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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Models implemented in the methodological approach to design the initial STEP first wall contour

The official Spherical Tokamak for Energy Production mission aims to demonstrate the ability to generate net electricity from fusion with the STEP Prototype Power plant. One of the key technological and engineering challenges in fusion power plants is managing the loads on the first wall within acceptable limits. Therefore, the conceptual design development of the STEP Prototype Power plant needs to be based on load estimates derived using legitimate plasma physics assumptions through dynamic and flexible tools. The current design foresees the STEP main chamber first wall to withstand steady-state heat loads of up to ~1 MW/m 2 , excluding critical regions expected to receive higher heat loads such as the baffle regions approaching the divertors. These critical areas will require ad hoc assessments and will be designed with the presence of limiters. This article focuses on the models and methodology adopted for designing the 2-D poloidal contour of the STEP first wall, based on the anticipated charged particle and radiation heat loads during normal operation. Firstly, the models adopted for calculating the charged particle and radiation heat loads are introduced. The first model is validated through benchmarking against the particle tracing code SMARDDA, while the second model is verified by comparing it with data from the MAST-U experiment. Secondly, the model used to design the 2-D first wall contour according to the heat loads is explained. We acknowledge that this preliminary design stage assumes certain simplifications, notably an axisymmetric geometry, for computational efficiency and clarity in presentation. It is understood that subsequent design phases will address the complexities of real-world engineering, including non-axisymmetric effects, transient plasma scenarios, and the impact of disruptions on the first wall design. Finally, an automatic procedure based on these models is presented for defining the 2-D poloidal contour of the STEP first wall to minimize heat loads, taking into account the need to radiate most of the alpha-particle and auxiliary heating power. Here, by providing an overview of the models, methodology, and an automatic procedure, this paper contributes to the design process of the STEP first wall, addressing the engineering challenges associated with fusion power plant development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Contextual subspace variational quantum eigensolver calculation of the dissociation curve of molecular nitrogen on a superconducting quantum computer

Abstract We present an experimental demonstration of the Contextual Subspace Variational Quantum Eigensolver on superconducting hardware. Calculating the potential energy curve of molecular nitrogen proves challenging for many conventional quantum chemistry techniques, since static correlation dominates in the dissociation limit. Our quantum simulations retain good agreement with the Full Configuration Interaction energy, outperforming all benchmarked single-reference wavefunction techniques in capturing the bond-breaking appropriately. Moreover, our methodology is competitive with multiconfigurational approaches but at a saving of quantum resource, meaning larger active spaces can be treated for a fixed qubit allowance. To achieve this result, we deploy an error mitigation/suppression strategy comprised of Dynamical Decoupling, Measurement-Error Mitigation and Zero-Noise Extrapolation. Circuit parallelization also provides passive noise-averaging and improves the effective shot yield to reduce the measurement overhead. Furthermore, we introduce a modified adaptive ansatz construction algorithm that incorporates hardware awareness into our variational circuits, minimizing the transpilation cost for the target qubit topology.

Physics↗
DOE OSTI2024

Exploration of Novel Neuromorphic Methodologies for Materials Applications

Many of today's most interesting questions involve understanding and interpreting complex relationships within graph-based structures. For instance, in materials science, predicting material properties often relies on analyzing the intricate network of atomic interactions. Graph neural networks (GNNs) have emerged as a popular approach for these tasks; however, they suffer from limitations such as inefficient hardware utilization and over-smoothing. Recent advancements in neuromorphic computing offer promising solutions to these challenges. In this work, we evaluate two such neuromorphic strategies known as reservoir computing and hyperdimensional computing. We compare the performance of both approaches for bandgap classification and regression using a subset of the Materials Project dataset. Our results indicate recent advances in hyperdimensional computing can be applied effectively to better represent molecular graphs.

Gobin, Derek [George Mason University, Virginia]↗
DOE OSTI2025

Dynamic Line Rating Models and Their Potential for a Cost‐Effective Transition to Carbon‐Neutral Power Systems

Most transmission system operators (TSOs) currently use seasonally steady-state models considering limiting weather conditions that serve as reference to compute the transmission capacity of overhead power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false congestions, and the degradation of lines in a cost-effective way. DLR can also be used in the long run in grid extension and new power capacity planning. In the short run, it should be used to help operate power systems with congested lines. The operation of the power systems is planned to have the market trading into account; thus, it computes transactions hours ahead of real-time operation, using power flow forecasts affected by large errors. In the near future, within a “smart grid” environment, in real-time operation conditions, TSOs should be able to rapidly compute the capacity rating of overhead lines using DLR models and the most reliable weather information, forecasts, and line measurements, avoiding the current steady-state approach that, in many circumstances, assumes ampacities above the thermal limits of the lines. Here, this work presents a review of the line rating methodologies in several European countries and the United States. Furthermore, it presents the results of pilot projects and studies considering the application of DLR in overhead power lines, obtaining significant reductions in the congestion of internal networks and cross-border transmission lines.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Frontiers in divalent $f$-block chemistry

Divalent f-block chemistry has undergone rapid expansion in recent years, driven by advances in the stabilization of low-valent lanthanide and actinide complexes. Although f-elements were historically accessed in the +3 or higher oxidation states, the isolation of +2 species has revealed unusual spectroscopic signatures, distinct bonding motifs, and multiple accessible electronic configurations that influence their chemical and physical properties. These developments have generated opportunities in areas including quantum information science, molecular magnetism, catalysis, and luminescence. Computational chemistry has played a pivotal role in interpreting the electronic structure and reactivity of these systems. However, accurately modeling divalent f-block complexes remains challenging because of strong electronic correlation, multiconfigurational character, and the presence of close-in-energy competing electronic states. As experimental capabilities and theoretical methodologies continue to advance, a comprehensive assessment of divalent chemistry is both timely and needed. In this Review, we integrate experimental and theoretical perspectives to provide a systematic analysis of divalent lanthanide and actinide complexes across the f-block series. We critically assess the role of ligands in determining competing ground-state configurations (f n d 1 vs. f n+1 ) resulting in their distinct spectroscopic, magnetic, and bonding properties. Finally, we discuss emerging strategies for predictive modeling and rational design of low-valent f-block molecular systems, with potential applications ranging from dinitrogen reduction to quantum technologies.

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

Algorithm to extract direction in 2D discrete distributions and a continuous Frobenius norm

In this study, we present a novel algorithm for determining directionality in 2D distributions of discrete data. We compare a reference dataset with a known direction to a measured dataset with an unknown direction by the Frobenius norm of the difference (FND) to find the unknown direction. To generalize this concept, we develop a continuous Frobenius norm of the difference (CFND) as a continuous analog of the FND and derive its analytical expression. By relating fitted and normalized 2D Gaussian distributions, we show that the CFND approximates the FND, and we validate this relationship with computer simulations. We find that a first-order approximation of the CFND between two similar Gaussian distributions takes the form of an absolute sine function, offering a simple analytical form with potential applications in specialized areas such as segmented inverse beta decay neutrino detectors, astronomy, machine learning, and more. Our methodology consists of modeling a 2D Gaussian distribution, binning the data into a histogram, and encoding it as a square matrix. Rotating this matrix around its geometric center and comparing it to a measured dataset using the FND gives us rotational data that we fit with an absolute sine function. The location of the minimum of this fit is the angle closest to the true angle of the direction in the measured dataset. We present the derivation and discuss initial applications of the CFND in our novel algorithm, demonstrating its success in approximating directionality in 2D distributions.

Physics↗
DOE OSTI2025

Accelerating Floating-Point Computations with Intel AMX

Intel AMX is a built-in component of recent Intel CPU architectures, first supported by the Intel Sapphire Rapids in 2023, that enables efficient dense matrix multiplications using mixed precision with low-precision data types. The popularity of mixed-precision algorithms has grown recently, primarily due to their use on GPUs to enhance the efficiency of HPC applications, particularly for the training of large language models. The availability of mixed precision on CPUs represents a cost-effective solution for applications where high speed is not critical. This report shows how to use the Intel AMX accelerator through examples in C++ and Python. The examples will focus on mixed-precision floating-point operations obtained by the use of bfloat16 (or BF16) to accelerate code in single precision. We employ a bottom-up methodology, starting from specific register instructions (TMUL operation) to higher-level applications in libraries such as Intel MKL, PyTorch, and TensorFlow, ensuring a comprehensive understanding of the accelerator's potential. Additionally, we provide insights into the expected performance gains when leveraging the accelerator on the Kestrel HPC machine at the National Renewable Energy Laboratory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

In silico λ-dynamics predicts protein binding specificities to modified RNAs

Abstract RNA modifications shape gene expression through a variety of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Reimagining Disassembly Interfaces With Visualization: Combining Instruction Tracing and Control Flow With DisViz

In applications where efficiency is critical, developers may examine their compiled binaries, seeking to understand how the compiler transformed their source code and what performance implications that transformation may have. This analysis is challenging due to the vast number of disassembled binary instructions and the many-to-many mappings between them and the source code. These problems are exacerbated as source code size increases, giving the compiler more freedom to map and disperse binary instructions across the disassembly space. Interfaces for disassembly typically display instructions as an unstructured listing or sacrifice the order of execution. Here, we design a new visual interface for disassembly code that combines execution order with control flow structure, enabling analysts to both trace through code and identify familiar aspects of the computation. Central to our approach is a novel layout of instructions grouped into basic blocks that displays a looping structure in an intuitive way. We add to this disassembly representation a unique block-based mini-map that leverages our layout and shows context across thousands of disassembly instructions. Finally, we embed our disassembly visualization in a web-based tool, DisViz, which adds dynamic linking with source code across the entire application. DizViz was developed in collaboration with program analysis experts following design study methodology and was validated through evaluation sessions with ten participants from four institutions. Participants successfully completed the evaluation tasks, hypothesized about compiler optimizations, and noted the utility of our new disassembly view. Our evaluation suggests that our new integrated view helps application developers in understanding and navigating disassembly code.

Computer science↗
DOE OSTI2024

Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools

Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Reduced-order condensed-phase kinetic models for polyethylene, polypropylene and polystyrene thermochemical recycling

Thermochemical recycling of plastic waste (PW) into chemicals and energy vectors requires coupling particle and reactor-scale simulations to accurate condensed phase pyrolysis mechanisms for each constituent. This work proposes a methodology to derive reduced-order condensed-phase kinetic models from validated semi-detailed kinetic mechanisms. Two types of kinetic models are obtained for polyethylene (PE), polypropylene (PP) and polystyrene (PS): reduced semi-detailed models and multi-step fully lumped ones. These families offer different compromises between accuracy and computational cost. The former employ 50–100 gas + liquid species and describe both the radical degradation and the detailed carbon distribution of the products. Conversely, the latter involves 5–10 species per polymer tracking only the main petroleum cuts. The kinetic mechanisms are complemented by the definition of thermochemical properties of gas, liquid, and solid-phase species, accounting for phase-transitions through pseudo-chemical reactions. Model validations are performed by comparison with experimental data and the original semi-detailed mechanisms in terms of mass loss, heat fluxes and product distribution profiles. The resulting CHEMKIN-like condensed-phase models are attached as Supplementary Material and as a GitHub repository. Extending the proposed approach to other polymers and coupling it with existing subsets in the CRECK kinetic framework (e.g., biomass, PVC, PET) offers a powerful tool to model thermochemical recycling of PW and biomass/PW mixtures.

kinetics↗
DOE OSTI2024

Evaluating the potential of disaggregated memory systems for HPC applications

Summary Disaggregated memory is a promising approach that addresses the limitations of traditional memory architectures by enabling memory to be decoupled from compute nodes and shared across a data center. Cloud platforms have deployed such systems to improve overall system memory utilization, but performance can vary across workloads. High‐performance computing (HPC) is crucial in scientific and engineering applications, where HPC machines also face the issue of underutilized memory. As a result, improving system memory utilization while understanding workload performance is essential for HPC operators. Therefore, learning the potential of a disaggregated memory system before deployment is a critical step. This paper proposes a methodology for exploring the design space of a disaggregated memory system. It incorporates key metrics that affect performance on disaggregated memory systems: memory capacity, local and remote memory access ratio, injection bandwidth, and bisection bandwidth, providing an intuitive approach to guide machine configurations based on technology trends and workload characteristics. We apply our methodology to analyze thirteen diverse workloads, including AI training, data analysis, genomics, protein, fusion, atomic nuclei, and traditional HPC bookends. Our methodology demonstrates the ability to comprehend the potential and pitfalls of a disaggregated memory system and provides motivation for machine configurations. Our results show that eleven of our thirteen applications can leverage injection bandwidth disaggregated memory without affecting performance, while one pays a rack bisection bandwidth penalty and two pay the system‐wide bisection bandwidth penalty. In addition, we also show that intra‐rack memory disaggregation would meet the application's memory requirement and provide enough remote memory bandwidth.

Ding, Nan↗
DOE OSTI2026

Continuous Counter‐Current Microfluidic Liquid–Liquid Extraction Achieved Using a Pair of Wettable Screen Meshes

Continuous counter‐current microfluidic liquid–liquid extraction performs separations by flowing immiscible liquids in opposing directions within a single flow channel. In principle, this flow arrangement enables a large number of theoretical separation units in a small footprint, without using interstage valving, pumping, and phase separation. Despite its potential for excellent separation performance, this microfluidic scheme rarely appears in literature due to the requirement for capillary forces to be greater than hydrodynamic forces for stable flow. We present a novel microfluidic device and flow approaches that overcome this force‐balance challenge, enabling stable, long‐duration continuous counter‐current flow. Additionally, we cover a suite of methodologies for quantifying the performance of the microfluidic device, revealing the number of theoretical equilibrium stages achieved. The enabling technologies include a woven mesh screen‐based microfluidic device architecture that is easily fabricated outside of a clean room, surface functionalization strategies to promote conjugate (organic/aqueous) wettability, flow approaches to eliminate bubbles and carryover, and computer‐aided flow automation with optical measurement of extraction performance. The reported experiments lasted for over 36 h, terminated only at experiment conclusion, where the device still exhibited good performance. Automated Raman spectroscopy was used for solute quantitation of the ternary system tert‐butanol in a toluene/water matrix, a ternary system that was specifically chosen to analyze the device's performance with a small solute partition ratio and to enable in‐line Raman measurements of solute concentrations in both phases. The microfluidic device possessed a 55 mm contact length and a 38.5 µL internal volume. During counter‐current flow, we observed approximately 37 equilibrium stages (37 ± 13) based on a best‐fit of the solute fraction remaining in the aqueous phase using a Kremser Group Method analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluation of Converter Performance Considering Static and Dynamic Device Part-to-Part Variability

This paper presents a methodology to incorporate and analyze the impact of semiconductor device part-to-part variation on power converter performance. By integrating extensive static and dynamic device characterization data with an automated compact model generation process that reflects manufacturing variability, device models with inherent variability features are utilized in converter simulations for a comprehensive assessment of performance impacts. The traditional converter performance evaluation process typically yields fixed efficiency values, often dismissing the inherent part-to-part variability caused by the manufacturing process of semiconductor devices. To address this limitation, a large population of devices was characterized to capture variations in static parameters-such as transfer, output, and capacitance characteristics-as well as dynamic behaviors, including switching losses. This data-driven approach enables the development of individual compact models, which were then integrated into converter simulations to evaluate efficiency ranges rather than single point estimated values. The converter simulation results show that part-to-part component variation can lead to significant efficiency deviations, exceeding several percentage points in high-power conversion applications. By offering a more accurate representation of converter behavior under real-world manufacturing conditions, this methodology enables designers to anticipate performance variability, improving the robustness of power converter designs.

device characterization↗
DOE OSTI2024

Nanoindentation Stress Relaxation to Quantify Dislocation Velocity–Stress Exponent

This work reports a new methodology using indentation stress relaxation to characterize the dislocation velocity–stress exponent. Through the indentation stress relaxation process, the dislocation structure builds up at the rate governed by dislocation velocity, which is a function of the externally applied stress. The relationship between the dislocation velocity and stress can thus be derived from the indentation stress relaxation data of the stress as a function of time. In this study, instrumented nanoindentation stress relaxation experiments were performed on pure aluminum samples, following three different initial displacement rates of 100, 400, and 800 nm/s. Based on the scaling properties of dislocation kinetics, the data were interpreted to derive a dislocation velocity–stress exponent of 2.5 ± 0.5 for room-temperature aluminum. Crystal plasticity finite-element simulations were performed to illustrate the sensitivity of the proposed nanoindentation stress relaxation methodology to the dislocation velocity–stress exponent value.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Enabling Dynamic Probabilistic Risk Assessment of Physical Security Using EMRALD and MAAP (Presentation)

The optimization of physical security in nuclear power plants requires sophisticated methodologies that integrate operator actions and plant behavior through advanced simulation tools. Idaho National Laboratory has developed the Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF) methodology, an approach that integrates force-on-force simulations, dynamic probabilistic risk assessment, and thermal hydraulics modeling to enhance security planning while reducing costs. A reduced order model for thermal hydraulic simulations performed by the Modular Accident Analysis Program (MAAP) was developed to evaluate reactor core behavior during attack scenarios. MAAP simulations are computationally intensive and must be run in a secure environment, complicating analysis and validation. By pre-computed scenario outcomes for a small number of modified parameters, the reduced order model significantly decreases the computational cost and enables offsite review of the results.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗