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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 OSTI2017

Funding for the 2ND IAEA technical meeting on fusion data processing, validation and analysis

The International Atomic Energy Agency (IAEA) will organize the second Technical Meeting on Fusion Da Processing, Validation and Analysis from 30 May to 02 June, 2017, in Cambridge, MA USA. The meeting w be hosted by the MIT Plasma Science and Fusion Center (PSFC). The objective of the meeting is to provide a platform where a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolation needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge based understanding of the physical processes governing the dynamics of these plasmas. The meeting will aim at fostering, in particular, discussions of research and development results that set out or underline trends observed in the current major fusion confinement devices. General information on the IAEA, including its mission and organization, can be found at the IAEA websit Uncertainty quantification (UQ) Model selection, validation, and verification (V&V) Probability theory and statistical analysis Inverse problems & equilibrium reconstru ction Integrated data analysis Real time data analysis Machine learning Signal/image proc essing & pattern recognition Experimental design and synthetic diagnostics Data management

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Process-Based Balance-of-System Cost Modeling for Offshore Wind Power Plants in the United States: Preprint

This work describes the development of a process based and open source balance of system cost model which provides the capability to evaluate both existing and novel offshore wind technologies. Individual design and installation steps are represented with bottom-up engineering models that compute the times and costs associated with the process; furthermore, operational constraints are assigned to each process so that delays due to weather and presence of marine mammals may be accounted for in the overall project timeline. The model structure, assumptions, inputs, and results are vetted with industry partners and compared against actual projects for validation; installation times are shown to agree with real data with relative errors of less than 35\%. Case studies are presented to demonstrate the functionality of the model. First, individual vessel efficiencies are computed for varying numbers of installation vessels and weather time series to show the diminishing returns of more than two feeder barges. Then, array cable capital costs and installation times are determined for a representative project with different turbine sizes. This quantifies the cost benefit tradeoffs of decreasing number of turbines, increased turbine spacing, and fewer turbine terminations and shows a net cost savings. These results demonstrate that the BOS model features the accuracy, functionality, and accessibility to serve as the foundation for a wide range of analyses to identify cost reduction potentials for offshore wind energy in the United States.

17 WIND ENERGY↗
DOE OSTI2018

Optimizing Voltage Control for Large-Scale Solar

Learn how NREL researchers, working with GE’s Grid Solution business and Duke Energy, used an advanced distribution management system to better manage voltage swings from utility-scale photovoltaic systems. The research brought NREL’s extensive power-hardware-in-the-loop and modeling capabilities together with real data from Duke Energy and GE’s advanced distribution management system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Invertible neural networks for E3SM land model calibration and simulation

We apply an invertible neural network (INN) for E3SM land model calibration and simulation with eight parameters at the Missouri Ozark AmeriFlux forest site. INN provides bijective (two-way) mappings between inputs and outputs, thus it can solve probabilistic inverse problems and forward approximations simultaneously. We demonstrate INN's inverse and forward capability in both synthetic and real-data applications. Results indicate that INN produces accurate parameter posterior distributions similar to Markov Chain Monte Carlo sampling and it generates model outputs close to the forward model simulations. Additionally, both the inverse and forward evaluations in INN are computationally efficient which allows for rapid integration of observations for parameter estimation and fast model predictions.

Lu, Dan↗
DOE OSTI2022

Secure Control Regions for Distributed Stochastic Systems with Application to Distributed Energy Resource Dispatch: Preprint

With the increasing connectedness and interdependence of systems that are stochastic in nature, the issue of how to manage and coordinate them for safe operation has evidently become more important. In many networked system architectures, the system-wide output has to be delicately managed; often within a prescribed set of bounds. In this paper, a novel control framework is proposed where the bounds on the outputs are translated into independent bounds on the controllable inputs of each subsystem. The main benefit of this framework is that respecting the individual control bounds suffices to guarantee that the system-wide outputs will remain within safe boundaries. Since the systems are assumed to be stochastic, the bounds on the output are introduced as probabilistic chance constraints. The benefits of this framework are demonstrated by applying it to the control of distributed energy resources in a distribution networks where main goal is to keep the voltage magnitudes with their prescribed bounds. The control bounds are evaluated using real data on an IEEE test system.

chance constrained optimization↗
DOE OSTI2022

Identifying Opportunities and Recommendations for the Integration of Advanced Reactors for Industrial Heat and Electricity Users

Nuclear power in the United States has been used traditionally to provide baseload electric power to the grid. Advancements in nuclear power to create smaller and safer reactors have renewed interest in nuclear as a source of both heat and electricity for a variety of applications. There is great interest in coupling nuclear reactors with industrial applications because nuclear power is a low carbon energy source that can be utilized for process heating, hydrogen generation, on-site electricity demand, and more. Idaho National Laboratory is developing guidelines to identify and assist industrial heat and electricity decarbonization by integrating with nuclear power plants (NPPs) to provide clean, abundant, and dispatchable energy. Considerations include specific industrial hazards which impact NPP siting, heat transport requirements and associated technologies, and implementation feasibility based on site-specific demand profiles. To assess integration feasibility, facility process models are developed based on real data obtained from a survey of baseline requirements and process information from industrial facilities in the United States. An assessment of safety and siting requirements is also performed to determine how nuclear industrial pairings could meet licensing requirements for NPPs. In addition, site characterization of an industrial plant is essential to determining the feasibility and suitable integration methods for each industry. Characterization includes facility distance from the nearest population center, site size, rail or water transport availability, and proximity to undeveloped land. These characteristics are important to determine reactor- or industry-side design requirements for safe, efficient operation. The siting and technical data assessment will reveal opportunities for single-use nuclear integration and co-location opportunities for industries to share benefits from a single reactor. In addition to existing facilities, this “energy-park” style cooperation could include new construction like data centers which can cost-share energy investments or provide a stable demand-and-revenue stream to the investor. Deliverables will contain a library of documents and models to guide various industries toward understanding nuclear technologies based on their users’ needs. This paper is a summary of the current project status, and provides insights on suitable pairings for specific industries and reactor technologies

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Systems usability case in stepwise control room validation - 125

We present a Systems Usability Case (SUC) approach enabling a requirement-based human factors evaluation of complex technical systems. The approach is especially suitable for stepwise verification and validation (V and V) of nuclear power plant (NPP) control room (CR) systems. A SUC is based on the Safety Case approach and on the Systems Usability (SU) construct. A series of V and V test activities carried throughout the design process and constructed according to SUC enables us to monitor how evaluation evidence is aggregated over variety of V and V steps and how the design solution is maturing towards a tool of a good quality and usability. The paper demonstrates an application of the SUC approach to real data from a stepwise CR validation. The results suggest that SUC has some clear advantages. It makes it easier to conduct the V and V activities in a more systematic fashion and it makes the reasoning process more explicit and transparent. Moreover, it enables building a longitudinal view of the progress of the design process. Most importantly, constructing the SUC enables monitoring of the fulfilment of the requirements from the human factors (i.e., SU) perspective. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Advancing earth system model calibration: a diffusion-based method

Understanding of climate impact on ecosystems globally requires site-specific model calibration. Here we introduce a novel diffusion-based uncertainty quantification (DBUQ) method for efficient model calibration. DBUQ is a score-based diffusion model that leverages Monte Carlo simulation to estimate the score function and evaluates a simple neural network to quickly generate samples for approximating parameter posterior distributions. DBUQ is stable, efficient, and can effectively calibrate the model given diverse observations, thereby enabling rapid and site-specific model calibration on a global scale. This capability significantly advances Earth system modeling and our understanding of climate impacts on Earth systems. We demonstrate DBUQ's capability in E3SM land model calibration at the Missouri Ozark AmeriFlux forest site. Both synthetic and real-data applications indicate that DBUQ produces accurate parameter posterior distributions similar to those generated by Markov Chain Monte Carlo sampling but with 30X less computing time. This efficiency marks a significant stride in model calibration, paving the way for more effective and timely climate impact analyses.

Liu, Yanfang↗
DOE OSTI2025

Dimuon production in the MicroBooNE detector

This analysis studies the production of the rare signal with two muons (dimuon) in the finalstate at µB using the Booster Neutrino Beam (BNB) as source of neutrino interactions. Themain background source for the dimuon signal are pions. Differentiating muons and pions hasbeen challenging at µB because the tracks left by these particles are almost identical. Thisanalysis faces this problem using a supervised classifier algorithm called boosted decisiontree (BDT). A Monte Carlo simulation (MC) enhanced dimuon signal sample was producedto train a BDT capable of recognizing this signal. A set of variables that show somediscrimination characteristics was used to perform the training. A MC neutrino interactionsimulation sample was used to estimate the background. Estimations of dimuon productionin real data samples were made for different cases. Additionally, an analysis of the systematicuncertainties, fake data studies, and BDT performance checks were obtained.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Decarbonizing Industrial Heat and Electricity Applications Using Advanced Nuclear Energy

Idaho National Laboratory (INL) is investigating the technical pathways to assist industrial heat and electricity users to meet their decarbonization goals through integration with advanced nuclear power plants (NPPs). This project will deliver a library of process models and accompanying documents that guide specific industries in choosing potential nuclear technologies based on their needs. Considerations in providing this guidance include specific hazards from the industrial facility, heat transport requirements and associated technologies, and feasibility with site-specific demand profiles. The library of facility process models will be based on real data from industrial facilities in the United States. The industrial processes will be identified in this project based on the following: (1) operational heat characteristics that nuclear systems can provide, (2) sufficient energy requirements to merit the capital investment for nuclear plant construction, and (3) environmental benefits of replacing existing energy production with carbon-free nuclear power. Other decarbonization opportunities considered are the addition of nuclear-powered electrolysis processes or high-temperature electric heating where the thermal requirements exceed nuclear generation conditions. In addition to assessing the technical feasibility, INL is evaluating the impact of hazards introduced by the industrial facilities on the siting requirements of advanced NPPs. Site characterization of an industrial plant is essential to determine the feasibility and suitable integration methods for each industry. The assessment of siting and technical data will reveal opportunities for a single-use nuclear integration as well as integration of multiple industrial facilities with a single NPP.

02 PETROLEUM↗
DOE OSTI2025

Muon momentum reconstruction in ICARUS-T600 LArTPC via Multiple Scattering

The SBN program at Fermilab investigates the possible existence of eV-scale sterile neutrinos. This work focuses on reconstructing muon momentum in $\nu$-Ar interactions within the ICARUS-T600 LArTPC detector using Multiple Coulomb Scattering (MCS), the only viable method for non-contained muons. Two algorithms are evaluated: the Gran Sasso algorithm (2D, Collection view) and the MicroBooNE algorithm (3D angles), applied to 2391 simulated and 2255 real stopping muon tracks (0.4-1 GeV/c). Performance is assessed via bias and resolution, using range-based momentum as a reference. Two enhanced versions of the Gran Sasso algorithm are also developed: one combining all three wire plane views, and one using 3D angles. Results show that the MicroBooNE algorithm performs slightly better, with resolution down to 14\% in simulation and 16\% in real data. These improvements significantly boost $\nu_\mu$ CC statistics, enhancing sensitivity to $\nu_\mu$ disappearance and 3+1 oscillation signals.

Chiello, Giovanni [Pisa U.] (ORCID:000900064571071↗
DOE OSTI2025

DNN-based Signal Processing for Liquid Argon Time Projection Chambers

We investigate a deep learning-based signal processing for liquid argon time projection chambers (LArTPCs), a leading detector technology in neutrino physics. Identifying regions of interest (ROIs) in LArTPCs is challenging due to signal cancellation from bipolar responses and various detector effects observed in real data. We approach ROI identification as an image segmentation task, and employ a U-ResNet architecture. The network is trained on samples that incorporate detector geometry information and include a range of detector variations. Our approach significantly outperforms traditional methods while maintaining robustness across diverse detector conditions. This method has been adopted for signal processing in the Short-Baseline Neutrino program and provides a valuable foundation for future experiments such as the Deep Underground Neutrino Experiment.

Bhat, Avinay [Chicago U.]↗
DOE OSTI2026

Alcock-Paczynski Blinding Scheme for the Ly-$α$ Forest Analysis

We present and validate a blinding method for the Lyman-$α$ (Ly$α$) forest analysis based on a modification of the Alcock-Paczynski test. In order to hide the background expansion history, the method employs a geometrical shift of each quasar (QSO) forest in wavelength space, once the quasar continuum has been fitted and the fluctuation field is extracted. The redshift positions for the QSO sample are also changed in a consistent manner. We show that the method remains effective when applied to real data, where contamination from metals and Lyman-$β$ is intrinsically mixed with the Lyman-$α$ forest. This limitation is primarily visible in the 1D correlation function, where other blinding strategies can mitigate the effect. To assess its effectiveness, the prescription is tested against a series of datasets of increasing complexity: from idealized low-noise mocks, to realistic DESI year one synthetic datasets, and finally to data from DESI first data release (DR1), using both the auto (Ly$α\times$Ly$α$) and cross (Ly$α\times$ QSO) correlations. We find that the method robustly shifts the BAO peak position from the 3D correlation functions to the expected value for cosmology changes of around 5% in the matter content, without altering the shape of the posteriors in the model parameters. In conclusion, this catalog-level blinding strategy is a viable method for cosmological inference with the Lyman-$α$ forest, particularly if a cross-analysis with other tracers using the same blinding strategy is pursued.

Perez-Sanchez, G. [Guanajuato U.] (ORCID:000900096↗
DOE OSTI2021

Cinema:Snap: Real-time tools for analysis of dynamic diamond anvil cell experiment data

We report we developed tools and a workflow for real-time analysis of data from dynamic diamond anvil cell experiments performed at user light sources. These tools allow users to determine the phases of matter observed during the compression of materials in order to make decisions during an experiment to improve the quality of experimental results and maximize the use of scarce experimental facility time. The tools fill a gap in dynamic compression data analysis tools that are real-time, are flexible to the needs of high-pressure scientists, connect to automated processing of results, can be easily incorporated into workflows with existing tools and data formats, and support remote experimental data analysis workflows. Specific analytics developed include novel automated two-peak analysis for overlapping peaks and multiple phases, coordinated views of pressure and temperature values, full-compression contour plots, and configurable views of integrated x-ray diffraction. We present an experimental use case to show how the tools produce real-time analytics that help the scientists revise parameters for the next compression

47 OTHER INSTRUMENTATION↗
NASA NTRS1999

Simulation of a Real-Time Local Data Integration System over East-Central Florida

The Applied Meteorology Unit (AMU) simulated a real-time configuration of a Local Data Integration System (LDIS) using data from 15-28 February 1999. The objectives were to assess the utility of a simulated real-time LDIS, evaluate and extrapolate system performance to identify the hardware necessary to run a real-time LDIS, and determine the sensitivities of LDIS. The ultimate goal for running LDIS is to generate analysis products that enhance short-range (less than 6 h) weather forecasts issued in support of the 45th Weather Squadron, Spaceflight Meteorology Group, and Melbourne National Weather Service operational requirements. The simulation used the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) software on an IBM RS/6000 workstation with a 67-MHz processor. This configuration ran in real-time, but not sufficiently fast for operational requirements. Thus, the AMU recommends a workstation with a 200-MHz processor and 512 megabytes of memory to run the AMU's configuration of LDIS in real-time. This report presents results from two case studies and several data sensitivity experiments. ADAS demonstrates utility through its ability to depict high-resolution cloud and wind features in a variety of weather situations. The sensitivity experiments illustrate the influence of disparate data on the resulting ADAS analyses.

Case, Jonathan↗
NASA NTRS1989

Application of a PC based, real-time, data-aquisition system in rotorcraft wind-tunnel testing

Data has been acquired for a rotocraft test in the NASA Langley Transonic Dynamics Tunnel using a desktop data acquisition system. The system, which consists of an IBM Personal Computer AT (PC-AT) and an Omega Engineering OM-900 Stand-Alone Interface System, is well suited for acquiring high speed data on a limited number of channels. The data acquisition system and the interrupt driven software which provides the capability for near real-time cyclic data acquisition as well as data storage and display are described.

Wilbur, Matthew L.↗
DOE OSTI2014

Map Matching and Real World Integrated Sensor Data Warehousing (Presentation)

The inclusion of interlinked temporal and spatial elements within integrated sensor data enables a tremendous degree of flexibility when analyzing multi-component datasets. The presentation illustrates how to warehouse, process, and analyze high-resolution integrated sensor datasets to support complex system analysis at the entity and system levels. The example cases presented utilizes in-vehicle sensor system data to assess vehicle performance, while integrating a map matching algorithm to link vehicle data to roads to demonstrate the enhanced analysis possible via interlinking data elements. Furthermore, in addition to the flexibility provided, the examples presented illustrate concepts of maintaining proprietary operational information (Fleet DNA) and privacy of study participants (Transportation Secure Data Center) while producing widely distributed data products. Should real-time operational data be logged at high resolution across multiple infrastructure types, map matched to their associated infrastructure, and distributed employing a similar approach; dependencies between urban environment infrastructures components could be better understood. This understanding is especially crucial for the cities of the future where transportation will rely more on grid infrastructure to support its energy demands.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗