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Results for “probabilistic modeling”

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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

A methodology for evaluating the reliability and risk of structures under complex service environments

The theoretical basis and numerical implementation of NESSUS (Numerical Evaluation of Stochastic Structures Under Stress; Cruse et al., 1988), a computer code for probabilistic structural analyses of aerospace components, are described, with an emphasis on the use of NESSUS for reliability and risk assessment. Topics addressed include the structure of NESSUS, the material-properties model, computational procedures, probabilistic models of fatigue-crack initiation, risk/cost evaluation, fatigue-fracture analyses, and fatigue-crack propagation. Numerical results from typical applications are presented in extensive graphs and briefly characterized; the usefulness of NESSUS predictions for establishing inspection and retirement schedules and for component certification is indicated.

Shiao, Michael C.↗
DOE OSTI2025

Improving the Quasi‐Biennial Oscillation via a Surrogate‐Accelerated Multi‐Objective Optimization

Accurate simulation of the quasi-biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM for a realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency from complex observations, we extract interpretable and physically meaningful quantities capturing key attributes. Building on this, we train a probabilistic surrogate model that approximates the fundamental characteristics of the QBO as functions of critical physics parameters governing gravity wave generation. Leveraging the Karhunen–Loève decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, capturing cross-correlations among multiple physics quantities evaluated at different pressure levels and enabling rapid surrogate-based inference at a fraction of the computational cost of full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution. To navigate this, we quantify the bi-criteria trade-off and generate a set of Pareto optimal parameter values that balance the conflicting objectives. This integrated workflow improves the fidelity of QBO simulations and offers a versatile template for uncertainty quantification in complex geophysical models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Use of probabilistic design methods for NASA applications

This paper presents a reliability evaluation process designed to improve the reliability of advanced launch systems. The work performed includes the development of a reliability prediction methodology to be used in the design phase of the Space Transportation Main Engine (STME). This includes prediction techniques which use historical data bases as well as deterministic and probabilistic engineering models for predicting design reliability. In summary, this paper describes a probabilistic design approach for the next-generation liquid rocket engine, the STME.

Safie, Fayssal M.↗
DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗
NASA NTRS1982

A survey of nongravitational forces and space environmental torques with applications to the Galileo spacecraft

A detailed survey of nongravitational forces and space environmental torques acting upon the Galileo spacecraft during its interplanetary flight to Jupiter is given. It includes simple analytic equations to model the first order effect of: solar, planetary and spacecraft radiation, solar wind, meteoroids, cosmic rays, magnetic fields, atmospheric forces and gas leakage of the propulsion system. The model parameters are taken from recent spaceflight data. The result is a probabilistic error model of the magnitudes of the disturbing forces and torques. It provides a useful tool for the analysis of the Galileo and future spaceflight missions.

Longuski, J. M.↗
NASA NTRS2025

Assessment of Model Outcomes Between the Integrated Medical Model (IMM) and the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT)

The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is a computational model that provides human health and medical risk predictions for crewed spaceflight missions. MEDPRAT utilizes discrete event modeling and dynamic probabilistic simulation to predict critical mission outcomes (total medical events, crew health index, quality time lost, loss of crew life, removal to definitive care), condition occurrences, and resource consumption. Input parameters for MEDPRAT include crew attributes (e.g., sex), types of mission activities (e.g., whether and where crew members perform an extravehicular activity (EVA)), available resources, treatment information, and probability distributions for medical conditions. As an evolution of the Integrated Medical Model (IMM), MEDPRAT provides enhanced capabilities and higher fidelity, and incorporates more appropriate assumptions for long-duration spaceflight. IMM is the currently accepted standard for quantifying spaceflight mission medical risk in NASA operations that uses a probabilistic risk assessment (PRA) approach. MEDPRAT builds on the same logical foundation as IMM but implements the model architecture through highly optimized Monte Carlo sampling methods. An analysis is performed comparing the outputs from IMM with those from MEDPRAT V1.0 and V2.0 for the same reference missions in order to quantify similarities and differences in the model outcomes. The juxtaposition between IMM and MEDPRAT V1.0 and 2.0 shown in this report demonstrates that these two models generate very similar results; where differences in outcomes are shown, these are in accordance with the underlying assumptions and differences in the model architectures. This validation effort further establishes the credibility and reliability of the MEDPRAT software.

Matthew T Prelich↗
NASA NTRS2022

Intent Modeling and Conflict Probability Calculation for Operations in Upper Class E Airspace

This work presents a probabilistic operational intent model for vehicles operating in upper Class E airspace. A hybrid method is developed to calculate the intent conflict probability leveraging and extending past works on probabilistic conflict computation. Simulation results verify that the probabilistic intent model can accurately encompass the uncertain positions of each vehicle type, that are a result of wind prediction errors and vehicle performance. A comparison with past methods showed that the proposed hybrid method captures the intent conflict probability with better accuracy, especially for a larger look-ahead horizon, and computational time is reasonable for real-time applications. An example is presented to illustrate the use of the intent conflict probability in strategic planning applications.

Air traffic management↗
NASA NTRS2022

Intent Modeling and Conflict Probability Calculation for Operations in Upper Class E Airspace

This work presents a probabilistic operational intent model for vehicles operating in upper Class E airspace. A hybrid method is developed to calculate the intent conflict probability leveraging and extending past works on probabilistic conflict computation. Simulation results verify that the probabilistic intent model can accurately encompass the uncertain positions of each vehicle type, that are a result of wind prediction errors and vehicle performance. A comparison with past methods showed that the proposed hybrid method captures the intent conflict probability with better accuracy, especially for a larger look-ahead horizon, and computational time is reasonable for real-time applications. An example is presented to illustrate the use of the intent conflict probability in strategic planning applications.

Air traffic management, operational intent, confli↗
NASA NTRS1989

Fatigue strength reduction model: RANDOM3 and RANDOM4 user manual, appendix 2

The FORTRAN programs RANDOM3 and RANDOM4 are documented. They are based on fatigue strength reduction, using a probabilistic constitutive model. They predict the random lifetime of an engine component to reach a given fatigue strength. Included in this user manual are details regarding the theoretical backgrounds of RANDOM3 and RANDOM4. Appendix A gives information on the physical quantities, their symbols, FORTRAN names, and both SI and U.S. Customary units. Appendix B and C include photocopies of the actual computer printout corresponding to the sample problems. Appendices D and E detail the IMSL, Version 10(1), subroutines and functions called by RANDOM3 and RANDOM4 and SAS/GRAPH(2) programs that can be used to plot both the probability density functions (p.d.f.) and the cumulative distribution functions (c.d.f.).

Boyce, Lola↗
NASA NTRS2002

C/sic Life Prediction for Propulsion Applications

Accurate life prediction is critical to successful use of ceramic matrix composites (CMC). The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many reusable and single mission launch vehicle propulsion and airframe applications. This paper describes an approach and process made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation of C/SiC. Issues such as oxidation, steam and hydrogen effects on material behavior are discussed. Preliminary tests indicate that steam will aggressively remove SiC seal coat and matrix in line with past experience. The kinetics of water vapor reaction with carbon fibers is negligible at 600 C, but comparable to air attack at 1200 C. The mitigating effect of steam observed in fiber oxidation studies has also been observed in stress rupture tests. Detailed microscopy of oxidized specimens is being carried out to develop the oxidation model. Carbon oxidation kinetics are reaction controlled at intermediate temperatures and diffusion controlled at high temperatures (approximately 1000 C). Activation energies for T-300 and interface pyrolytic carbon were determined as key inputs to the oxidation model. Crack opening as a function of temperature and stress was calculated. Mechanical property tests to develop and verify the probabilistic life model are very encouraging except for residual strength prediction. Gage width is a key variable governing edge oxidation of seal coated specimens. Future efforts will include architectural effects, enhanced coatings, biaxial tests, and LCF. Modeling will need to account for combined effects.

Levine, Stanley R.↗
NASA NTRS2002

C/SIC Life Prediction for Propulsion Applications

Accurate life prediction is critical to successful use of ceramic matrix composites (CMC). The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many reusable and single mission launch vehicle propulsion and airframe applications. This paper describes an approach and progress made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation of C/SiC. Issues such as oxidation, steam and hydrogen effects on material behavior are discussed. Preliminary tests indicate that stream will aggressively remove SiC seal coat and matrix in line with past experience. The kinetics of water vapor reaction with carbon fibers is negligible at 600 C, but comparable to air attack at 1200 C. The mitigating effect of steam observed in fiber oxidation studies has also been observed in stress rupture tests. Detailed microscopy of oxidized specimens is being carried out to develop the oxidation model. Carbon oxidation kinetics are reaction controlled at intermediate temperatures and diffusion controlled at high temperatures (approx. 1000 C). Activation energies for T-300 and interface pyrolytic carbon were determined as key inputs to the oxidation model. Crack opening as a function of temperature and stress was calculated. Mechanical property tests to develop and verify the probabilistic life model are very encouraging except for residual strength prediction. Gage width is a key variable governing edge oxidation of seal coated specimens. Future efforts will include architectural effects, enhanced coatings, biaxial tests, and LCF. Modeling will need to account for combined effects.

Levine, Stanley R.↗
NASA NTRS2002

Probabilistic Fatigue: Computational Simulation

Fatigue is a primary consideration in the design of aerospace structures for long term durability and reliability. There are several types of fatigue that must be considered in the design. These include low cycle, high cycle, combined for different cyclic loading conditions - for example, mechanical, thermal, erosion, etc. The traditional approach to evaluate fatigue has been to conduct many tests in the various service-environment conditions that the component will be subjected to in a specific design. This approach is reasonable and robust for that specific design. However, it is time consuming, costly and needs to be repeated for designs in different operating conditions in general. Recent research has demonstrated that fatigue of structural components/structures can be evaluated by computational simulation based on a novel paradigm. Main features in this novel paradigm are progressive telescoping scale mechanics, progressive scale substructuring and progressive structural fracture, encompassed with probabilistic simulation. These generic features of this approach are to probabilistically telescope scale local material point damage all the way up to the structural component and to probabilistically scale decompose structural loads and boundary conditions all the way down to material point. Additional features include a multifactor interaction model that probabilistically describes material properties evolution, any changes due to various cyclic load and other mutually interacting effects. The objective of the proposed paper is to describe this novel paradigm of computational simulation and present typical fatigue results for structural components. Additionally, advantages, versatility and inclusiveness of computational simulation versus testing are discussed. Guidelines for complementing simulated results with strategic testing are outlined. Typical results are shown for computational simulation of fatigue in metallic composite structures to demonstrate the versatility of this novel paradigm in predicting a priori fatigue life.

Chamis, Christos C.↗
NASA NTRSDate not supplied

Sampling Functions from Gaussian Processes and Structured Covariance Gaussian Networks

When learning aerodynamic models from data, it is critical to incorporate estimates of model uncertainty. This motivates the design of probabilistic aerodynamic databases which can be sampled to generate physically and statistically plausible aerodynamic models. In this talk we discuss how to sample deterministic functions from two different kinds of probabilistic models and demonstrate their use. First, Gaussian Process Regressors (GPRs) are a widely used probabilistic kernel-based model which can be thought of as Gaussian distributions over functions. GPRs are generally trained by maximizing the marginal likelihood of seeing the training data over the kernel parameter space. Sample functions are easily generated by drawing points from the Gaussian distribution at desired input points. However, when the points are not known ahead of time, the classical sampling approach is not possible since successive function samples will generate different function realizations. We present an approach for sampling consistent function evaluations from a GPR over multiple samples. Second, we describe a neural network architecture which learns a conditional Gaussian distribution by maximizing the marginal likelihood at each point in the input space. We then discuss and compare several options for generating sample functions which match this distribution. Finally, we demonstrate the use of these probabilistic aerodynamic models in an atmospheric reentry simulation.

Gaussian process regression↗
NASA NTRS2013

Simulation of Inflated Pahoehoe Lava Flows

A new stochastic model simulates late-stage pahoehoe lobes where random processes dominate emplacement. The model prescribes probabilistic rules for determining where and when parcels of lava move within the lobe. Unlike a classical Brownian motion random walk, the model allows individual parcels to remain dormant, but fluid, for multiple time steps. The randomness of parcel volume transfers within the lobe interior as well as at the margins qualitatively reflects inflation processes observed in the field. The fraction of inflated volume to total volume increases with the total volume, with greater than 75% of the lobe volume contributed through inflation for typical lobes. The influence on planform shape and topographic cross-sectional profiles of total volume, source area and shape, topographic confinement, and sequential breakouts at the lobe margins, are all explored with the stochastic model. Each of these factors influences the overall lobe thickness and width. The model provides a means for assessing the relative importance of these processes through comparisons with field data. For the first time, Gaussian and parabolic functions are quantitatively fit to field measurements of pahoehoe lobes. Both functional forms provide adequate description of the cross-sectional flow shapes. When comparing simulated lobes to field data, sequential breakouts at the lobe margins are found to be an important process controlling the final topographic distribution of observed pahoehoe lobes.

modeling↗
DOE OSTI2024

Reduced-dimension Bayesian optimization for model calibration of transient vapor compression cycles

Development and calibration of first-principles dynamic models of vapor compression cycles (VCCs) is of critical importance for applications that include control design and fault detection and diagnostics. Nevertheless, the inherent complexity of models that are represented by large systems of differential–algebraic equations leads to significant challenges for model calibration processes that utilize classical gradient-based methods. Bayesian optimization (BO) is a sample-efficient and gradient-free approach using a probabilistic surrogate model and optimal search over a feasible parameter space. Despite the benefits of BO in reducing computational costs, challenges remain in dealing with a high-dimensional calibration task resulting from a large set of parameters that have significant impacts on system behavior and need to be calibrated simultaneously. This paper presents a reduced-dimension BO framework for calibrating transient VCCs models where the calibration space is projected to a low-dimensional subspace for accelerating convergence of the solution algorithm and consequently reducing the number of transient simulations. The proposed approach was demonstrated via two case studies associated with different VCC applications where 10 parameters were calibrated in each case using laboratory measurements. The reduced-dimension BO framework only required 1 / 8 th of the iterations associated with a standard BO method that deals with high-dimensional calibration parameters for converged solutions and yielded comparable accuracy. Furthermore, both calibrated models revealed significant accuracy improvements compared to uncalibrated models.

Ma, Jiacheng↗
NASA NTRS2011

Probabilistic Path Planning of Montgolfier Balloons in Strong, Uncertain Wind Fields

Lighter-than-air vehicles such as hot-air balloons have been proposed for exploring Saturn s moon Titan, as well as other bodies with significant atmospheres. For these vehicles to navigate effectively, it is critical to incorporate the effects of surrounding wind fields, especially as these winds will likely be strong relative to the control authority of the vehicle. Predictive models of these wind fields are available, and previous research has considered problems of planning paths subject to these predicted forces. However, such previous work has considered the wind fields as known a priori, whereas in practical applications, the actual wind vector field is not known exactly and may deviate significantly from the wind velocities estimated by the model. A probabilistic 3D path-planning algorithm was developed for balloons to use uncertain wind models to generate time-efficient paths. The nominal goal of the algorithm is to determine what altitude and what horizontal actuation, if any is available on the vehicle, to use to reach a particular goal location in the least expected time, utilizing advantageous winds. The solution also enables one to quickly evaluate the expected time-to-goal from any other location and to avoid regions of large uncertainty. This method is designed for balloons in wind fields but may be generalized for any buoyant vehicle operating in a vector field. To prepare the planning problem, the uncertainty in the wind field is modeled. Then, the problem of reaching a particular goal location is formulated as a Markov decision process (MDP) using a discretized space approach. Solving the MDP provides a policy of what actuation option (how much buoyancy change and, if applicable, horizontal actuation) should be selected at any given location to minimize the expected time-to-goal. The results provide expected time-to-goal values from any given location on the globe in addition to the action policy. This stochastic approach can also provide insights not accessible by deterministic methods; for example, one can evaluate variability and risk associated with different scenarios, rather than only viewing the expected outcome.

Wolf, Michael↗
NASA NTRSDate not supplied

Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.

quantum↗