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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Control System Architectures, Technologies and Concepts for Near Term and Future Human Exploration of Space

Technologies that facilitate the design and control of complex, hybrid, and resource-constrained systems are examined. This paper focuses on design methodologies, and system architectures, not on specific control methods that may be applied to life support subsystems. Honeywell and Boeing have estimated that 60-80Y0 of the effort in developing complex control systems is software development, and only 20-40% is control system development. It has also been shown that large software projects have failure rates of as high as 50-65%. Concepts discussed include the Unified Modeling Language (UML) and design patterns with the goal of creating a self-improving, self-documenting system design process. Successful architectures for control must not only facilitate hardware to software integration, but must also reconcile continuously changing software with much less frequently changing hardware. These architectures rely on software modules or components to facilitate change. Architecting such systems for change leverages the interfaces between these modules or components.

Boulanger, Richard↗
NASA NTRS1991

Getting expert systems off the ground: Lessons learned from integrating model-based diagnostics with prototype flight hardware

As an initial attempt to introduce expert system technology into an onboard environment, a model based diagnostic system using the TRW MARPLE software tool was integrated with prototype flight hardware and its corresponding control software. Because this experiment was designed primarily to test the effectiveness of the model based reasoning technique used, the expert system ran on a separate hardware platform, and interactions between the control software and the model based diagnostics were limited. While this project met its objective of showing that model based reasoning can effectively isolate failures in flight hardware, it also identified the need for an integrated development path for expert system and control software for onboard applications. In developing expert systems that are ready for flight, artificial intelligence techniques must be evaluated to determine whether they offer a real advantage onboard, identify which diagnostic functions should be performed by the expert systems and which are better left to the procedural software, and work closely with both the hardware and the software developers from the beginning of a project to produce a well designed and thoroughly integrated application.

Stephan, Amy↗
NASA NTRS2010

Modeling and Performance Considerations for Automated Fault Isolation in Complex Systems

The purpose of this paper is to document the modeling considerations and performance metrics that were examined in the development of a large-scale Fault Detection, Isolation and Recovery (FDIR) system. The FDIR system is envisioned to perform health management functions for both a launch vehicle and the ground systems that support the vehicle during checkout and launch countdown by using suite of complimentary software tools that alert operators to anomalies and failures in real-time. The FDIR team members developed a set of operational requirements for the models that would be used for fault isolation and worked closely with the vendor of the software tools selected for fault isolation to ensure that the software was able to meet the requirements. Once the requirements were established, example models of sufficient complexity were used to test the performance of the software. The results of the performance testing demonstrated the need for enhancements to the software in order to meet the demands of the full-scale ground and vehicle FDIR system. The paper highlights the importance of the development of operational requirements and preliminary performance testing as a strategy for identifying deficiencies in highly scalable systems and rectifying those deficiencies before they imperil the success of the project

Ferrell, Bob↗
NASA NTRS1986

An experiment in software reliability

The results of a software reliability experiment conducted in a controlled laboratory setting are reported. The experiment was undertaken to gather data on software failures and is one in a series of experiments being pursued by the Fault Tolerant Systems Branch of NASA Langley Research Center to find a means of credibly performing reliability evaluations of flight control software. The experiment tests a small sample of implementations of radar tracking software having ultra-reliability requirements and uses n-version programming for error detection, and repetitive run modeling for failure and fault rate estimation. The experiment results agree with those of Nagel and Skrivan in that the program error rates suggest an approximate log-linear pattern and the individual faults occurred with significantly different error rates. Additional analysis of the experimental data raises new questions concerning the phenomenon of interacting faults. This phenomenon may provide one explanation for software reliability decay.

Dunham, J. R.↗
DOE OSTI2020

Exploratory Investigation of Disengagements and Crashes in Autonomous Vehicles Under Mixed Traffic: An Endogenous Switching Regime Framework

Autonomous Vehicles (AVs) have a large potential to improve traffic safety but also pose some critical challenges. While AVs may help reduce crashes caused by human error, they still may experience failures of technologies and sensing, as well as decision-making errors in a mixed traffic environment. A disengagement refers to an AV transitioning control from autonomous systems to the trained test driver. The safety critical nature of disengagements makes it imperative to analyze disengagements and crashes together. In this study, we analyze both crashes and disengagements from real-world AV driving in California to fill the knowledge gap regarding the relationship between disengagements and crashes in a mixed traffic environment. A nested logit model was calibrated using three different outcomes: (1) disengagement with a crash, (2) disengagement with no crash, and (3) no disengagement with a crash. Furthermore, endogenous switching regime models were also calibrated to draw distinctions between the relation of disengagements and crashes while accounting for endogeneity effects. The results show that factors related to AV systems (such as software failures) and other roadway participants increase the propensity of a disengagement without a crash. Furthermore, AVs were observed to disengage less often as the technology matured over time. Marginal effects revealed an 8% decrease. The results thus suggest that disengagements are a part of AVs' safe performance and disengagement alerts may need to be triggered in order to avoid certain failures with current technology.

42 ENGINEERING↗
DOE OSTI2023

Advanced Monitoring and Control in the ANL METL Facility Using an Engineering Digital Twin

The potential benefits of using an engineering digital twin to achieve greater autonomy for monitoring and control functions in advanced reactors was investigated for the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The METL sodium purification system served as a representative system as it requires significant human in-the-loop interaction to accomplish its design function. The objective was to demonstrate how real-time operation could be automated while preserving oversight of the operator for ensuring that the system design functions are met. A digital twin model of the purification system was developed for both the cold trap purification loop and plugging meter diagnostic loop using information from the METL piping and instrumentation diagram (P&ID). Automated monitoring and diagnosis of component degradation in the METL facility was demonstrated in tests using the PRO-AID health monitoring software with the digital twin model incorporated in the library of components. Component failures were introduced and were successfully diagnosed in real time. These tests serve to demonstrate an advanced monitoring capability able to differentiate sensor degradation from component degradation, to generate a rank ordering of probabilities of different failure mechanisms that serves to circumvent the false alarm problem with current anomaly detection methods, and how facility monitoring can be transformed from anomaly detection to identification of a specific fault. Automated control of the purification system was demonstrated through simulations that exercised a model predictive controller designed using the digital twin model. Results of these simulations compared favorably with experimental data showing very good reference tracking response with negligible overshoot. In conclusion, these pilot tests and simulations successfully demonstrated the use of a digital twin for improved automation of monitoring and control. It was shown how the digital twin enables switching between control modes from cold trap operation where impurities are removed to plugging meter operation where impurity concentrations are measured.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1990

DEPEND - A design environment for prediction and evaluation of system dependability

The development of DEPEND, an integrated simulation environment for the design and dependability analysis of fault-tolerant systems, is described. DEPEND models both hardware and software components at a functional level, and allows automatic failure injection to assess system performance and reliability. It relieves the user of the work needed to inject failures, maintain statistics, and output reports. The automatic failure injection scheme is geared toward evaluating a system under high stress (workload) conditions. The failures that are injected can affect both hardware and software components. To illustrate the capability of the simulator, a distributed system which employs a prediction-based, dynamic load-balancing heuristic is evaluated. Experiments were conducted to determine the impact of failures on system performance and to identify the failures to which the system is especially susceptible.

Goswami, Kumar K.↗
NASA NTRS2010

TEAMS Model Analyzer

The TEAMS model analyzer is a supporting tool developed to work with models created with TEAMS (Testability, Engineering, and Maintenance System), which was developed by QSI. In an effort to reduce the time spent in the manual process that each TEAMS modeler must perform in the preparation of reporting for model reviews, a new tool has been developed as an aid to models developed in TEAMS. The software allows for the viewing, reporting, and checking of TEAMS models that are checked into the TEAMS model database. The software allows the user to selectively model in a hierarchical tree outline view that displays the components, failure modes, and ports. The reporting features allow the user to quickly gather statistics about the model, and generate an input/output report pertaining to all of the components. Rules can be automatically validated against the model, with a report generated containing resulting inconsistencies. In addition to reducing manual effort, this software also provides an automated process framework for the Verification and Validation (V&V) effort that will follow development of these models. The aid of such an automated tool would have a significant impact on the V&V process.

Tijidjian, Raffi P.↗
DOE OSTI2019

Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials

In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for brittle failure that accurately predict these QoI exist but are highly computationally intensive, making them infeasible to incorporate in upscaling and uncertainty quantification frameworks. The goal of this paper is to provide a fast heuristic to reasonably estimate quantities such as failure path and damage in the process of brittle failure. Towards this goal, we first present a method to predict failure paths under tensile loading conditions and low-strain rates. The method uses a k-nearest neighbors algorithm built on fracture process zone theory, and identifies the set of all possible pre-existing cracks that are likely to join early to form a large crack. The method then identifies zone of failure and failure paths using weighted graphs algorithms. We compare these failure paths to those computed with a high-fidelity fracture mechanics model called the Hybrid Optimization Software Simulation Suite (HOSS). A probabilistic evolution model for average damage in a system is also developed that is trained using 150 HOSS simulations and tested on 40 simulations. A non-parametric approach based on confidence intervals is used to determine the damage evolution over time along the dominant failure path. For upscaling, damage is the key QoI needed as an input by the continuum models. This needs to be informed accurately by the surrogate models for calculating effective moduli at continuum-scale. We show that for the proposed average damage evolution model, the prediction accuracy on the test data is more than 90%. In terms of the computational time, the proposed models are ≈ O ( 10 6 ) times faster compared to high-fidelity fracture simulations by HOSS. These aspects make the proposed surrogate model attractive for upscaling damage from micro-scale models to continuum models. We would like to emphasize that the surrogate models are not a replacement of physical understanding of fracture propagation. The proposed method in this paper is limited to tensile loading conditions at low-strain rates. This loading condition corresponds to a dominant fracture perpendicular to tensile direction. The proposed method is not applicable for in-plane shear, out-of-plane shear, and higher strain rate loading conditions.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Application of Diagnostic Analysis Tools to the Ares I Thrust Vector Control System

The NASA Ares I Crew Launch Vehicle is being designed to support missions to the International Space Station (ISS), to the Moon, and beyond. The Ares I is undergoing design and development utilizing commercial-off-the-shelf tools and hardware when applicable, along with cutting edge launch technologies and state-of-the-art design and development. In support of the vehicle s design and development, the Ares Functional Fault Analysis group was tasked to develop an Ares Vehicle Diagnostic Model (AVDM) and to demonstrate the capability of that model to support failure-related analyses and design integration. One important component of the AVDM is the Upper Stage (US) Thrust Vector Control (TVC) diagnostic model-a representation of the failure space of the US TVC subsystem. This paper first presents an overview of the AVDM, its development approach, and the software used to implement the model and conduct diagnostic analysis. It then uses the US TVC diagnostic model to illustrate details of the development, implementation, analysis, and verification processes. Finally, the paper describes how the AVDM model can impact both design and ground operations, and how some of these impacts are being realized during discussions of US TVC diagnostic analyses with US TVC designers.

Maul, William A.↗
DOE OSTI2023

The Peridigm Meshfree Peridynamics Code

Abstract Peridigm is a meshfree peridynamics code written in C++ for use on large-scale parallel computers. It was originally developed at Sandia National Laboratories and is currently managed as an open-source, community driven software project. Its primary features include bond-based, state-based, and non-ordinary state-based constitutive models, bond failure laws, contact, and support for explicit and implicit time integration. To date, Peridigm has been used primarily by methods developers focused on solid mechanics and material failure. Peridigm utilizes foundational software components from Sandia’s Trilinos project and was designed for extensibility. This paper provides an overview of the solution methods implemented in Peridigm , a discussion of its software infrastructure, and demonstrates the use of Peridigm for the solution of several example problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1998

Integration of the Remote Agent for the NASA Deep Space One Autonomy Experiment

This paper describes the integration of the Remote Agent (RA), a spacecraft autonomy system which is scheduled to control the Deep Space 1 spacecraft during a flight experiment in 1999. The RA is a reusable, model-based autonomy system that is quite different from software typically used to control an aerospace system. We describe the integration challenges we faced, how we addressed them, and the lessons learned. We focus on those aspects of integrating the RA that were either easier or more difficult than integrating a more traditional large software application because the RA is a model-based autonomous system. A number of characteristics of the RA made integration process easier. One example is the model-based nature of RA. Since the RA is model-based, most of its behavior is not hard coded into procedural program code. Instead, engineers specify high level models of the spacecraft's components from which the Remote Agent automatically derives correct system-wide behavior on the fly. This high level, modular, and declarative software description allowed some interfaces between RA components and between RA and the flight software to be automatically generated and tested for completeness against the Remote Agent's models. In addition, the Remote Agent's model-based diagnosis system automatically diagnoses when the RA models are not consistent with the behavior of the spacecraft. In flight, this feature is used to diagnose failures in the spacecraft hardware. During integration, it proved valuable in finding problems in the spacecraft simulator or flight software. In addition, when modifications are made to the spacecraft hardware or flight software, the RA models are easily changed because they only capture a description of the spacecraft. one does not have to maintain procedural code that implements the correct behavior for every expected situation. On the other hand, several features of the RA made it more difficult to integrate than typical flight software. For example, the definition of correct behavior is more difficult to specify for a system that is expected to reason about and flexibly react to its environment than for a traditional flight software system. Consequently, whenever a change is made to the RA it is more time consuming to determine if the resulting behavior is correct. We conclude the paper with a discussion of future work on the Remote Agent as well as recommendations to ease integration of similar autonomy projects.

Dorais, Gregory A.↗
NASA NTRS2018

Comparison of Test Methods to Determine Failure Parameters for MAT162 Calibration

MAT162, a laminated composite failure material model developed by The Material Sciences Corporation for the commercial finite element software LS-Dyna, is widely used within the aerospace industry to predict damage events under a range of dynamic conditions. The material model involves numerous inputs consisting of both physical material properties and numerical calibration parameters. Due to the large number material card inputs, often there is a lack of uniqueness to MAT162 material cards that limits the predictive capability to only the directly calibrated space. To expand this space, MAT162 requires a prudent and robust calibration process in which significant parameters are calibrated to high confidence damage events observed in experiment. Critical to this success is fully defining the material properties correctly, namely the fiber crush (SFC) and fiber shear (SFS) values, prior to calibrating the numerical parameters. In this paper, the effect of the determination of SFS and SFC on subsequent calibration steps is examined using two different experimental techniques.

Molitor, Matthew↗
DOE OSTI2017

URBAN-NET-TOOLKIT

Strengthening and maintaining secure, functioning, and resilient Critical Infrastructure Systems (CISs) is one of our nation's primary goals, and it requires proactive and coo0rdinated efforts among federal, state, local, public and private owners and operators of CISs. Recent worldwide extreme events have shown how interdependencies across CISs (electric grid, telecommunications, transportation, water networks, etc.) can drastically increase system vulnerability. To understand these complex interdependencies, ORNL has instantiated Urban-NET, a novel decision support system for CIS operators who need to identify vulnerabilities for minimizing the cascading impacts - saving utilities, the federal and state government, and private sector companies potentially billions by avoiding a complete CI failure and/or shutdown. The developed system includes a stack of software tools for preprocessing, modeling, interconnecting multiple CIS datasets as an integrated heterogeneous graph (i.e. network), identifying vulnerabilities through topological analysis, and predicting consequences based on simulations along with visualization.

Chinthavali, Supriya↗
NASA NTRS1992

Estimating the probability of failure when testing reveals no failures

Formulas for estimating the probability of failure when testing reveals no errors are introduced. These formulas incorporate random testing results, information about the input distribution, and prior assumptions about the probability of failure of the software. The formulas are not restricted to equally likely input distributions, and the probability of failure estimate can be adjusted when assumptions about the input distribution change. The formulas are based on a discrete sample space statistical model of software and include Bayesian prior assumptions. Reusable software and software in life-critical applications are particularly appropriate candidates for this type of analysis.

Miller, Keith W.↗
DOE OSTI2022

PyPVRPM: Photovoltaic Reliability and Performance Model in Python

The ability to perform accurate techno-economic analysis of solar photovoltaic (PV) systems is essential for bankability and investment purposes. Most energy yield models assume an almost flawless operation (i.e., no failures); however, realistically, components fail and get repaired stochastically. This package, PyPVRPM, is a Python translation and improvement of the Language Kit (LK) based PhotoVoltaic Reliability Performance Model (PVRPM), which was first developed at Sandia National Laboratories in Goldsim software (Granata et al., 2011) (Miller et al., 2012). PyPVRPM allows the user to define a PV system at a specific location and incorporate failure, repair, and detection rates and distributions to calculate energy yield and other financial metrics such as the levelized cost of energy and net present value (Klise, Lavrova, et al., 2017). Our package is a simulation tool that uses NREL’s Python interface for System Advisor Model (SAM) (National Renewable Energy Laboratory, 2020b) (National Renewable Energy Laboratory, 2020a) to evaluate the performance of a PV plant throughout its lifetime by considering component reliability metrics. Besides the numerous benefits from migrating to Python (e.g., speed, libraries, batch analyses), it also expands on the failure and repair processes from the LK version by including the ability to vary monitoring strategies. These failures, repairs, and monitoring processes are based on user-defined distributions and values, enabling a more accurate and realistic representation of cost and availability throughout a PV system’s lifetime.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1985

A theoretical basis for the analysis of redundant software subject to coincident errors

Fundamental to the development of redundant software techniques fault-tolerant software, is an understanding of the impact of multiple-joint occurrences of coincident errors. A theoretical basis for the study of redundant software is developed which provides a probabilistic framework for empirically evaluating the effectiveness of the general (N-Version) strategy when component versions are subject to coincident errors, and permits an analytical study of the effects of these errors. The basic assumptions of the model are: (1) independently designed software components are chosen in a random sample; and (2) in the user environment, the system is required to execute on a stationary input series. The intensity of coincident errors, has a central role in the model. This function describes the propensity to introduce design faults in such a way that software components fail together when executing in the user environment. The model is used to give conditions under which an N-Version system is a better strategy for reducing system failure probability than relying on a single version of software. A condition which limits the effectiveness of a fault-tolerant strategy is studied, and it is posted whether system failure probability varies monotonically with increasing N or whether an optimal choice of N exists.

Eckhardt, D. E., Jr.↗
DOE OSTI2022

RDPM: An Extensible Tool for Resilience Design Patterns Modelling

Resilience to faults, errors, and failures in extreme-scale high-performance computing (HPC) systems is a critical challenge. Resilience design patterns offer a new, structured hardware and software design approach for improving resilience. While prior work focused on developing performance, reliability, and availability models for resilience design patterns, this paper extends it by providing a Resilience Design Patterns Modeling (RDPM) tool which allows (1) exploring performance, reliability, and availability of each resilience design pattern, (2) offering customization of parameters to optimize performance, reliability, and availability, and (3) allowing investigation of trade-off models for combining multiple patterns for practical resilience solutions.

Kumar, Mohit↗