Impact and casualty prediction for malfunctioning multi-stage vehicles.
Monte Carlo prediction technique for impact and casualties in populated land masses for malfunctioning multistage vehicles
MONTE CARLO METHOD↗
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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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Predicting the performance of solar illuminated thermal louvers for the GOES I, J, K instruments
The new series of Geostationary Operational Environmental Satellites (GOES I,J,K,L/M) will have two earth viewing optical instruments which use passive, bi-metallic actuated, blade-type louvers for their thermal control. GOES is a three-axis stabilized spacecraft in geostationary orbit, which means that sun can enter the instrument apertures for several hours before and after local midnight. The solar heat absorbed in the instruments causes the louvers to open thus allowing the radiators to reject the heat. These louvered radiators are located on the north face of the instruments. To perform thermal analyses of the instruments, a modeling method was needed for predicting the thermal characteristics of the louvers. This task was complicated by the fact that sun shines on the louvers during the summer season. A Monte Carlo analytical technique was used to develop absorbed power and effective emissivity tables for the louvers. These tables could be used with the instrument math models to predict the thermal behavior of the instruments. Data from the Monte Carlo analysis showed that solar entrapment could have a significant effect on the heat rejection capability of the louvered radiators.
Mengers, David↗
Self-Learning Kinetic Monte Carlo Simulations of Radiation Damage in Nuclear Fuels
Understanding how irradiation affects the thermo-physical and mechanical properties of nuclear materials, such as thermal conductivity degradation in fuels and embrittlement of structural components, is critical to the safety and efficiency of nuclear reactors. These effects are largely governed by the formation and evolution of atomic-scale point defects and defect clusters. Due to their small sizes, however, these defects are invisible under high-resolution scanning transmission electron microscopy. This project aims to fill this experimental knowledge gap by integrating density functional theory (DFT), machine learning interatomic potential (MLIP), and kinetic Monte Carlo (KMC) techniques to predict longtime evolution of irradiation-induced defects in nuclear fuels.
36 - MATERIALS SCIENCE↗
Physics-informed machine learning modeling for predictive control using noisy data
Due to the occurrence of over-fitting at the learning phase, the modeling of chemical processes via artificial neural networks (ANN) by using corrupted data (i.e., noisy data) is an ongoing challenge. Therefore, this work investigates the effect of both Gaussian and non-Gaussian noise on the performance of process-structure based recurrent neural networks (RNN) models, which take the form of partially-connected RNN models in this work, that are used to approximate a class of multi-input-multi-outputs nonlinear systems. Furthermore, two different techniques, specifically Monte Carlo dropout and co-teaching, are utilized in the development of partially-connected RNN models. Here, these two techniques are employed to reduce the over-fitting in ANNs when noisy data is used in the training process and, hence, to improve the open-loop accuracy as well as the closed-loop performance under a Lyapunov-based model predictive controller (MPC). Aspen Plus Dynamics, a well-known high-fidelity process simulator, is used to simulate a large-scale chemical process application in order to demonstrate the anticipated improvements in both open-loop approximation and closed-loop controller performance in the presence of Gaussian and non-Gaussian noise in the data set using physics-informed RNNs.
97 MATHEMATICS AND COMPUTING↗
A STUDY OF CRYOPUMP CONFIGURATIONS IN FREE MOLECULAR FLOW REGIONS
Monte carlo computer technique applied to focusing of cryopump configurations in free molecular flow region
FREE MOLECULAR FLOW↗
Implementation of Speed Variation in the Structural Dynamic Assessment of Turbomachinery Flow-Path Components
During the design of turbomachinery flow path components, the assessment of possible structural resonant conditions is critical. Higher frequency modes of these structures are frequently found to be subject to resonance, and in these cases, design criteria require a forced response analysis of the structure with the assumption that the excitation speed exactly equals the resonant frequency. The design becomes problematic if the response analysis shows a violation of the HCF criteria. One possible solution is to perform "finite-life" analysis, where Miner's rule is used to calculate the actual life in seconds in comparison to the required life. In this situation, it is beneficial to incorporate the fact that, for a variety of turbomachinery control reasons, the speed of the rotor does not actually dwell at a single value but instead dithers about a nominal mean speed and during the time that the excitation frequency is not equal to the resonant frequency, the damage accumulated by the structure is diminished significantly. Building on previous investigations into this process, we show that a steady-state assumption of the response is extremely accurate for this typical case, resulting in the ability to quickly account for speed variation in the finite-life analysis of a component which has previously had its peak dynamic stress at resonance calculated. A technique using Monte Carlo simulation is also presented which can be used when specific speed time histories are not available. The implementation of these techniques can prove critical for successful turbopump design, as the improvement in life when speed variation is considered is shown to be greater than a factor of two.
Brown, Andrew M.↗
Implementation of Speed Variation in the Structural Dynamic Assessment of Turbomachinery Flow-Path Components
During the design of turbomachinery flow path components, the assessment of possible structural resonant conditions is critical. Higher frequency modes of these structures are frequently found to be subject to resonance, and in these cases, design criteria require a forced response analysis of the structure with the assumption that the excitation speed exactly equals the resonant frequency. The design becomes problematic if the response analysis shows a violation of the HCF criteria. One possible solution is to perform "finite-life" analysis, where Miner's rule is used to calculate the actual life in seconds in comparison to the required life. In this situation, it is beneficial to incorporate the fact that, for a variety of turbomachinery control reasons, the speed of the rotor does not actually dwell at a single value but instead dithers about a nominal mean speed and during the time that the excitation frequency is not equal to the resonant frequency, the damage accumulated by the structure is diminished significantly. Building on previous investigations into this process, we show that a steady-state assumption of the response is extremely accurate for this typical case, resulting in the ability to quickly account for speed variation in the finite-life analysis of a component which has previously had its peak dynamic stress at resonance calculated. A technique using Monte Carlo simulation is also presented which can be used when specific speed time histories are not available. The implementation of these techniques can prove critical for successful turbopump design, as the improvement in life when speed variation is considered is shown to be greater than a factor of two
Brown, Andrew M.↗
Radiation configuration factors for annular rings and hemispherical sectors.
In the application of Monte Carlo computer techniques to the study of problems in the free molecular flow regime, the analogy between radiation heat transfer with diffuse emissions and the free molecular flow field makes it possible to use the radiation configuration factors as a test on the operation of the Monte Carlo program. A source plane which was an annular ring of arbitrary radius around a sphere with the center of the disk coincident with the center of the sphere was examined. Since various indicators seemed to verify the correct operation of the program, the program was used to calculate the radiation configuration factors. The study adds a new geometry to the general literature on radiation configuration factors.
Ballance, J. O.↗
Probabilistic Micromechanics and Macromechanics for Ceramic Matrix Composites
The properties of ceramic matrix composites (CMC's) are known to display a considerable amount of scatter due to variations in fiber/matrix properties, interphase properties, interphase bonding, amount of matrix voids, and many geometry- or fabrication-related parameters, such as ply thickness and ply orientation. This paper summarizes preliminary studies in which formal probabilistic descriptions of the material-behavior- and fabrication-related parameters were incorporated into micromechanics and macromechanics for CMC'S. In this process two existing methodologies, namely CMC micromechanics and macromechanics analysis and a fast probability integration (FPI) technique are synergistically coupled to obtain the probabilistic composite behavior or response. Preliminary results in the form of cumulative probability distributions and information on the probability sensitivities of the response to primitive variables for a unidirectional silicon carbide/reaction-bonded silicon nitride (SiC/RBSN) CMC are presented. The cumulative distribution functions are computed for composite moduli, thermal expansion coefficients, thermal conductivities, and longitudinal tensile strength at room temperature. The variations in the constituent properties that directly affect these composite properties are accounted for via assumed probabilistic distributions. Collectively, the results show that the present technique provides valuable information about the composite properties and sensitivity factors, which is useful to design or test engineers. Furthermore, the present methodology is computationally more efficient than a standard Monte-Carlo simulation technique; and the agreement between the two solutions is excellent, as shown via select examples.
Murthy, Pappu L. N.↗
Heat transfer and forces on concave surfaces in free molecule flow.
A Monte Carlo modeling technique is described for mathematically simulating free molecular flows over a concave spherical surface and a concave cylindrical surface of finite length. The half-angle of the surfaces may vary from 0 to 90 degrees, and the incident flow may have an arbitrary speed ratio and an arbitrary angle of attack. Partial diffuse reflection and imperfect energy accommodation for molecules colliding with the surfaces are also considered. Results of heat transfer, drag and lift coefficients are presented for a variety of flow conditions. The present Monte Carlo results are shown to be in very good agreement with certain available theoretical solutions.
Fan, C.↗
Solution of the stochastic control problem in unbounded domains.
Bellman's dynamic programming equation for the optimal index and control law for stochastic control problems is a parabolic or elliptic partial differential equation frequently defined in an unbounded domain. Existing methods of solution require bounded domain approximations, the application of singular perturbation techniques or Monte Carlo simulation procedures. In this paper, using the fact that Poisson impulse noise tends to a Gaussian process under certain limiting conditions, a method which achieves an arbitrarily good approximate solution to the stochastic control problem is given. The method uses the two iterative techniques of successive approximation and quasi-linearization and is inherently more efficient than existing methods of solution.
Robinson, P.↗
Multivariate Testing of Sampling Techniques to Address Class Imbalance in Building Use Type Classification
This study addresses the challenges inherent in building use type classification, particularly focusing on the issue of class imbalance in the training datasets for machine learning classifiers. We comprehensively analyze the efficacy of various class-balancing sampling techniques. Employing Monte Carlo simulations and Bayesian optimization, we evaluated the performance of multiple sampling methods, including Random Oversampling, Random Undersampling, SMOTE, Borderline-SMOTE, and ADASYN, across a dataset encompassing nine southeastern coastal states of the United States. Our findings reveal that simple random over- and undersampling techniques outperform more sophisticated methods. Additionally, we show inherent value in creating an imbalance in training data to effectively train a machine learning classifier for distinguishing between residential and nonresidential buildings. This study provides valuable guidance for future research on building use type classification research and lays essential groundwork for developing attribute-rich building stock datasets.
Adams, Daniel↗
Simulated performance of the NASA 30/20 GHz test transponder using multi-H phase coded modulation
The performance of a proposed NASA 30/20 GHz satellite communications system is studied for multi-h phase coded modulation (MHPM) schemes. The techniques used to model and simulate a satellite communications channel including transmitter, receiver, filters, nonlinearities, and interferers are presented. The performance of various MHPM schemes is compared for several different channel configurations. As a measure of performance, the probability of bit error vs Eb/NO is computed using a Monte Carlo simulation technique. It is found that, regardless of the channel configuration, MHPM schemes can provide power efficiency over serial minimum shift keying modulation.
Meader, C. B.↗
Issues/considerations and performance prediction of LEO protective coatings
Organic materials can be used in low earth orbit for long periods only if they are made to be durable to atomic oxygen in addition to the threats of ultraviolet radiation, micrometeoroid and debris impact, thermal cycling, and charged particle radiation. In many materials applications, it is more cost-effective to utilize atomic oxygen protective coatings over materials which are vulnerable to attack by atomic oxygen rather than developing alternative materials which are inherently durable. Many metal and metal oxide coatings have been shown to be potentially suitable for long term protection of organic polymers in low earth orbit. The protection afforded by thin film coatings on polymeric substrates is highly dependent on the completeness of coverage of the coating. Thus, the prime indicator of durability is the size and abundance of pin windows and scratches in the protective coating. Such defects depend on the deposition technique, smoothness of the surface to be protected, and presence of particulate contaminants. Issues and considerations relevant to substrate preparation, deposition of protective coatings, ground laboratory simulation and evaluation of threats in low earth orbit to protective materials, results of in-space tests, and the use of Monte Carlo modeling techniques to predict in-space durability are presented in graphic form.
Banks, Bruce A.↗
A Monte Carlo Laplace Transform Estimator for Radiation Transport
This work formulates and implements a Laplace transform estimator in a simple Monte Carlo radiation transport code. The estimator maps flux-based quantities of interest, like reaction rates, from a desired phase-space dimension to the complex Laplace domain. This on-the-fly Monte Carlo integration technique enables the spectral analysis of arbitrary nuclear systems via the Laplace transform. A simple code tests the estimator in neutron slowing-down problems across various infinite media, and the results compare well with Ganapol’s uninverted analytical solution of the neutron slowing-down equation.
97 MATHEMATICS AND COMPUTING↗
Post-Calibration Uncertainty Analysis for Travel Times at a Naval Weapons Industrial Reserve Plant
The Naval Weapons Industrial Reserve Plant (NWIRP) in McGregor, Texas began manufacturing explosives in 1980 and several hazardous chemicals were discovered in lakes and streams surrounding the site in 1998. Contaminants traveled to local lakes and streams much faster than initially predicted. This research estimated contaminant travel times and identified locations where monitoring wells should be installed to yield the greatest reductions in uncertainties in travel-time predictions. To this end, groundwater and particle-tracking models for NWIRP site were built to predict hydraulic heads and contaminant travel times. Next, parameter (hydraulic conductivities) uncertainties, parameter identifiabilities, observation (hydraulic heads) worth, and predictive (contaminant travel times) uncertainties were quantified. Parameter uncertainties were reduced by up to 92%; a total of 19 of 158 parameters were at least moderately identifiable; travel-time uncertainties were reduced up to 92%. Additionally, travel-time predictions and post-calibration parameter distributions were generated using the null-space Monte Carlo (NSMC) technique. NSMC predicted that conservative tracers exited the flow system within a year, which matches with field data. Finally, an observations-worth analysis found that additional 11 more measurements would reduce travel-time uncertainties by factors from 1.04 to 4.3 over existing data if monitoring wells were installed at the suggested locations.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Quantifying the Sensitivity of Condition Incidence Parameters in the Evidence Library
One approach to quantifying spaceflight risk at NASA makes use event driven probabilistic techniques. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is such a tool that estimates medical risk metrics via simulation and enables optimization of medical resources subject to mission constraints [1]. Previous analyses have informed medical set composition, exercise countermeasures, and water intake, where each analysis quantifies the risk associated with proposed variations in system design. As future mission profiles extend beyond Low-Earth Orbit (LEO) and lengthen in duration, understanding these risks and contributing factors is critical. MEDPRAT employs Monte Carlo sampling techniques to simulate missions and track the occurrence of medical events. These events follow fault-tree-like progressions through levels of severity and mitigation via medical treatment to many possible outcomes and these are reported throughout the mission. Making this possible, are the medical databases that contain evidence gathered by the Human Research Program (HRP). Quantifying the impact of uncertainty or variability in the input data is an important step in evaluating the credibility of modeling and simulation results. In this work, we investigate the sensitivity of medical risk metrics with respect to the condition incidence parameters within the Evidence Library (EL) [2] as the medical database input for MEDPRAT. The medical conditions, contained in the EL, are equipped with incidence rates that describe the likelihood that the condition will occur. These incidence rates reflect historical spaceflight data or when appropriate, terrestrial data. In this presentation, we will explore how uncertainty in these rates propagate to the medical risk described by MEDPRAT. These results identify the conditions and parameters with the largest contribution to medical risks.
Ian Lim↗
Demonstration of Linear Covariance Analysis Techniques to Evaluate Entry Descent and Landing Guidance Algorithms, Vehicle Configurations, Analysis Techniques, and Trajectory Profiles
Linear covariance analysis techniques have been previously developed to analyze closed-loop entry, descent, and landing (EDL) scenarios and the initial validation efforts are under-way confirming the generated GN&C system performance results. Given both the theoretical foundation and previous conceptual demonstration, this work begins to flex the potential of linear covariance analysis for atmospheric flight and highlight its versatility and reliability by evaluating multiple entry guidance algorithms, vehicle configurations, trajectory profiles, environment conditions, and analysis techniques for a variety of trade studies. To demonstrate the benefit linear covariance analysis can provide in producing rapid yet accurate performance data, two entry profiles are adopted including the NASA Mars Science Laboratory (MSL) and Exploration Flight Test-1 (EFT-1) while utilizing two different guidance algorithms, the Apollo Final Phase (AFP) and the Fully Numeric Predictor-Corrector Entry Guidance (FNPEG) with different navigation sensor suites in a 6 degree-of-freedom (6-DOF) simulation environment. Results are shown using both linear covariance and Monte Carlo analysis techniques to high-light the consistency between the two methodologies and continue the validation maturation of linear covariance analysis for entry, descent, and landing.
EDL↗