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NASA NTRS2003

Development of Testing Methodologies for the Mechanical Properties of MEMS

This effort is to investigate and design testing strategies to determine the mechanical properties of MicroElectroMechanical Systems (MEMS) as well as investigate the development of a MEMS Probabilistic Design Methodology (PDM). One item of potential interest is the design of a test for the Weibull size effect in pressure membranes. The Weibull size effect is a consequence of a stochastic strength response predicted from the Weibull distribution. Confirming that MEMS strength is controlled by the Weibull distribution will enable the development of a probabilistic design methodology for MEMS - similar to the GRC developed CARES/Life program for bulk ceramics. However, the primary area of investigation will most likely be analysis and modeling of material interfaces for strength as well as developing a strategy to handle stress singularities at sharp corners, filets, and material interfaces. This will be a continuation of the previous years work. The ultimate objective of this effort is to further develop and verify the ability of the Ceramics Analysis and Reliability Evaluation of Structures Life (CARES/Life) code to predict the time-dependent reliability of MEMS structures subjected to multiple transient loads.

Ekwaro-Osire, Stephen↗

NASA NTRS2014

Implementing Badhwar-O'Neill Galactic Cosmic Ray Model for the Analysis of Space Radiation Exposure

For the analysis of radiation risks to astronauts and planning exploratory space missions, accurate energy spectrum of galactic cosmic radiation (GCR) is necessary. Characterization of the ionizing radiation environment is challenging because the interplanetary plasma and radiation fields are modulated by solar disturbances and the radiation doses received by astronauts in interplanetary space are likewise influenced. A model of the Badhwar‐O'Neill 2011 (BO11) GCR environment, which is represented by GCR deceleration potential theta, has been derived by utilizing all of the GCR measurements from balloons, satellites, and the newer NASA Advanced Composition Explorer (ACE). In the BO11 model, the solar modulation level is derived from the mean international sunspot numbers with time‐delay, which has been calibrated with actual flight instrument measurements to produce better GCR flux data fit during solar minima. GCR fluxes provided by the BO11 model were compared with various spacecraft measurements at 1 AU, and further comparisons were made for the tissue equivalent proportional counters measurements at low Earth orbits using the high‐charge and energy transport (HZETRN) code and various GCR models. For the comparison of the absorbed dose and dose equivalent calculations with the measurements by Radiation Assessment Detector (RAD) at Gale crater on Mars, the intensities and energies of GCR entering the heliosphere were calculated by using the BO11 model, which accounts for time‐dependent attenuation of the local interstellar spectrum of each element. The BO11 model, which has emphasized for the last 24 solar minima, showed in relatively good agreement with the RAD data for the first 200 sols, but it was resulted in to be less well during near the solar maximum of solar cycle 24 due to subtleties in the changing heliospheric conditions. By performing the error analysis of the BO11 model and the optimization in reducing overall uncertainty, the resultant BO13 model corrects the fit at solar maxima as well as being accurate at solar minima. The BO13 model is implemented to the NASA Space Cancer Risk model for the assessment of radiation risks. Overall cumulative probability distribution of solar modulation parameters represents the percentile rank of the average interplanetary GCR environment, and the probabilistic radiation risks can be assessed for various levels of GCR environment to support mission design and operational planning for future manned space exploration missions.

Kim, Myung-Hee Y.↗

NASA NTRS2000

Probabilistic Multi-Scale, Multi-Level, Multi-Disciplinary Analysis and Optimization of Engine Structures

Aircraft engines are assemblies of dynamically interacting components. Engine updates to keep present aircraft flying safely and engines for new aircraft are progressively required to operate in more demanding technological and environmental requirements. Designs to effectively meet those requirements are necessarily collections of multi-scale, multi-level, multi-disciplinary analysis and optimization methods and probabilistic methods are necessary to quantify respective uncertainties. These types of methods are the only ones that can formally evaluate advanced composite designs which satisfy those progressively demanding requirements while assuring minimum cost, maximum reliability and maximum durability. Recent research activities at NASA Glenn Research Center have focused on developing multi-scale, multi-level, multidisciplinary analysis and optimization methods. Multi-scale refers to formal methods which describe complex material behavior metal or composite; multi-level refers to integration of participating disciplines to describe a structural response at the scale of interest; multidisciplinary refers to open-ended for various existing and yet to be developed discipline constructs required to formally predict/describe a structural response in engine operating environments. For example, these include but are not limited to: multi-factor models for material behavior, multi-scale composite mechanics, general purpose structural analysis, progressive structural fracture for evaluating durability and integrity, noise and acoustic fatigue, emission requirements, hot fluid mechanics, heat-transfer and probabilistic simulations. Many of these, as well as others, are encompassed in an integrated computer code identified as Engine Structures Technology Benefits Estimator (EST/BEST) or Multi-faceted/Engine Structures Optimization (MP/ESTOP). The discipline modules integrated in MP/ESTOP include: engine cycle (thermodynamics), engine weights, internal fluid mechanics, cost, mission and coupled structural/thermal, various composite property simulators and probabilistic methods to evaluate uncertainty effects (scatter ranges) in all the design parameters. The objective of the proposed paper is to briefly describe a multi-faceted design analysis and optimization capability for coupled multi-discipline engine structures optimization. Results are presented for engine and aircraft type metrics to illustrate the versatility of that capability. Results are also presented for reliability, noise and fatigue to illustrate its inclusiveness. For example, replacing metal rotors with composites reduces the engine weight by 20 percent, 15 percent noise reduction, and an order of magnitude improvement in reliability. Composite designs exist to increase fatigue life by at least two orders of magnitude compared to state-of-the-art metals.

Chamis, Christos C.↗

NASA NTRS1990

A methodology for probabilistic prediction of structural failures of launch vehicle propulsion systems

A quantitative methodology for estimating the probability of occurrence of critical structural failure modes of launch vehicle propulsion systems during mission operation is presented. In this approach, engineering analysis models that characterize specific failure modes based on the physics and mechanics of the failure phenomenon are used in a prescribed probabalistic structure in which quantified uncertainty in the information used in the analysis and quantified uncertainty of the engineering analysis models themselves are both considered in estimating failure risk. The methodology is applicable to high cycle fatigue, low cycle fatigue, flaw propagation stress rupture, and other failure modes of launch vehicle propulsion systems and other space-flight systems. The probabalistic failure assessment presented provides the capability of performing quantitative analyses to rank options to improve reliability, thereby enabling limited financial resources of development and improvement programs to be more effectively allocated.

Moore, N.↗

NASA NTRS2003

Testing and Analysis of NEXT Ion Engine Discharge Cathode Assembly Wear

Experimental and analytical investigations were conducted to predict the wear of the discharge cathode keeper in the NASA Evolutionary Xenon Thruster. The ion current to the keeper was found to be highly dependent upon the beam current, and the average beam current density was nearly identical to that of the NSTAR thruster for comparable beam current density. The ion current distribution was highly peaked toward the keeper orifice. A deterministic wear assessment predicted keeper orifice erosion to the same diameter as the cathode tube after processing 375 kg of xenon. A rough estimate of discharge cathode assembly life limit due to sputtering indicated that the current design exceeds the qualification goal of 405 kg. Probabilistic wear analysis showed that the plasma potential and the sputter yield contributed most to the uncertainty in the wear assessment. It was recommended that fundamental experimental and modeling efforts focus on accurately describing the plasma potential and the sputtering yield.

Domonkos, Matthew T.↗

NASA NTRS2016

Application of Interval Predictor Models to Space Radiation Shielding

This paper develops techniques for predicting the uncertainty range of an output variable given input-output data. These models are called Interval Predictor Models (IPM) because they yield an interval valued function of the input. This paper develops IPMs having a radial basis structure. This structure enables the formal description of (i) the uncertainty in the models parameters, (ii) the predicted output interval, and (iii) the probability that a future observation would fall in such an interval. In contrast to other metamodeling techniques, this probabilistic certi cate of correctness does not require making any assumptions on the structure of the mechanism from which data are drawn. Optimization-based strategies for calculating IPMs having minimal spread while containing all the data are developed. Constraints for bounding the minimum interval spread over the continuum of inputs, regulating the IPMs variation/oscillation, and centering its spread about a target point, are used to prevent data over tting. Furthermore, we develop an approach for using expert opinion during extrapolation. This metamodeling technique is illustrated using a radiation shielding application for space exploration. In this application, we use IPMs to describe the error incurred in predicting the ux of particles resulting from the interaction between a high-energy incident beam and a target.

Crespo, Luis G.↗

NASA NTRSDate not supplied

CFD2030 Grand Challenge: CFD-in-the-Loop Monte Carlo Simulation for Space Vehicle Design

Space vehicle design and certification differs widely from aircraft design relying more on probabilistic approaches than deterministic. Monte Carlo simulation plays an important role in the probabilistic design of space vehicles to ensure robust and reliable operation. Today, Monte Carlo flight simulation requires 1000’s of trajectory simulations that use databases to provide aerosciences models. These databases can be extremely expensive and time consuming to develop. Replacing these databases with unsteady computational fluid dynamics directly in the simulation loop has potential to significantly reduce the time required to analyze space vehicle concepts, improve simulation accuracy, and reduce the cost of space vehicle development. The CFD Vision 2030 Study outlined gaps and roadblocks to meeting the vision described in the study. The geometric, physical, and computational challenges associated with CFD-in-the-loop Monte Carlo simulation for space vehicle design are substantial and serve as an excellent grand challenge to advance the CFD 2030 vision.

CFD2030↗

NASA NTRS2002

Probabilistic and Possibilistic Analyses of the Strength of a Bonded Joint

The effects of uncertainties on the predicted strength of a single lap shear joint are examined. Probabilistic and possibilistic methods are used to account for uncertainties. A total of ten variables are assumed to be random, with normal distributions. Both Monte Carlo Simulation and the First Order Reliability Method are used to determine the probability of failure. Triangular membership functions with upper and lower bounds located at plus or minus three standard deviations are used to model uncertainty in the possibilistic analysis. The alpha cut (or vertex) method is used to evaluate the possibility of failure. Linear and geometrically nonlinear finite element analyses are used calculate the response of the joint; fracture in the adhesive and material strength failure in the strap are used to evaluate its strength. Although probabilistic and possibilistic analyses provide significantly more information than do conventional deterministic analyses, they are computationally expensive. A novel scaling approach is developed and used to substantially reduce the computational cost of the probabilistic and possibilistic analyses. The possibilistic approach for treating uncertainties appears to be viable during the conceptual and preliminary design stages when limited data are available and high accuracies are not needed. However, this viability is mixed with several cautions that are discussed herein.

Stroud, W. Jefferson↗

NASA NTRS1994

A statistical inference approach for the retrieval of the atmospheric ozone profile from simulated satellite measurements of solar backscattered ultraviolet radiation

NASA's Mission to Planet Earth (MTPE) will address important interdisciplinary and environmental issues such as global warming, ozone depletion, deforestation, acid rain, and the like with its long term satellite observations of the Earth and with its comprehensive Data and Information System. Extensive sets of satellite observations supporting MTPE will be provided by the Earth Observing System (EOS), while more specific process related observations will be provided by smaller Earth Probes. MTPE will use data from ground and airborne scientific investigations to supplement and validate the global observations obtained from satellite imagery, while the EOS satellites will support interdisciplinary research and model development. This is important for understanding the processes that control the global environment and for improving the prediction of events. In this paper we illustrate the potential for powerful artificial intelligence (AI) techniques when used in the analysis of the formidable problems that exist in the NASA Earth Science programs and of those to be encountered in the future MTPE and EOS programs. These techniques, based on the logical and probabilistic reasoning aspects of plausible inference, strongly emphasize the synergetic relation between data and information. As such, they are ideally suited for the analysis of the massive data streams to be provided by both MTPE and EOS. To demonstrate this, we address both the satellite imagery and model enhancement issues for the problem of ozone profile retrieval through a method based on plausible scientific inferencing. Since in the retrieval problem, the atmospheric ozone profile that is consistent with a given set of measured radiances may not be unique, an optimum statistical method is used to estimate a 'best' profile solution from the radiances and from additional a priori information.

Bonavito, N. L.↗

NASA NTRS2008

Probalistic Assessment of Radiation Risk for Solar Particle Events

For long duration missions outside of the protection of the Earth's magnetic field, exposure to solar particle events (SPEs) is a major safety concern for crew members during extra-vehicular activities (EVAs) on the lunar surface or Earth-to-moon or Earth-to-Mars transit. The large majority (~90%) of SPEs have small or no health consequences because the doses are low and the particles do not penetrate to organ depths. However, there is an operational challenge to respond to events of unknown size and duration. We have developed a probabilistic approach to SPE risk assessment in support of mission design and operational planning. Using the historical database of proton measurements during the past 5 solar cycles, the functional form of hazard function of SPE occurrence per cycle was found for nonhomogeneous Poisson model. A typical hazard function was defined as a function of time within a non-specific future solar cycle of 4000 days duration. Distributions of particle fluences for a specified mission period were simulated ranging from its 5th to 95th percentile. Organ doses from large SPEs were assessed using NASA's Baryon transport model, BRYNTRN. The SPE risk was analyzed with the organ dose distribution for the given particle fluences during a mission period. In addition to the total particle fluences of SPEs, the detailed energy spectra of protons, especially at high energy levels, were recognized as extremely important for assessing the cancer risk associated with energetic particles for large events. The probability of exceeding the NASA 30-day limit of blood forming organ (BFO) dose inside a typical spacecraft was calculated for various SPE sizes. This probabilistic approach to SPE protection will be combined with a probabilistic approach to the radiobiological factors that contribute to the uncertainties in projecting cancer risks in future work.

Kim, Myung-Hee Y.↗

NASA NTRS2013

Managing the Risk of Command File Errors

Command File Error (CFE), as defined by the Jet Propulsion Laboratory's (JPL) Mission Operations Assurance (MOA) is, regardless of the consequence on the spacecraft, either: an error in a command file sent to the spacecraft, an error in the process for developing and delivering a command file to the spacecraft, or the omission of a command file that should have been sent to the spacecraft. The risk consequence of a CFE can be mission ending and thus a concern to space exploration projects during their mission operations. A CFE during space mission operations is often the symptom of some kind of imbalance or inadequacy within the system that comprises the hardware & software used for command generation and the human experts involved in this endeavour. As we move into an era of enhanced collaboration with other NASA centers and commercial partners, these systems become more and more complex and hence it is all the more important to formally model and analyze CFEs in order to manage the risk of CFEs. Here we will provide a summary of the ongoing efforts at JPL in this area and also explain some more recent developments in the area of developing quantitative models for the purpose of managing CFE's.

Bayesian Belief Networks↗

NASA NTRS2012

Model-Based Method for Sensor Validation

Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

Vatan, Farrokh↗

NASA NTRS2016

Applying Simulated Annealing to Problems in Model-Based Diagnosis

Generating all diagnoses is computationally intractable. Therefore, many of the state-of-the-art approaches are incomplete. Quantum computers may however offer a solution. The first commercially available quantum computer is being used to minimize polynomials that are difficult for classical simulated annealing but easy for quantum annealing. All problems in Model-based Diagnosis (MBD) can be transformed into a polynomial minimization problem, allowing one to apply a quantum algorithm called quantum annealing to solve MBD problems. To better understand the need for this quantum approach, we designed two simulated annealingdiagnostic algorithms tailored to run on a polynomial representation of MBD. These algorithms differ on their policy for random neighborhood variable selection. In addition, enhanced metrics were devised to provide more diagnostic coverage. Finally, these two simulated annealing algorithms were analyzed and empirically evaluated and compared against state-of-the-art probabilistic methods for MBD such as SAFARI using ISCAS-85.

Simulated annealing↗

NASA NTRSDate not supplied

Consequences of Asteroid Characterization on the State of Knowledge about Inferred Physical Properties and Impact Risk

Physical characteristics of Near-Earth Objects (NEOs) are essential inputs to planetary defense assessments. The size, density, and strength of an NEO are critical inputs to modeling behavior during atmospheric entry as well as assessing the risk of impact. Similarly, knowledge of the physical characteristics of an object are necessary to evaluate the probable result of a mitigation mission. Usually, these attributes cannot be directly measured, but increasingly sophisticated methods have been developed to infer physical properties from related measurements of asteroids, meteors, and/or meteorites. Fortuitously, some of these measurements have been obtained for enough NEOs to elucidate the distribution of values across the sampled population. However, the situation becomes more challenging when considering a specific asteroid, since it is unlikely that all the relevant measurements have been made for any given object. We have developed a Bayesian network that can combine available information about a particular NEO with knowledge of the larger population to infer probabilistic values and uncertainties for physical characteristics of interest. Distributions of asteroid population albedos, taxonomic classes, and macroporosities, along with meteorite density distributions and associations between taxonomic classes and meteorite classes, provide the default distributions for the network’s parameter nodes. The inference network links parameters for each virtual asteroid either deterministically or probabilistically as appropriate, and eliminates any unphysical combinations of parameters. Within the context of planetary defense, our Bayesian network can be used to constrain the ranges of likely impactor properties, which can subsequently reduce the uncertainty in modelling of atmospheric entry, mitigation efficacy, and impact risk assessment. When additional measurements become available for a specific object, the network incorporates those measurements to generate virtual asteroids with property distributions that are consistent with the measurements. We will use the 2023 PDC scenario to demonstrate how the inference network can be combined with plausible characterization measurements to refine the state of knowledge about likely combinations of physical parameters and the resulting impact risk.

risk assessment↗

NASA NTRSDate not supplied

Consequences of Asteroid Characterization on the State of Knowledge about Inferred Physical Properties and Impact Risk

risk assessment↗

NASA NTRS1976

Computation-based reliability analysis

A reliability analysis method for computing systems is considered in which the underlying criteria for 'success' are based on the computations the system must perform in the use environment. Beginning with a general model of a 'computer with faults', intermediate concepts of a 'tolerance relation' and an 'environment space' are introduced which account for the computational needs of the user and the probabilistic nature of the use environment. These concepts are then incorporated to obtain a precisely defined class of computation-based reliability measures. Formulation of a particular measure is illustrated and results, applying this measure, are compared with those of a typical structure-based analysis.

Meyer, J. F.↗

NASA NTRS2007

Probabilistic Thermal Analysis During Mars Reconnaissance Orbiter Aerobraking

A method for performing a probabilistic thermal analysis during aerobraking has been developed. The analysis is performed on the Mars Reconnaissance Orbiter solar array during aerobraking. The methodology makes use of a response surface model derived from a more complex finite element thermal model of the solar array. The response surface is a quadratic equation which calculates the peak temperature for a given orbit drag pass at a specific location on the solar panel. Five different response surface equations are used, one of which predicts the overall maximum solar panel temperature, and the remaining four predict the temperatures of the solar panel thermal sensors. The variables used to define the response surface can be characterized as either environmental, material property, or modeling variables. Response surface variables are statistically varied in a Monte Carlo simulation. The Monte Carlo simulation produces mean temperatures and 3 sigma bounds as well as the probability of exceeding the designated flight allowable temperature for a given orbit. Response surface temperature predictions are compared with the Mars Reconnaissance Orbiter flight temperature data.

Dec, John A.↗

NASA NTRS2003

Advanced Software for Analysis of High-Speed Rolling-Element Bearings

COBRA-AHS is a package of advanced software for analysis of rigid or flexible shaft systems supported by rolling-element bearings operating at high speeds under complex mechanical and thermal loads. These loads can include centrifugal and thermal loads generated by motions of bearing components. COBRA-AHS offers several improvements over prior commercial bearing-analysis programs: It includes innovative probabilistic fatigue-life-estimating software that provides for computation of three-dimensional stress fields and incorporates stress-based (in contradistinction to prior load-based) mathematical models of fatigue life. It interacts automatically with the ANSYS finite-element code to generate finite-element models for estimating distributions of temperature and temperature-induced changes in dimensions in iterative thermal/dimensional analyses: thus, for example, it can be used to predict changes in clearances and thermal lockup. COBRA-AHS provides an improved graphical user interface that facilitates the iterative cycle of analysis and design by providing analysis results quickly in graphical form, enabling the user to control interactive runs without leaving the program environment, and facilitating transfer of plots and printed results for inclusion in design reports. Additional features include roller-edge stress prediction and influence of shaft and housing distortion on bearing performance.

Poplawski, J. V.↗