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NASA NTRS1991

An empirical approach to predicting long term behavior of metal particle based recording media

Alpha iron particles used for magnetic recording are prepared through a series of dehydration and reduction steps of alpha-Fe2O3-H2O resulting in acicular, polycrystalline, body centered cubic (bcc) alpha-Fe particles that are single magnetic domains. Since fine iron particles are pyrophoric by nature, stabilization processes had to be developed in order for iron particles to be considered as a viable recording medium for long term archival (i.e., 25+ years) information storage. The primary means of establishing stability is through passivation or controlled oxidation of the iron particle's surface. Since iron particles used for magnetic recording are small, additional oxidation has a direct impact on performance especially where archival storage of recorded information for long periods of time is important. Further stabilization chemistry/processes had to be developed to guarantee that iron particles could be considered as a viable long term recording medium. In an effort to retard the diffusion of iron ions through the oxide layer, other elements such as silicon, aluminum, and chromium have been added to the base iron to promote more dense scale formation or to alleviate some of the non-stoichiometric behavior of the oxide or both. The presence of water vapor has been shown to disrupt the passive layer, subsequently increasing the oxidation rate of the iron. A study was undertaken to examine the degradation in magnetic properties as a function of both temperature and humidity on silicon-containing iron particles between 50-120 deg C and 3-89 percent relative humidity. The methodology to which experimental data was collected and analyzed leading to predictive capability is discussed.

Hadad, Allan S.↗

DOE OSTI2022

Turbulence generation by shock interaction with a highly nonuniform medium

An initially planar shock wave propagating into a medium of nonuniform density will be perturbed, leading to the generation of postshock velocity perturbations. Using numerical simulations we study this phenomenon in the case of highly nonuniform density (order-unity normalized variance, $\sigma_\rho/\bar{\rho}\sim1$) and strong shocks (shock Mach numbers $\bar{M}_s\gtrsim10$). This leads to a highly disrupted shock and a turbulent postshock flow. We simulate this interaction for a range of shock drives and initial density configurations meant to mimic those which might be presently achieved in experiments. Theoretical considerations lead to scaling relations, which are found to reasonably predict the postshock turbulence properties. The turbulent velocity dispersion and turbulent Mach number are found to depend on the preshock density dispersion and shock speed in a manner consistent with the linear Richtymer-Meshkov instability prediction. We also show a dependence of the turbulence generation on the scale of density perturbations. The postshock pressure and density, which can be substantially reduced relative to the unperturbed case, are found to be reasonably predicted by a simplified analysis that treats the extended shock transition region as a single normal shock.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2024

Integrating Manned Aircraft and UAVs for the Prediction, Tracking, and Eradication of Desert Locust Swarms

Desert locusts (Schistocerca gregaria) present an acute threat to the agriculture of certain regions, capable of annihilating vast expanses of farmland and destabilizing food security. In this paper, an aviation-based framework concept is introduced that integrates both manned aircraft and Unmanned Aerial Vehicles (UAVs) to preemptively forecast, localize, and eliminate locust swarms efficiently. The framework begins by leveraging predictive modeling methods to refine search areas and pinpoint high-value Points of Interest (POIs) where locust swarms are most likely to materialize. From there, the proposed two-step model uses optical, infrared, and hyperspectral sensors onboard manned aircraft to conduct long-range surveying of probable swarm locations. Upon detection of a swarm, UAVs equipped with sophisticated sensors, including pheromone detectors, thermal and optical imaging systems, are deployed directly from the aircraft to carry out targeted biopesticide applications. This approach, which capitalizes on the combined strengths of both vehicles, provides the rapid response and large-scale intervention necessary to control locust outbreaks. By targeting locust swarms during their most destructive phase, this paper aims to disrupt the locust life cycle and mitigate their severe agricultural impact, offering a scalable and sustainable solution for food security in afflicted regions.

locust monitoring systems↗

NASA NTRS2022

Vermont & New Hampshire Ecological Forecasting: Monitoring Trends in Tree Defoliation Due to Lymantria dispar Outbreaks to Predict Future Hardwood Tree Mortality and Health Impacts

The invasive, herbivorous insect Lymantria dispar is a major defoliator of hardwood trees in the northeastern United States. Established populations of L. dispar typically rest at low levels but undergo recurring outbreaks that cause tree mortality if they occur in quick succession or are combined with other stressors on tree health. Widespread defoliation events disrupt local wildlife, economies, and livelihoods. Accurate monitoring of defoliation events is necessary to implement effective land management practices that support tree health. There are challenges to accurately monitoring L. dispar outbreaks that include the ephemeral character of defoliation disturbances and the difficulties of conducting large-scale surveys of L. dispar populations using existing aerial and ground-based data collection methods. To better monitor the impact of L. dispar on forests in Vermont and New Hampshire, the NASA DEVELOP team partnered with organizations responsible for supporting land and invasive species management including the Forest Ecosystem Monitoring Cooperative, the Vermont Agency of Agriculture, Food and Markets, the University of New Hampshire Cooperative Extension, and the New Hampshire Division of Forests and Lands, Forest Health Program. The team used NASA Earth observations collected by the Terra, Soil Moisture Active Passive (SMAP), Shuttle Radar Topography Mission (SRTM), Landsat 7, and Landsat 8 satellites along with ancillary datasets to map historical tree defoliation from 2012 to 2021. In support of partners’ future land management efforts, the team created a Google Earth Engine tool that displays annual defoliation extent.

Seamore Zhu↗

DOE OSTI2023

Structural Basis of Clostridium perfringens Enterotoxin Activation and Oligomerization by Trypsin

Clostridium perfringens enterotoxin (CpE) is a β-pore forming toxin that disrupts gastrointestinal homeostasis in mammals by binding membrane protein receptors called claudins. Although structures of CpE fragments bound to claudins have been determined, the mechanisms that trigger CpE activation and oligomerization that lead to the formation of cytotoxic β-pores remain undetermined. Proteolysis of CpE in the gut by trypsin has been shown to play a role in this and subsequent cytotoxicity processes. Here, we report solution structures of full-length and trypsinized CpE using small-angle X-ray scattering (SAXS) and crystal structures of trypsinized CpE and its C-terminal claudin-binding domain (cCpE) using X-ray crystallography. Mass spectrometry and SAXS uncover that removal of the CpE N-terminus by trypsin alters the CpE structure to expose areas that are normally unexposed. Crystal structures of trypsinized CpE and cCpE reveal unique dimer interfaces that could serve as oligomerization sites. Moreover, comparisons of these structures to existing ones predict the functional implications of oligomerization in the contexts of cell receptor binding and β-pore formation. This study sheds light on trypsin’s role in altering CpE structure to activate its function via inducing oligomerization on its path toward cytotoxic β-pore formation. Its findings can incite new approaches to inhibit CpE-based cytotoxicity with oligomer-disrupting therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

ExaWind: A multifidelity modeling and simulation environment for wind energy

We introduce the open-source ExaWind modeling and simulation environment for wind energy. The primary physics codes of ExaWind are Nalu-Wind and OpenFAST. Nalu-Wind is a wind-focused computational fluid dynamics (CFD) code that is coupled to the whole-turbine simulation code OpenFAST. The ExaWind environment was created under U.S. Department of Energy funding to achieve the highest-fidelity simulations of wind turbines and wind farms to date, with the goal of enabling disruptive changes to turbine and plant design and operation. Innovation will be gleaned through better understanding of the complex flow dynamics in wind farms, including wake evolution and the impact of wakes on downstream turbines and turbulent flow from complex terrain. High-fidelity predictive simulations employ hybrid turbulence models, geometry/boundary-layer-resolving CFD meshes, atmospheric turbulence, nonlinear structural dynamics, and fluid-structure interaction. While there is an emphasis on very high-fidelity simulations (e.g., blade resolved with full fluid-structure coupling), the ExaWind environment supports lower-fidelity modeling capabilities including actuator-line and -disk methods. Important in the development of ExaWind codes is that the codes scale well on today's largest petascale supercomputers and on the next-generation platforms that will enable exascale computing.

17 WIND ENERGY↗

DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗

DOE OSTI2022

Quantum Theory of the Classical: Einselection, Envariance, Quantum Darwinism and Extantons

Core quantum postulates including the superposition principle and the unitarity of evolutions are natural and strikingly simple. I show that—when supplemented with a limited version of predictability (captured in the textbook accounts by the repeatability postulate)—these core postulates can account for all the symptoms of classicality. In particular, both objective classical reality and elusive information about reality arise, via quantum Darwinism, from the quantum substrate. This approach shares with the Relative State Interpretation of Everett the view that collapse of the wavepacket reflects perception of the state of the rest of the Universe relative to the state of observer’s records. However, our “let quantum be quantum” approach poses questions absent in Bohr’s Copenhagen Interpretation that relied on the preexisting classical domain. Thus, one is now forced to seek preferred, predictable, hence effectively classical but ultimately quantum states that allow observers keep reliable records. Without such (i) preferred basis relative states are simply “too relative”, and the ensuing basis ambiguity makes it difficult to identify events (e.g., measurement outcomes). Moreover, universal validity of quantum theory raises the issue of (ii) the origin of Born’s rule, p k =|ψ k | 2 , relating probabilities and amplitudes (that is simply postulated in textbooks). Last not least, even preferred pointer states (defined by einselection—environment—induced superselection)—are still quantum. Therefore, unlike classical states that exist objectively, quantum states of an individual system cannot be found out by an initially ignorant observer through direct measurement without being disrupted. So, to complete the ‘quantum theory of the classical’ one must identify (iii) quantum origin of objective existence and explain how the information about objectively existing states can appear to be essentially inconsequential for them (as it does for states in Newtonian physics) and yet matter in other settings (e.g., thermodynamics). I show how the mathematical structure of quantum theory supplemented by the only uncontroversial measurement postulate (that demands immediate repeatability—hence, predictability) leads to preferred states. These (i) pointer states correspond to measurement outcomes. Their stability is a prerequisite for objective existence of effectively classical states and for events such as quantum jumps. Events at hand, one can now enquire about their probability—the probability of a pointer state (or of a measurement record). I show that the symmetry of entangled states—(ii) entanglement—assisted invariance or envariance—implies Born’s rule. Envariance also accounts for the loss of phase coherence between pointer states. Thus, decoherence can be traced to symmetries of entanglement and understood without its usual tool—reduced density matrices. A simple and manifestly noncircular derivation of p k =|ψ k | 2 follows. Monitoring of the system by its environment in course of decoherence typically leaves behind multiple copies of its pointer states in the environment. Only pointer states can survive decoherence and can spawn such plentiful information-theoretic progeny. This (iii) quantum Darwinism allows observers to use environment as a witness—to find out pointer states indirectly, leaving systems of interest untouched. Quantum Darwinism shows how epistemic and ontic (coexisting in epiontic quantum state) separate into robust objective existence of pointer states and detached information about them, giving rise to extantons—composite objects with system of interest in the core and multiple records of its pointer states in the halo comprising of environment subsystems (e.g., photons) which disseminates that information throughout the Universe.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Disruption of the tagF Orthologue in the epa Locus Variable Region of Enterococcus faecalis Causes Cell Surface Changes and Suppresses an eep -Dependent Lysozyme Resistance Phenotype

The disease-producing capacity of the opportunistic pathogen Enterococcus faecalis is enhanced by the ability of the bacterium to evade killing by antimicrobial agents. Survival of E. faecalis in the presence of the human antimicrobial enzyme lysozyme is mediated in part by the site 2 metalloprotease Eep; however, a complete model of enterococcal lysozyme resistance has not been elucidated. To better understand the molecular basis for lysozyme resistance in E. faecalis, we analyzed Δeep suppressor mutants that acquire resistance to lysozyme through mutation of the gene OG1RF_11713, a predicted teichoic acid biosynthesis-encoding gene located within the variable region of the enterococcal polysaccharide antigen (epa) locus. Sequence comparisons revealed that OG1RF_11713 is most similar to the cytidine-5'-diphosphate (CDP)-glycerol:poly-(glycerolphosphate)glycerophosphotransferase TagF from Staphylococcus epidermidis. Inactivation of OG1RF_11713 in both the wild-type and Δeep genetic backgrounds was sufficient to increase the resistance of E. faecalis OG1RF to lysozyme. Minimal amounts of N-acetylgalactosamine were detectable in cell wall carbohydrate extracts of OG1RF_11713 deletion mutants, and this was associated with a reduction in negative cell surface charge. Targeted disruption of OG1RF_11713 was also associated with increased susceptibility to the antibiotic polymyxin B and membrane-targeting detergents and decreased susceptibility to the lantibiotic nisin. Furthermore, this work implicates OG1RF_11713 as a major determinant of cell envelope integrity and provides further validation that lysozyme resistance is intrinsically linked to the modification of enterococcal cell wall polysaccharides.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Turbulence theories and statistical closure approaches

When discussing research in physics and in science more generally, it is common to ascribe equal importance to the three components of the scientific trinity: theoretical, experimental, and computational studies. This review will explore the future of modern turbulence theory by tracing its history, which began in earnest with Kolmogorov’s 1941 analysis of turbulence cascade and inertial range [A.N. Kolmogorov, Dokl. Akad. Nauk SSSR, 30, 299, (1941); 32, 19, (1941)]. The 80th Anniversary of Kolmogorov’s landmark study is a welcome opportunity to survey the achievements and evaluate the future of the theoretical approach of turbulence research. Over the years, turbulence theories have been critically important in laying the foundation of our understanding of the nature of turbulent flows. In particular, the Direct Interaction Approximation (DIA) [R.H. Kraichnan, J. Fluid Mech., 5, 497 (1959)] and its subsequent development, known as the statistical closure approach, can be identified as perhaps the most profound single advancement. The remarkable success of the statistical closure has furnished a platform to study such essential concepts as the energy transfer process and interacting scales, and the roles of the straining and sweeping motions. More recently, the quasi-Lagrangian formulation of V. L’vov & I. Procaccia and Kraichnan’s solvable passive scalar model provided powerful ways to explore another fundamental aspect of turbulent flows, the phenomena of intermittency, and the associated anomalous scaling exponents. In the meantime, the theory of fluid equilibria has been developed to describe the large-scale structures that can emerge from turbulent cascades of two-dimensional and geophysical flows at a later time. And yet, despite all these successes, analytical treatments suffer from mathematical complexities. As a result, the utility of theoretical approaches has been limited to relatively idealized flows. On the other hand, in recent decades, computational abilities and experimental facilities have reached an unprecedented scale. Looking beyond the horizon, the imminent deployment of exascale supercomputers will generate complete datasets of the entire flow field of key benchmark flows, allowing researchers to extract additional measurements concerning fully developed, complex turbulent flow fields far beyond those available from the statistical closure theories. Some other developments that could potentially influence the future course of turbulence theories include the advancement of machine learning, artificial intelligence, and data science; likely disruptions arising from the advent of quantum computation; and the increasingly prominent role of turbulence research in providing more accurate climate scientific data. Finally, turbulence theorists can leverage these developments by asking the right questions and developing advanced, sophisticated frameworks that will be able to predict and correlate vast amounts of data from the other two components of the trinity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1992

Stability limits for bioconvective fractals - Microgravity prospects

Fractal objects are delicate aggregates which show self-similar behavior and vanishing density for increasing length scales. In practice real fractals in nature however possess only a limited region of verifiable self-similarity. As natural fractal objects increase in size, they become easier to disrupt mechanically. Herein the effects of thermal vibrations and gravity are investigated as deforming forces on fractal aggregation. Example calculations are carried out on a biological fractal formed from the surface aggregation of various cells such as alga and bacteria. For typical cell parameters, the predicted diameter of this so-called 'bioconvective' fractal agrees well with the observed limits of about 5 cm. On earth, this size represents an experimental maximum for finding bioconvective fractal objects. To extend this size range of fractals available for statistical study, a reduced gravity environment offers one way to achieve larger fractals. For these enhanced sizes, the present scaling predicts that microgravity can yield up to a 35-fold improvement in extending statistical resolution.

Noever, David A.↗

DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

42 ENGINEERING↗

NASA NTRS1987

Impact and cratering processes on asteroids, satellites, and planets

A new model for scaling the outcome of catastrophic collisions from laboratory scale experiments was developed and applied to large bodies where gravitational binding is dominant. This algorithm includes a pressure dependent impact strength which predicts that large asteroids behave as intrinsically strong objects due to compressive loading of overburden throughout their interiors. For small bodies, where the gravitational loading is negligible, this model assumes that the impact strength is independent of size. There was considerable discussion of scaling laws for disruption when these results were presented to one authors colleagues. The pressure strengthening impact strength model was viewed as physically quite plausible; however, it was argued that the impact strength should decrease with increasing size in the size range where gravitational compression is negligible.

Chapman, Clark R.↗

NASA NTRS2008

Optical Images of an Exosolar Planet 25 Light Years from Earth

Fomalhaut is a bright star 7.7 parsecs (25 light years) from Earth that harbors a belt of cold dust with a structure consistent with gravitational sculpting by an orbiting planet. Here, we present optical observations of an exoplanet candidate, Fomalhaut b. In the plane of the belt, Fomalhaut b lies approximately 119 astronomical units (AU) from the star and 18 AU from the dust belt, matching predictions. We detect counterclockwise orbital motion using Hubble Space Telescope observations separated by 1.73 years. Dynamical models of the interaction between the planet and the belt indicate that the planet's mass is at most three times that of Jupiter for the belt to avoid gravitational disruption. The flux detected at 0.8 m is also consistent with that of a planet with mass no greater than a few times that of Jupiter. The brightness at 0.6 micron and the lack of detection at longer wavelengths suggest that the detected flux may include starlight reflected off a circumplanetary disk, with dimension comparable to the orbits of the Galilean satellites. We also observed variability of unknown origin at 0.6 micron.

Kalas, Paul↗

DOE OSTI2021

Cybersecurity Resiliency of Marine Renewable Energy Systems Part 2: Cybersecurity Best Practices and Risk Management

Marine renewable energy (MRE) is an emerging source of power for marine applications, marine devices, and coastal communities. This energy source relies on industrial control systems and IT to support operations and maintenance activities, which create a pathway for an adversary to gain unauthorized access to systems and data and disrupt operations. Incorporating cybersecurity risk prevention measures and mitigation capabilities from inception, development, operation, to decommissioning of the MRE system and components is paramount to the protection of energy generation and the security of network architecture and infrastructure. To improve the resilience of MRE systems as a predictable, affordable, and reliable source of energy, cybersecurity guidance was developed to enable operators to assess cybersecurity risks and implement security measures commensurate with the risk. This publication is the second of a two-part series, with Part 1 addressing a framework to determine cybersecurity risk by assessing the vulnerability of an MRE system to potential cyber threats and the consequences a cyberattack would have on the end user. This Part 2 publication describes an approach to select appropriate cybersecurity best practices commensurate with the MRE system's cybersecurity risk. The guidance includes 86 cybersecurity best practices, which are associated with 36 cybersecurity domains and grouped into nine categories. The best practices follow the core functions of the National Institute of Science and Technology Cybersecurity Framework (e.g., identify, detect, protect, respond, and and recover) and insights from both maritime and energy industry guidance documents to identify security measures effective in protecting information and operational technology assets prevalent in MRE systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Simulating Alpha Particles Incident on MKID Chips for Quantum Sensitivity Analysis

Superconducting quantum devices, such as microwave kinetic inductance detectors (MKIDs), are highly sensitive instruments used in quantum computing and advanced sensing technologies. However, their extreme sensitivity also makes them vulnerable to background noise from natural sources like radiation. One significant contributor to this noise is alpha particles emitted by 210Po, a radon decay daughter that accumulates on surfaces near the detector. This project investigates how alpha particles emitted from 210Po interact with MKID chips. These particles can deposit energy on the detector surface, disrupting its operation and generating false signals. Understanding the energy and behavior of these particles is crucial for improving the design and reliability of quantum devices. To explore this, we first modeled the decay chain starting from 210Pb to 210Po using differential equations. This allowed us to predict how the activity of alpha-emitting isotopes changes over time, reaching a steady state after about two years. Next, we simulated alpha particle interactions with the MKID chip using the Geant4 software toolkit. We built a detailed computer model of the detector housing, including the copper lid where alpha particles originate, the silicon chip, and a thin aluminum sensor layer. Alpha particles were emitted isotropically from just beneath the copper lid’s surface, mimicking natural decay conditions. The simulation tracked how these particles deposit energy on the chip, generating electron-hole pairs and phonons. The results provide insight into the behavior of the resultant electron-hole pairs and phonons, giving us a clear understanding of the energy deposition distribution on the chip. This work supports efforts to mitigate background noise in superconducting sensors, advancing their use in quantum computing and sensitive physics experiments.

Hall, Matthew [Fermilab; UCLA]↗

NASA NTRS2012

Bed Rest is an Analog to Study the Physiological Changes of Spaceflight and to Evaluate Countermeasures

The UTMB/NASA Flight Analog Research Unit is an inpatient unit with a bionutrition kitchen and unique testing areas for studying subjects subjected to 6 degree head-down complete bed rest for prolonged periods as an analog for zero gravity. Bed rest allows study of physiological changes and performance of functional tasks representative of critical interplanetary mission operations and measures of the efficacy of countermeasures designed to protect against the resulting deleterious effects. METHODS/STUDY POPULATION: Subjects are healthy adults 24-55 years old; 60 75 in tall; body mass index 18.5-30; and bone mineral density normal by DXA scan. Over 100 subjects have been studied in 7 campaigns since 2004. The iRAT countermeasure combines high intensity interval aerobic exercises on alternating days with continuous aerobic exercise. Resistance exercise is performed 3 days per week. Subjects are tested on an integrated suite of functional and interdisciplinary physiological tests before and after 70 days of total bed rest. RESULTS/ANTICIPATED RESULTS: It is anticipated that post-bed rest functional performance will be predicted by a weighted combination of sensorimotor, cardiovascular and muscle physiological factors. Control subjects who do not participate in the exercise countermeasure will have significantly greater decreases in these parameters. DISCUSSION/SIGNIFICANCE OF IMPACT: Astronauts experience alterations in multiple physiological systems due to exposure to the microgravity, leading to disruption in the ability to perform functional tasks after reintroduction to a gravitational environment. Current flight exercise countermeasures are not fully protective of cardiovascular, muscle and bone health. There is a need to refine and optimize countermeasures to mitigate health risks associated with long-term space missions.

Pfannenstiel, P.↗