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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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60 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ML-Based Rock Properties and Seismic Volume Enhancement

This project aims to improve field-scale Carbon Capture and Storage (CCS) assessments by enhancing petrophysical and geophysical log predictions through machine learning and neural networks. In our work during EY23, we applied Conditional Variational Autoencoders (CVAEs) to predict compressional velocity (Vp) and assess CO2 saturation levels in geological formations at the Illinois Basin Decatur Project (IBDP). In another task, we improved full-waveform inversion (FWI) methods with machine-learning approaches using lithological constraints. Full-waveform inversion (FWI) of seismic data estimates the elastic properties of subsurface rocks with high spatial resolution.

Nathanail, Athanasios↗
DOE OSTI2025

Design Basis Document / Owner’s Technical Specification for Nitrate Salt Systems in CSP Projects: Volume 1 Specifications for Parabolic Trough Projects, Volume 2 - Specifications for Central Receiver Projects, Volume 3 - Narrative

Design Basis Documents / Owner’s Technical Specifications are developed for parabolic trough and central receiver power plants using nitrate salt as the heat transport fluid and the thermal storage medium. The goals were to 1) distill the successful experience with nitrate salt systems from as many commercial projects as possible, 2) provide technical bases for equipment design/selection that an owner can impose on an EPC contractor, 3) compile this information in one location to provide guidance on salt systems that is as broadly applicable to as many projects as possible, and 4) work toward an industry consensus on a Design Basis Document that reflects the lessons learned from earlier commercial projects. The document allows an owner to provide design guidance, and to impose a minimum set of requirements, above and beyond those in the normal Codes and Standards, on an EPC contractor in an effort to avoid a repeat of past mistakes. If successful, this would allow salt systems to achieve the same levels of reliability and availability as commercial parabolic trough plants using diphenyl oxide / biphenyl (i.e., Dowtherm A, Therminol VP-1) as the heat transfer fluid. The report consists of 3 volumes: • Volume 1 - Specifications for Parabolic Trough Projects • Volume 2 - Specifications for Central Receiver Projects • Volume 3 - Narrative Volumes 1 and 2 include discussions of the following topics: • Plant functional requirements for the salt systems • Plant operating states, and transitions between states, for the salt systems • Risk analysis of the principal salt components • Plant requirements for the salt systems to meet the functional and the operating requirements • Type of specification for the principal salt components: functional; or prescriptive • Current state of the art for salt systems. Volume 3 discusses a number of cases in which the salt equipment at commercial parabolic trough and central receiver projects has not met the projected levels of reliability and availability. Possible reasons for the sources of the problems are discussed, as is a range of possible alternate designs that could avoid the known problems.

14 SOLAR ENERGY↗
DOE OSTI2025

Sound Velocities in Vanadium Reveal Complex Elastic Behavior at High Pressures

Compressional (VP) and shear (VS) wave velocities of polycrystalline vanadium were measured simultaneously up to 11.5 GPa at room temperature using ultrasonic interferometry in a multi-anvil press. Complex softening behavior in VS and resulting shear moduli are discovered, possibly revealing a precursor to the reported phase transition within 30–60 GPa. The current data enables a comprehensive assessment of the elastic and mechanical properties of vanadium at high pressures, including bulk and shear moduli, Young’s modulus, Poisson’s ratio, and Pugh’s ratio. Through fitting to the 3rd-order finite strain equations, the elastic moduli and their pressure derivatives were determined to be K S0 = 151 (2) GPa, G 0 = 46.9 (8) GPa, K$^{’}_{S0}$ = 3.47 (5), and G$^{’}_{0}$ = 0.62 (1). These experimental results allow us to compare with and benchmark the existing Steinberg–Guinan models for extrapolations to extreme pressure and temperature conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

The Performance of FBMC-SS in Challenging HF Channel Environments

Provide overview of FBMC-SS waveform Discuss parameters used for simulated results herein Present simulated results in challenging channel environments Cases: 2 path channel with 2nd path frequency offset 2 path MLD and “VP” channels Large narrowband interferer Highly congested interference case Highly dynamic interference case Clarify previously presented results Make conclusions from presented results

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

ML–Enabled FPGA Framework for Fast Quantum State Discrimination in Mid-Circuit Measurement Regimes

Accurate and low-latency quantum state discrimination is essential for protocols involving mid-circuit measurement (MCM) and conditional feed-forward. In superconducting quantum systems, conventional readout pipelines transfer measurement data to host processors for post-processing, introducing millisecond-scale delays that far exceed qubit coherence times. To overcome this bottleneck, we present an in-situ machine learning (ML) inference engine implemented on an FPGA for real-time quantum state discrimination. Our design performs inference directly on digitized readout signals with 40 ns latency, supports both qubit and qutrit readout, and enables conditional operations without host-side intervention. This capability is critical for MCM and for feedback-driven protocols such as quantum error correction. We validate the system on superconducting transmon hardware, demonstrating robust discrimination fidelity across multiple qubit and qutrit channels. We further demonstrate conditional qutrit logic driven by FPGA-resident classification, highlighting the potential of low-latency ML-on-FPGA control for NISQ applications and scalable fault-tolerant quantum computing.

Vora, Neel [Lawrence Berkeley National Laboratory ↗