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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quantifying Uncertainty in All-to-All Estimates of Space Object Conjunction Probabilities using U-Statistics

Predicting space object conjunctions is inherently probabilistic due to initial state and orbit model uncertainty. A commonly considered Monte Carlo estimator of the conjunction probability is the ’all-to-all’ estimator. Given independent random samples of the trajectories of both objects, the estimator is the percentage of all pairs of trajectories that result in a conjunction. Intuitively, the all-to-all estimator is the best possible estimator of the conjunction probability since it considers all pairs of Monte Carlo samples. However, its distribution is not available in closed-form, which limits its use in practice and makes this intuition difficult to make rigorous. In this paper, the all-to-all estimator is identified as a U-statistic, which implies that it has several favorable properties. Specifically, the estimator is the minimum variance unbiased estimator of the conjunction probability and is asymptotically Gaussian distributed. An approximate confidence interval for the conjunction probability is obtained from an estimate of the asymptotic Gaussian distribution. We show how to efficiently compute the confidence interval and demonstrate that the interval has the nominal coverage level. The confidence intervals are also seen to be narrower than those based on the commonly-used each-to-each estimator. Furthermore, the all-to-all estimator is shown to allow different Monte Carlo sample sizes, whereas the each-to-each estimator requires equal sample sizes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Sensitivity and Uncertainty of the IFR-1 BISON Benchmark

The fuel performance code BISON is being used to evaluate metallic fuel for a new fast-spectrum test reactor called the Versatile Test Reactor, which is being considered by the US Department of Energy. To quantify the accuracy of BISON predictions, researchers at Oak Ridge National Laboratory have been developing a series of benchmarks based on legacy metallic fuel experiments. As part of this effort, the sensitivity of BISON predictions to variations in model inputs and the uncertainties associated with BISON predictions must be established. This report summarizes efforts to perform a comprehensive sensitivity analysis (SA) and uncertainty quantification (UQ) on a benchmark based on the IFR-1 experiment. For the SA, at least one input was chosen from every BISON model and physics module used in the benchmark. The inputs were varied individually in a series of BISON simulations. The resulting variations in benchmark predictions were normalized to calculate sensitivities. The strongest sensitivities were identified and used to inform input selections for the UQ. The UQ was performed using the Monte Carlo UQ method. A literature review was conducted to estimate uncertainty distributions for the selected inputs, and values were sampled randomly from each distribution in a series of BISON simulations. Variations in the benchmark predictions were used to estimate uncertainty distributions and confidence intervals. It was found that nearly 100% of benchmark predictions matched the corresponding legacy values within the confidence intervals. However, this is at least partially because of the wide confidence intervals associated with the benchmark predictions. The uncertainty contributions of assumptions in the benchmark, experimental uncertainties, and BISON models were quantified. Some analysis was performed to identify inputs that contributed to the uncertainties. Finally, recommendations are made for future benchmark development and future BISON development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Monte Carlo Transport: Computational Physics Summer Workshop [Slides]

In general, Monte Carlo methods simulate large numbers of random trials in order to observe numerical behavior of systems described by probabilistic behavior. In radiation transport, pseudo-random number generators are used to randomly sample individual particle lives. Information about the particles are tallied.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Simultaneous Discovery of Positive and Negative Interactions Among Rhizosphere Bacteria Using Microwell Recovery Arrays

Understanding microbe-microbe interactions is critical to predict microbiome function and to construct communities for desired outcomes. Investigation of these interactions poses a significant challenge due to the lack of suitable experimental tools available. Here we present the microwell recovery array (MRA), a new technology platform that screens interactions across a microbiome to uncover higher-order strain combinations that inhibit or promote the function of a focal species. One experimental trial generates 10 4 microbial communities that contain the focal species and a distinct random sample of uncharacterized cells from plant rhizosphere. Cells are sequentially recovered from individual wells that display highest or lowest levels of focal species growth using a high-resolution photopolymer extraction system. Interacting species are then identified and putative interactions are validated. Using this approach, we screen the poplar rhizosphere for strains affecting the growth of Pantoea sp. YR343, a plant growth promoting bacteria isolated from Populus deltoides rhizosphere. In one screen, we montiored 3,600 microwells within the array to uncover multiple antagonistic Stenotrophomonas strains and a set of Enterobacter strains that promoted YR343 growth. The later demonstrates the unique ability of the platform to discover multi-membered consortia that generate emergent outcomes, thereby expanding the range of phenotypes that can be characterized from microbiomes. This knowledge will aid in the development of consortia for Populus production, while the platform offers a new approach for screening and discovery of microbial interactions, applicable to any microbiome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A fast Monte Carlo cell-by-cell simulation for radiobiological effects in targeted radionuclide therapy using pre-calculated single-particle track standard DNA damage data

Introduction: We developed a new method that drastically speeds up radiobiological Monte Carlo radiation-track-structure (MC-RTS) calculations on a cell-by-cell basis. Methods: The technique is based on random sampling and superposition of single-particle track (SPT) standard DNA damage (SDD) files from a “pre-calculated” data library, constructed using the RTS code TOPAS-nBio, with “time stamps” manually added to incorporate dose-rate effects. This time-stamped SDD file can then be input into MEDRAS, a mechanistic kinetic model that calculates various radiation-induced biological endpoints, such as DNA double-strand breaks (DSBs), misrepairs and chromosomal aberrations, and cell death. As a benchmark validation of the approach, we calculated the predicted energy-dependent DSB yield and the ratio of direct-to-total DNA damage, both of which agreed with published in vitro experimental data. We subsequently applied the method to perform a superfast cell-by-cell simulation of an experimental in vitro system consisting of neuroendocrine tumor cells uniformly incubated with 177 Lu. Results and discussion: The results for residual DSBs, both at 24 and 48 h post-irradiation, are in line with the published literature values. Our work serves as a proof-of-concept demonstration of the feasibility of a cost-effective “in silico clonogenic cell survival assay” for the computational design and development of radiopharmaceuticals and novel radiotherapy treatments more generally.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Coreset Clustering on Small Quantum Computers

Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging.

42 ENGINEERING↗
DOE OSTI2023

A Study of the Properties and Dynamics of the Disk of Satellites in a Milky-Way-like Galaxy System

The dynamics of the satellite systems of Milky-Way-like galaxies offer a useful means by which to study the galaxy formation process in the cosmological context. It has been suggested that the currently observed anisotropic distribution of the satellites in such galaxy systems is inconsistent with the concordance ΛCDM cosmology model on the galactic scale if the observed satellites are random samples of the dark matter (DM) sub-halos that are nearly isotropically distributed around the central galaxy. In this study, we present original high-resolution zoom-in studies of central galaxies and satellite systems based upon initial conditions for the DM distribution from the Aquarius simulations but with substantial high-resolution baryon physics added. We find that the galaxy most like the Milky Way in this study does indeed contain a disk of satellites (DOS). Although one galaxy DOS system does not answer the question of how common such disks are, it does allow the opportunity to explore the properties and dynamics of the DOS system. Our investigation centers on the spatial arrangement (distances, angles, etc.) of satellites in this Milky-Way-like galaxy system with a specific emphasis on identifying and analyzing the disk-like structure along with its dynamical and morphological properties. Among the conclusions from this study, we find that the satellites and DM sub-halos in the galaxy simulations are anisotropically distributed. The dynamical properties of the satellites, however, indicate that the direction of the angular momentum vector of the whole satellite system is different from the normal direction of the fitted DOS and from the normal direction of the velocity dispersion of the system. Hence, the fitted DOS appears to be comprised of infalling sub-halos and is not a rotationally supported system.

Zhao, Xinghai↗
DOE OSTI2024

Long-term Evolution of Relativistic Unmagnetized Collisionless Shocks

Abstract We study a relativistic collisionless electron–positron shock propagating into an unmagnetized ambient medium using 2D particle-in-cell simulations of unprecedented duration and size. The shock generates intermittent magnetic structures of increasingly larger size as the simulation progresses. Toward the end of our simulation, at around 26,000 plasma times, the magnetic coherence scale approaches λ ∼ 100 plasma skin depths, both ahead and behind the shock front. We anticipate a continued growth of λ beyond the time span of our simulation, as long as the shock accelerates particles to increasingly higher energies. The post-shock field is concentrated in localized patches, which maintain a local magnetic energy fraction ε B ∼ 0.1. Particles randomly sampling the downstream fields spend most of their time in low field regions ( ε B ≪ 0.1) but emit a large fraction of the synchrotron power in the localized patches with strong fields ( ε B ∼ 0.1). Our results have important implications for models of gamma-ray burst afterglows.

Astronomy & Astrophysics↗
DOE OSTI2026

Testing Classical Properties from Quantum Data

Many properties of Boolean functions can be tested far more efficiently than the function itself can be learned. However, this dramatic advantage often disappears when testers are limited to random samples of ƒ instead of adaptively chosen queries to f. In this work we investigate the quantum version of this restriction: quantum algorithms that test properties of a Boolean function f solely from copies of either the function state |ƒ⟩ ∝ ∑ x |x, ƒ(x)⟩ or the phase state |(-1) ƒ ⟩ ∝ ∑ x (-1) ƒ(x) |x⟩. For monotonicity, symmetry, and triangle-freeness, we show passive quantum testers are unboundedly or super-polynomially better than their classical passive testing counterparts. They are competitive with classic query -based testers in each case. Our new testers use techniques beyond quantum Fourier sampling, and it turns out this is necessary: we show a certain class of bent functions can be tested from 𝒪(1) function states but has a sample complexity lower bound of 2 Ω(n) for any tester relying exclusively on Fourier and classical samples. Our passive quantum testers are competitive with classical query -based testers, but this isn't universal: we exhibit a testing problem that can be solved from 𝒪(1) classical queries but requires Ω(2 n/2 ) function state copies. The Forrelation problem provides a separation of the same magnitude in the opposite direction, so we conclude that quantum data and classical queries are "maximally incomparable" resources for testing. We also begin the study of lower bounds for testing from quantum data. For quantum monotonicity testing, we prove that the ensembles of [Goldreich et al., 2000; Black, 2024], which give exponential lower bounds for classical sample-based testing, do not yield any nontrivial lower bounds for testing from quantum data. New insights specific to quantum data will be required for proving copy complexity lower bounds for testing in this model.

Boolean Functions↗
DOE OSTI2023

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A parcel-level evaluation of distributed wind opportunity in the contiguous United States

This study examines the potential for distributed wind (DW) energy across the contiguous United States, leveraging advancements in the National Renewable Energy Laboratory's distributed wind model, dWind. The novel modeling approach described here utilizes a high-resolution dataset and analyzes over 150 million parcels, a significant improvement from prior methods that extrapolated results from a smaller random sample. This achievement is enabled through key model performance improvements, such as transitioning to multiprocessing, which reduces runtime by 97 %. This optimized, high-resolution approach allows the inspection of technology deployment potential and impact on a variety of scales tailored to individual properties and regions. The results here align with prior work showing substantial opportunity for energy generation using DW technologies. Key findings reveal a substantial increase from prior results in estimated technical and economic potential for DW. Metrics tuned to highlight economic potential also show increased incentives supporting rural adoption. Results are spatially aggregated for usability and published via the U.S. Department of Energy Wind Data Portal and a custom scenario visualization platform, aiding policymakers, industry, and property owners in assessing DW viability across various scenarios and spatial scales.

17 WIND ENERGY↗
DOE OSTI2021

State of Stress Uncertainty Quantification and Geomechanical Risk Analysis for Subsurface Engineering

All subsurface stress measurement methods available for deep subsurface applications are indirect and involve a significant amount of uncertainty. Quantifying these uncertainties is critical for applications where the state of stress is a component of a risk analysis with significant safety, environmental, and economic considerations. A publicly available and open source tool called the State of Stress Analysis Tool (SOSAT) has been developed to aid in performing a Bayesian uncertainty quantification and geomechanical risk analysis using many types of commonly available data. A new feature of this tool is using data from the FutureGen 2.0 site. The new feature of the tool was uses information about the absence or presence of borehole breakouts to constrain the state of stress. This new feature allows the user to specify probability distributions for the relevant parameters such as the drilling mud pressure, temperature, and the formation strength properties. The tool computes the posterior joint probability distribution of the horizontal principal stresses. Using random samples from this posterior distribution the tool then computes the probability of activating a critically oriented fault at a range of pore pressures. This latter feature can be used to aid in the assessment of the risk of induced seismicity and leakage in geologic carbon sequestration and enhanced geothermal systems.

Burghardt, Jeffrey A.↗
DOE OSTI2022

Evolving Metrics for Resource Adequacy Assessment

Resource adequacy analysis quantifies the likelihood of capacity shortfall on a power system in a probabilistic manner. Using a combination of statistical techniques and power system fundamentals, the analysis typically evaluates hundreds or thousands of stochastic random samples (replications) of varying load, generator outages, variable renewable energy availability, and other aspects of power system uncertainty. In this range of uncertainty, there are - at times - periods where the power system's available resources are insufficient to meet system demand, referred to as a shortfall event. Today's power systems' rapidly evolving generation mix is changing the types of data needed by system planners and regulators, which can often render traditional resource adequacy metrics insufficient for ensuring resource adequacy for tomorrow's grid. In this paper we provide a critical assessment of traditional measures of shortfall risk in power systems, discussing their shortcomings and how they compare to metrics used in other domains. From this analysis we propose four steps forward for improving power system resource adequacy risk metrics in the future.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗
DOE OSTI2023

Beyond Expected Values Evolving Metrics for Resource Adequacy Assessment

Resource adequacy analysis quantifies the likelihood of capacity shortfall on a power system in a probabilistic manner. Using a combination of statistical techniques and power system fundamentals, the analysis typically evaluates hundreds or thousands of stochastic random samples (replications) of varying load, generator outages, variable renewable energy availability, and other aspects of power system uncertainty. In this range of uncertainty, there are - at times - periods where the power system's available resources are insufficient to meet system demand, referred to as a shortfall event. Today's power systems' rapidly evolving generation mix is changing the types of data needed by system planners and regulators, which can often render traditional resource adequacy metrics insufficient for ensuring resource adequacy for tomorrow's grid. In this paper we provide a critical assessment of traditional measures of shortfall risk in power systems, discussing their shortcomings and how they compare to metrics used in other domains. From this analysis we propose four steps forward for improving power system resource adequacy risk metrics in the future.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Method and apparatus for constructing informative outcomes to guide multi-policy decision making

In Multi-Policy Decision-Making (MPDM), many computationally-expensive forward simulations are performed in order to predict the performance of a set of candidate policies. In risk-aware formulations of MPDM, only the worst outcomes affect the decision making process, and efficiently finding these influential outcomes becomes the core challenge. Recently, stochastic gradient optimization algorithms, using a heuristic function, were shown to be significantly superior to random sampling. In this disclosure, it was shown that accurate gradients can be computed-even through a complex forward simulation—using approaches similar to those in dep networks. The proposed approach finds influential outcomes more reliably, and is faster than earlier methods, allowing one to evaluate more policies while simultaneously eliminating the need to design an easily-differentiable heuristic function.

Olson, Edwin↗
DOE OSTI2023

Machine Learning for Advanced Building Construction: Preprint

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)- enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

building retrofits↗
DOE OSTI2023

Machine Learning for Advanced Building Construction

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

build scans↗
DOE OSTI2022

An influence of manufacturing tolerances on Pin-Cell k-infinity of MOX fuel using data from the FUBILA experiment program

An influence of manufacturing tolerances (MTs) was evaluated on the pin-cell k-infinity of MOX fuel through the random sampling of CASMO5 calculations. The data from the FUBILA experiment program was used as the manufacturing parameters. For the uncertainties of element/isotope mass fractions, their covariance matrices were calculated by the generalized least square method. The total k-infinity uncertainty was 120-250 pcm (percent mille). From the breakdown of k-infinity uncertainty per materials, the MTs of fuel pellet had a dominant influence. The individual influences were also evaluated for element/isotope mass fractions, an inner/outer diameter, and a density. Those of element/isotope mass fractions were less than a few dozen pcm. Since the perturbations of fuel pellet diameter and density caused large variations to the total amount of heavy metals and effect of spatial self-shielding, they had a negative correlation with the k-infinity perturbation. On the AG3 (Al-Mg alloy) over-cladding, the inner/outer diameter perturbations had both negative and positive correlations. The negative one was due to decrease in amount of light water and the positive one was due to increase in amount of AG3. Since the effect of neutron slowing-down by light water had a dominant influence, the influence of the former negative correlation was larger than that of the latter positive correlation. It is, therefore, concluded that the consideration of the MTs that had a large influence on the effect of neutron slowing-down is important for precise quantification of the pin-cell k-infinity uncertainty of MOX fuel. (author)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗