Results for “Tools” — Page 83

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗

NASA NTRSDate not supplied

Impact, A Tool Suite for Crew Health and Performance System Trade Analyses and Decision to Support - Transition to Operations

Mission planners, systems engineers, and clinicians that support crew health and performance face very difficult choices on upcoming exploration missions. Given that there will be a heavily constrained mass and volume allocation for a medical system on these missions, what medical capability should be manifested to minimize both medical risk and mission risk? Given that not all promising research and technology proposals can be funded, how can proposals be prioritized so that those funded research investments produce the maximum benefit in reducing overall medical risk? The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) project seeks to answer these kinds of questions and others to support upcoming exploration missions. IMPACT enables risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This presentation will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include an updated medical database, consisting of an Evidence Library for medical conditions and a medical item database (MedID) for medical resources, dynamic Probabilistic Risk Assessment (PRA) capabilities, System Modeling Language (SysML) models, and contextual data visualizations of output data. IMPACT is the result of a multi-center collaborative effort. The trade space analyses performed by IMPACT can directly inform mission, vehicle, and habitat development by quantifying medical risk, given a design reference mission, crew attributes and a set of medical capabilities. This presentation will update the audience on the development status of the IMPACT tool suite as it comes out of its System Acceptance Review (SAR) and nears Transition to Operations (TTO). It will review IMPACT’s constituent parts, briefly discuss typical outputs, and outline the plans for transitioning to operations, currently scheduled for later in FY24. Recent development successes on the IMPACT project include the integration of the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) v2.0 to accommodate segmented missions with multiple carriers and medical systems, full onboarding of the IMPACT Medical Database (IMPACT-MD), clustering medical resources and skills into medical capabilities and mutually-dependent bundles, verification of IMPACT-MD, and the ability to perform trade analyses on different medical sets, different design reference missions (DRM), with different crew complements and extra-vehicular activity (EVA) schedules.

IMPACT↗

NASA NTRSDate not supplied

IMPACTing Medical System Design with a Risk Analysis Tool

Background: Following the success of Artemis I, NASA is preparing for human extended duration missions. Ongoing efforts are focused on mitigating mission-related risks, including those affecting crew health and performance. Communication latency, logistics of resupply and time frame of medical evacuation are barriers to provision of healthcare for these missions, especially with respect to constraints in mass, volume, and crew training. An in-depth assessment of medical risks, capabilities and resources for a specific mission design is necessary to determine an optimal balance that maximizes likelihood of mission success. Overview: IMPACT (Informed Mission Planning via Analysis of Complex Tradespaces) is a dynamic tool designed to estimate medical risk and outcomes for a specific mission design. In its current iteration, a list of medical conditions selected based on likelihood of occurrence and/or consequence was linked to a set of clinical capabilities and resources necessary for diagnosis and management. A probabilistic risk analysis tool was then used to identify and estimate the likelihood and consequence of risks through the following outcome metrics: loss of crew life (inflight mortality due to medical conditions), need for medical evacuation (return to definitive care), and crew disability (task time affected based on how medical conditions influence the ability to perform specific exploration mission crew tasks). Finally, the model’s optimization algorithm provides recommendations for medical capabilities that maximize risk mitigation relative to mass and volume constraints. In the Spring of 2023, IMPACT was utilized to estimate outcome metrics for a design reference mission that would be representative of an extended duration Artemis mission. Notional data generated were then used to determine a recommended set of medical capabilities and resources relative to user-defined mass and volume constraints. A multidisciplinary team has also been updating IMPACT to strengthen the model’s fidelity. Figure 1 shows how updates to outcome metric inputs for the conditions resulted in different capability and resource allocation recommendations. Discussion: This presentation will discuss the IMPACT tool and share the latest data generated for a representative extended duration Artemis mission. Efforts to improve the fidelity of data generated by the model’s algorithm will also be discussed.

K A Shair↗

NASA NTRSDate not supplied

Aviary: An Open-Source Multidisciplinary Design, Analysis, and Optimization Tool for Modeling Aircraft With Analytic Gradients

Demands on aircraft design methods in recent years have begun to require increasingly higher amounts of coupling between disciplines and have also begun to require optimization in order to satisfy competing objectives involving large numbers of parameters that define unconventional configurations. These expanding requirements have amplified a need for new and improved aircraft design, analysis, and optimization codes that are capable of performing coupled design exploiting analytic gradients where possible. Aviary is a multidisciplinary design optimization and analysis framework which allows for tightly coupled simultaneous aircraft and subsystem design using analytic gradients. Aviary has employed the methods of two legacy aircraft analysis tools to provide native analytically differentiated calculations for five different disciplines, and it also has the ability to couple in external discipline analysis tools, whether or not those tools can provide analytic gradients. Preliminary examples and modeling efforts have shown Aviary’s ability to effectively model novel concepts and explore large and non-intuitive design spaces. Finally, a multi-level user interface in Aviary creates an easy entry point for users with any level of multidisciplinary design, analysis, and optimization experience.

multidisciplinary↗

NASA NTRS2024

Usage of ChatGPT for Engineering Design and Analysis Tool Development

ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT’s application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an open-source, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT’s help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn’s MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model—less than one-fifth of the stress value in the baseline model.

Design↗

NASA NTRSDate not supplied

Usage of ChatGPT for Engineering Design and Analysis Tool Development

ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT’s application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an open-source, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT’s help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn’s MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model—less than one-fifth of the stress value in the baseline model.

Design↗

NASA NTRSDate not supplied

Ultrapure Water System at JSC for High Purity Cleaning of Flight Hardware, Astromaterial Handling Tools and Solar Wind Samples

Use of Ultrapure Water (UPW) for cleaning of astromaterial sample handling tools and containers was driven by the discontinuation of Freon 113 as a cleaning solvent, due to environmental concerns. Freon 113, recycled using distillation, was very effective at removing organic residues from sample handling hardware. The initial switch to high purity water for astromaterial tool cleaning was modeled after efforts at White Sands Test Facility (WSTF) and current semiconductor UPW production practices in the early 1990s. JSC curation’s first system was comprised of ion exchange polishing cylinders, 55 gallon PVDF-lined storage tank, a few tens of feet of PVDF piping, UV and particle filtration, and this system supported cleaning for lunar, meteorite and Cosmic Dust collectors lab operations. By 2000 a major expansion and upgrade was providing UPW of quality E-1 (ASTM D5127-90) or better at 10 gpm for cleaning of the Genesis solar wind payload for flight, in addition to sample handling tools for other astromaterials. This expansion included a 1000 gallon storage tank and several hundred feet of PVDF piping located in B. 31 and 31N. An extra reverse osmosis process unit was added to incoming water in 2005. Subsequently several major components were replaced, and in anticipation of adding new labs, an enlarged UPW system was designed in 2018 and completed in 2019. This system produces grade E1.1 UPW at 15 gpm in a continuous 1100 ft. loop with 5000 gal. storage tank, supplying 8 laboratories.

Ultrapure water↗

NASA NTRSDate not supplied

IMPACT, A Tool Suite for Crew Health and Performance System Trade Analyses and Decision Support- Transition to Operations

Mission planners, systems engineers, and clinicians that support crew health and performance face very difficult choices on upcoming exploration missions. Given that there will be a heavily constrained mass and volume allocation for a medical system on these missions, what medical capability should be manifested to minimize both medical risk and mission risk? Given that not all promising research and technology proposals can be funded, how can proposals be prioritized so that those funded research investments produce the maximum benefit in reducing overall medical risk? The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) project seeks to answer these kinds of questions and others to support upcoming exploration missions. IMPACT enables risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This presentation will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include an updated medical database, consisting of an Evidence Library for medical conditions and a medical item database (MedID) for medical resources, dynamic Probabilistic Risk Assessment (PRA) capabilities, System Modeling Language (SysML) models, and contextual data visualizations of output data. IMPACT is the result of a multi-center collaborative effort. The trade space analyses performed by IMPACT can directly inform mission, vehicle, and habitat development by quantifying medical risk, given a design reference mission, crew attributes and a set of medical capabilities. This presentation will update the audience on the development status of the IMPACT tool suite as it comes out of its System Acceptance Review (SAR) and nears Transition to Operations (TTO). It will review IMPACT’s constituent parts, briefly discuss typical outputs, and outline the plans for transitioning to operations, currently scheduled for later in FY24. Recent development successes on the IMPACT project include the integration of the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) v2.0 to accommodate segmented missions with multiple carriers and medical systems, full onboarding of the IMPACT Medical Database (IMPACT-MD), clustering medical resources and skills into medical capabilities and mutually-dependent bundles, verification of IMPACT-MD, and the ability to perform trade analyses on different medical sets, different design reference missions (DRM), with different crew complements and extra-vehicular activity (EVA) schedules.

IMPACT↗

NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗

NASA NTRSDate not supplied

Aviary: Open-Source Software Tool for Optimizing Next-Generation Aircraft Designs

Existing aircraft design tools are limited in their ability to provide meaningful trends for hybrid-electric & all-electric aircraft. Aviary is an open-source software tool that modernizes legacy design tools and enables multidisciplinary analysis and optimization of unconventional vehicles like X-66A. Aviary has the potential to revolutionize aircraft analysis by making aircraft conceptual designs more efficient.

Jennifer Gratz↗

NASA NTRSDate not supplied

On the Modeling of Urban Air Mobility Vehicle Takeoff and Landing Operations in the FAA Aviation Environmental Design Tool

Urban air mobility (UAM) vehicles are anticipated to operate in close proximity to the public. A possible barrier to the introduction of UAM vehicles as a transit solution is their community noise impact, particularly around vertiports. The Federal Aviation Administration Aviation Environmental Design Tool (AEDT) is the mandated tool to assess aircraft noise and other environmental impacts due to federal actions at civilian airports, vertiports, or in U.S. airspace for commercial flight operations. However, AEDT was designed to model fixed-wing aircraft and conventional helicopter operations, not UAM vehicles. Prior work by the authors showed significant differences in noise contours of UAM departures and approaches when modeling operations as fixed-wing and helicopter types in AEDT. This paper identifies the sources of those differences. Additionally, a NASA time-marching simulation tool is used to generate noise exposure predictions for equivalent operations to help identify differences associated with the noise model implemented in AEDT and offer possible changes to assess UAM community noise impact more consistently with simulation-based modeling.

aeroacoustics↗

NASA NTRSDate not supplied

On the Modeling of Urban Air Mobility Vehicle Takeoff and Landing Operations in the FAA Aviation Environmental Design Tool

Urban air mobility (UAM) vehicles are anticipated to operate in close proximity to the public. A possible barrier to the introduction of UAM vehicles as a transit solution is their community noise impact, particularly around vertiports. The Federal Aviation Administration Aviation Environmental Design Tool (AEDT) is the mandated tool to assess aircraft noise and other environmental impacts due to federal actions at civilian airports, vertiports, or in U.S. airspace for commercial flight operations. However, AEDT was designed to model fixed-wing aircraft and conventional helicopter operations, not UAM vehicles. Prior work by the authors showed significant differences in noise contours of UAM departures and approaches when modeling operations as fixed-wing and helicopter types in AEDT. This paper identifies the sources of those differences. Additionally, a NASA time-marching simulation tool is used to generate noise exposure predictions for equivalent operations to help identify differences associated with the noise model implemented in AEDT and offer possible changes to assess UAM community noise impact more consistently with simulation-based modeling.

aeroacoustics↗

NASA NTRSDate not supplied

Aviary: An Open-Source Multidisciplinary Design, Analysis, and Optimization Tool for Modeling Aircraft with Analytic Gradients

Aviary is an open-source aircraft design, analysis, and optimization tool built on OpenMDAO and leveraging the legacy tools of FLOPS and GASP. This presentation will introduce the basics of Aviary, more details for which can be found in the paper. Specifically, this presentation will discuss an overview of why new aircraft design tools are necessary and how Aviary meets those needs, the structure and subsystems of Aviary, and the validation and interface that Aviary provides.

optimization↗

NASA NTRS2024

Valkyrie Robot Capability Demonstration: Autonomous Grab of Body Stored Hand-Tool

A video that provides an automated tool grasp industrial scenario for the NASA Valkyrie Robot from the NASA Dexterous Robotics Team. The scenario is where the Valkyrie uses externally mounted sensors and tools to investigate a room for objects of interest and removes any hazards. This video highlights the tool pick component of the full demonstration. Video run time is 3 min. 28 sec, in color with no sound.

Valkyrie↗

NASA NTRSDate not supplied

AI-Enhanced Computational Tools for Entry Systems Modeling

To advance the understanding of complex atmospheric entry phenomena, NASA’s Entry Systems Modeling (ESM) team [1] has developed high-fidelity computational tools addressing multiscale challenges, from material microstructures to full-scale heatshield response. This abstract highlights a subset of ESM tools, focusing on AI integration to enhance workflows and predictive modeling. - PuMA [2] computes effective material properties from high-resolution micro-CT scans, supporting TPS analysis for NASA missions. - TomoSAM [3] automates 3D tomography dataset segmentation for PuMA using the Segment Anything Model, reducing manual effort and improving accuracy. - PATO [4] models porous reactive materials under extreme conditions, with advancements such as unified solvers, mechanical erosion, and TPS coatings for NASA missions. - arcjetCV [5] employs deep learning to analyze arc jet test footage, measuring recession rates, shape changes, and shock standoff distances, bridging simulations, and experiments to reveal TPS ablation behavior. - ARCHeS [6] simulates arc heater plasma flows, modeling turbulence, radiation, and electromagnetic interactions to optimize arc heater performance, validate TPS under extreme conditions, and serve as a foundation for developing digital twins of arc heater facilities. - SPARTA [7] simulates rarefied hypersonic flows and gas-surface interactions for planetary entry missions, leveraging GPU architectures for scalable and efficient aerothermal and ablation analyses. AI-driven solutions, such as deep learning segmentation, have streamlined workflows in ESM tools and still hold significant potential to further accelerate processes and enhance automation in entry systems modeling. [1] Haskins, J.B. (2023), [2] Ferguson, J.C. (2018), [3] Meurisse, J.B.E. (2018), [4] Semeraro, F. (2023), [5] Quintart, A. (2024) [6] Meurisse, J.B.E. (2022), [7] Plimpton, S.J. (2019)

Predictive Modeling↗

NASA NTRSDate not supplied

Performance Risk Model (PRisM) Proof-of-Concept: An Operational Decision Support Tool to Predict Crew Performance in Space from Available Performance Tests

The Crew Health and Performance-Probabilistic Risk Assessment (CHP-PRA) team at NASA Glenn Research Center has developed a range of tools to evaluate astronaut health during spaceflight and to optimize the medical set required for missions. Among these, the Performance Risk Model (PRisM) represents a novel advancement, extending CHP-PRA’s focus beyond medical systems into the domain of human performance. Such tool could be pivotal in optimizing astronaut capabilities during space travel, thereby enhancing overall mission success. PRisM leverages data from well-established performance assessments conducted during previous crewed space missions and Earth analogs to strategically predict outcomes for planned tasks, even when direct performance testing has not been conducted for those specific tasks. To evaluate performance, PRisM references the various metrics outlined in NASA-STD-3001 and employs a methodology to integrate different performance scales. This framework analyzes the contribution of various human system task categories (HSTCs) to task execution and compares these contributions to the HSTCs’ involvement in other known tests. The model further employs a Monte Carlo simulation to sample performance scores from their distribution in operationally relevant tests such as those in Mulavara et al. (2018) and, by leveraging similarities in HSTC involvement, transfers this knowledge to actual mission tasks, such as those outlined in the "Human Exploration of Mars: Preliminary List of Crew Tasks”. The current PRisM proof-of-concept includes analyses of the impact of exercise and specific medications on astronauts’ performance, with provisions to incorporate additional countermeasures as data becomes available. Furthermore, the tool is customizable to include any system necessary to fully encompass the domain of human systems and can be adapted to evaluate performance for any spaceflight activities as requested by operational stakeholders. PRisM has the potential to assist the Human Research Program in exploring the capabilities trade space for optimized crew performance.

performance modeling↗

DOE OSTI2013

Use of SCALE Continuous-Energy Monte Carlo Tools for Eigenvalue Sensitivity Coefficient Calculations

The TSUNAMI code within the SCALE code system makes use of eigenvalue sensitivity coefficients for an extensive number of criticality safety applications, such as quantifying the data-induced uncertainty in the eigenvalue of critical systems, assessing the neutronic similarity between different critical systems, and guiding nuclear data adjustment studies. The need to model geometrically complex systems with improved fidelity and the desire to extend TSUNAMI analysis to advanced applications has motivated the development of a methodology for calculating sensitivity coefficients in continuous-energy (CE) Monte Carlo applications. The CLUTCH and Iterated Fission Probability (IFP) eigenvalue sensitivity methods were recently implemented in the CE KENO framework to generate the capability for TSUNAMI-3D to perform eigenvalue sensitivity calculations in continuous-energy applications. This work explores the improvements in accuracy that can be gained in eigenvalue and eigenvalue sensitivity calculations through the use of the SCALE CE KENO and CE TSUNAMI continuous-energy Monte Carlo tools as compared to multigroup tools. The CE KENO and CE TSUNAMI tools were used to analyze two difficult models of critical benchmarks, and produced eigenvalue and eigenvalue sensitivity coefficient results that showed a marked improvement in accuracy. The CLUTCH sensitivity method in particular excelled in terms of efficiency and computational memory requirements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Development of a Groundwater Transport Simulation Tool for Remedial Process Optimization

The groundwater remedy for hexavalent chromium at the Hanford Site includes operation of five large pump-and-treat systems along the Columbia River. The systems at the 100-HR-3 and 100-KR-4 groundwater operable units treat a total of about 9,840 liters per minute (2,600 gallons per minute) of groundwater to remove hexavalent chromium, and cover an area of nearly 26 square kilometers (10 square miles). The pump-and-treat systems result in large scale manipulation of groundwater flow direction, velocities, and most importantly, the contaminant plumes. Tracking of the plumes and predicting needed system modifications is part of the remedial process optimization, and is a continual process with the goal of reducing costs and shortening the timeframe to achieve the cleanup goals. While most of the initial system evaluations are conducted by assessing performance (e.g., reduction in contaminant concentration in groundwater and changes in inferred plume size), changes to the well field are often recommended. To determine the placement for new wells, well realignments, and modifications to pumping rates, it is important to be able to predict resultant plume changes. In smaller systems, it may be effective to make small scale changes periodically and adjust modifications based on groundwater monitoring results. Due to the expansive nature of the remediation systems at Hanford, however, additional tools were needed to predict the plume reactions to system changes. A computer simulation tool was developed to support pumping rate recommendations for optimization of large pump-and-treat groundwater remedy systems. This tool, called the Pumping Optimization Model, or POM, is based on a 1-layer derivation of a multi-layer contaminant transport model using MODFLOW and MT3D.

Ivarson, Kristine A.↗