Results for “Tools” — Page 23

NASA NTRS2020

Enabling Space Exploration Medical System Development Using a Tool Ecosystem

The NASA Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element is utilizing a Model Based Systems Engineering (MBSE) approach to enhance the development of systems engineering products that will be used to advance medical system designs for exploration missions beyond Low Earth Orbit. In support of future missions, the team is capturing content such as system behaviors, functional decompositions, architecture, system requirements and interfaces, and recommendations for clinical capabilities and resources in Systems Modeling Language (SysML) models. As these products mature, SysML models provide a way for ExMC to capture relationships among the various products, which includes supporting more integrated and multi-faceted views of future medical systems. In addition to using SysML models, HRP and ExMC are developing supplementary tools to support two key functions: 1) prioritizing current and future research activities for exploration missions in an objective manner; and 2) enabling risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This paper will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include dynamic Probabilistic Risk Assessment (PRA) capabilities, additional SysML models, a database of system component options, and data visualizations. It also includes a review of an initial Pilot Project focused on enabling medical system trade studies utilizing data that is coordinated across tools for consistent outputs (e.g., mission risk metrics that are associated with medical system mass values and medical conditions addressed). This first Pilot Project demonstrated successful operating procedures and integration across tools. Finally, the paper will also cover a second Pilot Project that utilizes tool enhancements such as medical system optimization capabilities, post-processing, and visualization of generated data for subject matter expert review, and increased integration amongst the tools themselves.

Amador, Jennifer R.↗

NASA NTRS2020

Enabling Space Exploration Medical System Development Using a Tool Ecosystem

The NASA Human Research Program's (HRP) Exploration Medical Capability (ExMC) Element is utilizing a Model Based Systems Engineering (MBSE) approach to enhance the development of systems engineering products that will be used to advance medical system designs for exploration missions beyond Low Earth Orbit. In support of future missions, the team is capturing content such as system behaviors, functional decompositions, architecture, system requirements and interfaces, and recommendations for clinical capabilities and resources in Systems Modeling Language (SysML) models. As these products mature, SysML models provide a way for ExMC to capture relationships among the various products, which includes supporting more integrated and multi-faceted views of future medical systems. In addition to using SysML models, HRP and ExMC are developing supplementary tools to support two key functions: 1) prioritizing current and future research activities for exploration missions in an objective manner; and 2) enabling risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This paper will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include dynamic Probabilistic Risk Assessment (PRA) capabilities, additional SysML models, a database of system component options, and data visualizations. It also includes a review of an initial Pilot Project focused on enabling medical system trade studies utilizing data that is coordinated across tools for consistent outputs (e.g., mission risk metrics that are associated with medical system mass values and medical conditions addressed). This first Pilot Project demonstrated successful operating procedures and integration across tools. Finally, the paper will also cover a second Pilot Project that utilizes tool enhancements such as medical system optimization capabilities, post-processing, and visualization of generated data for subject matter expert review, and increased integration amongst the tools themselves.

Amador, Jennifer R.↗

NASA NTRS2019

Development of a Design for Manufacturing Tool for Automated Fiber Placement Structures

Existing design processes for laminates constructed with automated fiber placement lack significant integration between the various software tools that compose the process. Tools for finite element analysis, computer aided drafting, stress analysis, tool path simulation, and manufacturing defect prediction are all critical parts of the design process. With traditional hand-layup laminates, the analysis performed with each of these tools could be fairly well decoupled from one another. However, for laminates generated by automated fiber placement, the disciplines can become significantly coupled, especially on structures with curvature. This gives rise to a need for integrated “design for manufacturing” software tools that are able to balance the competing objectives from each discipline. This paper describes the preliminary development of such a tool.

Noevere, August T.↗

NASA NTRS2021

Topic Modeling Tool for PeTaL (Periodic Table of Life)

A topic modeling tool is constructed for the purpose of providing insights from biology to the engineer within the framework of PeTaL (Periodic Table of Life). The machine learning text mining tools–latent Dirichlet allocation (LDA) and nonnegative matrix factorization (NMF) with Kullback-Leibler (KL) divergence—are used to provide topic clusters to the user. Topic clusters are the underlying themes of a paper. For the text modeling problem, NMF-KL is the equivalent of probabilistic latent semantic analysis. Both LDA and NMF-KL are top-performing modeling tools. These tools are used to identify biological specimens relevant to the user. Various organisms solve a particular survival problem in nature differently. The topic clusters allow people without domain expertise to find these cross-topic themes in the body of documents and then branch out and examine papers whose target organisms solve the engineer’s problem. Abstracts from the Journal of Experimental Biology were used as input for the clustering tool in addition to a curated set of articles for validation. The tool is able to accept alternate input sources.

Machine learning↗

NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Update and Future Direction

NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at: NASA SSRI Knowledge Base | Explore. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content; development of an Application Programming Interface (API); improved user interfaces; scalable and searchable Best Practices and Lessons Learned (BPLL) lists with ratings; and custom website analytics. This presentation and paper will discuss the motivation for and development of the SSRI Knowledge Base, demonstrate the existing tool, and outline plans for further development. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.

Small Spacecraft↗

NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Update and Future Direction

NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at: NASA SSRI Knowledge Base | Explore. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content; development of an Application Programming Interface (API); improved user interfaces; scalable and searchable Best Practices and Lessons Learned (BPLL) lists with ratings; and custom website analytics. This presentation and paper will discuss the motivation for and development of the SSRI Knowledge Base, demonstrate the existing tool, and outline plans for further development. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.

Small Spacecraft↗

NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗

NASA NTRS2023

Flight Mechanics Analysis Tools Interoperability and Component Sharing

As multiple flight mechanics (FM) analysis tools are developed to meet the unique scientific and operational requirements of NASA missions, sharing data, transferring models and trajectory information between tools can be complicated. The NESC recently explored ways that increase interoperability of three mission analysis tools: Copernicus, General Mission Analysis Tool (GMAT), and Mission-Analysis Operations Navigation Toolkit Environment (MONTE). These tools are used to generate a variety of products throughout all phases of a mission including: maneuver planning, trajectory optimization and design, orbit determination, performance and error analysis, trade studies and sizing. Establishing a framework to share models, component data and trajectory information is an efficient way to leverage the benefits of an analysis tool without expending development costs to duplicate functionality.

Flight Mechanics Simulation↗

NASA NTRSDate not supplied

Applying Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗

NASA NTRSDate not supplied

Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗

NASA NTRSDate not supplied

Benchmarking Computational Tools for Calling SNPs and Indels in Complex Microbial Populations

The NASA BioNutrients missions seek to understand the suitability of microorganisms for bioproduction during space flight. One topic of interest is the stability of microbial genomes during long-term ambient storage and subsequent rehydration and growth. To address these questions, samples from 8 species were flown to ISS for 5 years of desiccated storage at ambient temperature (Stasis Packs) and 2 species were packaged along with powdered media inside a bioreactor system to allow hydration and growth in microgravity (Production Packs). For both systems, Whole Genome Sequencing (WGS) of the DNA extracted from the returned samples and paired ground controls will be conducted to identify changes in genome stability due to time, storage conditions and growth in space. Across the technical replicates, ground controls, 10 timepoints, and multiple experimental conditions, ~300 samples have been selected for initial analysis with WGS sequencing to 100x coverage. A flexible and resource efficient mutation calling pipeline is needed to process this large dataset and allow for comparisons between species. Many bioinformatics tools for calling Indels and Single Nucleotide Variants (SNVs) are designed for use with pure isolates, where true variations from the reference genome are expected to dominate the reads aligning to the location of mutation. In contrast, DNA from the Stasis Pack (SP) samples was collected directly after recovery from desiccated storage and the Production Pack (PP) samples were collected after fermentation. In this context, reads with mutations are expected to be less frequent than reads that align with the reference genome, as each sample will include multiple lines of cells. Thus, BioNutrients samples are expected to be similar to samples from cancer cell or “pooled” sequencing approaches. In preparation for the analysis of the BioNutrients samples, we have tested three mutation calling tools (GATK for Microbes, BreSeq and DiscoSNP) designed for complex samples. A challenge of validating mutation identification pipelines is a lack of “Ground Truth” datasets, especially for complex samples. To compare these three tools, we sought to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO as part of the Space Algae-1 mission. Here we present a summary of these tools against the analysis originally conducted using the CRISP tool. Critical metrics are compared such as runtime, the number of SNPs, the number and size of Indels, and patterns of transversion and transitions identified by each tool are reported. By sharing these benchmarking results collected in support of the BioNutrients mission, we aim to guide others seeking to identify SNVs in similarly complex microbial samples.

Biology↗

DOE OSTI2019

NIST Infiltration Tools for Commercial Buildings

The Department of Energy estimates that infiltration through building envelopes accounted for 6 % of the total energy consumed by commercial buildings and 21 % of the heating and cooling energy. Air barrier requirements are now being included in ASHRAE Standards 90.1 and 189.1 and other construction requirements. The energy impacts of unintended infiltration on building energy use can be significant, but current energy simulation software and design methods are generally not able to accurately account for envelope infiltration and the impacts of improved airtightness. NIST has been developing tools to better account for infiltration in building energy models. The tools that will be discussed in this paper include: CONTAM Results Export Tool; weather-correlated infiltration inputs for prototype commercial buildings and the associated Open Studio Measures; and a web-based tool to evaluate the energy savings of building envelope airtightening, which was developed with the Oak Ridge National Laboratory and the Air Barrier Association of America. This paper briefly describes each tool, their applications, and the benefits of using each. All of the tools are publicly available at no cost.

Ng, Lisa↗

DOE OSTI2020

Probabilistic Look-ahead Contingency Analysis Integration with Commercial Tool and Practical Data

This paper presents an initial effort of integrating a smart sampling-based probabilistic look-ahead contingency analysis algorithm with General Electric (GE) Grid Solutions’ commercial energy management system (EMS) tool as a proof-of-concept for a seamless research tool integration using real world large-scale grid data. With the increasing impact of random forces such as variable generation and load, their stochastic behaviors cannot be ignored. However, the current practices are still dominated by deterministic tools. They are becoming increasingly inadequate for the future grid. The developed look-ahead contingency analysis algorithm incorporates forecast errors of variable energy and load to address the challenges brought by the increasing uncertainty of power system. The algorithm can reveal the potential violations caused by the variance of variable energy and load that are not normally detected by traditional deterministic approaches. To test its performance under practical environments ( real data with real commercial tool), significant efforts have been made to prepare test cases, modify GE EMS tool, and adapt an extreme value distribution algorithm to analyze the GE EMS’s violation-only outputs. The test results clearly demonstrate the effectiveness of the developed algorithm as new transformer violations that were not previously detected have been identified. This performance provides better situational awareness to engineers for their decision-making process under uncertainty. Moreover, with the discussion of computational performance and future work, this paper has shown a clear path for integrating the probabilistic algorithm with commercial tools to make us better equipped for the changing power system.

Modeling and simulation of power systems, constrai↗

DOE OSTI2022

Binary-level Software Compatibility Tool Agreement

Application Binary Interface (ABI) compatibility is essential for system or software updates to ensure that libraries continue to function. Tools that can assess a binary or library ABI can thus be used to make predictions about compatibility, and predict downstream bugs by informing developers and users about issues. In this work, we are interested in describing a set of well-known tools for assessing ABI, and testing them in a controlled set experiments to assess tool agreement. We run 7660 smaller experiments across tools (N=30,640 total results) to evaluate not only predictions, but also each tool’s ability to provide detail about underlying issues. In this paper, along with highlighting the problem of assessing ABI compatibility and critiquing the pros and cons of currently available tools, we provide guidance to developers interested to test ABI based on our empirical results and suggestions for future work.

ABI↗

DOE OSTI2016

Strategic Planning and Decisions Tools to Enhance EM Mission Execution - 16552

Maximizing the success of an organization depends on many things with a key one being the focus and quality of the management decisions made by the organisation's senior leadership team. Successfully advancing an organisation's agenda requires operational, tactical, and strategic decisions. These decisions involve both decisions on specific actions, as well as decisions about planning. In addition to the day-to-day decisions of running the business, the leadership team should be allocating appropriate time toward developing organizational strategic objectives. Strategic planning is an organisation's process of defining its direction and strategies, and plotting how to accomplish those strategies. Strategic planning is essential to the long term viability of the organization. The pathway to success with strategic planning is not nearly so obvious and straight forward as with operational decisions. Organizational leaders must resist the temptation to minimize the time allocated to strategic thinking and strategic planning. Strategic thinking and strategic planning are centered around making sure that your organization is doing the right things, instead of just doing things right. Management guru Peter Drucker noted that 'There is nothing more wasteful than becoming highly efficient at doing the wrong things'. Strategic thinking is used in deciding what to do. Tactical thinking is used in deciding how to do it. Decision tools can be utilized to improve the results of the strategic planning process. In certain situations, properly selected decision tools will provide a mechanism or a process for making better decisions, or for channeling strategic thought and strategic planning toward enlightened alternative approaches. Decision tools can also provide for assigning a quantitative measure of merit or goodness, or at least a semi-quantitative measure of merit to multiple decision options. Properly selected and utilized, decision tools provide the leadership team with additional insight into the consequences of their decisions. Appropriately selected decision tools can be utilized to maximize the effectiveness of your strategic planning efforts. Decision tools can be used to quantify strategic decision options within a particular framework, providing insights into alternatives to improve an organisation's most effective execution of the DOE EM clean up mission. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Successes and Lessons Learned in the Design, Construction and Use of a Tool for Sampling the WR-1 Reactor Core - 1900z

Whiteshell Laboratories, located in Manitoba, Canada, provided research facilities for the Canadian nuclear industry since the early 1960's. The facility was centered on an organically cooled, heavy water moderated nuclear reactor in the world, Whiteshell Reactor 1 (WR-1). WR-1 was safely shut down in 1985, de-fueled and drained, and has been in a safe Storage with Surveillance (Deferred Decommissioning) state until present day. In support of an Environmental Assessment for proposed In Situ Decommissioning of WR-1, detailed characterization information regarding the reactor core components (calandria, calandria tubes, pressure tubes) was necessary to verify inventory estimates and neutron activation models produced circa 1992. The Fuel Channels of WR-1 (pressure tubes) were expected to be the most activated components of the reactor core, and to account for about 60% of the reactor core inventory. The interior surface of the fuel channel is also the most readily accessible surface within the reactor core. As such the decision was made to collect a sample of one of each of 3 Fuel Channel material types (Stainless Steel, Zr-2.5Nb, Ozhenite) for comparison to modelled estimates of neutron activation of various core materials. The expected dose rates from the samples were between 5 and 100 mSv (500-10,000 mrem) on contact based on a 200 mg sample. Sampling the Fuel Channels required verification the Fuel Channel was drained of organic coolant, removal of residual organic coolant from the Fuel Channel wall, removal of material from the interior surface of the Fuel Channel at the centerline of flux (∼25' below the operator position), and extraction of that material from the core. Several options for existing tooling and technologies, including hydraulic scrapers, and horizontal drill technologies, were looked at for their applicability to WR-1. While suitable approaches to higher radiation field sample collection, they were overly complex, costly and time consuming for the configuration at WR-1. In an effort to reduce costs, and to simplify the sampling process, a specialized and simple tool was developed to mechanically scrape a small sample of the interior wall of the Fuel Channels, collect the sample into a vial, and remove the sample from the core for analysis. The tool was designed, constructed, tested and implemented in the field entirely by Canadian Nuclear Laboratories Staff. The tool required no powered components, or electronic guidance or measurements; and was designed and constructed for a fraction of the cost of other commercially available technologies for sample collection within reactor cores. The samples were successfully collected without any injuries, contamination events or equipment failures. The tool remains operational in the event additional sampling is required. This paper provides additional details of the design, construction, testing and use of the tool and the successes and lessons learned during the various stages of design and implementation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Wire Arc Additive Manufacturing of Multi-material Parts Using H-13 Tool Steel

H-13 tools steel’s relative hardness, resistance to thermal fatigue, and high tolerance of thermal shock, make it very desirable for use in forging, pressing, casting, and extrusion processes[1]. H-13 is however typically quite expensive when compared to many other steel compositions. It is therefore desirable to encase a lower cost steel composition with H-13. This configuration allows for the benefits of H-13 tooling to be realized, at a considerably reduced cost. To demonstrate the viability of this concept, a casting tool was fabricated using a Wire Arc Additive Manufacturing (WAAM) process. Initial materials testing was conducted on a multi-material wall which consisted of one section of H-13 tool steel, and one section of 410NiNmo. These tests included scanning electron microscopy, energy dispersive X-ray spectroscopy, and hardness testing. This testing generated favorable results and subsequently a casting tool was fabricated for evaluation. Utilization of a Multi-Material WAAM process to fabricate large tooling in this manner could yield significant improvements in material cost, manufacturing agility, and supply chain complexity.

Lambert, Nathan [ORNL] (ORCID:0009000324134206)↗

DOE OSTI2018

Cybersecurity Framework Tool and Training Game Help Utilities ID Cyber Attack Vulnerabilities

PNNL cybersecurity experts developed two novel cyber tools to help utilities and organizations that serve them combat malicious cyber attacks. The first is the Cybersecurity Framework Tool, or CFT. This patented tool provides a way for utilities and their associated vendors to identify, protect, detect, respond to, and recover from emerging cyber threats or vulnerabilities to critical infrastructure. CFT provides an easy-to-follow set of best practices, policies, and procedures to improve the cybersecurity postures of critical infrastructure. The second tool is the Cybersecurity Training Game Simulator, a dynamic “train the trainer” tool. The game provides interactive training scenarios based on major cyberattacks that happened in the U.S. during the past decade. Both innovations are available for commercialization. Contact Commercialization Manager Peter Christensen at peter.christensen@pnnl.gov to learn more.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗