Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “software failure”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 883 records · Page 49

In-Flight Calibration of the Energetic Gamma-Ray Experiment Telescope (EGRET) on the Compton Gamma-Ray Observatory

The Energetic Gamma-Ray Experiment Telescope (EGRET) on the Compton Gamma-Ray Observatory has been operating for over seven years since its launch in 1991 April. This span of time far exceeds the design lifetime of two years. As the instrument has aged, several changes have occurred due to spark chamber gas exchanges as well as some hardware degradation and failures, all of which have an influence on the instrument sensitivity. This paper describes post-launch measurements and analysis that are done to calibrate the instrument response functions. The updated instrument characteristics are incorporated into the analysis software.

Esposito, J. A.↗

Single event upset suspectibility testing of the Xilinx Virtex II FPGA

Heavy ion testing of the Xilinx Virtex II was conducted on the configuration, block RAM and user flip flop cells to determine their static single-event upset susceptibility using LETs of 1.2 to 60 MeVcm^2/mg. A software program specifically designed to count errors in the FPGA was used to reveal L1/e, values (the LET at which the cross section is l/e times the saturation cross-section) and single-event functional-interrupt failures.

FPGA Virtex II SEU↗

Analyzing Distributed Functions in an Integrated Hazard Analysis

Large scale integration of today's aerospace systems is achievable through the use of distributed systems. Validating the safety of distributed systems is significantly more difficult as compared to centralized systems because of the complexity of the interactions between simultaneously active components. Integrated hazard analysis (IHA), a process used to identify unacceptable risks and to provide a means of controlling them, can be applied to either centralized or distributed systems. IHA, though, must be tailored to fit the particular system being analyzed. Distributed systems, for instance, must be analyzed for hazards in terms of the functions that rely on them. This paper will describe systems-oriented IHA techniques (as opposed to traditional failure-event or reliability techniques) that should be employed for distributed systems in aerospace environments. Special considerations will be addressed when dealing with specific distributed systems such as active thermal control, electrical power, command and data handling, and software systems (including the interaction with fault management systems). Because of the significance of second-order effects in large scale distributed systems, the paper will also describe how to analyze secondary functions to secondary functions through the use of channelization.

Morris, A. Terry↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

Sensor Data Management, Validation, Correction, and Provenance for Building Technologies

Oak Ridge National Laboratory (ORNL) conducts research on technologies that use a wide range of sensors to develop and characterize building energy performance. The management of high-resolution sensor data, analysis, and tracing lineage of such activities is challenging. Missing or corrupt data due to sensor failure, fouling, drifting, calibration error, or data logger failure is another issue. This paper focuses on sensor data management, validation, correction, and provenance to combat these issues, ensuring complete and accurate sensor datasets for building technologies applications and research. The design and development of two integrated software products are discussed: Sensor Data Validation and Correction (SensorDVC) and the Provenance Data Management System (ProvDMS) platform.

Data management↗

Software Support for Transiently Powered Computers

With the continued reduction in size and cost of computing, power becomes an increasingly heavy burden on system designers for embedded applications. While energy harvesting techniques are an increasingly desirable solution for many deeply embedded applications where size and lifetime are a priority, previous work has shown that energy harvesting provides insufficient power for long running computation. We present Ratchet, which to the authors knowledge is the first automatic, software-only checkpointing system for energy harvesting platforms. We show that Ratchet provides a means to extend computation across power cycles, consistent with those experienced by energy harvesting devices. We demonstrate the correctness of our system under frequent failures and show that it has an average overhead of 58.9% across a suite of benchmarks representative for embedded applications.

97 MATHEMATICS AND COMPUTING↗

Impact of Corrections to the Spallings Volume Calculation on Waste Isolation Pilot Plant Performance Assessment

The numerical code DRSPALL (from direct release spallings) is written to calculate the volume of Waste Isolation Pilot Plant solid waste subject to material failure and transport to the surface (i.e., spallings) as a result of a hypothetical future inadvertent drilling intrusion into the repository. An error in the implementation of the DRSPALL finite difference equations was discovered and documented in a software problem report in accordance with the quality assurance procedure for software requirements. This paper describes the corrections to DRSPALL and documents the impact of the new spallings data from the modified DRSPALL on previous performance assessment calculations. Updated performance assessments result in more simulations with spallings, which generally translates to an increase in spallings releases to the accessible environment. Total normalized radionuclide releases using the modified DRSPALL data were determined by forming the summation of releases across each potential release pathway, namely borehole cuttings and cavings releases, spallings releases, direct brine releases, and transport releases. Because spallings releases are not a major contributor to the total releases, the updated performance assessment calculations of overall mean complementary cumulative distribution functions for total releases are virtually unchanged. Therefore, the corrections to the spallings volume calculation did not impact Waste Isolation Pilot Plant performance assessment calculation results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Impact of Corrections to the Spallings Volume Calculation on Waste Isolation Pilot Plant Performance Assessment [Poster]

The numerical code DRSPALL (from direct release spallings) is written to calculate the volume of Waste Isolation Pilot Plant solid waste subject to material failure and transport to the surface (i.e., spallings) as a result of a hypothetical future inadvertent drilling intrusion into the repository. An error in the implementation of the DRSPALL finite difference equations was discovered and documented in a software problem report in accordance with the quality assurance procedure for software requirements. This paper describes the corrections to DRSPALL and documents the impact of the new spallings data from the modified DRSPALL on previous performance assessment calculations. Updated performance assessments result in more simulations with spallings, which generally translates to an increase in spallings releases to the accessible environment. Total normalized radionuclide releases using the modified DRSPALL data were determined by forming the summation of releases across each potential release pathway, namely borehole cuttings and cavings releases, spallings releases, direct brine releases, and transport releases. Because spallings releases are not a major contributor to the total releases, the updated performance assessment calculations of overall mean complementary cumulative distribution functions for total releases are virtually unchanged. Therefore, the corrections to the spallings volume calculation did not impact Waste Isolation Pilot Plant performance assessment calculation results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Impact of Corrections to the Spallings Volume Calculation on Waste Isolation Pilot Plant Performance Assessment - 16135

The numerical code DRSPALL (from direct release spallings) is written to calculate the volume of Waste Isolation Pilot Plant solid waste subject to material failure and transport to the surface (i.e., spallings) as a result of a hypothetical future inadvertent drilling intrusion into the repository. An error in the implementation of the DRSPALL finite difference equations was discovered and documented in a software problem report in accordance with the quality assurance procedure for software requirements. This paper describes the corrections to DRSPALL and documents the impact of the new spallings data from the modified DRSPALL on previous performance assessment calculations. Updated performance assessments result in more simulations with spallings, which generally translates to an increase in spallings releases to the accessible environment. Total normalized radionuclide releases using the modified DRSPALL data were determined by forming the summation of releases across each potential release pathway, namely borehole cuttings and cavings releases, spallings releases, direct brine releases, and transport releases. Because spallings releases are not a major contributor to the total releases, the updated performance assessment calculations of overall mean complementary cumulative distribution functions for total releases are virtually unchanged. Therefore, the corrections to the spallings volume calculation did not impact Waste Isolation Pilot Plant performance assessment calculation results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Cyber-Informed Engineering Principles: What’s in it for me?

CIE is an emerging method to integrate cybersecurity considerations into the conception, design, development, and operation of any physical system that has digital connectivity, monitoring, or control. CIE complements—but does not replace—the application of cybersecurity standards or practices currently in place within an organization. Rather, it expands cybersecurity decisions into the engineering space, not by asking engineers to become cyber experts, but by calling on engineers to apply engineering tools and make engineering decisions that improve cybersecurity outcomes. CIE examines the engineering consequences that a sophisticated cyber attacker could achieve, and drives engineering changes that may provide deterministic mitigations to limit or eliminate those consequences. Engineers and technicians that design critical energy infrastructure installations can integrate the 12 principles of CIE into each phase of the engineering lifecycle, from concept to retirement. These principles are aimed at system or design engineers, operators, and technicians, rather than software engineers or operational cybersecurity practitioners, because the engineers who design, build, operate, and maintain the physical infrastructure are best positioned to leverage a system’s engineering design to diminish the severity of cyber attacks or digital technology failures. This approach creates new opportunities for engineering teams—and not just cybersecurity teams—to secure the system using the physics and mechanics of engineering controls—not just digital monitoring and controls.

99 GENERAL AND MISCELLANEOUS↗

srlife : A software tool for estimating the life of high temperature concentrating solar receivers. Part II – Ceramic receivers

As Concentrating Solar Power (CSP) technologies aim for higher operating temperatures to enhance efficiency and meet industrial process heat demands, high-temperature metallic materials, including nickel-based superalloys, face challenges in maintaining structural integrity. Advanced ceramics offer a promising alternative due to their superior high-temperature strength. However, accurately assessing the performance of ceramic components requires a fundamentally different approach from that used for metallic components. This Part II of a two-part paper describes the integration of ceramic statistical failure models within srlife – an open-source tool for predicting the life of high-temperature CSP receivers. These models account for the inherent variability in ceramic strength, as well as the effects of subcritical crack growth (SCG) under high temperature cyclic loads. Here, the paper includes an example problem that demonstrate the process of evaluating ceramic receivers using srlife. Part I details the life estimation process for metallic receivers (i.e. creep-fatigue life) along with input and output data structure, thermohydraulic analysis, and structural analysis. The complete tool is available as open-source software at https://github.com/srlife-project/srlife and can be installed via the PyPi package manager (https://pypi.org). By supporting both ceramic and metallic receiver analyses, srlife facilitates fair comparisons between competing metallic and ceramic designs, enabling accurate evaluations of plant efficiency and the economic benefits of ceramic solar receivers and other components.

High temperature ceramic receivers↗

Encryption for Remote Control via Internet or Intranet

A data-communication protocol has been devised to enable secure, reliable remote control of processes and equipment via a collision-based network, while using minimal bandwidth and computation. The network could be the Internet or an intranet. Control is made secure by use of both a password and a dynamic key, which is sent transparently to a remote user by the controlled computer (that is, the computer, located at the site of the equipment or process to be controlled, that exerts direct control over the process). The protocol functions in the presence of network latency, overcomes errors caused by missed dynamic keys, and defeats attempts by unauthorized remote users to gain control. The protocol is not suitable for real-time control, but is well suited for applications in which control latencies up to about 0.5 second are acceptable. The encryption scheme involves the use of both a dynamic and a private key, without any additional overhead that would degrade performance. The dynamic key is embedded in the equipment- or process-monitor data packets sent out by the controlled computer: in other words, the dynamic key is a subset of the data in each such data packet. The controlled computer maintains a history of the last 3 to 5 data packets for use in decrypting incoming control commands. In addition, the controlled computer records a private key (password) that is given to the remote computer. The encrypted incoming command is permuted by both the dynamic and private key. A person who records the command data in a given packet for hostile purposes cannot use that packet after the public key expires (typically within 3 seconds). Even a person in possession of an unauthorized copy of the command/remote-display software cannot use that software in the absence of the password. The use of a dynamic key embedded in the outgoing data makes the central-processing unit overhead very small. The use of a National Instruments DataSocket(TradeMark) (or equivalent) protocol or the User Datagram Protocol makes it possible to obtain reasonably short response times: Typical response times in event-driven control, using packets sized .300 bytes, are <0.2 second for commands issued from locations anywhere on Earth. The protocol requires that control commands represent absolute values of controlled parameters (e.g., a specified temperature), as distinguished from changes in values of controlled parameters (e.g., a specified increment of temperature). Each command is issued three or more times to ensure delivery in crowded networks. The use of absolute-value commands prevents additional (redundant) commands from causing trouble. Because a remote controlling computer receives "talkback" in the form of data packets from the controlled computer, typically within a time interval < or =1 s, the controlling computer can re-issue a command if network failure has occurred. The controlled computer, the process or equipment that it controls, and any human operator(s) at the site of the controlled equipment or process should be equipped with safety measures to prevent damage to equipment or injury to humans. These features could be a combination of software, external hardware, and intervention by the human operator(s). The protocol is not fail-safe, but by adopting these safety measures as part of the protocol, one makes the protocol a robust means of controlling remote processes and equipment by use of typical office computers via intranets and/or the Internet.

Lineberger, Lewis↗

Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis

HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science, business, and other decision-making processes. However, understanding how ML jobs impact the operation of HPC datacenters, relative to generic jobs, remains desirable but understudied. In this work, we leverage long-term operational data, collected from a national-scale production HPC datacenter, and statistically compare how ML and generic jobs can impact the performance, failures, resource utilization, and energy consumption of HPC datacenters. Our study provides key insights, e.g., ML-related power usage causes GPU nodes to run into temperature limitations, median/mean runtime and failure rates are higher for ML jobs than for generic jobs, both ML and generic jobs exhibit highly variable arrival processes and resource demands, significant amounts of energy are spent on unsuccessfully terminating jobs, and concurrent jobs tend to terminate in the same state. We open-source our cleaned-up data traces on Zenodo (https://doi. org/10.5281/zenodo.13685426), and provide our analysis toolkit as software hosted on GitHub (https://github.com/atlarge-research/2024-icpads-hpc-workload-characterization). This study offers multiple benefits for data center administrators, who can improve operational efficiency, and for researchers, who can further improve system designs, scheduling techniques, etc.

crossanalysis↗

Simulation-Based Recovery Action Analysis Using the EMRALD Dynamic Risk Assessment Tool

A recovery action is defined as the action that prevents deviant conditions from producing unwanted effects. It generally indicates a kind of countermeasure performed in response to a failure of human action. The recovery actions especially play an important role in complex systems like nuclear power plants (NPPs), which consist of highly sophisticated controllers to ensure that desired performance and safety must be achieved and maintained. This is because a combination of human error and its recovery failure may be able to cause a catastrophic effect on a system. Analyzing recovery actions has been a critical part of HRA, which is a technique to evaluate human errors and provide human error probabilities (HEPs) for application in probabilistic safety assessment (PSA). If recovery actions are not adequately analyzed and applied to PSA models, the PSA results may be under-estimated or be not able to reasonably account for the failure of human actions in the context of PSA. For this reason, some regulatory documents such as ASME/ANS RA-Sb-2013 by the American Society for Mechanical Engineers and the American Nuclear Society and NUREG-1792 by U.S. Nuclear Regulatory Commission have emphasized the importance of recovery analysis within the HRA. A couple of existing HRA methods, such as the Technique for Human Error-Rate Prediction (THERP), the Cause-Based Decision Tree (CBDT), and the Korean Standard HRA (K-HRA), have respectively suggested their own approaches to the HRA recovery analysis. However, there are a couple of limitations to treating recovery actions using only the current HRA methods available. The biggest limitation is that the existing recovery analysis does not explicitly consider a variety of recovery action types and recovery sequences as they occur in actual NPPs. To handle the limitations of existing recovery analysis, this study proposes a simulation-based recovery analysis method using the Event Modeling Risk Assessment Using Linked Diagram (EMRALD) software. The EMRALD software is a dynamic simulation tool for PSA. It supports realistic and dynamic modeling of human actions as they would be performed at NPPs. It is also favorable to simultaneously model the specific moment at which an action is performed, the time it takes to perform the action, and the failure probability of that action. In this paper, a detailed methodology for modeling recovery actions in the simulation platform is proposed with a couple of examples. Then, outputs from the simulation are discussed as reviewing if this novel approach can complement the challenges of existing recovery analyses.

99 GENERAL AND MISCELLANEOUS↗