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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 127 records · Page 7

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↗

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↗

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↗

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.↗

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.↗

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↗

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.↗

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.↗

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

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.↗

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.↗

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↗

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↗

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'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.↗

Statistical models of lunar rocks and regolith

The mathematical, statistical, and computational approaches used in the investigation of the interrelationship of lunar fragmental material, regolith, lunar rocks, and lunar craters are described. The first two phases of the work explored the sensitivity of the production model of fragmental material to mathematical assumptions, and then completed earlier studies on the survival of lunar surface rocks with respect to competing processes. The third phase combined earlier work into a detailed statistical analysis and probabilistic model of regolith formation by lithologically distinct layers, interpreted as modified crater ejecta blankets. The fourth phase of the work dealt with problems encountered in combining the results of the entire project into a comprehensive, multipurpose computer simulation model for the craters and regolith. Highlights of each phase of research are given.

Marcus, A. H.↗

Exact and Approximate Probabilistic Symbolic Execution

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes.

Symbolic Execution↗

Probabilistic structural analysis methods for critical SSME propulsion components

The development of a three-dimensional inelastic analysis methodology for the Space Shuttle main engine (SSME) structural components is described. The methodology is composed of: (1) composite load spectra, (2) probabilistic structural analysis methods, (3) the probabilistic finite element theory, and (4) probabilistic structural analysis. The progress in the development of generic probabilistic models for various individual loads which consist of a steady state load, a periodic load, a random load, and a spike, is discussed. The capabilities of the Numerical Evaluation of Stochastic Structures Under Stress finite element code designed for probabilistic structural analysis of the SSME are examined. Variation principles for formulation probabilistic finite elements and a structural analysis for evaluating the geometric and material properties tolerances on the structural response of turbopump blades are being designed.

Chamis, C. C.↗

Crash Certification by Analysis - Are We There Yet?

This paper addresses the issue of crash certification by analysis. This broad topic encompasses many ancillary issues including model validation procedures, uncertainty in test data and analysis models, probabilistic techniques for test-analysis correlation, verification of the mathematical formulation, and establishment of appropriate qualification requirements. This paper will focus on certification requirements for crashworthiness of military helicopters; capabilities of the current analysis codes used for crash modeling and simulation, including some examples of simulations from the literature to illustrate the current approach to model validation; and future directions needed to achieve "crash certification by analysis."

Jackson, Karen E.↗