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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. As a result, a case study of several real-world distribution systems is presented to validate the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

A Novel Statistical Method for Measuring the Temperature–Density Relation in the IGM Using the b – N Hi Distribution of Absorbers in the Ly α Forest

We propose a new method for determining the thermal state of the intergalactic medium based on Voigt profile decomposition of the Lyα forest. The distribution of Doppler parameter and column density (b–N H i distribution) is sensitive to the temperature–density relation T = T 0 (ρ/ρ 0 ) γ-1 , and previous work has inferred T 0 and γ by fitting its low-b cutoff. This approach discards the majority of available data and is susceptible to systematics related to cutoff determination. We introduce an approach that exploits all information encoded in the b – N H i distribution by modeling its entire shape. We apply kernel density estimation to discrete absorption lines to generate model probability density functions, and then we use principal component decomposition to create an emulator that can be evaluated anywhere in thermal parameter space. We introduce a Bayesian likelihood based on these models enabling parameter inference via Markov Chain Monte Carlo. The method's robustness is tested by applying it to a large grid of thermal history simulations. By conducting 160 mock measurements, we establish that our approach delivers unbiased estimates and valid uncertainties for a 2D ( T 0 , γ) measurement. Furthermore, we conduct a pilot study applying this methodology to real observational data at z = 2. Using 200 absorbers, equivalent in path length to a single Lya forest spectrum, we measure $\mathrm{log}{T}_{0}={4.092}_{-0.055}^{+0.050}$ and $\gamma ={1.49}_{-0.074}^{+0.073}$ in excellent agreement with cutoff fitting determinations using the same data. Our method is far more sensitive than cutoff fitting, enabling measurements of log T 0 and γ with precision on $\mathrm{log}{T}_{0}$ (γ) nearly two (three) times higher for current data set sizes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Improvement of Drop‐Hammer Impact Testing for Safety Assessment of High Explosives Using 10‐mg Samples

Here, in this study, we established an improved method for drop-hammer impact testing of small quantities of high explosives (10 mg). We performed about seven hundred impact tests under various experimental conditions (e.g., sandpaper vs bare anvil, different sample masses, drop-weights, and striker diameters) to determine an optimal set of conditions and reaction detection methods (e.g., gas analysis, video, and sound recordings) that give the most statistically reliable results with 10 mg samples. We used both Frequentist and Bayesian statistical approaches to compare estimates of the drop height (DH50) that initiates a reaction 50% of the time, and to quantify the associated uncertainty. Gas analysis proved to be the most reliable reaction detection method, showing unambiguous rises in HE decomposition products (e.g., CO 2 ) even when the other indicators (e.g., sound, video) were inconclusive. The impact tests performed with a bare anvil showed much better reproducibility than those conducted with sandpaper, reducing the largest uncertainty observed in the data sets by a factor of 1.7. The DH 50 values obtained from three different sample masses (10, 20, and 35 mg) fell within the uncertainties of the measurements. We demonstrated the improved procedure (i.e., 10-mg samples, gas analysis, bare anvil, and Bayesian approach) on a variety of PETN samples having different surface areas and thermal histories.

PETN↗
DOE OSTI2024

Disentangling the Black Hole Mass Spectrum with Photometric Microlensing Surveys

Abstract From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens class of any single event. We investigate this method’s effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of 30 M ⊙ . On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ≈25%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

HyperSpace: Distributed Bayesian Hyperparameter Optimization

As machine learning models continue to increase in complexity, so does the potential number of free model parameters commonly known as hyperparameters. While there has been considerable progress toward finding optimal configurations of these hyperparameters, many optimization procedures are treated as black boxes. We believe optimization methods should not only return a set of optimized hyperparameters, but also give insight into the effects of model hyperparameter settings. To this end, we present HyperSpace, a parallel implementation of Bayesian sequential model-based optimization. HyperSpace leverages high performance computing (HPC) resources to better understand unknown, potentially non-convex hyperparameter search spaces. We show that it is possible to learn the dependencies between model hyperparameters through the optimization process. By partitioning large search spaces and running many optimization procedures in parallel, we also show that it is possible to discover families of good hyperparameter settings over a variety of models including unsupervised clustering, regression, and classification tasks.

Computer Science↗
DOE OSTI2021

Bayesian Monte Carlo Evaluation Framework for Imperfect Nuclear Data

Bayesian evaluation of resolved resonance region (RRR) nuclear data has historically been carried out using the generalized least squares (GLS) formalism, as implemented in, e.g., SAMMY. We have recently developed a prototype of Bayesian Monte Carlo (BMC) evaluation framework, implemented using a Markov Chain Monte Carlo (MCMC) method with a Metropolis-Hastings (MH) acceptance criterion. This was done in order to remove the approximations underlying the conventional GLS evaluations, namely, the linear approximation, and the approximation that all probability density functions (PDFs) are of the normal kind. Recent works by others have used similar stochastic approaches to quantify cross section uncertainties from ENDF evaluated co-variances, and/or, from integral benchmark data, but those have not been conceived as an evaluation framework like the one presented here.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Artificial intelligence for materials research at extremes

Abstract Materials development is slow and expensive, taking decades from inception to fielding. For materials research at extremes, the situation is even more demanding, as the desired property combinations such as strength and oxidation resistance can have complex interactions. Here, we explore the role of AI and autonomous experimentation (AE) in the process of understanding and developing materials for extreme and coupled environments. AI is important in understanding materials under extremes due to the highly demanding and unique cases these environments represent. Materials are pushed to their limits in ways that, for example, equilibrium phase diagrams cannot describe. Often, multiple physical phenomena compete to determine the material response. Further, validation is often difficult or impossible. AI can help bridge these gaps, providing heuristic but valuable links between materials properties and performance under extreme conditions. We explore the potential advantages of AE along with decision strategies. In particular, we consider the problem of deciding between low-fidelity, inexpensive experiments and high-fidelity, expensive experiments. The cost of experiments is described in terms of the speed and throughput of automated experiments, contrasted with the human resources needed to execute manual experiments. We also consider the cost and benefits of modeling and simulation to further materials understanding, along with characterization of materials under extreme environments in the AE loop. Graphical abstract AI sequential decision-making methods for materials research: Active learning, which focuses on exploration by sampling uncertain regions, Bayesian and bandit optimization as well as reinforcement learning (RL), which trades off exploration of uncertain regions with exploitation of optimum function value. Bayesian and bandit optimization focus on finding the optimal value of the function at each step or cumulatively over the entire steps, respectively, whereas RL considers cumulative value of the labeling function, where the latter can change depending on the state of the system (blue, orange, or green).

36 MATERIALS SCIENCE↗
NASA NTRS1992

Reliability evaluation methodology for NASA applications

Liquid rocket engine technology has been characterized by the development of complex systems containing large number of subsystems, components, and parts. The trend to even larger and more complex system is continuing. The liquid rocket engineers have been focusing mainly on performance driven designs to increase payload delivery of a launch vehicle for a given mission. In otherwords, although the failure of a single inexpensive part or component may cause the failure of the system, reliability in general has not been considered as one of the system parameters like cost or performance. Up till now, quantification of reliability has not been a consideration during system design and development in the liquid rocket industry. Engineers and managers have long been aware of the fact that the reliability of the system increases during development, but no serious attempts have been made to quantify reliability. As a result, a method to quantify reliability during design and development is needed. This includes application of probabilistic models which utilize both engineering analysis and test data. Classical methods require the use of operating data for reliability demonstration. In contrast, the method described in this paper is based on similarity, analysis, and testing combined with Bayesian statistical analysis.

Taneja, Vidya S.↗
NASA NTRS2003

Automated Monitoring with a BSP Fault-Detection Test

The figure schematically illustrates a method and procedure for automated monitoring of an asset, as well as a hardware- and-software system that implements the method and procedure. As used here, asset could signify an industrial process, power plant, medical instrument, aircraft, or any of a variety of other systems that generate electronic signals (e.g., sensor outputs). In automated monitoring, the signals are digitized and then processed in order to detect faults and otherwise monitor operational status and integrity of the monitored asset. The major distinguishing feature of the present method is that the fault-detection function is implemented by use of a Bayesian sequential probability (BSP) technique. This technique is superior to other techniques for automated monitoring because it affords sensitivity, not only to disturbances in the mean values, but also to very subtle changes in the statistical characteristics (variance, skewness, and bias) of the monitored signals.

Bickford, Randall L.↗
DOE OSTI2022

Uncertainty Quantification for Point Spread Functions in X-Ray Radiography

X-ray radiographers have tools for estimating the point spread function (PSF) which determines the extent of the blur in images. Knowledge of uncertainties in their estimations can be extremely valuable, and existing methods do not provide this information. We have developed a new technique using a Bayesian framework that will provide useful uncertainties to the estimated PSF.

point spread function↗
DOE OSTI2022

PyApprox: Enabling efficient model analysis

PyApprox is a Python-based one-stop-shop for probabilistic analysis of scientific numerical models. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent the most popular methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. Simple interfaces are provided for the most commonly-used algorithms to limit a user’s need to tune the various hyper-parameters of each algorithm. However, more advanced work flows that require customization of hyper-parameters is also supported. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of different algorithms for a wide range of model analyses. This paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in ground water.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System

The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, we, in this paper, propose a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

How to estimate soil organic carbon stocks of agricultural fields? perspectives using ex-ante evaluation

Estimating soil organic carbon (SOC) stocks of agricultural fields has a range of important applications from development of sustainable management practices to monitoring carbon stocks. There are many estimation strategies with the potential for more reliable estimates of SOC stock and more efficient use of soil sampling and analysis resources, especially by leveraging readily available auxiliary information such as remote sensing. However, concrete guidance for strategy selection is lacking. This study narrows this gap with a comparison of strategies for estimating deep SOC stock (0–60 cm) in a prototypical field. Using high density SOC stock measurements and simulation, we built on past studies by 1) ex-ante evaluating a large number of strategy options, 2) using a Bayesian approach to quantify the uncertainty of the comparison, and 3) considering multiple Bayesian models to assess sensitivity to this modeling choice. We found that, using readily available auxiliary information, both balanced and stratified sampling offer substantial improvements over simple random sampling. The auxiliary information most important for this improvement is a Sentinel-2 SOC index = blue / (green × red), followed by the topographic wetness index. We found that these results are robust to the choice of mapping method, but that there is uncertainty in the magnitude of improvement. Here, we recommend future studies implement this Bayesian approach for simulated ex-ante evaluation of SOC stock estimation strategies across more fields to investigate the generalizability of these findings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Uncertainty Quantification for a Multi-phase Carbon Equation of State Model

Many physics models have tunable parameters that are calibrated by matching the model output to experimental or calculated data. However, given that calibration data often contain uncertainty and that different model parameter sets might result in a very similar simulated output for a finite calibration data set, it is advantageous to provide an ensemble of parameter sets that are consistent with the calibration data. Uncertainty quantification (UQ) provides a means to generate such an ensemble in a statistically rigorous fashion. In this work, we perform UQ for a multi-phase equation of state (EOS) model for carbon containing the diamond, graphite, and liquid phases. We use a Bayesian framework for the UQ and introduce a novel strategy for including phase diagram information in the calibration. The method is highly general and accurately reproduces the calibration data without any material-specific prior knowledge of the EOS model parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Elastic Bayesian Model Calibration

Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantification methods for computer models with functional response have resulted in tools for emulation, sensitivity analysis, and calibration that are widely used. However, many of these tools do not perform well when the computer model’s parameters control both the amplitude variation of the functional output and its alignment (or phase variation). This paper introduces a framework for Bayesian model calibration when the model responses are misaligned functional data. The approach generates two types of data out of the misaligned functional responses: (1) aligned functions so that the amplitude variation is isolated and (2) warping functions that isolate the phase variation. These two types of data are created for the computer simulation data (both of which may be emulated) and the experimental data. The calibration approach uses both types so that it seeks to match both the amplitude and phase of the experimental data. The framework is careful to respect constraints that arise, especially when modeling phase variation, and is framed in a way that it can be done with readily available calibration software. In conclusion, we demonstrate the techniques on two simulated data examples and on two dynamic material science problems: a strength model calibration using flyer plate experiments and an equation of state model calibration using experiments performed on the Sandia National Laboratories’ Z-machine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Spatial‐Uniformity–Driven Bayesian Optimization for Rapid Development of Printed Perovskite Solar Cells

Printed metal halide perovskites can enable rapid, roll-to-roll manufacturing of a broad class of optoelectronics—flexible solar cells and imagers among them—while promising cost and speed advantages over incumbent silicon. However, though current methods offer high throughput and patterning capabilities, perovskite films’ spatial heterogeneity remains a challenge for large-area devices. Here, a spatial-uniformity-driven Bayesian optimization (BO) approach is leveraged to accelerate the development of printed perovskite solar cells and improve large-area device performance. Using a BO surrogate model, a 6D design space of ink chemistry and printing physics is explored via extensive iterative experimentation (≈100) informed by an objective function capturing spatial photoluminescence (PL) variance. It is discovered that optimizing for uniformity drives rapid advances in photovoltaic performance, yielding ≈20% power conversion efficiency (PCE) for small area (0.134 cm 2 ) devices and > 16% for large area (1 cm 2 ) devices. This machine-learning approach simultaneously enables rheological comparison of ink formulations that accelerate the leveling of Saffman-Taylor artifacts and improve film uniformity. Here, this showcases uniformity-driven BO as an efficient approach for uncovering the key printing physics and mitigating spatial heterogeneity to enable device scaling beyond small cell areas.

14 SOLAR ENERGY↗
DOE OSTI2022

General-Purpose Bayesian Tensor Learning With Automatic Rank Determination and Uncertainty Quantification

A major challenge in many machine learning tasks is that the model expressive power depends on model size. Low-rank tensor methods are an efficient tool for handling the curse of dimensionality in many large-scale machine learning models. The major challenges in training a tensor learning model include how to process the high-volume data, how to determine the tensor rank automatically, and how to estimate the uncertainty of the results. While existing tensor learning focuses on a specific task, this paper proposes a generic Bayesian framework that can be employed to solve a broad class of tensor learning problems such as tensor completion, tensor regression, and tensorized neural networks. We develop a low-rank tensor prior for automatic rank determination in nonlinear problems. Our method is implemented with both stochastic gradient Hamiltonian Monte Carlo (SGHMC) and Stein Variational Gradient Descent (SVGD). We compare the automatic rank determination and uncertainty quantification of these two solvers. We demonstrate that our proposed method can determine the tensor rank automatically and can quantify the uncertainty of the obtained results. We validate our framework on tensor completion tasks and tensorized neural network training tasks.

Bayesian inference↗