as we aim for higher fidelity operations and connect increasingly coherent modules. In this talk, we present our cryogenic microwave loss characterization setup, carefully designed to minimize losses external to the coaxial cables under test. We also share results for several commercially-available superconducting cables measured with this setup and briefly discuss our ongoing efforts to develop custom coaxial cables capable of achieving state-of-the-art performance.

Vallières, André [Northwestern U.]↗
DOE OSTI2025

AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women

Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the semantic relationships in the text document although it has been widely applied in text mining. BERTopic, created in 2022, leveraged advances in deep learning and can capture the contextual relationships between words. In this work, we integrated Artificial Intelligence (AI) modules to LDA and BERTopic and provided a comprehensive comparison on the analysis of prescription opioid-related cardiovascular risks in women. Opioid use can increase the risk of cardiovascular problems in women such as arrhythmia, hypotension etc. 1,837 abstracts were retrieved and downloaded from PubMed as of April 2024 using three Medical Subject Headings (MeSH) words: “opioid,” “cardiovascular,” and “women.” Machine Learning of Language Toolkit (MALLET) was employed for the implementation of LDA. BioBERT was used for document embedding in BERTopic. Eighteen was selected as the optimal topic number for MALLET and 23 for BERTopic. ChatGPT-4-Turbo was integrated to interpret and compare the results. The short descriptions created by ChatGPT for each topic from LDA and BERTopic were highly correlated, and the performance accuracies of LDA and BERTopic were similar as determined by expert manual reviews of the abstracts grouped by their predominant topics. The results of the t-SNE (t-distributed Stochastic Neighbor Embedding) plots showed that the clusters created from BERTopic were more compact and well-separated, representing improved coherence and distinctiveness between the topics. Our findings indicated that AI algorithms could augment both traditional and contemporary topic modeling techniques. In addition, BERTopic has the connection port for ChatGPT-4-Turbo or other large language models in its algorithm for automatic interpretation, while with LDA interpretation must be manually, and needs special procedures for data pre-processing and stop words exclusion. Therefore, while LDA remains valuable for large-scale text analysis with resource constraints, AI-assisted BERTopic offers significant advantages in providing the enhanced interpretability and the improved semantic coherence for extracting valuable insights from textual data.

Research & Experimental Medicine↗
DOE OSTI2025

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment

X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.

Wang, Cong↗
DOE OSTI2026

Impact of atmospheric turbulence on performance and loads of wind turbines: knowledge gaps and research challenges

Wind energy harvesting from the atmosphere takes place in the atmospheric boundary layer. The boundary layer shear and buoyancy create three-dimensional turbulent eddies spanning a range of scales that form a continuous forward cascade of kinetic energy to the smallest scales of motion where energy is dissipated. Large-scale atmospheric circulations modulate the boundary layer turbulence, characterized by coherence and intermittency. As wind turbines grow in size and the integrated control of both turbines and wind farms spans greater distances, the relationship between the scales of atmospheric turbulence and the design and operation of wind energy facilities has entered new territory. The boundary layer turbulence impacts both wind turbine power production and turbine loads. Optimizing wind turbine and wind farm performance requires an understanding of how turbulence affects both wind turbine efficiency and reliability. While the characteristics of atmospheric boundary layer turbulence have been observed and studied in detail over the last few decades, there are still significant gaps in our understanding of the impact of turbulence on wind power resources and wind farm operations. This paper outlines the current state of turbulence research relevant to wind energy applications and points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.

Kosović, Branko [Johns Hopkins Univ., Baltimore, M↗
DOE OSTI2026

High-Resolution ESM Projections for Energy Applications Over the CONUS

Assessing energy resources under future scenarios requires high-resolution meteorological information that is physically consistent and suitable for regional-scale analysis. While Earth system model (ESM) projections provide valuable large-scale information, their coarse resolution and systematic biases limit direct applicability for energy system modeling and planning. In this study, we develop a high-resolution dynamical downscaling framework based on the Weather Research and Forecasting (WRF) model to translate global-scale ESM data into energy-relevant regional projections over the contiguous United States (CONUS). The framework identifies an optimized WRF configuration through numerical experiments and evaluates raw and bias-corrected ESM initial and boundary conditions, with soil moisture (SM) and soil temperature (ST) bias correction implemented as an integral part of the bias-corrected ESM forcing to improve land-atmosphere coupling prior to WRF dynamical downscaling. Using an optimized WRF configuration at 4-km resolution, we show that raw ESM forcing introduces systematic dry and cold soil biases that propagate into pronounced warm biases in near-surface air temperature and positive biases in solar irradiance, particularly during summer. Applying bias-corrected atmospheric forcing together with bias-corrected SM and ST substantially reduces these downstream biases and improves the surface energy balance and near-surface atmospheric fields. These results demonstrate that bias-aware treatment of initial conditions is critical for producing high-resolution downscaled projections suitable for energy system modeling and planning applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1972

Development of advanced materials composites for use as insulations for LH2 tanks

A study of internal insulation materials and fabrication processes for space shuttle LH2 tanks is reported. Emphasis was placed on an insulation system capable of reentry and multiple reuse in the Shuttle environment. Results are given on the optimization and manufacturing process scale-up of a 3D fiberreinforced foam insulation, BX-251-3D, derived from the Saturn S-4B internal insulation. It is shown that BX-251-3D can be satisfactorily installed in large-scale tanks under conditions that will permit a significant cost saving over the existing S-4B technology.

Lemons, C. R.↗
NASA NTRS1973

On the decentralized control of large-scale systems

The decentralized control of stochastic large scale systems was considered. Particular emphasis was given to control strategies which utilize decentralized information and can be computed in a decentralized manner. The deterministic constrained optimization problem is generalized to the stochastic case when each decision variable depends on different information and the constraint is only required to be satisfied on the average. For problems with a particular structure, a hierarchical decomposition is obtained. For the stochastic control of dynamic systems with different information sets, a new kind of optimality is proposed which exploits the coupled nature of the dynamic system. The subsystems are assumed to be uncoupled and then certain constraints are required to be satisfied, either in a off-line or on-line fashion. For off-line coordination, a hierarchical approach of solving the problem is obtained. The lower level problems are all uncoupled. For on-line coordination, distinction is made between open loop feedback optimal coordination and closed loop optimal coordination.

Chong, C.↗
NASA NTRS1987

Decentralized control of large-scale systems: Fixed modes, sensitivity and parametric robustness

The problem of stabilization/pole placement under structural constraints of large scale linear systems is discussed. The existence of a solution to this problem is expressed in terms of fixed modes. The aim is to provide a bibliographic survey of the available results concerning the fixed modes (characterization, elimination, control structure selection to avoid them, control design in their absence) and to present the author's contribution to this problem which can be summarized by the use of the mode sensitivity concept to detect or to avoid them, the use of vibrational control to stabilize them, and the addition of parametric robustness considerations to design an optimal decentralized robust control.

Tarras, A.↗
NASA NTRS1987

Turbulence control on an airborne laser platform

An active flow control device to generate large-scale, periodic structures in a turbulent shear flow is developed. Together with adaptive optics, the device may be used on airborne laser platforms to reduce or eliminate optical distortion caused by the turbulence in the aircraft's boundary layer. A cyclic jet issuing from a spanwise slot is used to collect the turbulent boundary layer for a finite time and then release all of the flow instantaneously in one large eddy that convects downstream. Flow visualization and hot-film probe measurements are used together with pattern recognition algorithms to demonstrate the viability of the flow control method. A flat plate towed in a water channel is used as a test bed. The instantaneous velocity signal is used to compute important statistical quantities of the random velocity field, such as the mean, the root-mean-square, the spectral distribution, and the probability density function. When optimized for a given boundary layer, it is shown that the cyclic jet will produce periodic structures that are similar to the random, naturally occurring ones. These structures seem to trigger the onset of bursting events near the wall of the plate. Thus, the present device generates periodic structures in both the outer and inner regions of a turbulent boundary layer.

Gad-El-hak, Mohamed↗
NASA NTRS1987

Evaluation of on-line pulse control for vibration suppression in flexible spacecraft

A numerical simulation was performed, by means of a large-scale finite element code capable of handling large deformations and/or nonlinear behavior, to investigate the suitability of the nonlinear pulse-control algorithm to suppress the vibrations induced in the Spacecraft Control Laboratory Experiment (SCOLE) components under realistic maneuvers. Among the topics investigated were the effects of various control parameters on the efficiency and robustness of the vibration control algorithm. Advanced nonlinear control techniques were applied to an idealized model of some of the SCOLE components to develop an efficient algorithm to determine the optimal locations of point actuators, considering the hardware on the SCOLE project as distributed in nature. The control was obtained from a quadratic optimization criterion, given in terms of the state variables of the distributed system. An experimental investigation was performed on a model flexible structure resembling the essential features of the SCOLE components, and electrodynamic and electrohydraulic actuators were used to investigate the applicability of the control algorithm with such devices in addition to mass-ejection pulse generators using compressed air.

Masri, Sami F.↗
NASA NTRS1987

Turbulence control on airborne laser platform

An avctive flow control device to generate large-scale, periodic structures in a turbulent shear flow is developed. Together with adaptive optics, the device may be used on airborne laser platforms to reduce or eliminate optical distortion caused by the turbulence in the aircraft's boundary layer. A flat plate towed in a water channel is used as a test bed. A cyclic jet issuing from a spanwise slot is used to collect the turbulent boundary layer for a finite time during its 'on' period. When the jet is turned 'off', all of the turbulent fluid is released instantaneously in one large eddy that convects downstream. Flow visualization and hot-film probe measurements are used together with pattern recognition algorithms to demonstrate the viability of the flow control method. The instantaneous velocity signal is used to compute important statistical quantities of the random velocity field, such as the mean, the root-mean-square, the spectral distribution, and the probability density function. When optimized for a given boundary layer, the cyclic jet produces periodic structures that are qualitatively similar to the random, naturally occurring ones. These structures seem to trigger the onset of bursting events near the wall. Thus, the present device generates periodic structures in both the outer and inner regions of a turbulent boundary layer.

Gad-El-hak, Mohamed↗
DOE OSTI2024

QFw: A Quantum Framework for Large-scale HPC Ecosystems

This work extends Quantum Framework (QFw) by integrating it with Northwest Quantum Simulator (NWQ-Sim) and by introducing a lightweight python library that allows multiple frontends (e.g., Qiskit) to interact with QFw. This extension enables QFw to flexibly decouple frontends from backends (e.g., NWQ-Sim). We demonstrate this capability by executing a Greenberger-Horne-Zeilinger (GHZ) circuit using Qiskit and Pennylane with NWQ-Sim and Tensor-Network Quantum Virtual-Machine (TN-QVM). QFw enables easy scaling to multiple nodes. We showcase this with scaling tests using GHZ with up to 32 qubits for different number of nodes on the Frontier supercomputer. And, to demonstrate the use of QFw for real world problems, we solve a metamaterial optimization problem, using a Quantum Approximate Optimization Algorithm (QAOA). We observe that QFw over NWQ-Sim marginally improves Qiskit-aer’s accuracy in reaching the lowest energy state. These additions to QFw prepare it to run hybrid applications in a hybrid resource environment since it treats actual quantum hardware and simulators alike.

Chundury, Srikar↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

There and Back Again: Reimagining Cryogenic Cooling for Scalable Arrays of Dilution Refrigerators for future Quantum Datacenters

While pulse tube cryocoolers enabled the rapid expansion of dilution refrigerator technology over the past two decades, the transition to large-scale quantum systems is now driving a reassessment of the DR’s higher-temperature-stage cooling strategies and how these systems can be effectively scaled in a modular way. Quasi-wet architectures based on centralized cryoplants and forced-flow helium distribution offer compelling advantages in energy efficiency, operational cost, and scalability. With appropriate redundancy, standardized interfaces, and optimized distribution system designs, these architectures will provide a practical and robust path forward for the next generation of quantum computing infrastructure.

Hansen, B. [Fermilab]↗