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NASA NTRS2019

The Lunar Geophysical Network Mission

Overarching Principles: Must be better than Apollo (coverage, duration, instrument performance); Learn from the Apollo experience. Lunar Geophysical Network (LGN) New Frontiers (NF)-class mission, as part of the NF-5 call. “This mission consists of several identical landers distributed across the lunar surface, each carrying geophysical instrumentation. The primary science objectives are to characterize the Moon’s internal structure, seismic activity, global heat flow budget, bulk composition, & magnetic field.” Global distribution of multiple stations. Each station should contain a seismometer, heat flow probe, electromagnetic sounder, laser retroreflector (lunar nearside). Each station must be long-lived (e.g., approximately10 years)to allow other stations (from other countries?) to be integrated with the anchor nodes to form the International Lunar Network. Why LGN? Planetary Science: Moon represents an end-member in planetary evolution (large small body, small rocky planet); Primary planetary differentiation preserved; Key to understanding terrestrial planet initial differentiation. Lunar Science: Heat flow probes yield crustal heat budget estimates; Combined with EMS (ElectroMagnetic Sounding), the temperature profile of the deep interior can be modeled along with mineralogy; Seismic and LLR (Lunar Laser Ranging) data also yield structure and compositional information of the lunar interior; High fidelity data from LGN would enhance the usefulness of the GRAIL (Gravity Recovery and Interior Laboratory) and SELENE (Selenological and Engineering Explorer) gravity data. Human Exploration: LGN must be established prior to renewed human lunar activity - we do not know the exact locations or causes of the shallow moonquakes (SMQs) - the largest magnitude seismic events recorded by Apollo (1 event per year of magnitude greater than or equal to 5); Establishing surface infrastructure near SMQ epicenters must be avoided.

Neal, C. R.↗

NASA NTRS2011

Behavioral Health and Performance Operations During the Space Shuttle Program

Prior to the Columbia STS 107 disaster in 2003, the Johnson Space Center s Behavioral Health and Performance Group (BHP) became involved in Space Shuttle Operations on an as needed basis, occasionally acting as a consultant and primarily addressing crew-crew personality conflicts. The BHP group also assisted with astronaut selection at every selection cycle beginning in 1991. Following STS 107, an event that spawned an increased need of behavioral health support to STS crew members and their dependents, BHP services to the Space Shuttle Program were enhanced beginning with the STS 114 Return to Flight mission in 2005. These services included the presence of BHP personnel at STS launches and landings for contingency support, a BHP briefing to the entire STS crew at L-11 months, a private preflight meeting with the STS Commander at L-9 months, and the presence of a BHP consultant at the L-1.5 month Family Support Office briefing to crew and family members. The later development of an annual behavioral health assessment of all active astronauts also augmented BHP s Space Shuttle Program specific services, allowing for private meetings with all STS crew members before and after each mission. The components of each facet of these BHP Space Shuttle Program support services will be presented, along with valuable lessons learned, and with recommendations for BHP involvement in future short duration space missions

Beven, G.↗

NASA NTRS2011

Software Architecture to Support the Evolution of the ISRU RESOLVE Engineering Breadboard Unit 2 (EBU2)

The In-Situ Resource Utilization (ISRU) Regolith & Environmental Science and Oxygen & Lunar Volatiles Extraction (RESOLVE) software provides operation of the physical plant from a remote location with a high-level interface that can access and control the data from external software applications of other subsystems. This software allows autonomous control over the entire system with manual computer control of individual system/process components. It gives non-programmer operators the capability to easily modify the high-level autonomous sequencing while the software is in operation, as well as the ability to modify the low-level, file-based sequences prior to the system operation. Local automated control in a distributed system is also enabled where component control is maintained during the loss of network connectivity with the remote workstation. This innovation also minimizes network traffic. The software architecture commands and controls the latest generation of RESOLVE processes used to obtain, process, and quantify lunar regolith. The system is grouped into six sub-processes: Drill, Crush, Reactor, Lunar Water Resource Demonstration (LWRD), Regolith Volatiles Characterization (RVC) (see example), and Regolith Oxygen Extraction (ROE). Some processes are independent, some are dependent on other processes, and some are independent but run concurrently with other processes. The first goal is to analyze the volatiles emanating from lunar regolith, such as water, carbon monoxide, carbon dioxide, ammonia, hydrogen, and others. This is done by heating the soil and analyzing and capturing the volatilized product. The second goal is to produce water by reducing the soil at high temperatures with hydrogen. This is done by raising the reactor temperature in the range of 800 to 900 C, causing the reaction to progress by adding hydrogen, and then capturing the water product in a desiccant bed. The software needs to run the entire unit and all sub-processes; however, throughout testing, many variables and parameters need to be changed as more is learned about the system operation. The Master Events Controller (MEC) is run on a standard laptop PC using Windows XP. This PC runs in parallel to another laptop that monitors the GC, and a third PC that monitors the drilling/ crushing operation. These three PCs interface to the process through a CompactRIO, OPC Servers, and modems.

Moss, Thomas↗

DOE OSTI2023

Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are threefold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws governing vehicle dynamics into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Retrieval of Transuranic Drums with Deflagration to Detonation Potential from a Vault at the Oak Ridge National Laboratory - 20207

During the final phase of a long-term project, a total of 80 TRU waste drums at ORNL have been retrieved from a below-grade storage vault in which they have remained untouched for about 25 years. The drums, which house material generated from past ORNL operations, are now stored and ready for eventual transfer to the Transuranic Waste Processing Center, located on Highway 95 in Oak Ridge. The Transuranic Waste Processing Center will process and ship the drums to WIPP in Carlsbad, New Mexico, for disposal. Workers lifted the drums one-by-one from the underground storage vault with a 110-ton crane over a one-week period in October 2017. Although the physical work was performed in one week, years of detailed planning preceded the fieldwork. A key obstacle needed to be overcome before the project could safely proceed. This obstacle involved the need to ensure controls were developed for the Deflagration to Detonation Transition (DDT) potential. DDT refers to a phenomenon in ignitable mixtures of a flammable gas and air (or oxygen) when a sudden transition takes place from a deflagration type of combustion to a detonation type of explosion. Basically, these drums, under certain circumstances, have a potential to detonate and overpressurize. Extensive analysis of the drums verified that detonation, while unlikely, was possible. Of the total 103 drums that were retrieved or moved in preparation for retrieval, 18 had DDT potential. Extensive security requirements were also necessary, due to the nature of the material, and this added another layer of complexity to the fieldwork. Prior to retrieving the drums from the underground storage vault, space needed to be made for them within the various above-grade facilities. Inventory and security requirements dictated what could be stored where, and the first step of the field work involved a number of drum movements in preparation for storage of the newly retrieved drums. This effort involved moving 21 drums already in the storage area to facilitate the addition of the newly retrieved ones. The retrieval project required a total of 107 critical lifts using the 110-ton crane, under 3 critical lift plans. The TRU waste storage facility is a Category 2 Nuclear Facility and is managed under an extensive Documented Safety Analysis. The TRU retrieval project was conducted in accordance with DOE Order 425.1D and included three Implementation Verification Reviews, two Management Assessments, one Readiness Assessment, and seven mock-ups in preparation for the field work. Despite being stored for 25 years, the drums were in good shape due to the integrity of the facilities where the drums were stored. Of the 80 drums that were retrieved, 12 were placed in special containers called 'overpacks' based on their content. The drums were removed safely and without incident, thanks to a lot of preparation and the efforts of a highly skilled team. Lessons learned through the DDT control development and the drum retrieval and overpacking process could be beneficial to others having to manage similar drums of TRU waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1999

Visual-vestibular integration motion perception reporting

Self-orientation and self/surround-motion perception derive from a multimodal sensory process that integrates information from the eyes, vestibular apparatus, proprioceptive and somatosensory receptors. Results from short and long duration spaceflight investigations indicate that: (1) perceptual and sensorimotor function was disrupted during the initial exposure to microgravity and gradually improved over hours to days (individuals adapt), (2) the presence and/or absence of information from different sensory modalities differentially affected the perception of orientation, self-motion and surround-motion, (3) perceptual and sensorimotor function was initially disrupted upon return to Earth-normal gravity and gradually recovered to preflight levels (individuals readapt), and (4) the longer the exposure to microgravity, the more complete the adaptation, the more profound the postflight disturbances, and the longer the recovery period to preflight levels. While much has been learned about perceptual and sensorimotor reactions and adaptation to microgravity, there is much remaining to be learned about the mechanisms underlying the adaptive changes, and about how intersensory interactions affect perceptual and sensorimotor function during voluntary movements. During space flight, SMS and perceptual disturbances have led to reductions in performance efficiency and sense of well-being. During entry and immediately after landing, such disturbances could have a serious impact on the ability of the commander to land the Orbiter and on the ability of all crew members to egress from the Orbiter, particularly in a non-nominal condition or following extended stays in microgravity. An understanding of spatial orientation and motion perception is essential for developing countermeasures for Space Motion Sickness (SMS) and perceptual disturbances during spaceflight and upon return to Earth. Countermeasures for optimal performance in flight and a successful return to Earth require the development of preflight and in-flight training to help astronauts acquire and maintain a dual adaptive state. Despite the considerable experience with, and use of, an extensive set of countermeasures in the Russian space program, SMS and perceptual disturbances remain an unresolved problem on long-term flights. Reliable, valid perceptual reports are required to develop and refine stimulus rearrangements presented in the PAT devices currently being developed as countermeasures for the prevention of motion sickness and perceptual disturbances during spaceflight, and to ensure a less hazardous return to Earth. Prior to STS-8, crew member descriptions of their perceptual experiences were, at best, anecdotal. Crew members were not schooled in the physiology or psychology of sensory perception, nor were they exposed to the appropriate professional vocabulary. However, beginning with the STS-8 Shuttle flight, a serious effort was initiated to teach astronauts a systematic method to classify and quantify their perceptual responses in space, during entry, and after flight. Understanding, categorizing, and characterizing perceptual responses to spaceflight has been greatly enhanced by implementation of that training system.

Harm, Deborah L.↗

DOE OSTI2020

Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e., conversion from detected signals to descriptors specific to tip–surface interactions and subsequently to material’s properties. In this work, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We explore the nonlinear mechanical behaviors spatially in a classical ferroelectric material, PbTiO 3 . We observe the non-trivial evolution of the Duffing stiffness term and the nonlinearity of the sample surface, determine spatial clustering of the nonlinear response, and perform a Landau analysis on predicting the nonlinear coefficient, which indicates that ferroelectric behavior can be a cause of the observed results. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls may be broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO 2 capture process

In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO 2 capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO 2 capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

OLCF’s Advanced Computing Ecosystem (ACE): FY25 Update for Ongoing Efforts

The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2013

Friction Stir Weld Application and Tooling Design for the Multi-purpose Crew Vehicle Stage Adapter

The Multi-Purpose Crew Vehicle (MPCV), commonly known as the Orion capsule, is planned to be the United States' next manned spacecraft for missions beyond low earth orbit. Following the cancellation of the Constellation program and creation of SLS (Space Launch System), the need arose for the MPCV to utilize the Delta IV Heavy rocket for a test launch scheduled for 2014 instead of the previously planned Ares I rocket. As a result, an adapter (MSA) must be used in conjunction with the MPCV to account for the variation in diameter of the launch vehicles; 5.5 meters down to 5.0 meters. Prior to ight article fabrication, a path nder (test article) will be fabricated to ne tune the associated manufacturing processes. The adapter will be comprised of an aluminum frustum (partial cone) that employs isogrid technology and circumferential rings on each end. The frustum will be fabricated by friction stir welding (FSW) three individual panels together on a Vertical Weld Tool (VWT) at NASA Marshall Space Flight Center. Subsequently, each circumferential ring will be friction stir welded to the frustum using a Robotic Weld Tool (RWT). The irregular geometry and large mass of the MSA require that extensive tooling preparation be put into support structures for the friction stir weld. The tooling on the VWT will be comprised of a set of conveyors mounted on pre-existing stanchions so that the MSA will have the ability to be rotated after each of the three friction stir welds. The tooling requirements to friction stir weld the rings with the RWT are somewhat more demanding. To support the mass of the MSA and resist the load of the weld tool, a system of mandrels will be mounted to stanchions and assembled in a circle. The goal of the paper will be to explain the design, fabrication, and assembly of the tooling, to explain the use of friction stir welding on the MSA path nder, and also to discuss the lessons learned and modi cations made in preparation for ight article fabrication in support of the 2014 launch of the Orion MPCV.

Alcorn, John↗

DOE OSTI2024

AMIA KDDM Working Group Collaborative Workshop: Enriching Electronic Health Records with Social Determinants of Health to Improve Outcomes and Health Equity

Prior research has demonstrated that social determinants of health (SDoH) are major drivers of health outcomes and contributors to widespread health inequities. It was estimated that, in the United States, SDoH could be responsible for up to 40% of all preventable deaths, significantly higher than the 10-15% for which better medical care is responsible. Public health interventions that target SDoH are instrumental for improving health outcomes and reducing long-standing health inequities. Currently, most mainstream EHR vendors have implemented SDoH screeners in their EHR systems. However, the utility of the screeners is low, rendering patient-level SDoH still widely unavailable in the structured fields. SDoH are sometimes mentioned in free-text clinical notes (e.g., social context section) where natural language processing (NLP) can be applied to extract relevant information. Contextual-level SDoH can be identified from multiple data sources, many of which are publicly available and spatiotemporally linked to EHR data. As such, there is an opportunity for the KDDM research community to create innovative solutions to draw meaningful insights by creating and using rich data with SDoH to improve health outcomes while reducing disparities. In this workshop organized by AMIA Knowledge Discovery and Data Mining Working Group (AMIA KDDM WG), we will invite world-leading experts from academia, national laboratories, and life science industry with varied backgrounds in biomedical informatics, epidemiology, data science, machine learning, natural language processing, and pediatric cardiology to discuss the best practice of capturing, standardizing, and using SDoH information in various applications aiming at improving outcomes and health equity.

He, Zhe↗

NASA NTRS2005

A Testbed Demonstration of an Intelligent Archive in a Knowledge Building System

The last decade's influx of raw data and derived geophysical parameters from several Earth observing satellites to NASA data centers has created a data-rich environment for Earth science research and applications. While advances in hardware and information management have made it possible to archive petabytes of data and distribute terabytes of data daily to a broad community of users, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications in order to realize the full potential of these valuable datasets. In examining what is needed to enable this progress in the data provider environment that exists today and is expected to evolve in the next several years, we arrived at the concept of an Intelligent Archive in context of a Knowledge Building System (IA/KBS). Our prior work and associated papers investigated usage scenarios, required capabilities, system architecture, data volume issues, and supporting technologies. We identified six key capabilities of an IA/KBS: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting. Among these capabilities, large-scale data mining is perceived by many in the community to be an area of technical risk. One of the main reasons for this is that standard data mining research and algorithms operate on datasets that are several orders of magnitude smaller than the actual sizes of datasets maintained by realistic earth science data archives. Therefore, we defined a test-bed activity to implement a large-scale data mining algorithm in a pseudo-operational scale environment and to examine any issues involved. The application chosen for applying the data mining algorithm is wildfire prediction over the continental U.S. This paper reports a number of observations based on our experience with this test-bed. While proof-of-concept for data mining scalability and utility has been a major goal for the research reported here, it was not the only one. The other five capabilities of an WKBS named above have been considered as well, and an assessment of the implications of our experience for these other areas will also be presented. The lessons learned through the testbed effort and presented in this paper will benefit technologists, scientists, and system operators as they consider introducing IA/KBS capabilities into production systems.

Ramapriyan, Hampapuram↗

NASA NTRS2019

Managing Flagship Missions to Reduce Cost and Schedule

Flagship missions are highly complex with highly nested systems. This level of complexity poses unique management problems as complexity influences risk which, in turn, affects cost and schedule. Establishing a strong technical and programmatic leadership team is critical to mission success. Developing and using a mission architecture is critical to informing the management organization, product ownership, interface and integration relationships, schedule organization, and integration and test paths. In highly nested systems, the mission phasing can be significantly out of sync with product phasing. Targeted technology development prior to Phase A is critical to reducing risk. Early architecture, concept design, and requirements development is critical to reducing risk. Modular design; pathfinders; parallel manufacturing and integration and test paths; and properly handling institutional requirements across interfaces are all management techniques that can be applied to reduce risk. NASA’s large strategic missions, sometimes referred to as flagship missions, are designed to provide answers to some of the most compelling scientific questions being asked. These types of missions are a series of highly nested subsystems that pose unique management problems when compared to more traditional instrument and spacecraft designs. They typically have an overall architecture that is very complex and nested; they typically require a tremendous amount of technology development; they typically involve many contractors and subcontractors with many associated contracts; and they typically involve staff from all over the world. Successful management of a flagship requires the balance between science requirements, engineering and technology capabilities, and resource constraints. Mismanaging these flagship missions can and will lead to significant cost and schedule growth, both of which are detrimental to NASA’s overall reputation which, in turn, is detrimental to the development of future flagship missions. While many of the same management principles used on smaller instruments and spacecraft are relevant, managing flagship missions requires an evolution of those current best practices to better address the specific needs and additional complexity and vastness of these missions. This paper explores how to leverage lessons learned from previous flagship missions to better manage flagship missions in the future.

Hylan, Jason↗

DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

VAIM-CFF: a variational autoencoder inverse mapper solution to Compton form factor extraction from deeply virtual exclusive reactions

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted CFFs, use of uncertainty quantification technique, and inclusion of prior physics information. We then use dimensionality reduction techniques such as principal component analysis to visualize the missing physics information tracked in the latent space of the VAIM framework. Through re-framing the extraction of CFFs as an inverse problem, we gain access to fundamental properties of the problem not comprehensible in standard fitting methodologies: exploring the limits of the information encoded in deeply virtual exclusive experiments.

Accelerator Physics↗

DOE OSTI2023

Label Assist: Personalized Travel Models for Longitudinal Data Collection

Understanding travel behavior is crucial to transportation decarbonization. OpenPATH is an open-source mobility platform which collects and analyzes human travel behavior at the individual level. The mobile application passively senses trips and prompts users to label them. However, users find the labeling process burdensome; less than half the trips are typically labeled, making much of the data unusable in aggregate analyses of mobility patterns. Prior work has addressed the response fatigue challenge through automated mode inference using sensor data, but sensors cannot capture all aspects of travel behavior. We explore an alternative approach in which we leverage prior user input to predict travel choices in novel trips. We first explore trip clustering methods and develop a novel two-step pipeline using DBSCAN and SVMs to extract realistic geospatial clusters. We then propose two strategies to predict trip labels: (i) clustering trips and extrapolating labels for similar trips, and (ii) random forest classification. The random forest approach is able to achieve - $70-80% accuracy (purpose: 72%, mode: 79%, replaced mode: 81%). These novel approaches to trip classification allow us to increase the rate of user labeling by suggesting predicted labels to be verified by the user. Unlabeled trips can also contribute to aggregate analyses, using label predictions and their associated confidences as a substitute. While there exist other travel survey apps with the ability to infer travel choices, to our knowledge, this is the first paper to describe such a supervised system and rigorously evaluate it.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

NASA NTRS2001

Evolution of the Earth and Origin of Life: The Role of Gas/Fluid Interactions with Rocks

The work under the Cooperative Agreement will be centered on questions of the evolution of Life on the early Earth and possibly on Mars. It is still hotly debated whether the essential organic molecules were delivered to the early Earth from space (by comets, meteorites or interplanetary dust particles) or were generated in situ on Earth. Prior work that has shown that the matrix of igneous minerals is a medium in which progenitors of organic molecules assemble from H2O, C02 and N2 incorporated as minority "impurities" in minerals of igneous rocks during crystallization from H2O/CO2/N2-laden magmas. The underlying processes involve a redox. conversion whereby C, H, and N become chemically reduced, while 0 becomes oxidized to the peroxy state. During Year 02 the work will be divided into three tasks. Task 1: After carboxylic (fatty) acids and N-bearing compounds have been identified, other extractable organic molecules including lipids, oily substances and amino acids will be studied. Dedicated lipid analysis will be combined with gas chromatographic-mass spectroscopic (GCMS) analysis of organic compounds extracted from minerals and rocks. Task 2: Using infrared (IR) spectroscopy, C-H entities that are indicators for the organic progenitors in mineral matrices will be studied. A preliminary heating experiment with MgO single crystals has shown that the C-H entities can be pyrolyzed, causing the IR bands to disappear, but at room temperature the IR bands reappear in a matter of days to weeks. This work will be expanded, both by studying synthetic MgO crystals and olivine crystals from the Earth's upper mantle. The C-H bands will be compared to the published "organic" IR feature of dust in the interstellar medium (ISM) and interplanetary dust particles (IDP). Task 3: A paradox marks the evolution of early Life: Oxygen is highly toxic to primitive life, yet early organisms "learned" to detoxify reactive oxygen species, to utilize oxygen, and even produce it. Why would organisms on the early anaerobic Earth be under evolutionary pressure to evolve defenses against reactive oxygen species? Minerals in igneous rocks are now known to contain peroxy. When such minerals weather, the peroxy hydrolyzes to H2O2. The hypothesis will be tested whether organisms living in intimate contact with rock surfaces are subjected to a constant trickle of H202 and thus under stress to develop strategies to either detoxify the reactive oxygen species or repair the molecular damage that they cause. Understanding these processes is central to the Astrobiology mission. It opens new avenues toward understanding the evolution of early life on Earth, and the potential for aerobic life elsewhere. This Cooperative Agreement also has a strong educational and public outreach component involving high school, undergraduate students, and high school teachers.

Freund, Friedemann↗