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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 NTRS2017

Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment

Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This paper will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

Britton, Paul↗
NASA NTRS2002

Probabilistic Analysis and Density Parameter Estimation Within Nessus

This NASA educational grant has the goal of promoting probabilistic analysis methods to undergraduate and graduate UTSA engineering students. Two undergraduate-level and one graduate-level course were offered at UTSA providing a large number of students exposure to and experience in probabilistic techniques. The grant provided two research engineers from Southwest Research Institute the opportunity to teach these courses at UTSA, thereby exposing a large number of students to practical applications of probabilistic methods and state-of-the-art computational methods. In classroom activities, students were introduced to the NESSUS computer program, which embodies many algorithms in probabilistic simulation and reliability analysis. Because the NESSUS program is used at UTSA in both student research projects and selected courses, a student version of a NESSUS manual has been revised and improved, with additional example problems being added to expand the scope of the example application problems. This report documents two research accomplishments in the integration of a new sampling algorithm into NESSUS and in the testing of the new algorithm. The new Latin Hypercube Sampling (LHS) subroutines use the latest NESSUS input file format and specific files for writing output. The LHS subroutines are called out early in the program so that no unnecessary calculations are performed. Proper correlation between sets of multidimensional coordinates can be obtained by using NESSUS' LHS capabilities. Finally, two types of correlation are written to the appropriate output file. The program enhancement was tested by repeatedly estimating the mean, standard deviation, and 99th percentile of four different responses using Monte Carlo (MC) and LHS. These test cases, put forth by the Society of Automotive Engineers, are used to compare probabilistic methods. For all test cases, it is shown that LHS has a lower estimation error than MC when used to estimate the mean, standard deviation, and 99th percentile of the four responses at the 50 percent confidence level and using the same number of response evaluations for each method. In addition, LHS requires fewer calculations than MC in order to be 99.7 percent confident that a single mean, standard deviation, or 99th percentile estimate will be within at most 3 percent of the true value of the each parameter. Again, this is shown for all of the test cases studied. For that reason it can be said that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ; furthermore, the newest LHS module is a valuable new enhancement of the program.

Godines, Cody R.↗
NASA NTRS2017

Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment

"Uncertainty analysis itself is uncertain, therefore, you cannot evaluate it exactly," Source Uncertain Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This paper will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

Britton, Paul T.↗
NASA NTRS2015

Comparative Analysis of Static and Dynamic Probabilistic Risk Assessment

This study examines three different methodologies for producing loss-of-mission (LOM) and loss-of-crew (LOC) risks estimates for probabilistic risk assessments (PRA) of crewed spacecraft. The three bottom-up, component-based PRA approaches examined are a traditional static fault tree, a dynamic Monte Carlo simulation, and a fault tree hybrid that incorporates some dynamic elements. These approaches were used to model the reaction control system thruster pod of a generic crewed spacecraft and mission, and a comparative analysis of the methods is presented. The methodologies are assessed in terms of the process of modeling a system, the actionable information produced for the design team, and the overall fidelity of the quantitative risk evaluation generated. The system modeling process is compared in terms of the effort required to generate the initial model, update the model in response to design changes, and support mass-versus-risk trade studies. The results are compared by examining the top-level LOM/LOC estimates and the relative risk driver rankings at the failure mode level. The fidelity of each modeling methodology is discussed in terms of its capability to handle real-world system dynamics such as cold-sparing, changes in mission operations due to loss of redundancy, and common cause failure modes. The paper also discusses the applicability of each methodology to different phases of system development and shows that a single methodology may not be suitable for all of the many purposes of a spacecraft PRA. The fault tree hybrid approach is shown to be best suited to the needs of early assessments during conceptual design phases. As the design begins to mature, the level of detail represented in the risk model must go beyond redundancy and nominal mission operations to include dynamic, time- and state-dependent system responses as well as diverse system capabilities. This is best accomplished using the dynamic simulation approach, since these phenomena are not easily captured by static methods. Ultimately, once the design has been finalized and the goal of the PRA is to provide design validation and requirement verification, more traditional, static fault tree approaches may become as appropriate as the simulation method.

Mattenberger, Christopher J.↗
NASA NTRS2002

Software risk estimation and management techniques at JPL

In this talk we will discuss how uncertainty has been incorporated into the JPL software model, probabilistic-based estimates, and how risk is addressed, how cost risk is currently being explored via a variety of approaches, from traditional risk lists, to detailed WBS-based risk estimates to the Defect Detection and Prevention (DDP) tool.

software risk↗
NASA NTRS1997

Shape Optimization by Bayesian-Validated Computer-Simulation Surrogates

A nonparametric-validated, surrogate approach to optimization has been applied to the computational optimization of eddy-promoter heat exchangers and to the experimental optimization of a multielement airfoil. In addition to the baseline surrogate framework, a surrogate-Pareto framework has been applied to the two-criteria, eddy-promoter design problem. The Pareto analysis improves the predictability of the surrogate results, preserves generality, and provides a means to rapidly determine design trade-offs. Significant contributions have been made in the geometric description used for the eddy-promoter inclusions as well as to the surrogate framework itself. A level-set based, geometric description has been developed to define the shape of the eddy-promoter inclusions. The level-set technique allows for topology changes (from single-body,eddy-promoter configurations to two-body configurations) without requiring any additional logic. The continuity of the output responses for input variations that cross the boundary between topologies has been demonstrated. Input-output continuity is required for the straightforward application of surrogate techniques in which simplified, interpolative models are fitted through a construction set of data. The surrogate framework developed previously has been extended in a number of ways. First, the formulation for a general, two-output, two-performance metric problem is presented. Surrogates are constructed and validated for the outputs. The performance metrics can be functions of both outputs, as well as explicitly of the inputs, and serve to characterize the design preferences. By segregating the outputs and the performance metrics, an additional level of flexibility is provided to the designer. The validated outputs can be used in future design studies and the error estimates provided by the output validation step still apply, and require no additional appeals to the expensive analysis. Second, a candidate-based a posteriori error analysis capability has been developed which provides probabilistic error estimates on the true performance for a design randomly selected near the surrogate-predicted optimal design.

Patera, Anthony T.↗
NASA NTRS2017

Near Real-Time Probabilistic Damage Diagnosis Using Surrogate Modeling and High Performance Computing

This work investigates novel approaches to probabilistic damage diagnosis that utilize surrogate modeling and high performance computing (HPC) to achieve substantial computational speedup. Motivated by Digital Twin, a structural health management (SHM) paradigm that integrates vehicle-specific characteristics with continual in-situ damage diagnosis and prognosis, the methods studied herein yield near real-time damage assessments that could enable monitoring of a vehicle's health while it is operating (i.e. online SHM). High-fidelity modeling and uncertainty quantification (UQ), both critical to Digital Twin, are incorporated using finite element method simulations and Bayesian inference, respectively. The crux of the proposed Bayesian diagnosis methods, however, is the reformulation of the numerical sampling algorithms (e.g. Markov chain Monte Carlo) used to generate the resulting probabilistic damage estimates. To this end, three distinct methods are demonstrated for rapid sampling that utilize surrogate modeling and exploit various degrees of parallelism for leveraging HPC. The accuracy and computational efficiency of the methods are compared on the problem of strain-based crack identification in thin plates. While each approach has inherent problem-specific strengths and weaknesses, all approaches are shown to provide accurate probabilistic damage diagnoses and several orders of magnitude computational speedup relative to a baseline Bayesian diagnosis implementation.

Warner, James E.↗
NASA NTRS2017

Methods for Probabilistic Uncertainty Analysis and Bayesian Analysis with Examples of Statistically Analyzing Data to Revise MMOD Risk Estimates and Compare Models

Probabilistic methods are presented for characterizing and quantifying uncertainties in models and model predictions. Techniques are given for constructing specific uncertainty distributions based on available information. Alternative techniques are given for propagating uncertainties in model inputs to obtain the uncertainty in the model result or prediction. Bayesian techniques are also described for utilizing data and information to update and revise model results and predictions. The focus is on applications with numerous specific examples given. The use of data to revise Micrometeoroid and Orbital Debris (MMOD) risk prediction models are among the examples given.

Risk↗
NASA NTRS1992

Probabilistic evaluation of on-line checks in fault-tolerant multiprocessor systems

The analysis of fault-tolerant multiprocessor systems that use concurrent error detection (CED) schemes is much more difficult than the analysis of conventional fault-tolerant architectures. Various analytical techniques have been proposed to evaluate CED schemes deterministically. However, these approaches are based on worst-case assumptions related to the failure of system components. Often, the evaluation results do not reflect the actual fault tolerance capabilities of the system. A probabilistic approach to evaluate the fault detecting and locating capabilities of on-line checks in a system is developed. The various probabilities associated with the checking schemes are identified and used in the framework of the matrix-based model. Based on these probabilistic matrices, estimates for the fault tolerance capabilities of various systems are derived analytically.

Nair, V. S. S.↗
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 NTRS2010

Metrics for Offline Evaluation of Prognostic Performance

Prognostic performance evaluation has gained significant attention in the past few years. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements. Very little attention has been focused on establishing a standardized approach to compare different efforts. This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. These metrics have the capability of incorporating probabilistic uncertainty estimates from prognostic algorithms. In addition to quantitative assessment they also offer a comprehensive visual perspective that can be used in designing the prognostic system. Several methods are suggested to customize these metrics for different applications. Guidelines are provided to help choose one method over another based on distribution characteristics. Various issues faced by prognostics and its performance evaluation are discussed followed by a formal notational framework to help standardize subsequent developments.

Saxena, Abhinav↗
NASA NTRS2012

NASA Tech Briefs, October 2012

Topics discussed include: Detection of Chemical Precursors of Explosives; Detecting Methane From Leaking Pipelines and as Greenhouse Gas in the Atmosphere; Onboard Sensor Data Qualification in Human-Rated Launch Vehicles; Rugged, Portable, Real-Time Optical Gaseous Analyzer for Hydrogen Fluoride; A Probabilistic Mass Estimation Algorithm for a Novel 7-Channel Capacitive Sample Verification Sensor; Low-Power Architecture for an Optical Life Gas Analyzer; Online Cable Tester and Rerouter; A Three-Frequency Feed for Millimeter-Wave Radiometry; Capacitance Probe Resonator for Multichannel Electrometer; Inverted Three-Junction Tandem Thermophotovoltaic Modules; Fabrication of Single Crystal MgO Capsules; Inflatable Hangar for Assembly of Large Structures in Space; Mars Aqueous Processing System; Hybrid Filter Membrane; Design for the Structure and the Mechanics of Moballs; Pressure Dome for High-Pressure Electrolyzer; Cascading Tesla Oscillating Flow Diode for Stirling Engine Gas Bearings; Compact, Low-Force, Low-Noise Linear Actuator; Ultra-Compact Motor Controller; Extreme Ionizing-Radiation-Resistant Bacterium; Wideband Single-Crystal Transducer for Bone Characterization; Fluorescence-Activated Cell Sorting of Live Versus Dead Bacterial Cells and Spores; Nonhazardous Urine Pretreatment Method; Laser-Ranging Transponders for Science Investigations of the Moon and Mars; Ka-Band Waveguide Three-Way Serial Combiner for MMIC Amplifiers; Structural Health Monitoring with Fiber Bragg Grating and Piezo Arrays; Low-Gain Circularly Polarized Antenna with Torus-Shaped Pattern; Stereo and IMU- Assisted Visual Odometry for Small Robots; Global Swath and Gridded Data Tiling; GOES-R: Satellite Insight; Aquarius iPhone Application; Monitoring of International Space Station Telemetry Using Shewhart Control Charts; Theory of a Traveling Wave Feed for a Planar Slot Array Antenna; Time Manager Software for a Flight Processor; Simulation of Oxygen Disintegration and Mixing With Hydrogen or Helium at Supercritical Pressure; A Superfluid Pulse Tube Refrigerator Without Moving Parts for Sub-Kelvin Cooling; Sapphire Viewports for a Venus Probe; The Mobile Chamber; Electric Propulsion Induced Secondary Mass Spectroscopy; and Radiation-Tolerant DC-DC Converters.

Source record↗
NASA NTRS2013

Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

Roberts, J. Brent↗
NASA NTRS2015

On-Line Safe Flight Envelope Determination for Impaired Aircraft

The design and simulation of an on-line algorithm which estimates the safe maneuvering envelope of aircraft is discussed in this paper. The trim envelope is estimated using probabilistic methods and efficient high-fidelity model based computations of attainable equilibrium sets. From this trim envelope, a robust reachability analysis provides the maneuverability limitations of the aircraft through an optimal control formulation. Both envelope limits are presented to the flight crew on the primary flight display. In the results section, scenarios are considered where this adaptive algorithm is capable of computing online changes to the maneuvering envelope due to impairment. Furthermore, corresponding updates to display features on the primary flight display are provided to potentially inform the flight crew of safety critical envelope alterations caused by the impairment.

algorithms↗