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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

UAE6 - Wind Tunnel Tests Data - UAE6 - Sequence O - Raw Data

Sequence O: Sin AOA, Parked (P) This sequence was designed to quantify the blade 3-D unsteady aerodynamic response in the absence of rotational influences by varying blade pitch angle. This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speed ranged from 18.9 m/s to 39.3 m/s, and the yaw angle was 0° throughout the sequence. The rotor was parked with the instrumented blade fixed at 0° azimuth, and the rotor lock was installed (see Appendix A). Blade and probe pressure measurements were collected. The teeter dampers were replaced with rigid links, and these two channels were flagged as not applicable by setting the measured values in the data file to -99999.99 Nm. The teeter link load cell was pre-tensioned to 40,000 N. Sinusoidal angle-of-attack histories were designed to emulate 2-D S809 dynamic data obtained at the OSU wind tunnel (Reuss Ramsey et al. 1995) as well as 1P yawed flow angle-of-attack variations the turbine could be expected to encounter in routine operation. The reduced frequency (K), mean angle of attack (αm), and oscillation amplitude (αω) were computed for each of the five primary span locations. The file lengths varied according to the time required to complete 40 oscillations at the specified frequency. The file name convention used the sequence designation O, followed by two digits for the span location (30, 47, 80, or 95), followed by two digits representing the condition specified in the test matrix, followed by the repetition digit. Due to time constraints, the test conditions corresponding to the 63% span location were eliminated. Test points with a static pitch angle of 3° were collected throughout the sequence to ascertain instrumentation fidelity.

17 WIND ENERGY↗
DOE OSTI2017

Seventy-five years of nuclear criticality safety documents - A bibliography, part II - Links to full-text documents

A collection of nuclear criticality documents was begun in the 1970's at the University of California's Lawrence Livermore Laboratory (now Lawrence Livermore National Laboratory). Documents in this bibliography are dated as early as 1940. The early documents described experimental work that was of vital importance to the safe use of fissile materials at nuclear reactors, the facilities for preparing or transporting nuclear fuel, or its eventual storage. The experiments were of direct value to support safe operations with fissile materials but they were also needed for validating early physics data and calculational methods and then for validating computational methods used in criticality safety evaluations. These documents provided information for our computerized bibliographic database which was originally published in hard-copy format. Our original 'vintage' hardcopy document collection is preserved and is being scanned into digital format. The bibliography is available online as 'LLNL Database' within the U.S. Department of Energy's Nuclear Criticality Safety Program's NCSP web site. Bibliographic information and full-text documents continue to be downloaded from online databases. The online bibliography now contains close to 23,000 records. Links are available to over 3600 full-text documents within NCSP. This capability will be demonstrated at this conference. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Dynamic Modeling of Power Conversion Stages for an Exascale Supercomputer

In this paper a power conversion and energy consumption model for an exascale supercomputer is investigated. Power consumption, energy loss and efficiency are derived for the 27.2 MW liquid-cooled, centralized, High Performance Computing (HPC) power system, which is supplied directly from the 480 V three-phase mains. Two energy conversion stages are analyzed, measured and modeled. The model is developed in order to be adapted and implemented in a digital twin platform utilizing a Resource Allocator and Power Simulator (RAPS) module. RAPS enables estimation of potential energy savings in the direct AC power supply architecture via both conventional rectifier load sharing (commonly used in HPC systems), as well as smart rectifier load sharing. Moreover, besides the direct AC supply architecture analysis, the full direct DC supply architecture with with 1 kV DC bus were also studied. Comparison of 10 hour time frame operation of the system, with direct 480 V AC voltage supply with conventional and smart load sharing and medium dc voltage supply were done. For the direct AC supply architecture, with conventional and smart load sharing the predicted power loss was approximately 840 kW and 820 kW, respectively and the predicted total system efficiency was 92.87% and 93.05%, respectively. For the direct DC supply architecture with the 1000 V DC supply bus power loss was approximately 340 kW and the predicted total system efficiency was 97.02%.

Wojda, Rafal↗
DOE OSTI2025

Probing the Kitaev honeycomb model on a neutral-atom quantum computer

Quantum simulations of many-body systems are among the most promising applications of quantum computers. In particular, models based on strongly correlated fermions are central to our understanding of quantum chemistry and materials problems, and can lead to exotic, topological phases of matter. However, owing to the non-local nature of fermions, such models are challenging to simulate with qubit devices. Here we realize a digital quantum simulation architecture for two-dimensional fermionic systems based on reconfigurable atom arrays. We utilize a fermion-to-qubit mapping based on Kitaev’s model on a honeycomb lattice, in which fermionic statistics are encoded using long-range entangled states. We prepare these states efficiently using measurement and feedforward, realize subsequent fermionic evolution through Floquet engineering with tunable entangling gates interspersed with atom rearrangement, and improve results with built-in error detection. Leveraging this fermion description of the Kitaev spin model, we efficiently prepare topological states across its complex phase diagram and verify the non-Abelian spin-liquid phase by evaluating an odd Chern number. We further explore this two-dimensional fermion system by realizing tunable dynamics and directly probing fermion exchange statistics. Finally, we simulate strong interactions and study the dynamics of the Fermi–Hubbard model on a square lattice. These results pave the way for digital quantum simulations of complex fermionic systems for materials science, chemistry and high-energy physics.

atomic and molecular physics↗
DOE OSTI2021

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. Furthermore, this study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Markerless tracking of small lung tumors for stereotactic radiotherapy

Purpose: (1) To validate retrospective markerless tracking software for small lung tumors by comparing tracked motion in 4-dimensional planning computed tomography (4DCT) derived kV projection images and known tumor motion in the same 4DCT. (2) To evaluate variability of tumor motion using kV projection images from cone-beam computed tomography (CBCT) scans acquired on different days. Methods: Nonclinical tumor tracking software (TTS) used a normalized cross correlation algorithm to track the tumor on enhanced kV projection images (e.g., from a CBCT scan). The reference dataset consisted of digitally reconstructed radiographs (DRRs) from one phase of a planning 4DCT. TTS matches two in-plane coordinates and obtains the out-of-plane coordinate by triangulating with match results from other projections. (1) To validate TTS, tracking results were compared with known 4DCT tumor motion for two patients (A and B). Projection images (1 image/1°) were digitally reconstructed for each 4DCT phase. From these, kV projection series were composed simulating full breathing cycles every 20° of gantry rotation [breathing period = 20°/(6°/s) = 3.33 s]. Reference templates were 360 “tumor enhanced” DRRs from the 4DCT expiration phase. TTS-derived tumor motion was compared to known tumor motion on 4DCT. (2) For five patients, TTS-assessed motion during clinical CBCT acquisition was compared with motion on the planning 4DCT, and the motion component in the Y (cranio–caudal)-direction was compared with the motion of an external marker box (RPM, real-time position management). Results: (1) Validation results: TTS for case A (tumor 6.2 cm{sup 3}, 32 mm axial diameter) over 360° showed mean motion X (medial–lateral) = 3.4, Y = 11.5, and Z (ventral–dorsal) = 4.9 mm (1 SD < 1.0 mm). Corresponding 4DCT motion was X = 3.1, Y = 11.3, and Z = 5.1 mm. Correlation coefficients between TTS tumor motion and displacement of the tumor’s center of mass (CoM) on 4DCT were 0.64, 0.96, and 0.82 (X, Y, and Z, respectively). For case B (4.1 cm{sup 3}, 20 mm diameter), due to temporarily decreased tumor visibility preventing TTS from resolving the tumor, robust tracking data were only available between angles 300°–40° and 120°–220°. Mean motion according to TTS was X = 2.0, Y = 7.7, and Z = 8.2 mm (1 SD < 0.9 mm). Tumor motion on 4DCT was X = 1.8, Y = 7.6, and Z = 9.5 mm and correlation coefficients between TTS motion and CoM displacement were 0.59, 0.95, and 0.93 (X, Y, and Z, respectively). (2) Clinical results: TTS revealed a mean intrafraction variation in tumor motion in Y-direction of >2.0 mm (1 SD) in four of five patients. In addition, clinical tumor motion amplitude differed from that seen on planning 4DCT. Internal and external structures that create abrupt density change (e.g., table-top edge, interface between lung/mediastinum and lung/heart) were observed to prevent 360° tracking of the tumor. Correlation coefficients between TTS motion in the Y-direction and the RPM signal (22 observations) ranged from 0.78 to 0.96. In 2D, 241 TTS matches at end-inspiration and end-expiration were visually validated: mean difference was 0.8 mm (SD = 0.7) for both. Conclusions: TTS can track small lung tumors if these are visible in kV projections. A 4DCT dataset can be used to validate kV tracking of moving targets. TTS and 4DCT displacement agreed to within 2 mm. TTS and RPM motion were closely associated but tumor motion during CBCT can vary from the planning 4DCT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Digital Assurance for High Consequence Systems: 2024 Mission Campaign White Paper

This Sandia National Laboratories Mission Campaign (MC) seeks to create the technical basis that allows national leaders to efficiently assess and manage the digital assurance of high consequence systems. We will call for transformative research that enables efficient (1) development of provably secure systems and secure integration of untrusted products, (2) intelligent threat mitigation, and (3) digital risk-informed engineering trade-offs. Ultimately, this MC will impact multiple national security missions; it will develop an informed Digital Assurance for High Consequence Systems (DAHCS) community and expand Sandia partnerships to build this national capability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Data Acquisition with GPUs: The DAQ for the Muon $g$-$2$ Experiment at Fermilab

Graphical Processing Units (GPUs) have recently become a valuable computing tool for the acquisition of data at high rates and for a relatively low cost. The devices work by parallelizing the code into thousands of threads, each executing a simple process, such as identifying pulses from a waveform digitizer. The CUDA programming library can be used to effectively write code to parallelize such tasks on Nvidia GPUs, providing a significant upgrade in performance over CPU based acquisition systems. The muon $g$-$2$ experiment at Fermilab is heavily relying on GPUs to process its data. The data acquisition system for this experiment must have the ability to create deadtime-free records from 700 $\mu$s muon spills at a raw data rate 18 GB per second. Data will be collected using 1296 channels of $\mu$TCA-based 800 MSPS, 12 bit waveform digitizers and processed in a layered array of networked commodity processors with 24 GPUs working in parallel to perform a fast recording of the muon decays during the spill. The described data acquisition system is currently being constructed, and will be fully operational before the start of the experiment in 2017.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

From tensors to qubits

We discuss recent progress in Tensor Lattice Field Theory and economical, symmetry preserving, truncations suitable for quantum computations or simulations. We focus on spin and gauge models with continuous Abelian symmetries such as the Abelian Higgs model and emphasize noise-robust implementations of Gauss's law. We discuss recent progress concerning the comparison between field digitizations and character expansions, symmetry breaking in tensor language, wave-packet preparation and possible new implementations of Abelian models using Rydberg atoms.

Meurice, Yannick↗
DOE OSTI2025

Resistive Switching of Spinel Li 4 Ti 5 O 12 Lithium-Ion Battery Material for Neuromorphic Computing

The rapid rise of AI has exposed significant limitations in conventional Von Neumann computing architecture, particularly in regard to speed and energy efficiency. To address these challenges, researchers are exploring a brain-inspired neuromorphic architecture that mimics biological neural networks, enabling massive parallel processing with reduced power consumption for complex AI computational demands. Recent interest has focused on utilizing battery electrodes and solid electrolyte materials for their resistive switching properties in developing a neuromorphic architecture. These properties are precisely tuned through local- and bulk-level chemical composition modifications via voltage bias stimuli. In this study, we demonstrate fabricating a three-terminal lithium-ion electrochemical transistor based on lithium titanium oxide (Li 4 Ti 5 O 12 ), a popular lithium-ion battery anode material. We deposited and characterized LTO thin films using RF sputtering, demonstrating a 6 orders of magnitude increase in electronic conductivity upon lithiation, with conductivity plateauing after 20% lithiation. Density functional theory calculations revealed transformation from the insulating to conducting state, supported by experimental characterization through X-Ray Photoelectron Spectroscopy (XPS) and Direct Current (DC) polarization analyses. The fabricated transistor consisted of LTO as the channel layer, gold as source/drain terminals, lithium phosphorus oxynitride (LiPON) as the lithium-ion conductor, and copper as the gate terminal. The device exhibited clear hysteresis in transfer characteristics due to lithium insertion/extraction processes. Long-term potentiation (LTP) and long-term depression (LTD) measurements showed an asymmetric ratio of 1.425 and maximum/minimum conductance ratio of 7.83. When implemented in a deep neural network (DNN) for MNIST handwritten digit recognition, the device achieved 92.03% accuracy over 20 training epochs. Detailed transport mechanism analysis revealed the crucial role of oxygen vacancies and interface effects in device operation. Our preliminary findings establish LTO-based lithium-ion electrochemical transistors as promising candidates for energy-efficient neuromorphic computing applications, offering potential solutions to traditional Von Neumann architecture limitations.

25 ENERGY STORAGE↗
DOE OSTI2021

Simulation of Hurricane Harvey flood event through coupled hydrologic-hydraulic models: Challenges and next steps

Using the 2017 Hurricane Harvey flood event as a test case, this study set up a series of sensitivity analyses to highlight three challenges associated with large-scale flood inundation modeling, including (a) model parameterization, (b) errors in digital elevation models, and (c) effects of reservoir retention. Driven by radar-based hourly rainfall data, a series of hydrologic-hydraulic models including the VIC hydrologic model, RAPID routing model, and Flood2D-GPU hydrodynamic model are set up over Harris County, Texas, to simulate flood inundation and hazards. The results demonstrate the importance of hydrologic parameters in improving flood modeling. For a large flood event such as Hurricane Harvey, the effect of the initial water depths is insignificant. The Manning's n values may increase the peak water depth by ~1%, the flood extents by 65km 2 , and the high danger zone by ~6%. On the contrary, the bathymetry correction factors may reduce the flood extent by ~1.4% and the high-danger zone by ~4%. Reducing the reservoir storage capacity to 1% may increase the flood extent by ~4% and the high-danger zone by ~17%. This study may provide supporting information to guide and prioritize the development of future high-performance computing hydrodynamic large-scale flood simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Validation of Pronghorn for Natural-Circulation Molten Salt Loops

This paper presents the development and validation of a high-fidelity thermal-hydraulic model of a molten salt natural circulation flow loop, designed for integration within a digital twin framework. The study evaluates the performance of Idaho National Laboratory’s Pronghorn against experimental data from Texas A&M University Molten Salt Flow Loop (MSFL) four Hitec-salt test benchmark data. Natural circulation of high-Prandtl-number fluids exhibits complex, counter-intuitive flow patterns that make pointwise thermocouple readings unreliable. Experimental work at TAMU’s MSFL provides benchmark data, including flow visualization at a test-section and centerline steady-state temperature measurements along the loop. Validation includes four single-phase natural circulation test cases with Hitec salt. Key metrics include flow profile agreement and steady-state temperature accuracy. Pronghorn results for two-dimensional single-phase agree qualitatively with the experimental flow profile. This paper illustrates the importance of Computational Fluid Dynamics (CFD) in elucidating the behavior of high-Prandtl-number thermal-hydraulics, along with how misleading centerline temperature measurements can be. Pronghorn reproduces the axial and radial stratification that makes single thermocouple readings unreliable. Future research will focus on reduced-order modeling techniques to enable rapid simulation suitable for real-time digital twin applications. The validated cases provide a basis for developing reduced-order surrogates aimed at real-time digital-twin applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

BWR modernization project: installation of a new digital feedwater control system - 295

A European BWR plant carried out a relevant and pioneering modernization project regarding the Feedwater and Feed Pump Turbine Control System, replacing the legacy analogic control with the new General Electric MarkVIe digital system. This digital instrumentation and control modernization, motivated by obsolescence, had a great impact on the main control room and on the plant operation. Tecnatom collaborated with the nuclear power plant in the project performing the following activities: early installation of the system in the full scope and classroom simulators, Human Factors Engineering (HFE) Verification and Validation (V and V), Simulation Assisted Engineering (SAE), design and implementation of cyber secure communication with the Plant Process Computer (PPC), operating procedures modification and plant operators training. Those activities contributed to the success of the project that was completed during the refueling outage as planned. Scheduling HFE verification before the design freeze allowed the plant to modify the Human Machine Interface (HMI) to comply with the standards and increase safety without overcharging the project. SAE resulted in the reduction of costs and time during the system commissioning through the detection of discrepancies and improvements in the control, covering different plant scenarios. All the activities carried out in the simulators before the implementation of the system in the plant contributed to validate it and to build confidence in the new control among the plant personnel. New digital system communication interfaces with the PPC saved cable laying works and allowed future expansions. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level

Neuromorphic computing is non-von Neumann computer architecture for the post Moore’s law era of computing. Since a main focus of the post Moore’s law era is energy-efficient computing with fewer resources and less area, neuromorphic computing contributes effectively in this research. Here in this paper, we present a memristive neuromorphic system for improved power and area efficiency. Our particular mixed-signal approach implements neural networks with spiking events in a synchronous way. Moreover, the use of nano-scale memristive devices saves both area and power in the system. We also provide device-level considerations that make the system more energy-efficient. The proposed system additionally includes synchronous digital long term plasticity, an online learning methodology that helps the system train the neural networks during the operation phase and improves the efficiency in learning considering the power consumption and area overhead.

77 NANOSCIENCE AND NANOTECHNOLOGY↗