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DOE OSTI2023

Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.

autonomy loops↗

NASA NTRS2009

The Effect of Center of Gravity and Anthropometrics on Human Performance in Simulated Lunar Gravity

NASA EVA Physiology, Systems and Performance (EPSP) Project at JSC has been investigating the effects of Center of Gravity and other factors on astronaut performance in reduced gravity. A subset of the studies have been performed with the water immersion technique. Study results show correlation between Center of Gravity location and performance. However, data variability observed between subjects for prescribed Center of Gravity configurations. The hypothesis is that Anthropometric differences between test subjects could be a source of the performance variability.

Mulugeta, Lealem↗

NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗

DOE OSTI2020

Understanding Interactive and Reproducible Computing With Jupyter Tools at Facilities

Increasingly Jupyter tools are being adopted and incorporated into High Performance Computing (HPC) and scientific user facilities. Adopting Jupyter tools enables more interactive and reproducible computational work at facilities across data life cycles. As the volume, variety, and scope of data grow, scientists need to be able to analyze and share results in user friendly ways. Human-centered research highlights design challenges around computational notebooks, and our qualitative user study shifts focus to better characterize how Jupyter tools are being used in HPC and science user facilities today. We conducted twenty-nine interviews, and obtained 103 survey responses from NERSC Jupyter users, to better understand the increasing role of interactive computing tools in DOE sponsored scientific work. We examine a range of issues that emerge using and supporting Jupyter in HPC ecosystems, including: how Jupyter is being used by scientists in HPC and user facility ecosystems; how facilities are purposefully supporting Jupyter in their ecosystems; feedback NERSC users have about the facility’s deployment, and, discuss features NERSC indicated would be helpful. We offer a variety of takeaways for staff supporting Jupyter at facilities, Project Jupyter and related open source communities, and funding agencies supporting interactive computing work.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

BeyondFingerprinting: AI-guided discovery of robust materials & processes

BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject-matter expertise with algorithms enriched with physics-based constraints to unearth process-structure-property correlations. Such algorithms, trained on high-throughput experiments and simulations, are shown to serve as surrogate models that efficiently detect key “fingerprints” in materials data, prognose material performance, and guide effective process improvements. To accelerate broader adoption across mission areas, this AI-guided approach was demonstrated with three complex process-centric exemplars: electroplating, physical vapor deposition, and laser powder bed fusion. Together, these exemplars impact nearly every hardware component relevant to DOE and NNSA national security missions.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Simulating Physiological Response with a Passive Sensor Manikin and an Adaptive Thermal Manikin to Predict Thermal Sensation and Comfort

Reliable assessment of occupant thermal comfort can be difficult to obtain within automotive environments, especially under transient and asymmetric heating and cooling scenarios. Evaluation of HVAC system performance in terms of comfort commonly requires human subject testing, which may involve multiple repetitions, as well as multiple test subjects. Instrumentation (typically comprised of an array of temperature sensors) is usually only sparsely applied across the human body, significantly reducing the spatial resolution of available test data. Further, since comfort is highly subjective in nature, a single test protocol can yield a wide variation in results which can only be overcome by increasing the number of test replications and subjects. In light of these difficulties, various types of manikins are finding use in automotive testing scenarios. These manikins can act as human surrogates from which local skin and core temperatures can be obtained, which are necessary for accurately predicting local and whole body thermal sensation and comfort using a physiology-based comfort model (e.g., the Berkeley Comfort Model). This paper evaluates two different types of manikins, i) an adaptive sweating thermal manikin, which is coupled with a human thermoregulation model, running in real-time, to obtain realistic skin temperatures; and, ii) a passive sensor manikin, which is used to measure boundary conditions as they would act on a human, from which skin and core temperatures can be predicted using a thermophysiological model. The simulated physiological responses and comfort obtained from both of these manikin-model coupling schemes are compared to those of a human subject within a vehicle cabin compartment transient heat-up scenario.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Factorized visual representations in the primate visual system and deep neural networks

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether (‘invariance’), represented in non-interfering subspaces of population activity (‘factorization’) or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters – lighting, background, camera viewpoint, and object pose – in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1973

Vibration

Man's reactions to vibration are emphasized rather than his reactions to the vibrational characteristics of vehicles. Vibrational effects studies include: performance effects reflected in tracking proficiency, reaction time, visual impairment, and other measures related to man's ability to control a system; physiological reactions; biodynamic responses; subjective reactions; and human tolerance limits. Technological refinements in shaker systems and improved experimental designs are used to validate the data.

Hornick, R. J.↗

NASA NTRS1993

Enhancements to the Engine Data Interpretation System (EDIS)

The Engine Data Interpretation System (EDIS) expert system project assists the data review personnel at NASA/MSFC in performing post-test data analysis and engine diagnosis of the Space Shuttle Main Engine (SSME). EDIS uses knowledge of the engine, its components, and simple thermodynamic principles instead of, and in addition to, heuristic rules gathered from the engine experts. EDIS reasons in cooperation with human experts, following roughly the pattern of logic exhibited by human experts. EDIS concentrates on steady-state static faults, such as small leaks, and component degradations, such as pump efficiencies. The objective of this contract was to complete the set of engine component models, integrate heuristic rules into EDIS, integrate the Power Balance Model into EDIS, and investigate modification of the qualitative reasoning mechanisms to allow 'fuzzy' value classification. The results of this contract is an operational version of EDIS. EDIS will become a module of the Post-Test Diagnostic System (PTDS) and will, in this context, provide system-level diagnostic capabilities which integrate component-specific findings provided by other modules.

Hofmann, Martin O.↗

NASA NTRS1993

Enhancements to the Engine Data Interpretation System (EDIS)

The Engine Data Interpretation System (EDIS) expert system project assists the data review personnel at NASA/MSFC in performing post-test data analysis and engine diagnosis of the Space Shuttle Main Engine (SSME). EDIS uses knowledge of the engine, its components, and simple thermodynamic principles instead of, and in addition to, heuristic rules gathered from the engine experts. EDIS reasons in cooperation with human experts, following roughly the pattern of logic exhibited by human experts. EDIS concentrates on steady-state static faults, such as small leaks, and component degradations, such as pump efficiencies. The objective of this contract was to complete the set of engine component models, integrate heuristic rules into EDIS, integrate the Power Balance Model into EDIS, and investigate modification of the qualitative reasoning mechanisms to allow 'fuzzy' value classification. The result of this contract is an operational version of EDIS. EDIS will become a module of the Post-Test Diagnostic System (PTDS) and will, in this context, provide system-level diagnostic capabilities which integrate component-specific findings provided by other modules.

Hofmann, Martin O.↗

NASA NTRS2004

Carbon Dioxide Dispersion in the Combustion Integrated Rack Simulated Numerically

When discharged into an International Space Station (ISS) payload rack, a carbon dioxide (CO2) portable fire extinguisher (PFE) must extinguish a fire by decreasing the oxygen in the rack by 50 percent within 60 sec. The length of time needed for this oxygen reduction throughout the rack and the length of time that the CO2 concentration remains high enough to prevent the fire from reigniting is important when determining the effectiveness of the response and postfire procedures. Furthermore, in the absence of gravity, the local flow velocity can make the difference between a fire that spreads rapidly and one that self-extinguishes after ignition. A numerical simulation of the discharge of CO2 from PFE into the Combustion Integrated Rack (CIR) in microgravity was performed to obtain the local velocity and CO2 concentration. The complicated flow field around the PFE nozzle exits was modeled by sources of equivalent mass and momentum flux at a location downstream of the nozzle. The time for the concentration of CO2 to reach a level that would extinguish a fire anywhere in the rack was determined using the Fire Dynamics Simulator (FDS), a computational fluid dynamics code developed by the National Institute of Standards and Technology specifically to evaluate the development of a fire and smoke transport. The simulation shows that CO2, as well as any smoke and combustion gases produced by a fire, would be discharged into the ISS cabin through the resource utility panel at the bottom of the rack. These simulations will be validated by comparing the results with velocity and CO2 concentration measurements obtained during the fire suppression system verification tests conducted on the CIR in March 2003. Once these numerical simulations are validated, portions of the ISS labs and living areas will be modeled to determine the local flow conditions before, during, and after a fire event. These simulations can yield specific information about how long it takes for smoke and combustion gases produced by a fire to reach a detector location, how large the fire would be when the detector alarms, and the behavior of the fire until it has been extinguished. This new capability could then be used to optimize the location of fire detectors and fire-suppression ports as well as to evaluate the effectiveness of fire suppressants and response strategies. Numerical data collected from these simulations could also be used to develop a virtual reality fire event for crew training and fire safety awareness. This work is funded by NASA's Bioastronautics Initiative, which has the objective of ensuring and enhancing the health, safety, and performance of humans in space. As part of this initiative, the Microgravity Combustion Science Branch at the NASA Glenn Research Center is conducting spacecraft fire safety research to significantly improve fire safety on inhabited spacecraft.

Wu, Ming-Shin↗

NASA NTRS2010

Absorbed Dose and Dose Equivalent Calculations for Modeling Effective Dose

While in orbit, Astronauts are exposed to a much higher dose of ionizing radiation than when on the ground. It is important to model how shielding designs on spacecraft reduce radiation effective dose pre-flight, and determine whether or not a danger to humans is presented. However, in order to calculate effective dose, dose equivalent calculations are needed. Dose equivalent takes into account an absorbed dose of radiation and the biological effectiveness of ionizing radiation. This is important in preventing long-term, stochastic radiation effects in humans spending time in space. Monte carlo simulations run with the particle transport code FLUKA, give absorbed and equivalent dose data for relevant shielding. The shielding geometry used in the dose calculations is a layered slab design, consisting of aluminum, polyethylene, and water. Water is used to simulate the soft tissues that compose the human body. The results obtained will provide information on how the shielding performs with many thicknesses of each material in the slab. This allows them to be directly applicable to modern spacecraft shielding geometries.

Welton, Andrew↗

NASA NTRS2013

Low Profile Tunable Dipole Antenna Using BST Varactors for Biomedical Applications

In this paper a 2.4 GHz low profile (lambda/47) tunable dipole antenna is evaluated in the presence of a human core model (HCM) body phantom. The antenna uses a frequency selective surface (FSS) with interdigital barium strontium titanate (BST) varactor-tuned unit cells and its performance is compared to a similar low profile antenna that uses an FSS with semiconductor varactor diodes. The measured data of the antenna demonstrate tunability from 2.2 GHz to 2.55 GHz in free space and impedance match improvement in the presence of a HCM at different distances. This antenna has smaller size, lower cost and less weight compared to the semiconductor varactor diode counterpart.

frequency selective surfaces↗

NASA NTRS2013

Low Profile Tunable Dipole Antennas Using BST Varactors for Biomedical Applications

In this presentation a 2.4 GHz low profile (lambda45) tunable dipole antenna is evaluated in the presence of a human core model (HCM) body phantom. The antenna uses a frequency selective surface (FSS) with interdigital barium strontium titanate (BST) varactor-tuned unit cells and its performance is compared to a similar low profile antenna that uses an FSS with semiconductor varactor diodes. The measured data of the antenna demonstrate tunability from 2.2 GHz to 2.55 GHz in free space and impedance match improvement in the presence of a HCM at different distances. This antenna has smaller size, lower cost and less weight compared to the semiconductor varactor diode counterpart.

Tunable Dipole Antennas↗

DOE OSTI2017

Creating a Face from a DNA Sample

The idea of creating a recognizable face from a sample of DNA is moving closer to reality as Oak Ridge National Laboratory (ORNL) researchers employ a novel statistical representation of the face and a genome-wide association study (GWAS) to relate facial characteristics to genetic mutations. The reduction in cost of DNA sequencing and genotyping technology is leading to the availability of massive datasets of human genome sequences and human genotypes representing mutational variation among people. These large-scale data provide for the first time the potential to correlate an individual’s specific genetic code with their physical appearance. Using a specialized statistical method for representing the entire 3D shape of faces, ORNL researchers performed a GWAS to discover associations between genetic mutations and aspects of face shape. Data from 3,556 subjects resulted in the discovery of a very large set of statistically significant associations, which is strong evidence for the viability of predicting faces from DNA.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Exploration and Analysis of Legacy Data at the Hanford Site Using PHOENIX - 19044

Radioactive contaminants pose risks to human health and the environment. These risks are influenced by factors such as disposal/release and transport mechanisms, location in the environment, potential exposure receptors, and the presence of comingled contaminants. Understanding the behavior, transport, and distribution of radionuclides in the environment is essential to facilitating risk-informed decision-making related to remedial design, action, and monitoring. These decisions are based on significant amounts of complex data that must be accessed, integrated, and analyzed to effectively evaluate data and formulate appropriate conclusions and recommendations. Often, data volume, complexity, and legacy access issues pose severe challenges for analysts, regulators, and stakeholders interested in evaluating scientific data to support decision-making. To address these challenges at the U.S. Department of Energy Hanford Site, Pacific Northwest National Laboratory (PNNL) has developed PHOENIX (PNNL-Hanford Online Environmental Information exchange) to facilitate risk-informed decision-making by streamlining investigative and analytical processes through the integration and visualization of previously isolated datasets created under distinct regulatory or operational regimes, each serving a specific purpose. With the large volume of data dispersed across many Hanford environmental databases, finding the correct sources of relevant data, accessing them, and relating them in a meaningful way can prove challenging and time consuming. By analyzing Hanford databases, performing substantial optimization using modern data processing methods and developing extensive queries on the refactored data, PHOENIX enables users to access, integrate, display, visualize, and retrieve data that would have been historically unfeasible to examine. The data and information accessible through PHOENIX help inform remediation-related assessments and evaluations, predict and quantify the risks that radioactive and chemical contaminants pose to human health and the environment, and communicate this information to decision makers responsible for risk-based remediation actions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Control Board Digital Interface Input Devices – Touchscreen, Trackpad, or Mouse?

The authors collaborated with a power utility to evaluate input devices for use in the human system interface (HSI) for a new digital Turbine Control System (TCS) at a nuclear power plant (NPP) undergoing a TCS upgrade. A standalone dynamic software simulation of the new digital TCS and a mobile kiosk were developed to conduct an input device study to evaluate operator preference and input device effectiveness. The TCS software presented the anticipated HSI for the TCS and mimicked (i.e., simulated) the turbine systems’ responses to operator commands. Twenty-four licensed operators from the two nuclear power units participated in the study. Three input devices were tested: a trackpad, mouse, and touchscreen. The subjective feedback from the survey indicates the operators preferred the touchscreen interface. The operators subjectively rated the touchscreen as the fastest and most comfortable input device given the range of tasks they performed during the study, but also noted a lack of accuracy for selecting small targets. The empirical data suggest the mouse input device provides the most consistent performance for screen navigation and manipulating on screen controls. The trackpad input device was both empirically and subjectively found to be the least effective and least desired input device.

control room modernization↗