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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Facets of hydro power and future trends in a Nordic Context

Hydropower technologies bolster high penetration of variable renewable energies (VREs) in the net zero emissions scenarios. Nevertheless, there are various challenges to meeting the ambitious goal, such as stability, reliability, resiliency, security, lack of reactive power, voltage support and inertia, large-scale storage deployment and coordination, interconnectedness, demand-side response, higher thermal cycles with increased start/stops, and inadequate Levelized Cost of Energy (LCOE) for system-wise VRE integration and profitability. This survey conducts a bottom-up analysis to unveil the opportunities to utilize hydropower facilities and disentangle the nested problem for intertwining design features, control algorithms, operation, optimization approaches, incentives, services, and market mechanisms using a three-pillar framework perspective: grid owners, power producers, and machine designers. The survey identified emerging trends in real-time and capacity markets, flexible power systems, and enhanced grid capabilities, including advanced voltage support and updated grid codes. These developments present significant opportunities for hydropower, such as achieving super-flexibility through hybridization, expanded reactive power capabilities, and advanced operational modes like a synchronous condenser and power adequator functionalities. These opportunities require novel design philosophies — including new winding, stator, and rotor configurations, optimized ventilation, and active cooling systems — to enhance performance under stressed grid and climate conditions. Finally, integrating climate and energy models for multi-basin optimization with finer spatial and temporal granularity enhances the planning accuracy for water management of hydropower while addressing environmental challenges. The review delivers helpful prospective suggestions and tools that would serve researchers, power engineers, and stakeholders in making decisions about hydropower technologies and services in 2050 and beyond.

13 HYDRO ENERGY↗
DOE OSTI2021

Towards robust autonomous impedance spectroscopy analysis: A calibrated hierarchical Bayesian approach for electrochemical impedance spectroscopy (EIS) inversion

Distribution-based analyses, such as the distribution of relaxation times (DRT) and the distribution of diffusion times (DDT), present model-free alternatives to equivalent circuit modeling for analysis of electrochemical impedance spectroscopy (EIS) data. However, reconstructing such distributions from noisy impedance data is an ill-posed problem that must be solved with specialized inversion algorithms, requiring careful control and tuning. Furthermore, most inversion algorithms developed to date can only solve problems of limited complexity. Herein, we present a new hierarchical Bayesian method for EIS inversion, leveraging efficient algorithms for optimization and Hamiltonian Monte Carlo (HMC) sampling to solve models of arbitrary complexity. We overcome the challenge of ad-hoc parameter tuning by encoding intrinsic characteristics of the DRT and DDT into flexible prior distributions and “pre-calibrating” the model to simulated data. This approach is versatile, highly robust to noise, and provides quantitative estimates of both the error structure of the data and the uncertainty in the recovered distributions. The model is validated with simulated data to demonstrate accurate recovery of the DRT and the DDT. The method also shows promise for simultaneous recovery of multiple distributions, raising the intriguing possibility of semi-autonomous EIS analysis and ad-hoc model construction. Finally, the practical utility of the method is illustrated with experimental data. Throughout, we draw comparisons to several recently published EIS inversion methodologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Design and optimization of a modular hydrogen-based integrated energy system to maximize revenue via nuclear-renewable sources

Here, this paper demonstrates a novel modular distributed framework that uses optimal energy-dispatching strategies to enable greater flexibility and profitability in nuclear-renewable integrated energy systems (NR-IES). Hydrogen is used as a commodity in this framework since its production can improve grid stability and system operational flexibility, decarbonize heavy industry, and create an additional revenue stream for electricity generators, particularly nuclear power plants with high operational expenses. The proposed solution addresses the challenges associated with merging multiple software and services from various domains by using functional mock-up units (FMU) to co-simulate diverse subsystems designed in various platforms. The tightly coupled integrated energy system (IES) is optimized to maximize revenue by utilizing the deep reinforcement learning (DRL) technique to make smart dispatching decisions based on variable electricity prices and the availability of renewable energy. Proximal policy optimization (PPO) algorithm is used in training and testing the DRL agent. Over a period of 120 days, the proposed hydrogen-based IES framework showed about 10% revenue boost compared to a non-hydrogen generating baseline IES while also providing an easily-adoptable framework which can help to improve the flexibility of future generation nuclear power plants.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO 2 – Reservoir oil minimum miscibility pressure prediction

Carbon dioxide (CO 2 ) injection into oil reservoirs is considered a mature enhanced oil recovery (EOR) technique for conventional reservoirs. The local displacement efficiency of the CO 2 -EOR process is highly dependent on the minimum miscibility pressure (MMP), estimating this parameter is critical to design of the CO 2 injection process. Traditional empirical methods to test the CO 2 -oil MMP are time consuming and expensive; derived correlations are fast but not accurate. Therefore, an efficient and reliable method to determine MMP is beneficial. Here in this study, a mixed kernels function (MKF) based support vector regression (SVR) model was developed and used to predict the MMP for both pure and impure CO 2 injection cases. Four parameters were chosen as input parameters: (1) reservoir temperature; (2) average critical temperature; (3) molecular weight of pentane plus (C 5+ ) fraction of crude oil, and; (4) the ratio of volatile components to intermediate components in crude oil. MMP was selected as the desired output parameter to train and test this newly developed model. The performance of basic kernels function based SVR model is compared with that of this newly developed MKF-SVR model. The well-trained MFK-SVR was compared with three well-established published correlations, demonstrated the highest correlation coefficient (R of 0.9381), lowest root mean square error (RMSE of 1.9151), smallest average absolute error (AAE of 1.1406) and maximum absolute error (MAE of 4.6291). We believe that the proposed MFK-SVM model is a more reliable and stable regression model to predict MMP. In addition, a sensitivity analysis was conducted to evaluate the physical correctness. It indicates that the predicted results from the newly developed model are in excellent agreement with previous empirical work.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Estimating QSVT angles for matrix inversion with large condition numbers

Quantum Singular Value Transformation (QSVT) is a state-of-the-art, near-optimal quantum algorithm that can be used for matrix inversion. The QSVT circuit is parameterized by a sequence of angles that must be pre-calculated classically, with the number of angles increasing as the matrix condition number grows. Computing QSVT angles for ill-conditioned problems is a numerically challenging task. Here, we propose a numerical technique for estimating QSVT angles for large condition numbers. This technique allows one to avoid expensive numerical computations of QSVT angles and to emulate QSVT circuits for solving ill-conditioned problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

An efficient level set method for tracking many materials

Here, we present an efficient level set method to track an arbitrary number of materials. The algorithm is optimal in the sense that it only needs to store a single unsigned distance-like function and a single integer indicator function, independent of the number of materials or distinct regions being tracked. Furthermore, for smooth velocity fields and smooth interface shape, arbitrarily high order solutions can be demonstrated. For interfaces that are or become kinked, the solution is limited to second-order convergence rates in the L 1 norm and first-order in the L ∞ norm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A method to calibrate measurement instruments to optimize the spectrometer optics for experiment E94-107 at JLab

A method to calibrate measurement instruments through the fulfillment of physical laws is described. This method is particularly well suited to determine and/or improve magnetic spectrometer optics databases as well as to establish the best resolution achievable with them. Here, this method was applied to obtain the best resolution achievable in the excitation and binding energy spectra of several hypernuclei produced in the experiment E94-107 performed at JLab, allowing us to obtain sub-MeV resolutions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Efficient Fresnel zoneplate pattern data preparation for high-resolution nanofabrication

A Fresnel zoneplate is a diffractive optical element consisting of concentric rings (zones) for which the transmitted light produces a focal spot that is used in all wavelength regimes, including X-rays. The pattern of transmission openings determines the location of the spot and the sub-half wavelength size of the openings can adjust the intensity. Today, very general transmission zoneplate patterns are used for many special imaging and image compensation purposes. Manufacturing zoneplates require a zoneplate pattern file, which precisely describes the size, shape, and contour of the rings based on the desired optical properties of the lens. Generating such a pattern requires the delicate balance of achieving the required optical performance while maintaining manageable file sizes and computation times. Here we describe a new algorithm meeting these needs. In conclusion, by precisely controlling the number of shapes in each zone, the algorithm simultaneously optimizes the desired optical tolerances with the pattern file size.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Real time operation of high-capacity electric vehicle ridesharing fleets

We study the feasibility of using electric vehicles in online, high-capacity ridepooling systems. Prior work has shown that online algorithms perform well for centrally-controlled, high-capacity ridepool systems. First, we propose a mixed integer linear program to expand past algorithms on ridepooling to electric vehicle fleets with the objective of scheduling vehicle charging to maintain sufficient fleet sizes at various times of day. Then we show a faster, scalable algorithm with similar performance that is practical for full-scale systems. Furthermore, our contributions reinforce the importance of having knowledge and estimates of future demand even when operating in the online setting.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Universal Nuclear Accident Dosimeter

The Lawrence Livermore National Laboratory (LLNL) Universal Nuclear Accident Dosimetry (UNAD) project is a four-year initiative aimed at advancing nuclear accident dosimetry methods. This article presents an overview of the research, key findings, and the progress made throughout the project. The primary goals included a background into the history of nuclear accident dosimetry, consolidating current dosimetry techniques within the NNSA/DOE complex, fostering collaboration among subject matter experts, and exploring novel technologies for potential implementation. The technical focus centered on investigating new and novel technologies, instrumentation methods, and analysis methods to develop recommendations for a potential nuclear accident dosimeter (NAD) to be universally deployed through the DOE complex. A multilaboratory and multinational Usergroup was established, conducting periodic meetings to facilitate knowledge exchange. The UNAD team has participated in two international nuclear accident dosimetry intercomparison exercises and one characterization exercise, where the existing LLNL NAD and a prototype alanine electron paramagnetic dosimeter NAD were deployed. Ongoing improvements are being made to the prototype NAD based on results from the exercises, laboratory studies, and collaboration with other laboratories. A machine learning algorithm to optimize the geometry and conversion factors of the current LLNL NAD is being implemented, and the resulting design will be tested in the next exercise. In conclusion, key lessons learned and future directions for the project are discussed.

Electron paramagnetic resonance spectroscopy↗
DOE OSTI2020

Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line

Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs but attempts to combine different algorithms and optimize them on large datasets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in identification of 540 SAAVs in the mass spectrometry data, the most comprehensive coverage yet reported. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53 and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Southern Ocean Cloud Properties Derived From CAPRICORN and MARCUS Data

The properties of Southern Ocean (SO) liquid phase non precipitating clouds (hereafter clouds) are examined using shipborne data collected during the Measurements of Aerosols, Radiation and Clouds over the Southern Ocean and the Clouds Aerosols Precipitation Radiation and atmospheric Composition Over the SoutheRN ocean I and II campaigns that took place south of Australia during Autumn 2016 and Summer 2017–2018. Cloud properties are derived using data from W-band radars, lidars, and microwave radiometers using an optimal estimation algorithm. The SO clouds tended to have larger liquid water paths (LWP, 115 ± 117 g m -2 ), smaller effective radii (re, 8.7 ± 3 μm), and higher number concentrations (N d , 90 ± 107 cm-3) than typical values of eastern ocean basin stratocumulus. The clouds demonstrated a tendency for the LWP to increase with Nd presumably due to precipitation suppression up to Nd of approximately 100 cm -3 when mean LWP decreased with increasing Nd. Due to higher optical depth, cloud albedos were less susceptible to changes in Nd compared to subtropical stratocumulus. The highest latitude clouds of the datasets, observed along and near the Antarctic coast, presented a distinctly bimodal character. One mode had the properties of marine clouds further north. Additionally, the other mode occurred in an aerosol environment characterized by high cloud condensation nuclei concentrations and elevated sulfate aerosol without obvious continental aerosol markers. These regions of higher cloud condensation nuclei tended to have higher Nd, smaller re and higher LWP suggesting sensitivity of cloud properties to seasonal biogenic aerosol production in the high latitude SO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Cost function dependent barren plateaus in shallow parametrized quantum circuits

Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized quantum circuit V(θ) to minimize a cost function C. While VQAs may enable practical applications of noisy quantum computers, they are nevertheless heuristic methods with unproven scaling. Here, we rigorously prove two results, assuming V(θ) is an alternating layered ansatz composed of blocks forming local 2-designs. Our first result states that defining C in terms of global observables leads to exponentially vanishing gradients (i.e., barren plateaus) even when V(θ) is shallow. Hence, several VQAs in the literature must revise their proposed costs. On the other hand, our second result states that defining C with local observables leads to at worst a polynomially vanishing gradient, so long as the depth of V(θ) is Ο(logn). Our results establish a connection between locality and trainability. We illustrate these ideas with large-scale simulations, up to 100 qubits, of a quantum autoencoder implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.

Wang, Xin↗
DOE OSTI2016

Coherent diffraction of single Rice Dwarf virus particles using hard X-rays at the Linac Coherent Light Source

Single particle diffractive imaging data from Rice Dwarf Virus (RDV) were recorded using the Coherent X-ray Imaging (CXI) instrument at the Linac Coherent Light Source (LCLS). RDV was chosen as it is a well-characterized model system, useful for proof-of-principle experiments, system optimization and algorithm development. RDV, an icosahedral virus of about 70 nm in diameter, was aerosolized and injected into the approximately 0.1 μm diameter focused hard X-ray beam at the CXI instrument of LCLS. Diffraction patterns from RDV with signal to 5.9 Ångström were recorded. Here, the diffraction data are available through the Coherent X-ray Imaging Data Bank (CXIDB) as a resource for algorithm development, the contents of which are described here.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Extracting the turbulent flow-field from beam emission spectroscopy images using velocimetry

The 2D turbulent E × B flow-field is inferred from density fluctuation images obtained with the beam emission spectroscopy diagnostic on DIII-D using the orthogonal dynamic programming velocimetry algorithm. A synthetic turbulence model is used to test the algorithm and optimize it for measuring zonal flows. Zonal flow measurements are found to require a signal-to-noise ratio above ~10 and a zonal flow wavelength longer than ~2 cm. Comparison between the velocimetry-estimated flow-field and the E × B flow-field using a nonlinear gyrokinetic GENE simulation finds that the flow-fields have identical spatial structure and differ only by the mean turbulence phase velocity, which is spatially uniform in this flux tube simulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

In this paper we discuss recent advances in deep convolutional neural networks (CNN) for sequence learning, which allow identifying long-range, multi-scale phenomena in long sequences, such as those found in fusion plasmas. We point out several benefits of these deep CNN architectures, such as not requiring experts such as physicists to hand-craft input data features, the ability to capture longer range dependencies compared to the more common sequence neural networks (recurrent neural networks like long short-term memory (LSTM) networks), and the comparative computational efficiency. We apply this neural network architecture to the popular problem of disruption prediction in fusion energy tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. Initial results trained on a large ECEi dataset show promise, achieving an F 1 -score of ~91% on individual time-slices using only the ECEi data. This indicates the ECEi diagnostic by itself can be sensitive to a number of pre-disruption markers useful for predicting disruptions on timescales not only for mitigation but also avoidance. Future opportunities for utilizing these deep CNN architectures with fusion data are outlined, including impact of recent upgrades to the ECEi diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗