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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 NTRS1985

Overview of Structural Response: Probabilistic Structural Analysis

Advanced analysis methods are required to predict accurately the structural response (static, transient, cyclic, etc.) and accompanying local stresses in space propulsion system components operating in a fatigue environment consisting of complex thermal and mechanical load spectra. The probabilistic approach to structural response consists of the following program elements: (1) composite load spectra, (2) probabilistic structural analysis methods development, (3) probabilitic finite element theory, (4) probabilistic structural analysis application, (5) structural tailoring of turbopump blades, (6) unified theory of dynamic creep buckling/ratcheting, (7) creep buckling/ratcheting analyzer, and (8) nonlinear COBSTRAN development. Research activities on all of these program elements (except the creep buckling/ratcheting analyzer) are under way.

Chamis, C. C.↗

NASA NTRS2010

Verifying Algorithms for Autonomous Aircraft by Simulation Generalities and Example

An open question in Air Traffic Management is what procedures can be validated by simulation where the simulation shows that the probability of undesirable events is below the required level at some confidence level. The problem is including enough realism to be convincing while retaining enough efficiency to run the large number of trials needed for high confidence. The paper first examines the probabilistic interpretation of a typical requirement by a regulatory agency and computes the number of trials needed to establish the requirement at an equivalent confidence level. Since any simulation is likely to consider only one type of event and there are several types of events, the paper examines under what conditions this separate consideration is valid. The paper establishes a separation algorithm at the required confidence level where the aircraft operates under feedback control as is subject to perturbations. There is a discussion where it is shown that a scenario three of four orders of magnitude more complex is feasible. The question of what can be validated by simulation remains open, but there is reason to be optimistic.

White, Allan L.↗

NASA NTRS2017

A Probabilistic Asteroid Impact Risk Model

Asteroid threat assessment requires the quantification of both the impact likelihood and resulting consequence across the range of possible events. This paper presents a probabilistic asteroid impact risk (PAIR) assessment model developed for this purpose. The model incorporates published impact frequency rates with state-of-the-art consequence assessment tools, applied within a Monte Carlo framework that generates sets of impact scenarios from uncertain parameter distributions. Explicit treatment of atmospheric entry is included to produce energy deposition rates that account for the effects of thermal ablation and object fragmentation. These energy deposition rates are used to model the resulting ground damage, and affected populations are computed for the sampled impact locations. The results for each scenario are aggregated into a distribution of potential outcomes that reflect the range of uncertain impact parameters, population densities, and strike probabilities. As an illustration of the utility of the PAIR model, the results are used to address the question of what minimum size asteroid constitutes a threat to the population. To answer this question, complete distributions of results are combined with a hypothetical risk tolerance posture to provide the minimum size, given sets of initial assumptions. Model outputs demonstrate how such questions can be answered and provide a means for interpreting the effect that input assumptions and uncertainty can have on final risk-based decisions. Model results can be used to prioritize investments to gain knowledge in critical areas or, conversely, to identify areas where additional data has little effect on the metrics of interest.

Asteroid↗

NASA NTRS2000

An Approach to Experimental Design for the Computer Analysis of Complex Phenomenon

The ability to make credible system assessments, predictions and design decisions related to engineered systems and other complex phenomenon is key to a successful program for many large-scale investigations in government and industry. Recently, many of these large-scale analyses have turned to computational simulation to provide much of the required information. Addressing specific goals in the computer analysis of these complex phenomenon is often accomplished through the use of performance measures that are based on system response models. The response models are constructed using computer-generated responses together with physical test results where possible. They are often based on probabilistically defined inputs and generally require estimation of a set of response modeling parameters. As a consequence, the performance measures are themselves distributed quantities reflecting these variabilities and uncertainties. Uncertainty in the values of the performance measures leads to uncertainties in predicted performance and can cloud the decisions required of the analysis. A specific goal of this research has been to develop methodology that will reduce this uncertainty in an analysis environment where limited resources and system complexity together restrict the number of simulations that can be performed. An approach has been developed that is based on evaluation of the potential information provided for each "intelligently selected" candidate set of computer runs. Each candidate is evaluated by partitioning the performance measure uncertainty into two components - one component that could be explained through the additional computational simulation runs and a second that would remain uncertain. The portion explained is estimated using a probabilistic evaluation of likely results for the additional computational analyses based on what is currently known about the system. The set of runs indicating the largest potential reduction in uncertainty is then selected and the computational simulations are performed. Examples are provided to demonstrate this approach on small scale problems. These examples give encouraging results. Directions for further research are indicated.

Rutherford, Brian↗

NASA NTRS2003

Probabilistic Evaluation of Advanced Ceramic Matrix Composite Structures

The objective of this report is to summarize the deterministic and probabilistic structural evaluation results of two structures made with advanced ceramic composites (CMC): internally pressurized tube and uniformly loaded flange. The deterministic structural evaluation includes stress, displacement, and buckling analyses. It is carried out using the finite element code MHOST, developed for the 3-D inelastic analysis of structures that are made with advanced materials. The probabilistic evaluation is performed using the integrated probabilistic assessment of composite structures computer code IPACS. The affects of uncertainties in primitive variables related to the material, fabrication process, and loadings on the material property and structural response behavior are quantified. The primitive variables considered are: thermo-mechanical properties of fiber and matrix, fiber and void volume ratios, use temperature, and pressure. The probabilistic structural analysis and probabilistic strength results are used by IPACS to perform reliability and risk evaluation of the two structures. The results will show that the sensitivity information obtained for the two composite structures from the computational simulation can be used to alter the design process to meet desired service requirements. In addition to detailed probabilistic analysis of the two structures, the following were performed specifically on the CMC tube: (1) predicted the failure load and the buckling load, (2) performed coupled non-deterministic multi-disciplinary structural analysis, and (3) demonstrated that probabilistic sensitivities can be used to select a reduced set of design variables for optimization.

Abumeri, Galib H.↗

NASA NTRS1993

Specifying design conservatism: Worst case versus probabilistic analysis

Design conservatism is the difference between specified and required performance, and is introduced when uncertainty is present. The classical approach of worst-case analysis for specifying design conservatism is presented, along with the modern approach of probabilistic analysis. The appropriate degree of design conservatism is a tradeoff between the required resources and the probability and consequences of a failure. A probabilistic analysis properly models this tradeoff, while a worst-case analysis reveals nothing about the probability of failure, and can significantly overstate the consequences of failure. Two aerospace examples will be presented that illustrate problems that can arise with a worst-case analysis.

Miles, Ralph F., Jr.↗

NASA NTRS1991

Probabilistic evaluation of SSME structural components

The application is described of Composite Load Spectra (CLS) and Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) family of computer codes to the probabilistic structural analysis of four Space Shuttle Main Engine (SSME) space propulsion system components. These components are subjected to environments that are influenced by many random variables. The applications consider a wide breadth of uncertainties encountered in practice, while simultaneously covering a wide area of structural mechanics. This has been done consistent with the primary design requirement for each component. The probabilistic application studies are discussed using finite element models that have been typically used in the past in deterministic analysis studies.

Rajagopal, K. R.↗

NASA NTRS2012

A Markov Chain Approach to Probabilistic Swarm Guidance

This paper introduces a probabilistic guidance approach for the coordination of swarms of autonomous agents. The main idea is to drive the swarm to a prescribed density distribution in a prescribed region of the configuration space. In its simplest form, the probabilistic approach is completely decentralized and does not require communication or collabo- ration between agents. Agents make statistically independent probabilistic decisions based solely on their own state, that ultimately guides the swarm to the desired density distribution in the configuration space. In addition to being completely decentralized, the probabilistic guidance approach has a novel autonomous self-repair property: Once the desired swarm density distribution is attained, the agents automatically repair any damage to the distribution without collaborating and without any knowledge about the damage.

Markov chain↗

NASA NTRSDate not supplied

Adopting an Objectives-Driven Assurance Case Approach for Achieving Space Flight Mission Planetary Protection Objectives

Traditionally, the National Aeronautics and Space Administration (NASA) has utilized prescriptive technical and process requirements to ensure safety and mission assurance performance objectives for planetary protection are achieved during space flight missions. While prescriptive requirements may be easier to communicate and manage throughout the systems engineering process, the highly constrained nature of prescriptive requirements can limit the ability to take advantage of cost-saving opportunities and offer limited ability to explore other options or alternative designs, processes, and methods. It can also be difficult to develop prescriptive requirements for objectives that are probabilistic in nature or that cannot be satisfied by direct verification. In contrast, the development of an assurance case allows for a compelling, comprehensible, and valid argument to be developed with supporting evidence that shows safety and mission assurance objectives have been satisfied. Analogous to how patent applications are constructed for inventions, an assurance case has a high-level claim of meeting a safety and mission assurance objective, followed by a more specific set of sub-claims and technical evidence which supports the claims. The objectives-driven assurance case approach allows for a better understanding and exploration of the trade space, more flexibility to balance trades, and the ability to realize and implement technical and process innovations for resource, time, and cost savings. The assurance case is a living case that evolves over the entire program life cycle. Recently, NASA’s Office of Planetary Protection (OPP) has adopted the assurance case approach as an acceptable methodology for demonstrating avoidance of contamination of target solar system bodies explored by NASA space flight missions. This methodology has been incorporated into NASA’s new technical standard for planetary protection and is currently being utilized by the Mars Sample Return campaign for safe sample containment during sample return.

Assurance Case↗

DOE OSTI2024

Artificial-intelligence-driven shot reduction in quantum measurement

Variational Quantum Eigensolver (VQE) provides a powerful solution for approximating molecular ground state energies by combining quantum circuits and classical computers. However, estimating probabilistic outcomes on quantum hardware requires repeated measurements (shots), incurring significant costs as accuracy increases. Optimizing shot allocation is thus critical for improving the efficiency of VQE. Current strategies rely heavily on hand-crafted heuristics requiring extensive expert knowledge. This paper proposes a reinforcement learning (RL)-based approach that automatically learns shot assignment policies to minimize total measurement shots while achieving convergence to the minimum of the energy expectation in VQE. The RL agent assigns measurement shots across VQE optimization iterations based on the progress of the optimization. This approach reduces VQE's dependence on static heuristics and human expertise. When the RL-enabled VQE is applied to a small molecule, a shot reduction policy is learned. The policy demonstrates transferability across systems and compatibility with other wavefunction Ansätze. In addition to these specific findings, this work highlights the potential of RL for automatically discovering efficient and scalable quantum optimization strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2002

Reliability Analysis of Brittle Material Structures - Including MEMS(?) - With the CARES/Life Program

Brittle materials are being used, or considered, for a wide variety of high tech applications that operate in harsh environments, including static and rotating turbine parts. thermal protection systems, dental prosthetics, fuel cells, oxygen transport membranes, radomes, and MEMS. Designing components to sustain repeated load without fracturing while using the minimum amount of material requires the use of a probabilistic design methodology. The CARES/Life code provides a general-purpose analysis tool that predicts the probability of failure of a ceramic component as a function of its time in service. For this presentation an interview of the CARES/Life program will be provided. Emphasis will be placed on describing the latest enhancements to the code for reliability analysis with time varying loads and temperatures (fully transient reliability analysis). Also, early efforts in investigating the validity of using Weibull statistics, the basis of the CARES/Life program, to characterize the strength of MEMS structures will be described as as well as the version of CARES/Life for MEMS (CARES/MEMS) being prepared which incorporates single crystal and edge flaw reliability analysis capability. It is hoped this talk will open a dialog for potential collaboration in the area of MEMS testing and life prediction.

Nemeth, Noel N.↗

NASA NTRS2010

Applying Monte Carlo Simulation to Launch Vehicle Design and Requirements Analysis

This Technical Publication (TP) is meant to address a number of topics related to the application of Monte Carlo simulation to launch vehicle design and requirements analysis. Although the focus is on a launch vehicle application, the methods may be applied to other complex systems as well. The TP is organized so that all the important topics are covered in the main text, and detailed derivations are in the appendices. The TP first introduces Monte Carlo simulation and the major topics to be discussed, including discussion of the input distributions for Monte Carlo runs, testing the simulation, how many runs are necessary for verification of requirements, what to do if results are desired for events that happen only rarely, and postprocessing, including analyzing any failed runs, examples of useful output products, and statistical information for generating desired results from the output data. Topics in the appendices include some tables for requirements verification, derivation of the number of runs required and generation of output probabilistic data with consumer risk included, derivation of launch vehicle models to include possible variations of assembled vehicles, minimization of a consumable to achieve a two-dimensional statistical result, recontact probability during staging, ensuring duplicated Monte Carlo random variations, and importance sampling.

Hanson, J. M.↗

NASA NTRS2015

Simulation of Liquid Rocket Engine Failure Propagation Using Self-Evolving Scenarios

Traditional probabilistic risk assessment approaches often require failure scenarios to be explicitly defined through event sequences that are then quantified as part of the integrated analysis. This approach becomes difficult when failure propagation paths change as a function of the system operation. Additionally, if the propagation paths represent interactions among even a modest number of components, the scenario count becomes combinatorially intractable. This paper presents an alternate approach for quantifying the probability of failure propagation in such a case. Rather than explicitly defining scenario sequences, simple physical models are created for each of the components. In this way, only the physical states and rules of component interactions must be defined, rather than event sequences for each individual scenario. Initiating failures are introduced into the system, either randomly or as defined by relative likelihood, and the failures cascade through the system via the interaction rules. This process is repeated using Monte Carlo methods and, as a result, the most probable scenarios “self-evolve” in terms of both sequence path and frequency. This approach was applied to failures occurring in the engine compartment of a space launch vehicle with four liquid rocket engines and four high-pressure helium tanks. Each engine was modeled with key components, such as turbomachinery, combustion chamber, propellant lines, and additional support systems. Three test cases were conducted with different high-energy engine failures. End results of interest included an additional engine-out failure and tank burst, which represent the loss-of-mission (LOM) and loss-of-crew (LOC) failure environments, respectively. Observations show that almost every scenario outcome is unique and that many scenarios involve complex chain reactions that are difficult to predict. This validates the usefulness of the modeling approach in assessing the overall risks to the crew during a launch vehicle abort.

Rocket Engine↗

NASA NTRS2014

Preliminary Structural Sensitivity Study of Hypersonic Inflatable Aerodynamic Decelerator Using Probabilistic Methods

Acceptance of new spacecraft structural architectures and concepts requires validated design methods to minimize the expense involved with technology validation via flighttesting. This paper explores the implementation of probabilistic methods in the sensitivity analysis of the structural response of a Hypersonic Inflatable Aerodynamic Decelerator (HIAD). HIAD architectures are attractive for spacecraft deceleration because they are lightweight, store compactly, and utilize the atmosphere to decelerate a spacecraft during re-entry. However, designers are hesitant to include these inflatable approaches for large payloads or spacecraft because of the lack of flight validation. In the example presented here, the structural parameters of an existing HIAD model have been varied to illustrate the design approach utilizing uncertainty-based methods. Surrogate models have been used to reduce computational expense several orders of magnitude. The suitability of the design is based on assessing variation in the resulting cone angle. The acceptable cone angle variation would rely on the aerodynamic requirements.

Lyle, Karen H.↗

NASA NTRS2002

Probabilistic Design of a Plate-Like Wing to Meet Flutter and Strength Requirements

An approach is presented for carrying out reliability-based design of a metallic, plate-like wing to meet strength and flutter requirements that are given in terms of risk/reliability. The design problem is to determine the thickness distribution such that wing weight is a minimum and the probability of failure is less than a specified value. Failure is assumed to occur if either the flutter speed is less than a specified allowable or the stress caused by a pressure loading is greater than a specified allowable. Four uncertain quantities are considered: wing thickness, calculated flutter speed, allowable stress, and magnitude of a uniform pressure load. The reliability-based design optimization approach described herein starts with a design obtained using conventional deterministic design optimization with margins on the allowables. Reliability is calculated using Monte Carlo simulation with response surfaces that provide values of stresses and flutter speed. During the reliability-based design optimization, the response surfaces and move limits are coordinated to ensure accuracy of the response surfaces. Studies carried out in the paper show the relationship between reliability and weight and indicate that, for the design problem considered, increases in reliability can be obtained with modest increases in weight.

Stroud, W. Jefferson↗

NASA NTRS2012

Deployment Analysis of a Simple Tape-Spring Hinge Using Probabilistic Methods

Acceptance of new deployable structures architectures and concepts requires validated design methods to minimize the expense involved with technology validation flight testing. Deployable concepts for large lightweight spacecraft include booms, antennae, and masts. This paper explores the implementation of probabilistic methods in the design process for the deployment of a strain-energy mechanism, specifically a simple tape-spring hinge. Strain-energy mechanisms are attractive for deployment in very lightweight systems because they do not require the added mass and complexity associated with motors and controllers. However, designers are hesitant to include free deployment, strain-energy mechanisms because of the potential for uncontrolled behavior. In the example presented here, the tapespring cross-sectional dimensions have been varied and a target displacement during deployment has been selected as the design metric. Specifically, the tape-spring should reach the final position in the shortest time with the minimal amount of overshoot and oscillations. Surrogate models have been used to reduce computational expense. Parameter values to achieve the target response have been computed and used to demonstrate the approach. Based on these results, the application of probabilistic methods for design of a tape-spring hinge has shown promise as a means of designing strain-energy components for more complex space concepts.

Lyle, Karen H.↗

NASA NTRS2010

Overview of Graphical User Interface for ARRBOD (Acute Radiation Risk and BRYNTRN Organ Dose Projection)

Solar particle events (SPEs) pose the risk of acute radiation sickness (ARS) to astronauts, because organ doses from large SPEs may reach critical levels during extra vehicular activities (EVAs) or lightly shielded spacecraft. NASA has developed an organ dose projection model of Baryon transport code (BRYNTRN) with an output data processing module of SUMDOSE, and a probabilistic model of acute radiation risk (ARR). BRYNTRN code operation requires extensive input preparation, and the risk projection models of organ doses and ARR take the output from BRYNTRN as an input to their calculations. With a graphical user interface (GUI) to handle input and output for BRYNTRN, these response models can be connected easily and correctly to BRYNTRN in a user friendly way. The GUI for the Acute Radiation Risk and BRYNTRN Organ Dose (ARRBOD) projection code provides seamless integration of input and output manipulations required for operations of the ARRBOD modules: BRYNTRN, SUMDOSE, and the ARR probabilistic response model. The ARRBOD GUI is intended for mission planners, radiation shield designers, space operations in the mission operations directorate (MOD), and space biophysics researchers. Assessment of astronauts organ doses and ARS from the exposure to historically large SPEs is in support of mission design and operation planning to avoid ARS and stay within the current NASA short-term dose limits. The ARRBOD GUI will serve as a proof-of-concept for future integration of other risk projection models for human space applications. We present an overview of the ARRBOD GUI product, which is a new self-contained product, for the major components of the overall system, subsystem interconnections, and external interfaces.

Kim, Myung-Hee Y.↗

DOE OSTI2024

Data-driven projection pursuit adaptation of polynomial chaos expansions for dependent high-dimensional parameters

Uncertainty quantification (UQ) and inference involving a large number of parameters are valuable tools for problems associated with heterogeneous and non-stationary behaviors. The difficulty with these problems is exacerbated when these parameters are statistically dependent requiring statistical characterization over joint measures. Probabilistic modeling methodologies stand as effective tools in the realms of UQ and inference. Among these, polynomial chaos expansions (PCE), when adapted to low-dimensional quantities of interest (QoI), provide effective yet accurate approximations for these QoI in terms of an adapted orthogonal basis. These adaptation techniques have been cast as projection pursuits in Gaussian Hilbert space in what has been referred to as a projection pursuit adaptation (PPA) by Xiaoshu Zeng and Roger Ghanem (2023). The PPA method efficiently identifies an optimal low-dimensional space for representing the QoI and simultaneously evaluates an optimal PCE within that space. The quality of this approximation clearly depends on the size of the training dataset, which is typically a function of the adapted reduced dimension. Here, the complexity of the problem is thus mediated by the complexity of the low-dimensional quantity of interest and not the complexity of the high-dimensional parameter space.

Data-driven↗