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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

60 years of science in ICF: from conception to scientific breakeven on the National Ignition Facility

The recent achievements of a burning plasma, fusion ignition, and scientific energy gain with deuterium-tritium (DT) fuel at Lawrence Livermore National Laboratory’s National Ignition Facility (NIF) represents a major milestone in the development of inertial confinement fusion (ICF) and all of fusion research. In these experiments, fuel pressures well in excess of hundreds of GBars were achieved in the compressed fuel, and robust alpha heating of the fuel, far in excess of the energy provided by the implosion, were demonstrated for the first time. These achievements occurred 60 years after the inception of ICF and the first laser demonstration, and were made possible by more than five decades of research at laser facilities around the world. Advances in laser technology both in wavelength and precision, motivated by improved understanding of laser-plasma interaction physics and the demands of targets; improvements in target fabrication inspired by the need to control and minimize hydrodynamic instabilities in the implosion; and multi-dimensional simulations and diagnostics have been critical to this achievement. This paper will summarize the scientific and technical advances, the surprises, and the challenges that had to be overcome to achieve these goals.

fusion↗
DOE OSTI2020

Performance Evaluation of AG-1 FC HEPA Filters and Medium in Nuclear Complex Facilities - 20434

High Efficiency Particulate Air (HEPA) filters are credited as the final barrier against the release of radioactive aerosol contamination in nearly every operating U. S. Department of Energy (DOE) and National Nuclear Security Agency (NNSA) nuclear facility. The Institute for Clean Energy Technology (ICET) at Mississippi State University maintains a research program that studies various aspects of these components of containment systems and seeks to answer key questions from across the industry. This programmatic overview will include results from recent and ongoing studies, including a study on filter design and performance envelope, degradation due to aging or fatigue, fire event impact on HEPA filters, and accelerated aging of medium and component parts. ICET has completed a study helping to define the threshold for combined elevated temperature and relative humidity resistance of separator style and separator-less style HEPA filters that are used in DOE complexes. The study provides experimental data that considers elevated temperature, elevated relative humidity (RH), and target differential pressure (dP) in an attempt to gain a better understanding or more comprehensive insight into the operating envelope of different configurations of ASME (American Society for Mechanical Engineers) AG-1 Section FC filters. Test variables including air temperature ranges of 48.88 deg. C, 54.44 deg. C, or 60 deg. C (120, 130, or 140 deg. F); air RH ranges including 60-70%, 80%, or 90+% with initial filter dP of either 497.68 Pa, 746.52 Pa, or 995.36 Pa (2 inches water column (in. w. c.), 3 in. w. c., or 4 in. w. c.) included for full bracketing of each set of conditions. A study examining the effects of aging and fatigue on nuclear grade HEPA filters and medium elucidates the physical properties of media along with testing of new and aged ASME AG-1 HEPA filters. In addition to performing autopsies of the tested, aged filters to help better understand the service life of their performance, newly designed accelerated aging chambers allow new medium to undergo accelerated aging and exposure treatments. Evaluation of degradation in physical properties and functionality requires a high population of aged filters of different ages, designs, manufacturers, and operational histories. The limited availability of this population and the impropriety of looking solely at formulations and components from past years requires experimental design that will provide foresight into future filter performance. This undertaking therefore involves not only properties analysis of aged medium from different operational histories, ages, designs, and manufacturers, but also prescience in analytical goals via accelerated aging studies of newly manufactured media. A more complete understanding of the mechanisms of degradation from past formulations as well as current and future formulations is possible. Accelerated Aging of newly manufactured media utilizes exposure treatments based on the Arrhenius equation to artificially age medium and therefore glimpse into the future. Evaluation of current media properties This testing allows for the comparison of performance and durability of new filters under upset or design basis conditions with aged filters that were in service under ambient conditions and other aged filters retained in storage. Susceptibility of HEPA filters to the effects of combustion byproducts, heat, and water is also being studied in an attempt to gain understanding on the prevention of filter failures during fire events in nuclear facilities. ICET seeks to determine the effect of filter performance due to smoke loading using characterized smoke from variable fuel compositions, geometries, and loads; the smoke capacity of various filters with different burning conditions, heat release rates and transport, as well as mass transport from the air stream will be studied in low flow containment systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development and Experimental Optimization of High-Temperature Modeling Tools and Methods for Concentrated Solar Power Particle - Systems

A novel, open-source radiative modeling toolset was developed to extend the functionality of particle-based modeling software (e.g. discrete element method (DEM)) to environmental conditions relevant to concentrated solar power applications. This toolset was optimized for deployment on desktop workstations instead of high-performance computing systems, to render such tools more accessible to the research community. Both particle-based modeling and radiative exchange modeling are computationally expensive and often require specialized programming expertise, making these methods cumbersome to use. Recent developments in DEM software by DCS Computing have greatly reduced these challenges, providing a graphical-user-interface based platform and modeling optimization for desktop workstations, HPCs, and cloud computing. The University of Dayton leveraged the experience of DCS Computing in developing a user-friendly, open-source radiative heat transfer expansion for DEM modeling. The University of Dayton DEM+ radiative modeling toolset was developed using a combination of fundamental experimental measurements, modeling, and simplified flow experiments over a range of temperatures and flow conditions. The toolset provides researchers with access to multiple radiative models including an accelerated Monte-Carlo Ray Tracing (application agnostic, highly computationally expensive), an expanded database of distance-based approximations (application limited, computationally light), and a weighted blending of the two methods capable of achieving over 90% reduction in computation time with equivalent accuracy compared to Monte-Carlo Ray Tracing. Through a graphical user interface, users can customize the radiative models to match their desired accuracy and available computational resources, improving access to particle based modeling for the research community. Ceramic sintered bauxite proppants were used in modeling and experimentally as a baseline. Both the radiative heat transfer and flow properties for particulate systems were investigated at elevated temperatures up to 800 °C. The major accomplishments for this work include a verified, open-source radiative modeling toolset to be distributed amongst the research community and the fabrication of three small-scale test facilities to investigate particle behavior and tune DEM flow properties for operation up to 800 °C. The findings have been shared with the research community via conference modeling workshops, deployment of the tools in DCS Computing Aspherix®, and open-source access to the developed radiative modeling tool. The development of next-generation CSP facilities and thermal energy storage systems based on ceramic particles requires providing access to computationally efficient and accurate modeling tools. Particles will experience a wide range of environments (20-800 °C) and handling conditions (dilute curtains or dense packing), requiring specially designed and optimized equipment. Optimizing solid particle physics models and establishing best-practices for particle modeling in CSP environments will assist researchers with designing optimized equipment, accelerating the deployment of more economically-competitive CSP facilities.

14 SOLAR ENERGY↗
DOE OSTI2023

Design Aspects of the Emittance Diagnostic for the Scorpius Accelerator

Dear author, This is a reminder that we are expecting you to resubmit your IPAC'23 paper 1268 at https://indico.jacow.org/event/41/papers/2549/ in a format compliant with the IoP guidelines posted at https://publishingsupport.iopscience.iop.org/author-guidelines-for-conference-proceedings/ Could you please upload your paper as a PDF file in the IoP format at https://indico.jacow.org/event/41/papers/2549/ Title: The Design of the Emittance Diagnostic for the Scorpius Accelerator id: 1268 The deadline for resubmitting your paper is July 1st 2023 but we would appreciate if you could submit it earlier. Thank you in advance, Nicolas Delerue on behalf of the IPAC'23 LPR SPB

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION

The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Owoyele, Opeoluwa↗
NASA NTRS1993

Acceleration-Augmented LQG Control of an Active Magnetic Bearing

A linear-quadratic-gaussian (LQG) regulator controller design for an acceleration-augmented active magnetic bearing (AMB) is outlined. Acceleration augmentation is a key feature in providing improved dynamic performance of the controller. The optimal control formulation provides a convenient method of trading-off fast transient response and force attenuation as control objectives.

Feeley, Joseph J.↗
DOE OSTI2026

Atomic- and Molecular-Scale Interphase Engineering for High-Performance Solid-State Batteries

Solid-state batteries (SSBs) promise a decisive advance beyond conventional Li-ion systems, yet their development remains constrained by persistent solid–solid interfacial instabilities that degrade performance and durability. Interfaces between solid electrolytes and both cathodes and Li metal often exhibit poor wettability, limited physical contact, and high charge–transfer resistance, leading to chemical decomposition, mechanical failure, and impedance growth. Overcoming these limitations requires interphase engineering with atomic-scale precision—capabilities that conventional coating methods cannot reliably deliver. Atomic layer deposition (ALD) and molecular layer deposition (MLD) uniquely meet this need by enabling ultrathin, conformal, and composition-tunable films that stabilize reactive surfaces, suppress parasitic reactions, and regulate Li-metal morphology. Importantly, this Perspective highlights ALD/MLD systems that have already demonstrated effectiveness in liquid-electrolyte cells and discusses how these validated strategies can be deliberately translated to solid-state architectures. By grounding future directions in experimentally proven concepts rather than speculative hypotheses, we outline how atomic- and molecular-scale design principles can accelerate the development of robust, high-performance SSB technologies.

atomic and molecular layer deposition↗
DOE OSTI2025

An energetic link between order and strength in metals: A nanocrystalline strength limit in high-entropy alloys and intermetallic compounds

The metallurgy and materials communities have long understood and exploited fundamental links between chemical and structural ordering in metallic solids to tailor their mechanical properties. We extend these ideas to include prediction of the nanocrystalline strength limit in high-entropy alloys and intermetallic compounds, where a breakdown occurs in the classical Hall-Petch strengthening behavior. The highest reported strength achievable through alloying has rapidly climbed and given rise to new classifications of materials with extraordinary properties, with a notable case being nanocrystalline metals. High-entropy alloys (chemically disordered, concentrated solid solutions) and intermetallic compounds are two boundary cases of how tailored order can be used to manipulate mechanical behavior. Here, we show that the complex electronic-structure mechanisms governing the peak strength of alloys and pure metals can be reduced to a few physically meaningful parameters based on their atomic arrangements and used – with no fitting parameters – to predict the maximum strength of these materials. This includes a generalized energy-based accounting for the degree of structural and chemical ordering that allows for rapid and reasonably accurate prediction of peak strength (validated in the nanocrystalline limit) as a function of temperature. Predictions of maximum strength based on the activation energy (with all materials properties derived from DFT calculations or experiments) for a stress-driven transition to an amorphous state is shown to accurately describe the breakdown in Hall-Petch behavior at the smallest crystallite sizes for pure metals, intermetallic compounds, high-entropy alloys, and metallic glasses. Further, this activation energy is also shown to be directly proportional to interstitial electronic charge density, which is a good predictor of ductility, stiffness (moduli), and phase stability in high-entropy alloys and solid metals generally. The proposed framework suggests the possibility of coupling ordering and intrinsic strength to mechanisms like dislocation nucleation, hydrogen embrittlement, and transport properties, such as through correlations between the activation energies for amorphization with stacking-fault and grain boundary energies. It additionally opens the prospect for greatly accelerated structural materials design and development to address materials challenges limiting more sustainable and efficient use of energy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A sensitivity analysis to predict the neutronics behavior of samples irradiated in the VTR rabbit system

We report a low-order neutronics model is developed to carry out hundreds of simulations efficiently and investigate the neutronics behavior of samples being irradiated in a test reactor setting under different geometrical constraints. The low-order model allowed for simulations that yield the expected neutronics behavior of any irradiated sample in any environment and allows for the calculation of highly accurate spatially averaged statistics and idealized spatial distributions in the neutron flux. Several benchmarks are performed to evaluate the performance and limitations of the low-order model revealing many important findings. The low-order model predicted the LHGR in the EBR-II driver fuel to within 2.34% by only simulating the fuel rod by itself, which served as a validation for the model. Sensitivity studies investigated 3% enriched UO 2 and U-10Zr being irradiated in the Versatile Test Reactor rabbit system. The analyses investigated a range of combinations of 15 radii and 5 heights for each sample in the rabbit system. Similar data sets are also provided for irradiations in the Advanced Test Reactor’s B-10 irradiation position, which is a thermal neutron spectrum environment. Generalized fits and fit coefficients are obtained for sample heating, reaction rate densities, and local multiplication rate characteristics, allowing the predictions of the neutronics behavior of the samples based on their geometrical constraints. The analyses and fits laid the groundwork for developing a user-end Multiphysics analysis framework to assist and accelerate irradiation experiment design and optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Technologies and policies to decarbonize global industry: Review and assessment of mitigation drivers through 2070

Fully decarbonizing global industry is essential to achieving climate stabilization, and reaching net zero greenhouse gas emissions by 2050–2070 is necessary to limit global warming to 2 °C. This paper assembles and evaluates technical and policy interventions, both on the supply side and on the demand side. It identifies measures that, employed together, can achieve net zero industrial emissions in the required timeframe. Key supply-side technologies include energy efficiency (especially at the system level), carbon capture, electrification, and zero-carbon hydrogen as a heat source and chemical feedstock. There are also promising technologies specific to each of the three top-emitting industries: cement, iron & steel, and chemicals & plastics. These include cement admixtures and alternative chemistries, several technological routes for zero-carbon steelmaking, and novel chemical catalysts and separation technologies. Crucial demand-side approaches include material-efficient design, reductions in material waste, substituting low-carbon for high-carbon materials, and circular economy interventions (such as improving product longevity, reusability, ease of refurbishment, and recyclability). Strategic, well-designed policy can accelerate innovation and provide incentives for technology deployment. High-value policies include carbon pricing with border adjustments or other price signals; robust government support for research, development, and deployment; and energy efficiency or emissions standards. These core policies should be supported by labeling and government procurement of low-carbon products, data collection and disclosure requirements, and recycling incentives. In implementing these policies, care must be taken to ensure a just transition for displaced workers and affected communities. Similarly, decarbonization must complement the human and economic development of low- and middle-income countries.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗
DOE OSTI2020

High-Throughput Functional Genomics for Energy Production

Functional genomics remains a foundational field for establishing genotype-phenotype relationships that enable strain engineering. High-throughput (HTP) methods accelerate the Design-Build-Test-Learn cycle that currently drives synthetic biology towards a forward engineering future. Trackable mutagenesis techniques including transposon insertion sequencing and CRISPR-Cas-mediated genome editing allow for rapid fitness profiling of a collection, or library, of mutants to discover beneficial mutations. Due to the relative speed of these experiments compared to adaptive evolution experiments, iterative rounds of mutagenesis can be implemented for next-generation metabolic engineering efforts to design complex production and tolerance phenotypes. Further, the expansion of these mutagenesis techniques to novel bacteria are opening up industrial microbes that show promise for establishing a bio-based economy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Porting hypre to heterogeneous computer architectures: Strategies and experiences

We report that linear systems are occurring in many applications, and solving them can take a large amount of the total simulation time. The high performance library hypre provides a variety of interfaces and linear solvers, including various multigrid methods, that have achieved good scalability on a variety of homogeneous parallel computer architectures. Heterogeneous architectures with nodes that have both CPUs and accelerators provide new challenges, since they require more fine-grained parallelism and reduced data movement between different memories on a single node as well as across nodes. We will discuss our experiences and strategies to port hypre to heterogeneous computers with accelerators, including the design of a new memory model, the use of abstractions, the BoxLoop macros in the structured and semi-structured interfaces, and the restructuring of algebraic multigrid (AMG) into modular components. We present numerical experiments comparing CPU and GPU performance for several test problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Bifunctional Electrocatalysts with High-Entropy Alloys: Bridging Hydrogen Evolution and Oxygen Reduction

High-entropy alloys (HEAs) have emerged as a promising class of bifunctional electrocatalysts capable of simultaneously driving the hydrogen evolution reaction (HER) and the oxygen reduction reaction (ORR) with high activity and durability. Their near-equiatomic multicomponent compositions give rise to unique physicochemical characteristics, including lattice distortion, sluggish diffusion, high-entropy stabilization, and pronounced electronic heterogeneity, that collectively generate diverse and synergistic active sites inaccessible in conventional alloys. This review summarizes recent progress in HEA-based bifunctional electrocatalysis, with a focus on the fundamental mechanisms governing HER and ORR activity, stability, and selectivity. We discuss advances in synthesis strategies, ranging from confined growth and step-alloying to scalable continuous-flow methods, that enable precise control over composition, size, and surface structure. Complementary computational and data-driven approaches, including density functional theory, machine-learning-assisted screening, and descriptor development, are highlighted as essential tools for navigating the vast HEA design space and establishing structure−property relationships. Particular attention is paid to adsorption-energy distributions, multisite cooperativity, and environmental effects under realistic electrochemical conditions. Finally, we outline current challenges and future opportunities for integrating mechanistic understanding with AI-guided, closed-loop design frameworks to accelerate the discovery of next-generation HEA bifunctional electrocatalysts for sustainable energy conversion.

Alloys↗
DOE OSTI2025

Automated Strain Construction for Biosynthetic Pathway Screening in Yeast

Automation accelerates the Design-Build-Test-Learn (DBTL) cycle for synthetic biology; however, most strain construction pipelines lack robotic integration. Here, in this study, we present the workflow design and source code for a modular, integrated protocol that automates the Build step in Saccharomyces cerevisiae. We programmed the Hamilton Microlab VANTAGE to integrate off-deck hardware via its central robotic arm, enabling automated steps that increased throughput to 2,000 transformations per week. We developed a user interface with the Hamilton VENUS software to support on-demand parameter customization. As a proof of concept, we screened a gene library in an engineered yeast strain producing verazine, a key intermediate in the biosynthesis of steroidal alkaloids. Our pipeline rapidly identified pathway bottlenecks and genes that enhanced verazine production by 2.0- to 5-fold. This technical note provides resources for synthetic biologists designing yeast workflows for biofoundries to screen libraries for pathway discovery/optimization, combinatorial biosynthesis, and protein engineering.

automation↗
DOE OSTI2021

Unique structural solution from a V H 3-30 antibody targeting the hemagglutinin stem of influenza A viruses

Broadly neutralizing antibodies (bnAbs) targeting conserved influenza A virus (IAV) hemagglutinin (HA) epitopes can provide valuable information for accelerating universal vaccine designs. Here, we report structural details for heterosubtypic recognition of HA from circulating and emerging IAVs by the human antibody 3I14. Somatic hypermutations play a critical role in shaping the HCDR3, which alone and uniquely among V H 3-30 derived antibodies, forms contacts with five sub-pockets within the HA-stem hydrophobic groove. 3I14 light-chain interactions are also key for binding HA and contribute a large buried surface area spanning two HA protomers. Comparison of 3I14 to bnAbs from several defined classes provide insights to the bias selection of V H 3-30 antibodies and reveals that 3I14 represents a novel structural solution within the V H 3-30 repertoire. The structures reported here improve our understanding of cross-group heterosubtypic binding activity, providing the basis for advancing immunogen designs aimed at eliciting a broadly protective response to IAV.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Unifying thermochemistry concepts in computational heterogeneous catalysis

Thermophysical properties of adsorbates and gas-phase species define the free energy landscape of heterogeneously catalyzed processes and are pivotal for an atomistic understanding of the catalyst performance. These thermophysical properties, such as the free energy or the enthalpy, are typically derived from density functional theory (DFT) calculations. Enthalpies are species-interdependent properties that are only meaningful when referenced to other species. The widespread use of DFT has led to a proliferation of new energetic data in the literature and databases. However, there is a lack of consistency in how DFT data is referenced and how the associated enthalpies or free energies are stored and reported, leading to challenges in reproducing or utilizing the results of prior work. Additionally, DFT suffers from exchange–correlation errors that often require corrections to align the data with other global thermochemical networks, which are not always clearly documented or explained. In this review, we introduce a set of consistent terminology and definitions, review existing approaches, and unify the techniques using the framework of linear algebra. This set of terminology and tools facilitates the correction and alignment of energies between different data formats and sources, promoting the sharing and reuse of ab initio data. Standardization of thermochemistry concepts in computational heterogeneous catalysis reduces computational cost and enhances fundamental understanding of catalytic processes, which will accelerate the computational design of optimally performing catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗