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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Revolutionizing thermal Management in Next-Generation AI data centers: Challenges and breakthrough innovations

Data centers (DCs) serve as critical infrastructure for powering the growth and evolution of AI. Next-generation AI DCs present unique challenges in thermal management driven by unprecedented computational demands. This paper provides a comprehensive summary of key stakeholder perspectives on technology gaps, infrastructure requirements, test bed needs, emerging opportunities, and preliminary solutions related to thermal management for AI DCs. It establishes six strategic pillars of thermal management for next generation AI DC: reliability, deployability, efficiency, resilience, measurability, and valorization. The discussion spans a range of critical topics, including advanced cooling technologies, thermal strategies for emerging modular and edge DCs, system-level optimization and control frameworks, infrastructure planning and grid integration designs, benchmarking approaches, and pathways for waste heat recovery and reuse. The proposed research, development, and demonstration efforts are aimed at accelerating the deployment of AI DCs while ensuring energy efficiency, reliability, safety, and regulatory compliance.

Wang, Pengtao [ORNL] (ORCID:0000000214713429)↗
DOE OSTI2020

Optimized Quantum Compilation for Near-Term Algorithms with OpenPulse

Quantum computers are traditionally operated by programmers at the granularity of a gate-based instruction set. However, the actual device-level control of a quantum computer is performed via analog pulses. Here, we introduce a compiler that exploits direct control at this microarchitectural level to achieve significant improvements for quantum programs. Unlike quantum optimal control, our approach is bootstrapped from existing gate calibrations and the resulting pulses are simple. Our techniques are applicable to any quantum computer and realizable on current devices. We validate our techniques with millions of experimental shots on IBM quantum computers, controlled via the OpenPulse control interface. For representative benchmarks, our pulse control techniques achieve both 1.6x lower error rates and 2x faster execution time, relative to standard gate-based compilation. These improvements are critical in the near-term era of quantum computing, which is bottlenecked by error rates and qubit lifetimes.

Gokhale, Pranav↗
DOE OSTI2020

NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families

Comparative network analysis provides effective computational means for gaining novel insights into the structural and functional compositions of biological networks. In recent years, various methods have been developed for biological network alignment, whose main goal is to identify important similarities and critical differences between networks in terms of their topology and composition. A major impediment to advancing network alignment techniques has been the lack of gold-standard benchmarks that can be used for accurate and comprehensive performance assessment of such algorithms. The original NAPAbench (network alignment performance assessment benchmark) was developed to address this problem, and it has been widely utilized by many researchers for the development, evaluation, and comparison of novel network alignment techniques. In this work, we introduce NAPAbench 2—a major update of the original NAPAbench that was introduced in 2012. NAPAbench 2 includes a completely redesigned network synthesis algorithm that can generate protein-protein interaction (PPI) network families whose characteristics closely match those of the latest real PPI networks. Furthermore, the network synthesis algorithm comes with an intuitive GUI that allows users to easily generate PPI network families with an arbitrary number of networks of any size, according to a flexible user-defined phylogeny. In addition, NAPAbench 2 provides updated benchmark datasets—created using the redesigned network synthesis algorithm—which can be used for comprehensive performance assessment of network alignment algorithms and their scalability.

42 ENGINEERING↗
DOE OSTI2019

Photovoltaic Inverter Reliability Assessment

As the price of PV modules drops, the price of power electronics become more important. Power electronics now constitute 8%-12% of the total lifetime PV cost. As of 2010, the inverter and associated power conditioning components accounted for $\$0.25$/W, well above the DOE benchmark of $\$0.12$/W by 2020. As efforts to reduce PV module costs yield diminishing returns, the importance of understanding and reducing inverter costs becomes an increasingly critical and cost-effective investment toward achieving the DOE SunShot goals. With this in view, this report showcases and describes an approach to help assess and predict the reliability of photovoltaic (PV) inverters. To predict the reliability, thermal cycling is considered as a prominent stressor in the inverter system. To evaluate the impacts of thermal cycling, a detailed linearized model of the PV inverter is developed along with controllers. This research also develops models and methods to compute the losses of the power electronics switches and other components in a PV inverter. The losses are then used to estimate the junction and heat sink temperature. The model is verified by developing an in-house inverter. Additionally, to assess the scalability of the research, the hardware inverter is placed inside a thermal chamber to verify the losses for different ambient temperatures. After the verification of the model, a reduced-order model of the inverter is implemented to translate the profile of ambient temperature and solar irradiance into the profile of junction temperatures of the switches. The estimated junction temperature data are used to identify inverter reliability indices and predict the useful lifetime of the inverter system. After developing the models to predict the useful lifetime of the system, the impact of reactive power on the overall reliability of the system will be studied. The first chapter involves the motivation behind the research on assessing the reliability of PV inverters. The inverter power stage and controller design of the power converter used in this research is also explained in detail in this chapter. The second chapter presents the various losses of the power converter system and the translation of power loss into temperature increase with thermal modeling. The results of the loss modeling and the thermal modeling are also explained in detail in this chapter. The third chapter involves implementing the reduced-order model to translate the mission profile of ambient temperature and solar irradiance into profile of the junction temperatures of the switches. The final chapter involves identifying reliability indices, which are of interest for this system to predict the remaining useful life of the system, and concludes with the impact of reactive power on the overall reliability of the inverter system.

14 SOLAR ENERGY↗
DOE OSTI2020

Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems

This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs. This extension allows NODEs to learn multiple dynamics specified by the input parameter instances. Our extension is inspired by the concept of parameterized ordinary differential equations, which are widely investigated in computational science and engineering contexts, where characteristics of the governing equations vary over the input parameters. We apply the proposed parameterized NODEs (PNODEs) for learning latent dynamics of complex dynamical processes that arise in computational physics, which is an essential component for enabling rapid numerical simulations for time-critical physics applications. For this, we propose an encoder-decoder-type framework, which models latent dynamics as PNODEs. We demonstrate the effectiveness of PNODEs with important benchmark problems from computational physics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1996

An Analysis of NASA Technology Transfer

A review of previous technology transfer metrics, recommendations, and measurements is presented within the paper. A quantitative and qualitative analysis of NASA's technology transfer efforts is performed. As a relative indicator, NASA's intellectual property performance is benchmarked against a database of over 100 universities. Successful technology transfer (commercial sales, production savings, etc.) cases were tracked backwards through their history to identify the key critical elements that lead to success. Results of this research indicate that although NASA's performance is not measured well by quantitative values (intellectual property stream data), it has a net positive impact on the private sector economy. Policy recommendations are made regarding technology transfer within the context of the documented technology transfer policies since the framing of the Constitution. In the second thrust of this study, researchers at NASA Langley Research Center were surveyed to determine their awareness of, attitude toward, and perception about technology transfer. Results indicate that although researchers believe technology transfer to be a mission of the Agency, they should not be held accountable or responsible for its performance. In addition, the researchers are not well educated about the mechanisms to perform, or policies regarding, technology transfer.

Bush, Lance B.↗
NASA NTRS2010

Automatically Finding the Control Variables for Complex System Behavior

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the factors most likely to cause a mission-critical failure. The goal of this research is to comparatively assess treatment learning against state-of-the-art numerical optimization techniques. To achieve this, this paper benchmarks the TAR3 and TAR4.1 treatment learners against optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. The results clearly show that treatment learning is both faster and more accurate than traditional optimization methods.

Gay, Gregory↗
NASA NTRS2018

Unstructured Grid Adaptation and Solver Technology for Turbulent Flows

Unstructured grid adaptation is a tool to control Computational Fluid Dynamics (CFD) discretization error. However, adaptive grid techniques have made limited impact on production analysis workflows where the control of discretization error is critical to obtaining reliable simulation results. Issues that prevent the use of adaptive grid methods are identified by applying unstructured grid adaptation methods to a series of benchmark cases. Once identified, these challenges to existing adaptive workflows can be addressed. Unstructured grid adaptation is evaluated for test cases described on the Turbulence Modeling Resource (TMR) web site, which documents uniform grid refinement of multiple schemes. The cases are turbulent flow over a Hemisphere Cylinder and an ONERA M6Wing. Adaptive grid force and moment trajectories are shown for three integrated grid adaptation processes with Mach interpolation control and output error based metrics. The integrated grid adaptation process with a finite element (FE) discretization produced results consistent with uniform grid refinement of fixed grids. The integrated grid adaptation processes with finite volume schemes were slower to converge to the reference solution than the FE method. Metric conformity is documented on grid/metric snapshots for five grid adaptation mechanics implementations. These tools produce anisotropic boundary conforming grids requested by the adaptation process.

Park, Michael A.↗
DOE OSTI2022

Investigation of the OECD/NEA PWR MOX/UO{sub 2} core transient benchmark using a coupled whole core pin-by-pin route in WIMS

This paper investigates the new CAMELOT coupling route developed in WIMS, which uses prepared tabulated cross-sections combined with the flux solver MERLIN module and the integrated sub-channel thermal hydraulics solver ARTHUR module to solve for the coupled core state. This coupling route is applied to the OECD/NEA pressurized water reactor (PWR) core transient benchmark exercise, involving 3-D neutronics and thermal-hydraulics analysis of a full-sized PWR core featuring MOX fuel at various core states, as well as simulating a control rod ejection transient. Analysis of the results for each part of the benchmark demonstrate that the high-resolution pin-by-pin analysis used in WIMS closely matches the other participants in the benchmark, with differences of between 20-200 pcm for k-effective, 1-2.5% RMS difference for assembly-averaged powers, 2-8% difference in rod worth calculations, < 200 ppm for calculations of critical boron concentration, zero difference in delayed neutron fraction calculation and a very close match for power response to the rod ejection transient. This is the first application of the WIMS-CAMELOT approach for the transient analysis of a full-sized reactor and as such further improvements such as reducing computational cost through parallelization, optimisation and memory reduction, are currently in active development. This paper demonstrates the accuracy and flexibility of the CAMELOT coupling route, providing a straightforward and consistent user image that can be easily used for a variety of modelling problems involving coupled neutronics and thermal-hydraulics. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Range extender mediates long-distance enhancer activity

Although most mammalian transcriptional enhancers regulate their cognate promoters over distances of tens of kilobases, some enhancers act over distances in the megabase range1. The sequence features that enable such long-distance enhancer–promoter interactions remain unclear. Here we used in vivo enhancer-replacement experiments at the mouse Shh locus to show that short- and medium-range limb enhancers cannot initiate gene expression at long-distance range. We identify a cis-acting element, range extender (REX), that confers long-distance regulatory activity and is located next to a long-range limb enhancer of Sall1. The REX element has no endogenous enhancer activity. However, addition of the REX to other short- and mid-range limb enhancers substantially increases their genomic interaction range. In the most extreme example observed, addition of REX increased the range of an enhancer by an order of magnitude from its native 73 kb to 848 kb. The REX element contains highly conserved [C/T]AATTA homeodomain motifs that are critical for its activity. These motifs are enriched in long-range limb enhancers genome-wide, including the ZRS (zone of polarizing activity (ZPA) regulatory sequence), a benchmark long-range limb enhancer of Shh2. The ZRS enhancer with mutated [C/T]AATTA motifs maintains limb activity at short range, but loses its long-range activity, resulting in severe limb reduction in knock-in mice. In summary, we identify a sequence signature associated with long-range enhancer–promoter interactions and describe a prototypical REX element that is necessary and sufficient to confer long-distance activation by remote enhancers.

Bower, Grace↗
DOE OSTI2022

Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction

The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern X-ray facilities have allowed a dramatically increased fraction of this information to be captured. Now, the primary challenge is to understand and discover scientific principles from big datasets when a comprehensive analysis is beyond human reach. We report the development of an unsupervised machine learning approach, X-ray diffraction (XRD) temperature clustering (X-TEC), that can automatically extract charge density wave order parameters and detect intraunit cell ordering and its fluctuations from a series of high-volume X-ray diffraction measurements taken at multiple temperatures. We benchmark X-TEC with diffraction data on a quasi-skutterudite family of materials, (Ca x Sr 1–x ) 3 Rh 4 Sn 13 , where a quantum critical point is observed as a function of Ca concentration. We apply X-TEC to XRD data on the pyrochlore metal, Cd 2 Re 2 O 7 , to investigate its two much-debated structural phase transitions and uncover the Goldstone mode accompanying them. We demonstrate how unprecedented atomic-scale knowledge can be gained when human researchers connect the X-TEC results to physical principles. Specifically, we extract from the X-TEC–revealed selection rules that the Cd and Re displacements are approximately equal in amplitude but out of phase. This discovery reveals a previously unknown involvement of 5d 2 Re, supporting the idea of an electronic origin to the structural order. Our approach can radically transform XRD experiments by allowing in operando data analysis and enabling researchers to refine experiments by discovering interesting regions of phase space on the fly.

36 MATERIALS SCIENCE↗
DOE OSTI2015

A CONNECTION BETWEEN PLASMA CONDITIONS NEAR BLACK HOLE EVENT HORIZONS AND OUTFLOW PROPERTIES

Accreting black holes are responsible for producing the fastest, most powerful outflows of matter in the universe. The formation process of powerful jets close to black holes is poorly understood, and the conditions leading to jet formation are currently hotly debated. In this paper, we report an unambiguous empirical correlation between the properties of the plasma close to the black hole and the particle acceleration properties within jets launched from the central regions of accreting stellar-mass and supermassive black holes. In these sources the emission of the plasma near the black hole is characterized by a power law at X-ray energies during times when the jets are produced. We find that the photon index of this power law, which gives information on the underlying particle distribution, correlates with the characteristic break frequency in the jet spectrum, which is dependent on magnetohydrodynamical processes in the outflow. The observed range in break frequencies varies by five orders of magnitude in sources that span nine orders of magnitude in black hole mass, revealing a similarity of jet properties over a large range of black hole masses powering these jets. This correlation demonstrates that the internal properties of the jet rely most critically on the conditions of the plasma close to the black hole, rather than other parameters such as the black hole mass or spin, and will provide a benchmark that should be reproduced by the jet formation models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Fast Fault Injection and Sensitivity Analysis for Collective Communications, In: 2015 IEEE International Conference on Cluster Computing

The collective communication operations, which are widely used in parallel applications for global communication and synchronization are critical for application's performance and scalability. However, how faulty collective communications impact the application and how errors propagate between the application processes is largely unexplored. One of the critical reasons for this situation is the lack of fast evaluation method to investigate the impacts of faulty collective operations. The traditional random fault injection methods relying on a large amount of fault injection tests to ensure statistical significance require a significant amount of resources and time. These methods result in prohibitive evaluation cost when applied to the collectives. In this paper, we introduce a novel tool named Fast Fault Injection and Sensitivity Analysis Tool (FastFIT) to conduct fast fault injection and characterize the application sensitivity to faulty collectives. The tool achieves fast exploration by reducing the exploration space and predicting the application sensitivity using Machine Learning (ML) techniques. A basis for these techniques are implicit correlations between MPI semantics, application context, critical application features, and application responses to faulty collective communications. The experimental results show that our approach reduces the fault injection points and tests by 97% for representative benchmarks (NAS Parallel Benchmarks (NPB)) and a realistic application (Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)) on a production supercomputer. Further, we statistically generalize the application sensitivity to faulty collective communications for these workloads, and present correlation between application features and the sensitivity.

Computer Science↗
DOE OSTI2018

On the convergence of Neumann series for electrostatic fracture response

Here, the feasibility of Neumann series expansion of Maxwell’s equations in the electrostatic limit is investigated for potentially rapid and approximate subsurface imaging of geologic features proximal to metallic infrastructure in an oilfield environment. While generally useful for efficient modeling of mild conductivity perturbations in uncluttered settings, we raise the question of its suitability for situations, such as oilfield, where metallic artifacts are pervasive, and in some cases, in direct electrical contact with the conductivity perturbation on which the Neumann series is computed. Convergence of the Neumann series and its residual error are computed using the hierarchical finite element framework for a canonical oilfield model consisting of an “L” shaped, steel-cased well, energized by a steady state electrode, and penetrating a small set of mildly conducting fractures near the heel of the well. For a given node spacing h in the finite element mesh, we find that the Neumann series is ultimately convergent if the conductivity is small enough - a result consistent with previous presumptions on the necessity of small conductivity perturbations. However, we also demonstrate that the spectral radius of the Neumann series operator grows as ~ 1/ h , thus suggesting that in the limit of the continuous problem h → 0, the Neumann series is intrinsically divergent for all conductivity perturbation, regardless of their smallness. The hierarchical finite element methodology itself is critically analyzed and shown to possess the h 2 error convergence of traditional linear finite elements, thereby supporting the conclusion of an inescapably divergent Neumann series for this benchmark example. Application of the Neumann series to oilfield problems with metallic clutter should therefore be done with careful consideration to the coupling between infrastructure and geology. Here, the methods used here are demonstrably useful in such circumstances.

58 GEOSCIENCES↗
DOE OSTI2026

Thermodynamic Modeling of Intrinsic Defects in MnBi₂Te₄

This repository contains the computational data supporting the manuscript titled “The critical role of intrinsic defects and many-body interactions on the stability of MnBi₂Te₄.” It includes: 1. DFT data generated using VASP, used for training and benchmarking electronic structure models. 2. Quantum Monte Carlo (QMC) data produced with QMCPACK, used to apply many-body corrections and validate the electronic and magnetic properties of MnBi₂Te₄. 3. Relevant scripts used to run, analyze, and process the calculations, enabling reproducibility and transparency of the workflows.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Ready to Retrofit: The Process of Project Team Selection, Building Benchmarking, and Financing Commercial Building Energy Retrofit Projects

This guide presents a process for three key activities for the building owner in preparing to retrofit existing commercial buildings: selecting project teams, benchmarking the existing building, and financing the retrofit work. Although there are other essential steps in the retrofit process, the three activities presented in this guide are the critical elements where the building owner has the greatest influence on the outcome of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Integral Experiments Request 441 CED-3b Summary Report

Under IER-441, critical experiments were done with and without tantalum test rods within a central test region surrounded by 7uPCX fuel rods. The experiments were done in new critical assembly hardware designed to support the 7uPCX fuel in a 1.02 cm triangular-pitched array. Appendix I is a draft of section 1 of the ICSBEP benchmark evaluation of the experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗