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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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NASA NTRS2019

Probabilistic Risk Assessment of Aging Layered Pressure Vessels

The National Aeronautics and Space Administration (NASA) operates approximately 300 aging layered pressure vessels that were designed and manufactured prior to ASME Boiler and Pressure Vessel (B&PV) code requirements. In order to make decisions regarding the continued fitness-for-service of these non-code carbon steel vessels, it is necessary to perform a relative risk of failure assessment for each vessel. However, risk assessment of these vessels is confounded by uncertainties and variabilities related to the use of proprietary materials in fabrication, missing construction records, geometric discontinuities, weld residual stresses, and complex service stress gradients in and around the welds. Therefore, a probabilistic framework that can capture these uncertainties and variabilities has been developed to assess the fracture risk of flaws in regions of interest, such as longitudinal and circumferential welds, using the NESSUS® probabilistic modeling software and NASGRO® fracture mechanics software. In this study, the probabilistic framework was used to predict variability in the stress intensity factor associated with different reference flaws located in the head-to-shell circumferential welds of a 4-layer and 14-layer pressure vessel. The probabilistic studies predict variability in flaw behavior and the important uncertain parameters for each reference flaw location.

Riha, D. S.↗

NASA NTRS2009

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.

Duda, David P.↗

DOE OSTI2024

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2008

NASA's Human Research Program at The Glenn Research Center: Progress and Opportunities

The NASA Human Research Program is aimed at correcting problems in critical areas that place NASA human spaceflight missions at risk due to shortfalls in astronaut health, safety and performance. The Glenn Research Center (GRC) and partners from Ohio are significant contributors to this effort. This presentation describes several areas of GRC emphasis, the first being NASA s path to creating exercise hardware requirements and protocols that mitigate the effects of long duration spaceflight. Computational simulations will be a second area that is discussed. This includes deterministic models that simulate the effects of spaceflight on the human body, as well as probabilistic models that bound and quantify the probability that adverse medical incidents will happen during an exploration mission. Medical technology development for exploration will be the final area to be discussed.

Nall, Marsha↗

NASA NTRSDate not supplied

Characterizing Spatiotemporal Uncertainty in Interpolated Meteorological Data

Interpolated meteorological data invariably contain errors. These errors have structure in time and space, particularly autocorrelation, which can cause the effects of errors to compound when model outputs are aggregated temporally or spatially. One way to account for this uncertainty is with a probabilistic model from which samples can be drawn that are coherent with respect to underlying spatial and temporal covariance structure. This work describes a probabilistic method for spatial interpolation of point-wise meteorological time series. Observational data from weather stations are generally sparse in space and dense in time (but sometimes missing). The method works by projecting time series onto orthogonal basis vectors and spatially interpolating each resulting component independently. Under suitable assumptions, and data transformations to better satisfy those assumptions, Gaussian process regression provides a complete description of the joint predictive distribution over a Gaussian random field. Spatiotemporally coherent realizations are generated as the sum of conditional (spatial) simulations of each orthogonal (temporal) component. Data-derived and generic orthogonal bases are considered. In addition to spatial interpolation, imputation of missing observational data is examined. The method is applied using near-surface air temperature over the Western United States and validated by comparing theoretical versus actual coverage of predictive distributions and analyzing the degree to which spatial and temporal covariance structure is reproduced. Computational considerations, relating to conditional simulation of random fields, are also addressed.

Conor T Doherty↗

DOE OSTI2026

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials

Machine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods. Uncertainty quantification (UQ) of ML-IAPs is a growing area of research as UQ is critical in many applications of IAPs, such as developing curated datasets, active learning-based data augmentation, self-improving models, and estimating the uncertainty of molecular dynamics simulations. In this paper, we construct and benchmark a series of different neural network potentials (NNPs) with varying network architectures to determine the performance of these models with respect to both the mean and uncertainty calibration error. Each NNP method is specifically designed to predict either epistemic or aleatoric uncertainty with particular focus on the differences in behavior between the epistemic and aleatoric uncertainty estimates. We benchmark these methods using multiple datasets common in the ML-IAP literature. The results show that the aleatoric uncertainty from single-shot model architectures is a competitive alternative to ensemble-based epistemic uncertainty predictions in regions of sufficient data-density. However, in regions where the representative data is sparse, aleatoric uncertainty models tend to overpredict and epistemic methods tend to underpredict the actual model error. We conclude that the type of UQ is crucial when discussing performance of probabilistic model results as different methods have different performance characteristics depending on the regime in which they are evaluated. Therefore, the type of UQ method should be carefully evaluated against both the data characteristics and requirements for the intended application.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Towards Sheaf Theoretic Analyses for Delay Tolerant Networking

The goal of Delay Tolerant Networking (DTN) is to take a collection of heterogeneous, disparate connections between satellites, space assets, ground stations, and ground infrastructure and bring it together into a cohesive, functioning overlay network. Depending on the systems being considered, one can find links with a one-way light time exceeding minutes (and hours),periodic links which can sometimes be predicted by orbital mechanics, and restrictions based on the variety of capabilities built into these systems. These characteristics preclude traditional network models and routing techniques and have classically led to either rigid routing tables or purely probabilistic models. As the deeper underlying structures remain unknown, development of more DTN-optimized algorithms has lacked the necessary foundation. In a continuation of previous work, the goal of this paper is to identify and study these fundamental structures that exist in delay tolerant networks (DTN), with a focus on space networks. The current routing methodology has been to use contact graph routing (CGR) algorithms. CGR models a series of known contacts as a static graph. For CGR to work, this graph must be globally consistent and must have an accurate picture of the network. Because this is a globally controlled structure, there is little room for flexibility in the event of changes to the network which would naturally occur as the network grows. As a response to the desire for flexibility as the network changes, we introduced the mathematical structure known as sheaves to DTNs last year. The tag-line for sheaves is that they are a mathematically precise way of gluing local data together into unique global data. Thus, sheaves lend extra power to traditional models(and routing algorithms) by taking additional information and merging it, in as consistent a manner as possible, with the representation itself. The clearest example of how Earth-bound networks exhibit behavior that is “sheafy” is link state routers, which build a local-to-global picture of their network by gluing local information together into a global network, exactly as a sheaf would do. For routing within delay tolerant networks to truly exploit this structure, a deeper structure than a graph is required. In this paper, we develop sheaves that can work over directed graphs such as temporal flow networks, we construct a sheaf representation for Dijkstra’s algorithm, and we outline a construction for routing sheaves capable of modeling multicast scenarios. Finally, there is a section of future work suggesting follow-on research.

Robert Short↗

NASA NTRS1987

The NESSUS finite element code

The objective of this development is to provide a new analysis tool which integrates the structural modeling versatility of a modern finite element code with the latest advances in the area of probabilistic modeling and structural reliability. Version 2.0 of the NESSUS finite element code was released last February, and is currently being exercised on a set of problems which are representative of typical Space Shuttle Main Engine (SSME) applications. NESSUS 2.0 allows linear elastostatic and eigenvalue analysis of structures with uncertain geometry, material properties and boundary conditions, which are subjected to a random mechanical and thermal loading environment. The NESSUS finite element code is a key component in a broader software system consisting of five major modules. NESSUS/EXPERT is an expert system under development at Southwest Research Institute, with the objective of centralizing all component-specific knowledge useful for conducting probabilistic analysis of typical Space Shuttle Main Engine (SSME) components. NESSUS/FEM contains the finite element code used for the structural analysis and parameter sensitivity evaluation of these components. The task of parametrizing a finite element mesh in terms of the random variables present is facilitated with the use of the probabilistic data preprocessor in NESSUS/PRE. An external database file is used for managing the bulk of the data generated by NESSUS/FEM.

Dias, J. B.↗

NASA NTRS2004

Probabilistic Physics-Based Risk Tools Used to Analyze the International Space Station Electrical Power System Output

This paper describes the methods employed to apply probabilistic modeling techniques to the International Space Station (ISS) power system. These techniques were used to quantify the probabilistic variation in the power output, also called the response variable, due to variations (uncertainties) associated with knowledge of the influencing factors called the random variables. These uncertainties can be due to unknown environmental conditions, variation in the performance of electrical power system components or sensor tolerances. Uncertainties in these variables, cause corresponding variations in the power output, but the magnitude of that effect varies with the ISS operating conditions, e.g. whether or not the solar panels are actively tracking the sun. Therefore, it is important to quantify the influence of these uncertainties on the power output for optimizing the power available for experiments.

Patel, Bhogila M.↗

NASA NTRS1989

Probabilistic analysis for fatigue strength degradation of materials

This report presents the results of the first year of a research program conducted for NASA-LeRC by the University of Texas at San Antonio. The research included development of methodology that provides a probabilistic treatment of lifetime prediction of structural components of aerospace propulsion systems subjected to fatigue. Material strength degradation models, based on primitive variables, include both a fatigue strength reduction model and a fatigue crack growth model. Linear elastic fracture mechanics is utilized in the latter model. Probabilistic analysis is based on simulation, and both maximum entropy and maximum penalized likelihood methods are used for the generation of probability density functions. The resulting constitutive relationships are included in several computer programs, RANDOM2, RANDOM3, and RANDOM4. These programs determine the random lifetime of an engine component, in mechanical load cycles, to reach a critical fatigue strength or crack size. The material considered was a cast nickel base superalloy, one typical of those used in the Space Shuttle Main Engine.

Royce, Lola↗

NASA NTRS1987

Composite load spectra for select space propulsion structural components

The objective of this program is to develop generic load models to simulate the composite load spectra (CLS) that are induced in space propulsion system components representative of the space shuttle main engines (SSME). These models are being developed through describing individual component loads with an appropriate mix of deterministic and state-of-the-art probabilistic models that are related to key generic variables. Combinations of the individual loads are used to synthesize the composite loads spectra. A second approach for developing the composite loads spectra load model simulation, the option portion of the contract will develop coupled models which combine the individual load models. Statistically varying coefficients of the physical models will be used to obtain the composite load spectra.

Newell, J. F.↗

NASA NTRS1987

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system structural components

The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

Cruse, T. A.↗

NASA NTRS1988

Probabilistic Structural Analysis Methods for select space propulsion system structural components (PSAM)

Cruse, T. A.↗

NASA NTRS1978

The engineering analysis of solar radiation

A necessary precursor to construction of well-designed, efficient, and economically viable solar energy systems is the engineering analysis not only of the systems themselves but also of the solar radiation that will drive them. This paper presents the first steps in such an analysis to support the design of solar thermal power systems. A rationale for development of an integrated approach to this analysis is outlined, and elements of the approach are described. A dynamic computer simulation of a conceptual system was employed in an initial sensitivity analysis to explore how performance estimates might be affected by the precision and amount of detail in solar radiation data used as model input. A measurement program, including instrumentation, used to characterize precisely and in detail the solar resource at one location is described as is a probabilistic model derived from it, for predicting insolation as a function of time.

Reid, M. S.↗

NASA NTRS1994

Hidden Markov models and neural networks for fault detection in dynamic systems

Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.

Smyth, Padhraic↗

NASA NTRSDate not supplied

Analysis of Alternative Architectures for Cargo Lunar Landers

NASA’s Human Landing System (HLS) program has been working with commercial partners to develop human-class lunar landers to return the first American woman and next American man to the lunar surface in the mid 2020’s. In an effort to expand human presence beyond low Earth orbit, NASA’s Artemis program aims to facilitate a sustainable, long-term human presence in cis-lunar space. A component of this will require significant infrastructure to be delivered to the lunar surface. Delivering this infrastructure will require a significant lander capability that has yet to be developed. A thorough understanding of cargo lunar lander architectures is required such that select alternatives can be identified that best support the Artemis program’s objective of sustainability. The goal of this study is to aid NASA and its partners in the understanding of the cargo lunar lander trades space, as well as identify potential robust alternatives. The results will support NASA as it moves forward with key activities such as requirements formulation, agency strategic planning, and potential cargo lunar lander procurements. The study builds off of recent work performed by the Human Landing System program’s Architecture and Systems Analysis group to encompass a broad trade space of cargo lunar lander architecture alternatives. The current trade space as depicted by the morphological matrix and mission graph in Fig. 1 and Fig. 2, respectively, includes key alternative options that have become highly relevant due to current HLS activities and include on-orbit refueling, active cryogenic fluid management, Earth orbit aggregation, and global lunar access. The authors believe that there is also a statistically relevant impact of lander-payload configuration on the primary structure of the vehicle that could greatly impact alternative selection. Because of this, several conceptual lander-payload configurations will be evaluated to determine the level of impact. The current set of conceptual configurations are shown in Fig. 3 and Fig. 4. To aid the conceptual evaluation of these configurations, a catalogue of notional payloads has been developed that represent a wide range of masses and volumes that are expected to be delivered in support of a sustained human lunar presence, including pressurized and unpressurized rovers, surface habitats, power systems, and other support infrastructure. In order to execute this study in a timely fashion, a similar approach to that utilized in a similar 2019 study focused on 2024 human lunar sorties will be employed [1]. The team utilized a novel architecture synthesis framework currently being developed by NASA/MSFC to evaluate over 600,000 lunar lander architectures over a two month time frame [2]. From this large data set, varying ground rules and assumptions were applied as filters to explore the trade space to identify alternatives which exhibited robustness, as measured by launch vehicle payload margin, to absorb the natural growth that occurs during design maturation. The set of Earth-Moon system Delta-Vs assumed from the 2019 study, shown in Fig. 5, will be repurposed to accelerate model formulation for this effort. Additionally, current efforts in collaboration with the Georgia Institute of Technology’s Aerospace System Design Lab will be integrated to provide probabilistic modeling of the cargo lunar lander architectures to aid in identifying robust design alternatives [3]. The approach will help minimize potential impacts due to large levels of uncertainty inherent to pre phase-A conceptual design. By leveraging these past and present studies and partnerships, a highly detailed set of data can be generated in a short time period to aid NASA in the coming years to support the goal of a sustained human lunar presence.

Architectures↗

NASA NTRS2001

A Probabilistic Approach to Model Update

Finite element models are often developed for load validation, structural certification, response predictions, and to study alternate design concepts. In rare occasions, models developed with a nominal set of parameters agree with experimental data without the need to update parameter values. Today, model updating is generally heuristic and often performed by a skilled analyst with in-depth understanding of the model assumptions. Parameter uncertainties play a key role in understanding the model update problem and therefore probabilistic analysis tools, developed for reliability and risk analysis, may be used to incorporate uncertainty in the analysis. In this work, probability analysis (PA) tools are used to aid the parameter update task using experimental data and some basic knowledge of potential error sources. Discussed here is the first application of PA tools to update parameters of a finite element model for a composite wing structure. Static deflection data at six locations are used to update five parameters. It is shown that while prediction of individual response values may not be matched identically, the system response is significantly improved with moderate changes in parameter values.

Horta, Lucas G.↗

NASA NTRS2000

NASA's New Approach for Evaluating Risk Reduction Due to Space Shuttle Upgrades

As part of NASA's intensive effort to incorporate quantitative risk assessment (QRA) tools in the Agency's decision-making process concerning Space Shuttle risk, NASA has developed a powerful risk assessment tool called the Quantitative Risk Assessment System (QRAS). The QRAS is a tool designed to estimate Space Shuttle risk and evaluate Space Shuttle upgrades. This paper presents an overview of the QRAS with focus on its application for evaluating the risk reduction due to proposed Space Shuttle upgrades. The application includes a case study from the Space Shuttle main engine (SSME). The QRAS overview section of the paper includes the QRAS development process, the technical approach to model development, the QRA quantification methods and techniques, and observations concerning the complex modeling involved in QRAS. The application section of the paper describes a practical case study using QRAS models for evaluating critical Space Shuttle Program upgrades, specifically a proposed SSME nozzle upgrade. This paper presents the method for evaluating the proposed upgrade by comparing the current nozzle (old design with well-established probabilistic models) to the channel wall nozzle (new design at the preliminary design level).

Safie, Fayssal M.↗