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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Pumped Thermal Energy Storage Using Low-Cost Particles and a Fluid Bed Heat Exchanger for Maximum Power Efficiency

Robust, efficient, cost-effective long-duration electricity storage (LDES) solutions can enhance grid resiliency, support existing transmission and distribution infrastructure, and enable a greater share of low-cost, variable alternative energy sources to penetrate the market. To meet this need, the project team at the National Renewable Energy Laboratory developed a transformative LDES system based on pumped thermal energy storage (TES) using low-cost particles and a fluid bed heat exchanger for maximum power efficiency (PUMP). The PUMP system is composed of high-temperature, low-cost particle TES coupled with an advanced pressurized fluid bed heat exchanger (PFB HX) that supports a high-efficiency pumped thermal energy storage (PTES) system integrated with concentrating solar thermal power (CSP). The PUMP project developed and de-risked a PFB HX and particle CSP system intended to be integrated with reversible turbomachinery and a modeling tool to assess PTES cost and performance.

14 SOLAR ENERGY↗
DOE OSTI2021

In Vivo Entombment of Bacteria and Fungi during Calcium Oxalate, Brushite, and Struvite Urolithiasis

Human kidney stones form via repeated events of mineral precipitation, partial dissolution, and reprecipitation, which are directly analogous to similar processes in other natural and manmade environments, where resident microbiomes strongly influence biomineralization. High-resolution microscopy and high-fidelity metagenomic (microscopy-to-omics) analyses, applicable to all forms of biomineralization, have been applied to assemble definitive evidence of in vivo microbiome entombment during urolithiasis. Stone fragments were collected from a randomly chosen cohort of 20 patients using standard percutaneous nephrolithotomy (PCNL). Fourier transform infrared (FTIR) spectroscopy indicated that 18 of these patients were calcium oxalate (CaOx) stone formers, whereas one patient formed each formed brushite and struvite stones. This apportionment is consistent with global stone mineralogy distributions. Stone fragments from seven of these 20 patients (five CaOx, one brushite, and one struvite) were thin sectioned and analyzed using brightfield (BF), polarization (POL), confocal, super-resolution autofluorescence (SRAF), and Raman techniques. DNA from remaining fragments, grouped according to each of the 20 patients, were analyzed with amplicon sequencing of 16S rRNA gene sequences (V1–V3, V3–V5) and internal transcribed spacer (ITS1, ITS2) regions. Bulk-entombed DNA was sequenced from stone fragments in 11 of the 18 patients who formed CaOx stones, and the patients who formed brushite and struvite stones. These analyses confirmed the presence of an entombed low-diversity community of bacteria and fungi, including Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, and Aspergillus niger. Bacterial cells approximately 1 μm in diameter were also optically observed to be entombed and well preserved in amorphous hydroxyapatite spherules and fans of needle-like crystals of brushite and struvite. In conclusion, these results indicate a microbiome is entombed during in vivo CaOx stone formation. Similar processes are implied for brushite and struvite stones. This evidence lays the groundwork for future in vitro and in vivo experimentation to determine how the microbiome may actively and/or passively influence kidney stone biomineralization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A laser ultrasonics-based approach for rapid screening of high entropy alloys

This project demonstrates a laser ultrasonics-based characterization methodology for rapid metallic materials design and discovery via in situ determination of phases, microstructures and elastic properties with respect to temperature. Ultrasonic waves are strongly affected by material microstructure, and therefore serve as a facile means to probe elastic properties, phase content and their size distributions and volume fractions. In this study, a laser ultrasonic technique is used to systematically study the evolution of properties in a set of interrelated simple binary alloys and high entropy alloys (HEA). Phase transformations and microstructural changes inferred from the ultrasonic signals will be correlated with electron microscopy data and predictions using calculations of phase diagrams (CALPHAD). The non-contact and non-destructive ultrasonic testing approach developed in this study could overcome several limitations associated with current material characterization methods for materials discovery. It is expected that laser ultrasonics-based methodology developed in this work will be utilized to evaluate novel graded composition HEAs currently being developed at the Idaho National Laboratory (INL) using advanced manufacturing methods based on direct energy deposition and spark plasma sintering processes, and contribute to accelerate the discovery of HEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Optimal Design of Megahertz $LLC$ Converter for 48-V Bus Converter Application

Intermediate bus architecture employing 48-V bus converters is widely used in power supply applications. With the rapid increase of demanded power by these loads, higher efficiency and power density are driving better performance power management solutions. In this article, a Gallium Nitride (GaN) based design of a two-stage solution is proposed. The first stage is a multi-phase Buck for regulation. The second stage is an LLC converter with a fixed switching frequency for isolation. The detailed design and optimization of the LLC converter are studied. To achieve high power density and high efficiency, the transformer design becomes critical at megahertz frequency. The matrix transformer concept is applied and a merged winding structure is used for flux cancellation, which effectively reduces the ac winding losses. A novel primary termination and via structure is proposed, resulting in a great loss reduction. In addition, to study the current sharing of parallel winding layers, a 1-D analytic model is proposed, and a symmetrical winding layer scheme is used to balance the current distribution. Finally, the prototype for the two-stage bus converter is developed, with a peak efficiency of 96% and a power density of 615 W/in 3 .

42 ENGINEERING↗
DOE OSTI2024

Minimum entropy filtering for a single output non-Gaussian stochastic system using state transformation

This paper presents a novel filter design for the single-output stochastic non-linear systems subjected to non-Gaussian noises and the proposed assumptions. Based on a state transformation, the unmeasurable states of the systems can be estimated where non-linear terms in the systems have been eliminated. It has been shown that the estimation error is linearly dynamical regarding to the presented vector-valued filter gain which can be optimised by minimising the entropy-based performance criterion. In addition, the convergence of the presented algorithm is analysed in mean-square sense and a numerical example is given to verify the effectiveness of the presented filtering algorithm. Meanwhile, the extended Kalman filter, unscented particle filter and minimum entropy filter are given for the comparisons of the filtering performance. Following the presented framework, some extensions of the presented filtering algorithm are discussed to indicate the flexibility of the filter design. The contribution of this paper can be summarised as establishing a novel minimum entropy filtering framework which consists of model transformation, entropy optimisation and convergence analysis.

42 ENGINEERING↗
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
DOE OSTI2022

Large Scale Study of Ligand–Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols

The accurate and reliable prediction of protein–ligand binding affinities can play a central role in the drug discovery process as well as in personalized medicine. Of considerable importance during lead optimization are the alchemical free energy methods that furnish an estimation of relative binding free energies (RBFE) of similar molecules. Recent advances in these methods have increased their speed, accuracy, and precision. This is evident from the increasing number of retrospective as well as prospective studies employing them. However, such methods still have limited applicability in real-world scenarios due to a number of important yet unresolved issues. Here, we report the findings from a large data set comprising over 500 ligand transformations spanning over 300 ligands binding to a diverse set of 14 different protein targets which furnish statistically robust results on the accuracy, precision, and reproducibility of RBFE calculations. We use ensemble-based methods which are the only way to provide reliable uncertainty quantification given that the underlying molecular dynamics is chaotic. These are implemented using TIES (Thermodynamic Integration with Enhanced Sampling). Results achieve chemical accuracy in all cases. Ensemble simulations also furnish information on the statistical distributions of the free energy calculations which exhibit non-normal behavior. We find that the “enhanced sampling” method known as replica exchange with solute tempering degrades RBFE predictions. We also report definitively on numerous associated alchemical factors including the choice of ligand charge method, flexibility in ligand structure, and the size of the alchemical region including the number of atoms involved in transforming one ligand into another. Our findings provide a key set of recommendations that should be adopted for the reliable application of RBFE methods.

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

An immersed interface method for the 2D vorticity-velocity Navier-Stokes equations with multiple bodies

We present an immersed interface method for the vorticity-velocity form of the 2D Navier Stokes equations that directly addresses challenges posed by nonconvex immersed bodies, multiply connected domains, and the calculation of force distributions on immersed surfaces. The immersed interface method is re-interpreted as a polynomial extrapolation of flow quantities and boundary conditions into the immersed solid bodies, reducing computational cost and enabling simulations with nonconvex bodies that could not be discretized with previous immersed interface methods. In the flow, the vorticity transport equation is discretized using a conservative finite difference scheme and explicit Runge-Kutta time integration. The velocity reconstruction problem is transformed to a scalar Poisson equation that is discretized with conservative finite differences, and solved using an FFT-accelerated iterative algorithm. The use of conservative differencing throughout leads to exact enforcement of a discrete Kelvin's theorem, allowing for simulations with multiply connected domains and outflow boundaries that have challenged other immersed interface vortex methods. We also explore novel methods for recovering time-dependent pressure distributions on immersed bodies within a vorticity-based method and present a novel control volume formulation for recovering aerodynamic moments from only the vorticity and velocity fields. The method achieves second order spatial accuracy and third order temporal accuracy, and is validated on a variety of 2D flows in internal and free-space domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Multi-scale structural analysis of swift heavy ion-irradiated ZrO 2 and HfO 2

The radiation-induced monoclinic-to-tetragonal phase transition in ZrO 2 and HfO 2 has been the subject of many investigations, but the transformation pathways and underlying structural mechanisms are still not well understood. In this study, microcrystalline powder samples of ZrO 2 and HfO 2 were irradiated with 946 MeV and 1470 MeV Au ions to a wide fluence range up to 3 × 10 13 ions/cm 2 . To characterize beam-induced structural modifications across all spatial length scales, complementary experimental techniques such as synchrotron X-ray diffraction and spallation neutron total scattering were used. The phase evolution of the tetragonal polymorph with increasing ion fluence is accurately described by a heterogeneous track-overlap model that incorporates both direct- and double-impact processes. These damage accumulation processes are an expression of a core-shell ion track morphology that depends on irradiation conditions and target material. Neutron pair distribution function analysis revealed that ion-beam-induced tetragonal ZrO 2 is merely a configurational average of short-range orthorhombic (Pbcn) domains stabilized by a dense network of domain walls. Furthermore, this knowledge is critical for a better understanding of how crystalline-to-crystalline phase transformations proceed at the atomic scale under extreme conditions.

Monoclinic-to-tetragonal phase transformation↗
DOE OSTI2021

Leveraging Free Volume Manipulation to Improve the Membrane Separation Performance of Amine‐Functionalized PIM‐1

Abstract Gas‐separation polymer membranes display a characteristic permeability–selectivity trade‐off that has limited their industrial use. The most comprehensive approach to improving performance is to devise strategies that simultaneously increase fractional free volume, narrow free volume distribution, and enhance sorption selectivity, but generalizable methods for such approaches are exceedingly rare. Here, we present an in situ crosslinking and solid‐state deprotection method to access previously inaccessible sorption and diffusion characteristics in amine‐functionalized polymers of intrinsic microporosity. Free volume element (FVE) size can be increased while preserving a narrow FVE distribution, enabling below‐upper bound polymers to surpass the H 2 /N 2 , H 2 /CH 4 , and O 2 /N 2 upper bounds and improving CO 2 ‐based selectivities by 200 %. This approach can transform polymers into chemical analogues with improved performance, thereby overcoming traditional permeability–selectivity trade‐offs.

Mizrahi Rodriguez, Katherine↗
DOE OSTI2026

A Step-by-Step Protocol from METASPACE to Biological Interpretation

Mass spectrometry imaging (MSI) represents an exceptional tool for exploring complex biological systems spatially at the molecular level. However, due to its multidimensional nature and large-scale data output, it presents considerable challenges when it comes to extracting meaningful biological insights. Recent advancements, such as the METASPACE platform, have enabled researchers to efficiently process, annotate, and interpret MSI datasets by leveraging machine learning and cloud-based infrastructure. In this tutorial, we present a detailed and user-friendly R-pipeline designed to help METASPACE users navigate untargeted metabolomic annotations and transform them into practical insights about their biological systems. By combining METASPACE annotations with rapid R-based screening, this workflow not only streamlined the analytical process but also enhanced the understanding of spatial molecular distribution, especially for complex systems. Here, this easy-to-follow approach has the potential for applications in diagnostics, drug discovery, environmental and ecological processes, and more. We envision this pipeline to be particularly useful for newcomers to the field of MSI and

Moreno Pedraza, Abigail↗
DOE OSTI2025

Quantum stresses in the hydrogen atom

Gravitational form factors are often interpreted as providing access to stresses inside hadrons, in particular through Fourier transforms of the form factors 𝐷 and $\bar{𝑐}$. Some researchers, however, have expressed skepticism of this interpretation. I revisit the question, and argue that it is indeed appropriate to interpret these quantities as stress distributions. I consider the hydrogen atom’s ground state as a familiar example, and use the pilot wave interpretation of quantum mechanics to give the distributions a clear meaning. A striking result is that $\bar{𝑐}$—rather than 𝐷—quantifies the force law binding the system, which can be understood through Cauchy’s first law of motion.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Implementing Directive-Based Deferred Execution for Effective Network Aggregation

Remote direct memory access technology provides an efficient mechanism for one-sided communication that can be leveraged to implement a distributed shared memory programming model. However, when applications generate large numbers of small, irregular messages, network congestion often arises. Existing solutions address this small message problem by facilitating message aggregation but typically require disruptive code transformations that detract from the algorithmic intent of applications, or can be limited by dependent operations on aggregated data between synchronisation points. A solution is to use a directive-assisted approach that enables compilers to transform code dependent on aggregated communication for deferred execution. This paper presents an algorithm that a compiler can use to implement and optimise deferred execution for code dependent on aggregated data, based on an "aggregation context" extension for the OpenSHMEM partitioned global address space library. This new capability addresses a key challenge of message aggregation, allowing its full potential to reduce network congestion and enhance programmability to be realised.

Welch, Aaron [ORNL]↗
DOE OSTI2019

Strain Localization during Diametral Compression of ZrO{sub 2}(Y{sub 2}O{sub 3}) Ceramics

Deformation behavior of ZrO{sub 2}−Y{sub 2}O{sub 3} ceramics under conditions of diametral compression has been studied by method of digital image correlation. Spatiotemporal patterns of strain localization along the axis of a deformed sample (ε{sub xx}(x)) and across this axis (ε{sub yy}(y)) were obtained. It is established that the ε{sub xx} and ε{sub yy} deformations accumulated during the diametral compression test are inhomogeneously distributed over the sample. This is manifested by variation of the microstructure characteristics such as the size of the coherently diffracting domains of a tetragonal phase and microstresses and leads to the tetragonal–monoclinic phase transformation. This pattern of strain localization is correlated with the observed inhomogeneity of microstresses arising in the material volume.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Performance of Embedded Sensors in 3D Printed SiC

This report summarizes recent advances in embedding sensors in 3D printed silicon carbide (SiC) ceramic components under the Transformational Challenge Reactor (TCR) program. The additive manufacturing technologies developed under this program will enable fabrication of complex structures with embedded fuels and sensors. The sensors will be capable of characterizing fuel performance using spatially distributed measurements at the most strategic locations that would be otherwise inaccessible using conventional manufacturing techniques. While previous programmatic updates describe initial concepts for embedding sensors, materials selection, and initial characterization of the embedded sensors, the technology requires further demonstration, and quality-significant procedures must be established before the embedding technology is ready for adoption by industry. To this end, this report describes the most effective techniques that have been used to embed functional sensors in 3D printed components using a combination of binder-jet additive manufacturing and chemical vapor infiltration (CVI). A detailed procedure describes each step in the process and is available upon request. Molybdenum (Mo)-sheathed thermocouples have been successfully embedded in complex SiC components, and temperatures were monitored in situ during the embedding process. Post-embedding measurements showed no significant hysteresis, and characterization of the interface revealed qualitatively strong bonding around the entire perimeter of the sensor sheath. Distributed fiber-optic temperature sensors were able to briefly measure temperature profiles during CVI, but they ultimately failed prior to completion of the CVI run. The failure appears to be related to the fiber coating at temperatures close to 1,000°C. Future work will focus on irradiation testing of embedded thermocouples and other sheathed electrical sensors, as well as the identification of fiber-optic sensor coatings that can survive CVI.

42 ENGINEERING↗
DOE OSTI2024

Lattice Design and Advanced Modeling to Guide the Design of High-Performance Lightweight Structural Materials

Lightweight structural materials are required to increase the mobility of fission batteries. The materials must feature a robust combination of mechanical properties to demonstrate structural resilience. The primary objective of this project is to produce lightweight structural materials whose strength-to-weight ratios exceed those of the current widely used structural materials such as 316L stainless steels (316L SS). To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structures models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The preliminary results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison with the solid material and that the unit cell size of the lattice had a minimal effect. The novelty here is to apply up-front modeling to determine the best structure for the application before actually producing the sample. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for material development.

36 MATERIALS SCIENCE↗