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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery

Phasor measurement units (PMUs) provide high temporal-resolution synchrophasor measurements for power system monitoring and control. The frequent data quality issues, such as missing and bad data, prevent the incorporation of synchrophasor data in real-time operations. Most existing data-driven data recovery methods assume the power system dynamics can be approximated by a linear dynamical system, and the recovery performance degrades significantly when the power system is experiencing nonlinear dynamics during significant events. Here, this paper proposes a data-driven Bayesian nonlinear synchrophasor data recovery method (Ba-NSDR) that can recover a consecutive time period of simultaneous data losses or errors across all channels, even when the underlying system is highly nonlinear. The idea is to lift the Hankel matrix of the spatial-temporal synchrophasor data to a higher dimension such that the lifted Hankel matrix is low-rank in that space and can be processed with the kernel trick. Our proposed Bayesian method then infers the probabilistic distributions of synchrophasor from the partial observations. Some distinctive features of Ba-NSDR include an uncertainty index to measure the accuracy of the recovery result and the robustness to parameter selections. Our method is verified on both synthetic and recorded event datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

The simulation of fluid dynamic uncertainties in the SSME turbopump

The aerodynamic uncertainties in the reusable rocket engine turbopumps due to randomness in their cryogenic environment are investigated. The probabilistic simulation of the turbopumps' aerodynamic response was accomplished using the panel method coupled with Fast Probability Integration methods. The results are presented for the probabilistic rotor blades and splitter loading and their sensitivity to specified flow coefficient and rotor preswirl variance.

Hamed, Awatef↗
NASA NTRS2012

Space Radiation Cancer Risks and Uncertainities for Different Mission Time Periods

Space radiation consists of solar particle events (SPEs), comprised largely of medium energy protons (less than several hundred MeV); and galactic cosmic ray (GCR), which includes high energy protons and high charge and energy (HZE) nuclei. For long duration missions, space radiation presents significant health risks including cancer mortality. Probabilistic risk assessment (PRA) is essential for radiation protection of crews on long term space missions outside of the protection of the Earth s magnetic field and for optimization of mission planning and costs. For the assessment of organ dosimetric quantities and cancer risks, the particle spectra at each critical body organs must be characterized. In implementing a PRA approach, a statistical model of SPE fluence was developed, because the individual SPE occurrences themselves are random in nature while the frequency distribution of SPEs depends strongly upon the phase within the solar activity cycle. Spectral variability of SPEs was also examined, because the detailed energy spectra of protons are important especially at high energy levels for assessing the cancer risk associated with energetic particles for large events. An overall cumulative probability of a GCR environment for a specified mission period was estimated for the temporal characterization of the GCR environment represented by the deceleration potential (theta). Finally, this probabilistic approach to space radiation cancer risk was coupled with a model of the radiobiological factors and uncertainties in projecting cancer risks. Probabilities of fatal cancer risk and 95% confidence intervals will be reported for various periods of space missions.

Kim,Myung-Hee Y.↗
NASA NTRS2016

A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

Warner, James E.↗
NASA NTRS1989

Probability of failure and risk assessment of propulsion structural components

Because of the increasing need to account for the uncertainties in material properties, loading conditions, geometry, etc., a methodology was developed to determine structural reliability and to assess the associated risk. The methodology consists of a probabilistic structural analysis by a probabilistic finite element computer code, numerical evaluation of stochastic structures under stress (NESSUS), and a generic probabilistic material property model. The methodology is versatile and is equally applicable to structures operating at high and cryogenic temperature environments. Results obtained demonstrate that the issues of structural reliability and risk can be formally evaluated by using the methodology developed which is inclusive of the uncertainties in material properties, structural parameters, and loading conditions. The methodology is described in some detail with illustrative examples.

Shiao, Michael C.↗
NASA NTRS1989

Probability of Failure and Risk Assessment of Propulsion Structural Components

Due to increasing need to account for the uncertainties in material properties, loading conditions, or geometries, a methodology was developed to determine structural reliability and the assess the risk associated with it. The methodology consists of a probabilistic structural analysis by a probabilistic finite element computer code Nonlinear Evaluation of Stochastic Structures Under Stress (NESSUS) and a generic probabilistic material properties model. The methodology is versatile and is equally applicable to high and cryogenic temperature structures. Results obtained demonstrate that the whole issue of structural reliability and risk can be formally evaluated using the methodology developed which is inclusive of uncertainties in material properties, structural parameters and loading conditions. The methodology is described in some detail with illustrative examples.

Shiao, Michael C.↗
DOE OSTI2023

Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High-Fidelity and Physics-Agnostic Models

Applications of process-based models (PBM) for predictions are confounded by multiple uncertainties and computational burdens, resulting in appreciable errors. A novel modeling framework combining a high-fidelity PBM with surrogate and machine learning (ML) models is developed to tackle these challenges and applied for streamflow prediction. A surrogate model permits high computational efficiency of a PBM solution at a minimum loss of its accuracy. A novel probabilistic ML model partitions the PBM-surrogate prediction errors into reducible and irreducible types, quantifying their distributions that arise due to both explicitly perceived uncertainties (such as parametric) or those that are entirely hidden to the modeler (not included or unexpected). Using this approach, we demonstrate a substantial improvement of streamflow predictive accuracy for a case study urbanized watershed. Such a framework provides an efficient solution combining the strengths of high-fidelity and physics-agnostic models for a wide range of prediction problems in geosciences.

58 GEOSCIENCES↗
DOE OSTI2023

A Probabilistic Methodology for Determining Collision Risk of Marine Animals with Tidal Energy Turbines

Commercial development of tidal stream energy is hampered by technical and financial challenges, and impeded by uncertainty about potential environmental effects that drive environmental risk assessments and permitting (consenting) processes. The effect of greatest concern for operational tidal stream energy devices is the potential for marine animals to collide with turbine blades, resulting in injury or death. Due to the turbulent and often turbid waters that frequently characterize tidal turbine sites, there is an absence of empirical evidence about collisions with marine animals. This paucity of observations often leads to risk-averse permitting decisions that further restrict the deployment of tidal energy devices that are needed to collect this evidence. This paper relies on the framework of stressors and receptors that is widely used in marine energy studies and outlines a stepwise probabilistic methodology that applies existing knowledge to further elucidate the risk to marine animals from operational tidal turbines. A case study using striped bass from the Bay of Fundy, Canada, accompanies the methodology, to partially demonstrate its application.

16 TIDAL AND WAVE POWER↗
NASA NTRS2003

Exploration of Advanced Probabilistic and Stochastic Design Methods

The primary objective of the three year research effort was to explore advanced, non-deterministic aerospace system design methods that may have relevance to designers and analysts. The research pursued emerging areas in design methodology and leverage current fundamental research in the area of design decision-making, probabilistic modeling, and optimization. The specific focus of the three year investigation was oriented toward methods to identify and analyze emerging aircraft technologies in a consistent and complete manner, and to explore means to make optimal decisions based on this knowledge in a probabilistic environment. The research efforts were classified into two main areas. First, Task A of the grant has had the objective of conducting research into the relative merits of possible approaches that account for both multiple criteria and uncertainty in design decision-making. In particular, in the final year of research, the focus was on the comparison and contrasting between three methods researched. Specifically, these three are the Joint Probabilistic Decision-Making (JPDM) technique, Physical Programming, and Dempster-Shafer (D-S) theory. The next element of the research, as contained in Task B, was focused upon exploration of the Technology Identification, Evaluation, and Selection (TIES) methodology developed at ASDL, especially with regards to identification of research needs in the baseline method through implementation exercises. The end result of Task B was the documentation of the evolution of the method with time and a technology transfer to the sponsor regarding the method, such that an initial capability for execution could be obtained by the sponsor. Specifically, the results of year 3 efforts were the creation of a detailed tutorial for implementing the TIES method. Within the tutorial package, templates and detailed examples were created for learning and understanding the details of each step. For both research tasks, sample files and tutorials are attached in electronic form with the enclosed CD.

Marvis, Dimitri N.↗
NASA NTRS2003

Exploration of Advanced Probabilistic and Stochastic Design Methods

The primary objective of the three year research effort was to explore advanced, non-deterministic aerospace system design methods that may have relevance to designers and analysts. The research pursued emerging areas in design methodology and leverage current fundamental research in the area of design decision-making, probabilistic modeling, and optimization. The specific focus of the three year investigation was oriented toward methods to identify and analyze emerging aircraft technologies in a consistent and complete manner, and to explore means to make optimal decisions based on this knowledge in a probabilistic environment. The research efforts were classified into two main areas. First, Task A of the grant has had the objective of conducting research into the relative merits of possible approaches that account for both multiple criteria and uncertainty in design decision-making. In particular, in the final year of research, the focus was on the comparison and contrasting between three methods researched. Specifically, these three are the Joint Probabilistic Decision-Making (JPDM) technique, Physical Programming, and Dempster-Shafer (D-S) theory. The next element of the research, as contained in Task B, was focused upon exploration of the Technology Identification, Evaluation, and Selection (TIES) methodology developed at ASDL, especially with regards to identification of research needs in the baseline method through implementation exercises. The end result of Task B was the documentation of the evolution of the method with time and a technology transfer to the sponsor regarding the method, such that an initial capability for execution could be obtained by the sponsor. Specifically, the results of year 3 efforts were the creation of a detailed tutorial for implementing the TIES method. Within the tutorial package, templates and detailed examples were created for learning and understanding the details of each step. For both research tasks, sample files and tutorials are attached in electronic form with the enclosed CD.

Mavris, Dimitri N.↗
DOE OSTI2020

Probabilistic Look-ahead Contingency Analysis Integration with Commercial Tool and Practical Data

This paper presents an initial effort of integrating a smart sampling-based probabilistic look-ahead contingency analysis algorithm with General Electric (GE) Grid Solutions’ commercial energy management system (EMS) tool as a proof-of-concept for a seamless research tool integration using real world large-scale grid data. With the increasing impact of random forces such as variable generation and load, their stochastic behaviors cannot be ignored. However, the current practices are still dominated by deterministic tools. They are becoming increasingly inadequate for the future grid. The developed look-ahead contingency analysis algorithm incorporates forecast errors of variable energy and load to address the challenges brought by the increasing uncertainty of power system. The algorithm can reveal the potential violations caused by the variance of variable energy and load that are not normally detected by traditional deterministic approaches. To test its performance under practical environments ( real data with real commercial tool), significant efforts have been made to prepare test cases, modify GE EMS tool, and adapt an extreme value distribution algorithm to analyze the GE EMS’s violation-only outputs. The test results clearly demonstrate the effectiveness of the developed algorithm as new transformer violations that were not previously detected have been identified. This performance provides better situational awareness to engineers for their decision-making process under uncertainty. Moreover, with the discussion of computational performance and future work, this paper has shown a clear path for integrating the probabilistic algorithm with commercial tools to make us better equipped for the changing power system.

Modeling and simulation of power systems, constrai↗
DOE OSTI2023

Managing Uncertainty and Flexibility in Day-Ahead Electricity Markets

Net load imbalances from day ahead forecasts can lead to significant grid operations costs and are expected to increase as variable renewable energy adoption grows. We propose a new wholesale market product to manage the risk of net load imbalances called Flexibility Options. This product relies on probabilistic forecasts to estimate flexibility demand and would be co-optimized in the day-ahead market. We also propose stochastic methods that enable DER and flexible load aggregators to participate in flexibility markets while considering the uncertainty in weather and occupant behavior.

day-ahead market↗
NASA NTRS2007

Probabilistic Methods for Structural Reliability and Risk

A formal method is described to quantify structural reliability and risk in the presence of a multitude of uncertainties. The method is based on the materials behavior level where primitive variables with their respective scatters are used to describe that behavior. Computational simulation is then used to propagate those uncertainties to the structural scale where reliability and risk are usually specified. A sample case is described to illustrate the effectiveness, versatility, and maturity of the method. Typical results from this method demonstrate that the method is mature and that it can be used for future strategic projections and planning to assure better, cheaper, faster products for competitive advantages in world markets. The results also indicate that the methods are suitable for predicting remaining life in aging or deteriorating structures.

Chamis, Christos C.↗
NASA NTRS2015

Integration of Uncertain Ramp Area Aircraft Trajectories and Generation of Optimal Taxiway Schedules at Charlotte Douglas (CLT) Airport

The integration of aircraft maneuver characteristics into an optimal taxiway scheduling solution is challenging due to the uncertainties that are intrinsic to ramp area aircraft trajectories. To address the challenge, we build a stochastic model of ramp area aircraft trajectories that is used to generate a probabilistic measure of conflict within the Charlotte Douglas International Airport (CLT) ramp area. Parameters of the conflict distributions are estimated and passed to a Mixed Integer Linear Program that solves for an optimal taxiway schedule constrained to be conflict free in the presence of trajectory uncertainties. Here we extend our previous research by accounting for departing and arriving aircraft whereas our prior formulation only accounted for departing aircraft.

taxiway schedule↗
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
DOE OSTI2024

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models

Abstract Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Toxicology↗
DOE OSTI2024

Bayesian Tensor Decompositions for Scalable Supervised Learning of Scientific Data (Final Report)

In this document we highlight the detailed accomplishments and progress that we have made in this period. This progress seeks to address the three main objectives to provide new algorithms for quantifying uncertainty in low-multilinear-rank models and to leverage them for data analysis. These include: (1) develop probabilistic models for low-multilinear-rank functions; (2) develop a suite of Bayesian learning approaches to learn the probabilistic models from data; (3) apply the techniques on challenging problems arising in DOE-relevant applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2008

Effects of Moist Convection on Hurricane Predictability

This study exemplifies inherent uncertainties in deterministic prediction of hurricane formation and intensity. Such uncertainties could ultimately limit the predictability of hurricanes at all time scales. In particular, this study highlights the predictability limit due to the effects on moist convection of initial-condition errors with amplitudes far smaller than those of any observation or analysis system. Not only can small and arguably unobservable differences in the initial conditions result in different routes to tropical cyclogenesis, but they can also determine whether or not a tropical disturbance will significantly develop. The details of how the initial vortex is built can depend on chaotic interactions of mesoscale features, such as cold pools from moist convection, whose timing and placement may significantly vary with minute initial differences. Inherent uncertainties in hurricane forecasts illustrate the need for developing advanced ensemble prediction systems to provide event-dependent probabilistic forecasts and risk assessment.

Zhang, Fuqing↗