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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

The application of automated operations at the Institutional Processing Center

The JPL Institutional and Mission Computing Division, Communications, Computing and Network Services Section, with its mission contractor, OAO Corporation, have for some time been applying automation to the operation of JPL's Information Processing Center (IPC). Automation does not come in one easy to use package. Automation for a data processing center is made up of many different software and hardware products supported by trained personnel. The IPC automation effort formally began with console automation, and has since spiraled out to include production scheduling, data entry, report distribution, online reporting, failure reporting and resolution, documentation, library storage, and operator and user education, while requiring the interaction of multi-vendor and locally developed software. To begin the process, automation goals are determined. Then a team including operations personnel is formed to research and evaluate available options. By acquiring knowledge of current products and those in development, taking an active role in industry organizations, and learning of other data center's experiences, a forecast can be developed as to what direction technology is moving. With IPC management's approval, an implementation plan is developed and resources identified to test or implement new systems. As an example, IPC's new automated data entry system was researched by Data Entry, Production Control, and Advance Planning personnel. A proposal was then submitted to management for review. A determination to implement the new system was made and elements/personnel involved with the initial planning performed the implementation. The final steps of the implementation were educating data entry personnel in the areas effected and procedural changes necessary to the successful operation of the new system.

Barr, Thomas H.↗
NASA NTRS2014

Hindlimb Suspension as a Model to Study Ophthalmic Complications in Microgravity Status Report: Optimization of Rat Retina Flat Mounts Staining to Study Vascular Remodeling

Preliminary data from a prior tissue‐sharing experiment has suggested that early growth response protein‐1 (Egr1), a transcription factor involved in various stress responses in the vasculature, is induced in the rat retina after 14 days of hindlimb suspension (HS) and may be evidence that mechanical stress is occurring secondary to the cephalad fluid shift. This mechanical stress could cause changes in oxygenation of the retina, and the subsequent ischemia‐ or inflammation‐driven hypoxia may lead to microvascular remodeling. This microvascular remodeling process can be studied using image analysis of retinal vessels and can be then be quantified by the VESsel GENeration Analysis (VESGEN) software, a computational tool that quantifies remodeling patterns of branching vascular trees and capillary or vasculogenic networks. Our project investigates whether rodent HS is a valid model to study the effects of simulated‐weightlessness on ocular structures and their relationship with intracranial pressure (ICP). One of the hypotheses to be tested is that HS‐induced cephalad fluid shift is accompanied by vascular engorgement that produces changes in retinal oxygenation, leading to oxidative stress, hypoxia, microvascular remodeling, and cellular degeneration. We have optimized the procedure to obtain flat mounts of rat retina, staining of the endothelial lining in vasculature and acquisition of high quality images suitable for VESGEN analysis. Briefly, eyes were fixed in 4% paraformaldehyde for 24 hours and retinas were detached and then mounted flat on microscope slides. The microvascular staining was done with endothelial cell‐specific isolectin binding, coupled to Alexa‐488 fluorophore. Image acquisition at low magnification and high resolution was performed using a new Leica SP8 confocal microscope in a tile pattern across the X,Y plane and multiple sections along the Z‐axis. This new confocal microscope has the added capability of dye separation using the Linear Unmixing method and allows us to remove the autofluorescence originating from the photoreceptor layer. In summary, we have an improved method for studying the retinal microvasculature that will provide an increase in the quality of images captured and will be applied throughout the various animal cohorts of the recentlyinitiated study that will evaluate rodent HS as a model to study ophthalmic complications in microgravity.

Theriot, Corey A.↗
DOE OSTI2022

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

36 MATERIALS SCIENCE↗
DOE OSTI2020

SYMBA: An end-to-end VLBI synthetic data generation pipeline

Realistic synthetic observations of theoretical source models are essential for our understanding of real observational data. In using synthetic data, one can verify the extent to which source parameters can be recovered and evaluate how various data corruption effects can be calibrated. These studies are the most important when proposing observations of new sources, in the characterization of the capabilities of new or upgraded instruments, and when verifying model-based theoretical predictions in a direct comparison with observational data. We present the SYnthetic Measurement creator for long Baseline Arrays (SYMBA), a novel synthetic data generation pipeline for Very Long Baseline Interferometry (VLBI) observations. SYMBA takes into account several realistic atmospheric, instrumental, and calibration effects. Here, we used SYMBA to create synthetic observations for the Event Horizon Telescope (EHT), a millimetre VLBI array, which has recently captured the first image of a black hole shadow. After testing SYMBA with simple source and corruption models, we study the importance of including all corruption and calibration effects, compared to the addition of thermal noise only. Using synthetic data based on two example general relativistic magnetohydrodynamics (GRMHD) model images of M 87, we performed case studies to assess the image quality that can be obtained with the current and future EHT array for different weather conditions. Our synthetic observations show that the effects of atmospheric and instrumental corruptions on the measured visibilities are significant. Despite these effects, we demonstrate how the overall structure of our GRMHD source models can be recovered robustly with the EHT2017 array after performing calibration steps, which include fringe fitting, a priori amplitude and network calibration, and self-calibration. With the planned addition of new stations to the EHT array in the coming years, images could be reconstructed with higher angular resolution and dynamic range. In our case study, these improvements allowed for a distinction between a thermal and a non-thermal GRMHD model based on salient features in reconstructed images.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1990

Summary of presentation for research on social structure, agreement, and conflict in groups in extreme and isolated environments

Despite a vast amount of research, little is known concerning the effect of group structure, and individuals' understanding of that structure, on conflict in Antarctic groups. The overall objective of the research discussed is to determine the interrelationships of group structure, social cognition, and group function and conflict in isolated and extreme environments. In the two decades following WWII, a large body of research focused on the physiological, psychological, and social psychological factors affecting the functioning of individuals and groups in a variety of extreme and isolated environments in both the Arctic and Antarctic. There are two primary reasons for further research of this type. First, Antarctic polar stations are considered to be natural laboratories for the social and behavioral sciences and provide an opportunity to address certain theoretical and empirical questions concerned with agreement and conflict in social groups in general and group behavior in extreme, isolated environments in particular. Recent advances in the analysis of social networks and intracultural variation have improved the methods and have shifted the theoretical questions. The research is motivated by three classes of questions: (1) What are the characteristics of the social relations among individuals working and living together in extreme and isolated environments?; (2) What do individuals understand about their group, how does that understanding develop, and how is it socially distributed?; and (3) What is the relationship between that understanding and the functioning of the social group? Answers to these questions are important if we are to advance our knowledge of how individuals and groups adapt to extreme environments. Second, although Antarctic winter-over candidates may be evaluated as qualified on the basis of individual characteristics, they may fail to adapt because of certain characteristics of the social group. Consequently, the ability of winter-over-groups to adapt to these extreme conditions has varied dramatically from year to year. In the past, differences in personality, background, and social status have led to conflicts between individuals or cliques precipitating, in turn, an overall decline in morale, failure to accomplish work tasks, and increases in insomnia, depression, anxiety, and alcohol abuse. A better understanding of the role of group structure and social cognition in processes of group adaptation and conflict in Antarctica would contribute towards the revision of existing screening methods, potentially leading to a reduction of group conflict and improved performance of scientific research and support activities. An improved screening protocol for the Antarctic would also have applications for other isolated environments such as scientific outposts and the proposed NASA space station. In sum, this research will (1) contribute significantly to our theoretical understanding of the role of social structure and cognition in the functioning of groups in isolation; (2) complement current work on health and adaptation in polar environments; and (3) provide for models of the formation of group structure that will aid in the development of improved procedures for assembling groups for the Antarctic and other isolated environments (e.g., space stations).

Source record↗
NASA NTRS2000

A Parallel Genetic Algorithm for Automated Electronic Circuit Design

Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency issues in the GA, it is possible to have idle processors. However, as long as the load at each processing node is similar, the processors are kept busy nearly all of the time. In applying GAs to circuit design, a suitable genetic representation 'is that of a circuit-construction program. We discuss one such circuit-construction programming language and show how evolution can generate useful analog circuit designs. This language has the desirable property that virtually all sets of combinations of primitives result in valid circuit graphs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. For example, a figure shows an 85 dB amplifier design evolved by our system, and another figure shows the performance of that circuit (gain and frequency response). In all tasks, our system is able to generate circuits that achieve the target specifications.

Long, Jason D.↗
NASA NTRS2016

Basin-Scale Assessment of the Land Surface Water Budget in the National Centers for Environmental Prediction Operational and Research NLDAS-2 Systems

The purpose of this study is to evaluate the components of the land surface water budget in the four land surface models (Noah, SAC-Sacramento Soil Moisture Accounting Model, (VIC) Variable Infiltration Capacity Model, and Mosaic) applied in the newly implemented National Centers for Environmental Prediction (NCEP) operational and research versions of the North American Land Data Assimilation System version 2 (NLDAS-2). This work focuses on monthly and annual components of the water budget over 12 National Weather Service (NWS) River Forecast Centers (RFCs). Monthly gridded FLUX Network (FLUXNET) evapotranspiration (ET) from the Max-Planck Institute (MPI) of Germany, U.S. Geological Survey (USGS) total runoff (Q), changes in total water storage (dS/dt, derived as a residual by utilizing MPI ET and USGS Q in the water balance equation), and Gravity Recovery and Climate Experiment (GRACE) observed total water storage anomaly (TWSA) and change (TWSC) are used as reference data sets. Compared to these ET and Q benchmarks, Mosaic and SAC (Noah and VIC) in the operational NLDAS-2 overestimate (underestimate) mean annual reference ET and underestimate (overestimate) mean annual reference Q. The multimodel ensemble mean (MME) is closer to the mean annual reference ET and Q. An anomaly correlation (AC) analysis shows good AC values for simulated monthly mean Q and dS/dt but significantly smaller AC values for simulated ET. Upgraded versions of the models utilized in the research side of NLDAS-2 yield largely improved performance in the simulation of these mean annual and monthly water component diagnostics. These results demonstrate that the three intertwined efforts of improving (1) the scientific understanding of parameterization of land surface processes, (2) the spatial and temporal extent of systematic validation of land surface processes, and (3) the engineering-oriented aspects such as parameter calibration and optimization are key to substantially improving product quality in various land data assimilation systems.

NLDAS-2↗
DOE OSTI2022

Characterizing Plug Load Energy Use and Savings Potential in Army Buildings

The Assistant Secretary of the Army (Installations, Energy and Environment) tasked the Pacific Northwest National Laboratory to examine plug loads in typical Army buildings. Plug loads (also known as miscellaneous electric loads (MELs)) represent the electricity used by appliances and devices that are plugged in or hardwired and serve functions outside of a building’s core end uses. Common plug loads include computers, printers, copiers, networking devices, refrigerators, and vending machines. They also include personal electronic devices such as televisions, smart phones, tablets, and gaming systems. Examples of hardwired MELs include elevators, air compressors, and fire and security systems. The findings from this study confirm that significant energy is consumed within Army buildings by plug load devices and hardwired MEL equipment. A number of opportunities are identified for reducing unnecessary energy use that could save the Army over $5 million per year when broadly applied. Army regulations clearly spell out expectations for the purchase and operation of information technology equipment (computers, laptops, monitors, printers, and multi-function devices). However, the policies regarding the shutdown or activation of sleep and other lower power modes after 30 minutes of inactivity (15 minutes for monitors) do not appear to be consistently followed. There are many effective approaches and pathways for impacting change as it relates to improving awareness, implementing measures, and adjusting behaviors to identify and reduce plug load energy use. The Army should prioritize and consider deploying all of these to better understand and manage plug load equipment to save energy and enhance resilience across their facilities. Engaging the building occupants who use these devices daily via outreach and education should be a strong component of the strategy. The focus should be on reducing waste without sacrificing productivity or the benefits that many of these devices provide. Continued evaluation of plug loads beyond that performed here is important to gather lessons from additional building and equipment types, and to stay aware of evolving device technology and management options. This will highlight additional needs for policies, best practices, control technologies, and education of personnel to achieve real reductions in energy waste from plug load equipment. It is recommended that this study may serve as the foundation for a broader and sustained focus on plug loads and MELs, towards simultaneously enhancing the productivity, readiness, and resilience of the Army while reducing energy use and demand, and freeing up resources to better support the mission.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2020

Charged Particle Dynamics in the Lunar Environment

As the Artemis Program streams forward, organizations of scientists and engineers across the country have been coming together to solve the complex network of problems to once again achieve the milestone of successfully touching down on the moon. This project is no exception, and it has been an honor to work with the Electrostatics and Surface Physics Laboratory (ESPL), a lab within the Exploration Research and Technology Programs’ Spaceport Technologies Office (UB-G) located at the National Aeronautics and Space Administration at Kennedy Space Center (NASA KSC). The authors and their mentor James R. Phillips III3, in tandem with researchers from the Astrodynamics and Space Robotics Laboratory (ASRL) at the University of Central Florida (UCF), have been working on creating a state-of-the-art (SOA) granular gas dynamics model for particulate contamination prevention and mitigation purposes. In essence, the underlying objective for this project is to more accurately model the resulting electrodynamic interactions between lunar regolith grains with applications to dust mitigation and rocket engine plume surface interactions. To achieve this goal, the team has been expanding upon existing open source classical molecular dynamics code developed by Sandia National Laboratories (SNL). The following reports on the details of the problem at hand as well as the contributions that the authors have made towards resolution, including but not limited to the encoding of physical attributes and interactions for non-spherical polydisperse particle distributions within various bed geometries and preemptive data analysis implementations. Significant data analysis processes were utilized, and several original algorithms were created to perform critical evaluations, resulting in only a 0.006% error in discrepancy.

Annelisa B Esparza↗
DOE OSTI2025

Thermo-hydraulic steam pipe models for district heating simulations: Simplifications to balance accuracy and simulation speed

Steam piping networks are essential for optimizing performance in industrial processes and district heating systems. However, dynamic models that balance thermo-hydraulic accuracy with computational efficiency remain limited. In response, this paper presents a new discretized steam pipe model based on the plug flow approach, capturing key thermo-hydraulic behaviors while simplifying steam phase change processes. Implemented in Modelica, the model accurately calculates temperature and pressure distributions along steam pipelines. To improve computational efficiency for district-scale simulations, five model simplifications are introduced: lumped thermo-hydraulic functions, empirical correlations, fluid state approximations, steady-state dynamics and inclusion of flow derivatives. These simplified models achieve 85%-98% accuracy in predicting pressure drop and condensation losses, including dynamic condensate behavior during pipe warm-up—a factor often overlooked in existing models. The models support diverse network configurations, scaling effectively to systems with multiple distribution pipes and connected building loads. Discrete models provide detailed insights but exhibit a cubic increase in simulation time as the network scales by N connected building O(N 2.42 ). In contrast, lumped models simulate 10–28 times faster than discrete, offering quadratic scaling of simulation time O(N 1.73 ). However, they still require 6 times more computation time than a lossless network, highlighting the inherent computational challenges of modeling compressible fluid flow. In conclusion, the steady-state lumped variant, with its near-linear scalability in computational time O(N 1.01 ), emerges as an efficient solution for preliminary design evaluations and extensive parametric studies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

NASA’s Simulation Activities for Evaluating UAM Concept of Operations

NASA’s vision for Advanced Air Mobility (AAM) is to help emerging aviation markets to safely develop an air transportation system that moves people and cargo between places previously not served or underserved by aviation using revolutionary new aircraft types. Urban Air Mobility (UAM), the concept of expanding transportation networks by serving short flights to transport people and goods around metropolitan areas, is part of a larger paradigm shift toward AAM, in which new technologies and business models are enabling transformational applications of aviation. In NASA’s Concept of Operations for UAM, Providers of Services for UAM (PSU) play a key role to enable safe and efficient UAM operations by sharing operational data among UAM operators. PSUs provide a pre-departure strategic conflict management service that establishes operational plans for new flights and coordinates them with all relevant operations in shared airspace. The strategic conflict management aims to minimize the need for tactical separation provision, while considering anticipated traffic demand, vertiport capacity and availability, forecasted weather, and airspace restrictions. NASA is currently preparing a new lab evaluation activity with aviation industry partners, called the X4 simulation, to demonstrate their airspace services and capabilities and test new information exchange requirements for UAM operations. This simulation activity will support NASA’s National Campaign (NC) flight tests which are planned over the next several years to guide the collective community and stakeholders through a series of scenario-based test activities that involve vehicles and airspace management services operating in a live test environment. This presentation will provide an overview of the X4 simulation, including objectives, scenarios, assumptions, system architecture, and test schedule. The X4 simulation will evaluate the interconnectivity and operational intent sharing between PSUs for multiple operators, as well as the performance of strategic conflict management.

Urban Air Mobility, Simulation, Advanced Air Mobil↗
NASA NTRS1961

Study of Systems Using Inertia Wheels for Precise Attitude Control of a Satellite

Systems using inertia wheels are evaluated in this report to determine their suitability for precise attitude control of a satellite and to select superior system configurations. Various possible inertia wheel system configurations are first discussed in a general manner. Three of these systems which appear more promising than the others are analyzed in detail, using the Orbiting Astronomical Observatory as an example. The three systems differ from each other only by the method of damping, which is provided by either a rate gyro, an error-rate network, or a tachometer in series with a high-pass filter. An analytical investigation which consists of a generalized linear analysis, a nonlinear analysis using the switching-time method, and an analog computer study shows that all three systems are theoretically capable of producing adequate response and also of maintaining the required pointing accuracy for the Orbiting Astronomical Observatory of plus or minus 0.1 second of arc. Practical considerations and an experimental investigation show, however, that the system which uses an error-rate network to provide damping is superior to the other two systems. The system which uses a rate gyro is shown to be inferior because the threshold level causes a significant amount of limit-cycle operation, and the system which uses a tachometer with a filter is shown to be inferior because a device with the required dynamic range of operation does not appear to be available. The experimental laboratory apparatus used to investigate the dynamic performance of the systems is described, and experimental results are included to show that under laboratory conditions with relatively large extraneous disturbances, a dynamic tracking error of less than plus or minus 0.5 second of arc was obtained.

White, John S.↗
DOE OSTI2025

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding the interactions and regulatory relationships among biomolecules is essential for deciphering complex biological systems and elucidating the mechanisms behind diverse biological functions. Traditionally, the collection of such molecular interaction data has relied on expert curation, a process that is both time-consuming and labor-intensive. To address these limitations, this study explores the use of large language models (LLMs) to automate the genome-scale extraction of molecular interaction knowledge. Here, we evaluate the performance of various LLMs on key biological tasks, including the identification of protein-protein interactions, detection of genes associated with pathways influenced by low-dose radiation, and inference of gene regulatory relationships. Our findings demonstrate that larger LLMs tend to perform better, particularly in extracting intricate gene and protein interactions. Despite their strengths, these models face challenges in recognizing functionally diverse gene groups and highly correlated regulatory relationships. Through a comprehensive analysis using established molecular interaction and pathway databases, we show that LLMs possess the potential to identify relevant biomolecules and predict their interactions, offering valuable insights and marking a significant step toward AI-driven biological knowledge discovery.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS2006

Development and Implementation of Non-Newtonian Rheology Into the Generalized Fluid System Simulation Program (GFSSP)

One of the goals of this program was to develop the experimental and analytical/computational tools required to predict the flow of non-Newtonian fluids through the various system components of a propulsion system: pipes, valves, pumps etc. To achieve this goal we selected to augment the capabilities of NASA's Generalized Fluid System Simulation Program (GFSSP) software. GFSSP is a general-purpose computer program designed to calculate steady state and transient pressure and flow distributions in a complex fluid network. While the current version of the GFSSP code is able to handle various systems components the implicit assumption in the code is that the fluids in the system are Newtonian. To extend the capability of the code to non-Newtonian fluids, such as silica gelled fuels and oxidizers, modifications to the momentum equations of the code have been performed. We have successfully implemented in GFSSP flow equations for fluids with power law behavior. The implementation of the power law fluid behavior into the GFSSP code depends on knowledge of the two fluid coefficients, n and K. The determination of these parameters for the silica gels used in this program was performed experimentally. The n and K parameters for silica water gels were determined experimentally at CFDRC's Special Projects Laboratory, with a constant shear rate capillary viscometer. Batches of 8:1 (by weight) water-silica gel were mixed using CFDRC s 10-gallon gelled propellant mixer. Prior to testing the gel was allowed to rest in the rheometer tank for at least twelve hours to ensure that the delicate structure of the gel had sufficient time to reform. During the tests silica gel was pressure fed and discharged through stainless steel pipes ranging from 1", to 36", in length and three diameters; 0.0237", 0.032", and 0.047". The data collected in these tests included pressure at tube entrance and volumetric flowrate. From these data the uncorrected shear rate, shear stress, residence time, and viscosity were evaluated using formulae for non-Newtonian, power law fluids. The maximum shear rates (corrected for entrance effects) obtained in the rheometer with the current setup were in the 150,000 to 170,000sec- range. GFSSP simulations were performed with a flow circuit simulating the capillary rheometer and using Power Law gel viscosity coefficients from the experimental data. The agreement between the experimental data and the simulated flow curves was within +/-4% given quality entrance effect data.

DiSalvo, Roberto↗
DOE OSTI2023

LOCOMOTIVES - Comprehensive Impact and Cost Assessment Framework of Carbon Lowering Approaches for the US Rail Freight System

The goal of this project is to develop a tool to aid railroads and other stakeholders assess and approach the decarbonization of freight rail operations by identifying new, viable low-carbon energy storage and conversion systems for future locomotive systems and how they should be deployed on the existing US freight rail network. In the first quarter, the project focused on collecting data, establishing a simulation workflow, and engaging industry through the creation of the Industry Advisory Board (IAB). In the second quarter, the project focused on selecting fuel pathways and powertrain technologies, setting performance targets, conducting a techno-economic analyses, and developing the simulation framework that would serve as the backbone of the future toolhead. The third quarter involved developing an industry-oriented interactive dashboard powered by a five-step sequential framework, as well as holding industry advisory board meetings as per the initial technology-to-market plan. In the remaining project quarters, the NUFRIEND dashboard were fine-tuned with the help of IAB member feedback and in-depth scenario analyses were conducted to support the techno-economic analysis of energy sources. Additionally, dashboard documentation, project insights, and open-source code on GitHub were prepared and released. Throughout the project, the team completed testing and analysis of all model components, integrated all initial test scenarios, and conducted stakeholder engagement. Lower-carbon drop-in fuels can be deployed as admixtures and are considered uniform across the network at a desired penetration rate, while hydrogen and battery-electric technology deployment poses a more complex problem as they require significant investments to be made in the siting of refueling/charging facilities and the replacement of locomotive fleets. Thus, strategies for locating and sizing refueling/charging facilities on a railroad’s network to meet their energy demands were developed to inform deployment decisions. To address this challenge, the Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework presents a five-step sequential framework to select O-D paths, locate facilities, reroute flows, size facilities, and evaluate the deployment for alternative energy sources that require locomotive powertrains to be converted and new refueling infrastructure to be deployed. The NUFRIEND Framework is an industry-oriented tool for simulating the deployment of new energy technologies across the US freight rail network. The framework provides a comprehensive network-level optimization and scenario simulation tool for decarbonizing the freight rail sector, addressing the uncertainties surrounding technological developments by supporting sensitivity analyses for different operational and technological parameters through a transparent and flexible input module. It offers practical alternatives to diesel locomotives and can be applied for any railroad considering the specific network structure and freight demand, outputting evaluation metrics for the associated emissions and costs relative to diesel operations. A number of relevant simulation scenarios were run and analyzed for key insights on the value of different alternative technologies for freight rail decarbonization. The project developments and findings have been presented at numerous conferences and events.

08 HYDROGEN↗
NASA NTRS2006

Mars Reconnaissance Orbiter Ka-band (32 GHz) Demonstration: Cruise Phase Operations

The X-band (8.41 GHz) frequency currently used for deep space telecommunications is too narrow (50 MHz) to support future high rate missions. Because of this NASA has decided to transition to Ka-band (32 GHz) frequencies. As weather effects cause much larger fluctuations on Ka-band than on X-band, the traditional method of using a few dBs of margin to cover these fluctuations is wasteful of power for Ka-band; therefore, a different operations concept is needed for Ka-band links. As part of the development of the operations concept for Ka-band, NASA has implemented a fully functioning Ka-band communications suite on its Mars Reconnaissance Orbiter (MRO). This suite will be used during the primary science phase to develop and refine the Ka-band operations concept for deep space missions. In order to test the functional readiness of the spacecraft and the Deep Space Network's (DSN) readiness to support the demonstration activities a series of passes over DSN 34-m Beam Waveguide (BWG) antennas were scheduled during the cruise phase of the mission. MRO was launched on August 12, 2005 from Kennedy Space Center, Cape Canaveral, Florida, USA and went into Mars Orbit on March 10, 2006. A total of ten telemetry demonstration and one high gain antenna (HGA) calibration passes were allocated to the Ka-band demonstration. Furthermore, a number of "shadow" passes were also scheduled where, during a regular MRO track over a Ka-band capable antenna, Ka-band was identically configured as the X-band and tracked by the station. In addition, nine Ka-band delta differential one way ranging ((delta)DOR) passes were scheduled. During these passes, the spacecraft and the ground system were put through their respective paces. Among the highlights of these was setting a single day record for data return from a deep space spacecraft (133 Gbits) achieved during one 10-hour pass; achieving the highest data rate ever from a planetary mission (6 Mbps) and successfully demonstrating Ka-band DDOR. In addition, DSN performed well. However, there are concerns with the active pointing of the Ka-band antennas as well as delivery of the monitor data from the stations. The spacecraft also presented challenges not normally associated with planetary missions mostly because of its very high equivalent isotropic radiated power (EIRP). This caused problems in accurately evaluating the in-flight EIRP of the spacecraft which led to difficulties evaluating the quality of the HGA calibration data. These led to the development of additional measurement techniques that could be used for future high-power deep space missions.

Mars Reconnaissance Orbiter (MRO)↗
NASA NTRS2020

Medics: Medical Decision Support System for Long-Duration Space Exploration

The Autonomous Medical Operations (AMO) group at NASA Ames is developing a “medical decision support system” to enable astronauts on long-duration exploration missions to operate autonomously. The system will support clinical actions by providing medical interpretation advice and procedural recommendations during emergent care and clinical work performed by crew. The current state of development of the system, called MedICS (Medical Interpretation Classification and Segmentation) includes two separate aspects: a set of machine learning diagnostic models trained to analyze organ images and patient health records, and an interface to ultrasound diagnostic hardware and to medical repositories. Three sets of images of different organs and medical records were utilized for training machine learning models for various analyses, as follows: 1. Pneumothorax condition (collapsed lung). The trained model provides a positive or negative diagnosis of the condition. 2. Carotid artery occlusion. The trained model produces a diagnosis of 5 different occlusion levels (including “normal”). 3. Ocular retinal images. The model extracts optic disc pixels (image segmentation). This is a precursor step for advanced autonomous fundus clinical evaluation algorithms to be implemented in FY20. 4. Medical health records. The model produces a differential diagnosis for any particular individual, based on symptoms and other health and demographic information. A probability is calculated for each of 25 most common conditions. The same model provides the likelihood of survival. All results are provided with a confidence level. Item 1 images were provided by the US Army and were part of a data set for the clinical treatment of injured battlefield soldiers. This condition is relevant to possible space mishaps, due to pressure management issues. Item 2 images were provided by Houston Methodist Hospital, and item 3 health records were acquired from the MIT laboratory of computational physiology. The machine learning technology utilized is deep multilayer networks (Deep Learning), and new models will continue to be produced, as relevant data is made available and specific health needs of astronaut crews are identified. The interfacing aspects of the system include a GUI for running the different models, and retrieving and storing data, as well as support for integration with an augmented reality (AR) system deployed at JSC by Tietronix Software Inc. (HoloLens). The AR system provides guidance for the placement of an ultrasound transducer that captures images to be sent to the MedICS system for diagnosis. The image captured and the associated diagnosis appear in the technician’s AR visual display.

Colombano, Silvano↗