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NASA NTRS2023

NASA Passive Thermal Control Engineering Guidebook

The NASA Passive Thermal Control Engineering Guidebook provides recommendations, including best practices and lessons learned, related to the passive thermal control engineering discipline. Topics include analysis (including documentation and review), thermal hardware (design/selection, vendors, and integration), testing, and flight operations. The passive thermal discipline includes thermal control and thermal protection systems. The passive thermal control discipline, which is addressed in this Guidebook, is broad and covers internal and external systems, component passive thermal analysis, vehicle on-orbit attitude timeline analysis, integrated thermal analysis, various thermal control apparatus (heaters and controls, coatings, blankets and insulations, isolators, geometric design for view factors, materials emissivity/absorptivity properties), passive cooling of avionics, purge, vent, and drain for vehicle cavities and compartments, thermal model development and correlation, thermal cycle and thermal-vacuum testing. Topics related to thermal protection systems (TPS) are not within the scope of this document. The Guidebook provides a consolidated reference for early career as well as experienced engineers embarking on a new task. Leveraging the experience of the group minimizes the learning curve that exists at the start of new projects, reduces the risk of repeating mistakes, and improves the organization’s ability to deliver high-quality products efficiently. The intent of this document is to provide guidance based on collective experience, and is not intended impose constraints on project work. Best practices can vary substantially and it is important to apply judgment wisely based on careful consideration of the specific design and requirements. Implementation of the methods described will vary from project to project and should be applied in conjunction with the judgment of the thermal engineers performing and reviewing the work. The recommendations in this document are not intended to replace program, project, branch, Center, or NASA requirements and/or policies.

thermal↗

NASA NTRSDate not supplied

Low Leakage Valves for Long Duration Missions

I.Nomenclaturemtypical_mission=internal mass of propellant lost over the course of a mission with Qtypicalmmission_goal=mass of propellant loss over the course of a mission with Qgoaltmission=theoretical mission durationQtypical=internal leakage rate found in commercially available valvesQgoal=internal leakage rate design goalρH2_STP=density of hydrogen at standard temperature and pressure.1NASA AST, Liquid Propulsion Valve Engineer.Valves, Actuators, and Ducts Design and Development Branch (ER14)2NASA AST, Liquid Propulsion Valve Engineer.Valves, Actuators, and Ducts Design and Development Branch (ER14)3NASA AST, Liquid Propulsion Valve Engineer (Retired).Valves, Actuators, and Ducts Design and Development Branch (ER14)4JSEG ESSCA, Engineering Specialist.Valves Actuators and Ducts Design and Development Branch (ER14)5NASA AST, Liquid Propulsion Valve Engineer.Valves, Actuators, and Ducts Design and Development Branch (ER14)6JSEG ESSCA, Valve Design and Development Engineer.Valves Actuators and Ducts Design and Development Branch (ER14) II.IntroductionCurrent aerospace cryogenic valves present challenges to potential long duration missions that utilize cryogenic propellants. Small interplanetary and long-life communication satellites typically utilize hypergolic propellants that operate at higher temperatures, making it easier to achieve very low internal leakage rates. Larger vehicles for long duration missions will likely need to utilize cryogenic-based chemical and nuclear systems to achieve mission requirements. Some early propulsion concepts are projected to require valves with a nominal size ranging from 3” to 10”.Currently available cryogenic aerospace valves typically have internal leakage rates which can range from 100 to 300 SCIM for 3” valves, or upwards of 2,000 SCIM for 10” valves. With just a few of these valves in a system, internal leakage could account for multiple tons of propellant loss over the course of a potential Mars mission, as shown in Figure 1.Figure 1 - Potential Propellant Loss Over the Course of a Long Duration MissionMost internal leakage rates can be attributed to inherent imperfections and misalignments, which result in imperfect contact between sealing surfaces, as shown in Figure 2.Figure 2 - Imperfections and Misalignments Between Sealing Surfaces The ER14 Branch at Marshall Space Flight Center (MSFC) has created a self-aligning seat and poppet design (shown in Figure 3) that allows a valve to be more tolerant of imperfect contacts. This design utilizes a metallic poppet head with five degrees of freedom that allows the poppet toself-align with the seat, reducing the need for tight tolerances.Figure 3 - Self-Aligning Seat and Poppet DesignIII.Test Valve DescriptionA series of development tasks have been conducted to study potential improvements to internal leakage rates. These tasks include 3 test valves (shown in Figure 4) to demonstrate the potential application to various configurations and sizes:A 3” isolation valve for liquid flows (similar to a fill and drain valve)A 3” relief valve for low temperature gas flows.An 8” pre-valve (similar to the engine isolation valve needed on a Nuclear Thermal Propulsion (NTP) engine).Figure 4 - Low Leakage Development Test ValvesThese valves have been developed and are currently being tested at liquid nitrogen (LN2) temperatures, and are anticipated to undergo testing at liquid hydrogen (LH2) temperatures in late 2022

Cody L Gilliland↗

NASA NTRS2023

NASA Passive Thermal Control Engineering Guidebook

thermal↗

DOE OSTI2026

Intern Poster Session 08/13: Autonomous Nuclear Robotics: Applications in nuclear waste inspection and hot cell experiments

The nuclear industry is experiencing renewed interest in autonomous robotics, yet most deployed systems remain teleoperated with limited autonomy. This work presents two contributions toward fully autonomous nuclear robotic systems: autonomous waste inspection at the Hanford Site and an autonomous hot cell laboratory framework. Inspections of Hanford's underground waste storage tanks are performed manually at significant cost and personnel exposure. We developed a reinforcement-learning (RL) training pipeline for a custom-built inspection arm. In parallel, we are designing an autonomous laboratory framework for post-irradiation examination in hot cells at the Specimen Preparation Laboratory (SPL) that integrates computer vision, task and motion planning, hardware execution, and operator-in-the-loop control. These systems demonstrate a path toward safer, more efficient nuclear operations by reducing human exposure while maintaining rigorous human oversight at critical decision points.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2016

Development of a Protocol to Test Proprioceptive Utilization as a Predictor for Sensorimotor Adaptability

Astronauts returning from space flight show significant inter-subject variations in their abilities to readapt to a gravitational environment because of their innate sensory weighting. The ability to predict the manner and degree to which each individual astronaut will be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. We hypothesize participant's ability to utilize individual sensory information (vision, proprioception and vestibular) influences adaptation in sensorimotor performance after space flight. The goal of this study is to develop a reliable protocol to test proprioceptive utilization in a functional postural control task. Subjects "stand" in a supine position while strapped to a backpack frame holding a friction-free device using air-bearings that allow the subject to move freely in the frontal plane, similar to when in upright standing. The frame is attached to a pneumatic cylinder, which can provide different levels of a gravity-like force that the subject must balance against to remain "upright". The supine posture with eyes closed ensures reduced vestibular and visual contribution to postural control suggesting somatosensory and/or non-otolith vestibular inputs will provide relevant information for maintaining balance control in this task. This setup is called the gravity bed. Fourteen healthy subjects carried out three trials each with eyes open alternated with eyes closed, "standing" on their dominant leg in the gravity bed environment while loaded with 60 percent of their body weight. Subjects were instructed to: "use your sense of sway about the ankle and pressure changes under the foot to maintain balance." Maximum length of a trial was 45 seconds. A force plate underneath the foot recorded forces and moments during the trial and an inertial measurement unit (IMU) attached on the backpack's frame near the center of mass of the subject recorded upper body postural responses. Series of linear and non-linear analyses were carried out on several force plate and IMU data including stabilogram diffusion analysis on the center of pressure (COP) to find a subset of parameters that were sensitive to detect differences in postural performance between eyes open and closed conditions. Results revealed that seven parameters (root mean square (RMS) of medio-lateral (ML) COP, range of ML COP, RMS of roll moment, range of trunk roll, minimum time-to-boundary (TTB), integrated TTB, and critical mean square planar displacement (delta r (sup 2) (sub c)) were significantly different between eyes open and closed conditions. We will present data to show the efficacy of using performance in single leg stance with eyes closed on the gravity bed to assess individuals' ability to utilize proprioceptive information in a functional postural control task to predict re-adaptation for sensorimotor and functional performance.

Goel, R.↗

NASA NTRS2014

Human-Robot Interaction Directed Research Project

Human-robot interaction (HRI) is a discipline investigating the factors affecting the interactions between humans and robots. It is important to evaluate how the design of interfaces and command modalities affect the human's ability to perform tasks accurately, efficiently, and effectively when working with a robot. By understanding the effects of interface design on human performance, workload, and situation awareness, interfaces can be developed to appropriately support the human in performing tasks with minimal errors and with appropriate interaction time and effort. Thus, the results of research on human-robot interfaces have direct implications for the design of robotic systems. This DRP concentrates on three areas associated with interfaces and command modalities in HRI which are applicable to NASA robot systems: 1) Video Overlays, 2) Camera Views, and 3) Command Modalities. The first study focused on video overlays that investigated how Augmented Reality (AR) symbology can be added to the human-robot interface to improve teleoperation performance. Three types of AR symbology were explored in this study, command guidance (CG), situation guidance (SG), and both (SCG). CG symbology gives operators explicit instructions on what commands to input, whereas SG symbology gives operators implicit cues so that operators can infer the input commands. The combination of CG and SG provided operators with explicit and implicit cues allowing the operator to choose which symbology to utilize. The objective of the study was to understand how AR symbology affects the human operator's ability to align a robot arm to a target using a flight stick and the ability to allocate attention between the symbology and external views of the world. The study evaluated the effects type of symbology (CG and SG) has on operator tasks performance and attention allocation during teleoperation of a robot arm. The second study expanded on the first study by evaluating the effects of the type of navigational guidance (CG and SG) on operator task performance and attention allocation during teleoperation of a robot arm through uplinked commands. Although this study complements the first study on navigational guidance with hand controllers, it is a separate investigation due to the distinction in intended operators (i.e., crewmembers versus ground-operators). A third study looked at superimposed and integrated overlays for teleoperation of a mobile robot using a hand controller. When AR is superimposed on the external world, it appears to be fixed onto the display and internal to the operators' workstation. Unlike superimposed overlays, integrated overlays often appear as three-dimensional objects and move as if part of the external world. Studies conducted in the aviation domain show that integrated overlays can improve situation awareness and reduce the amount of deviation from the optimal path. The purpose of the study was to investigate whether these results apply to HRI tasks, such as navigation with a mobile robot.

Sandor, Aniko↗

DOE OSTI2020

Modeling Photovoltaics Innovation and Deployment Dynamics

PV’s historically rapid cost reduction is exceptional among technologies. Further cost reductions could play a major role in increasing deployment in the future. To enable such cost reductions, new modeling frameworks are needed to understand the determinants of innovation in PV. In this project, we study the mechanisms driving PV module and system cost reductions, delving deeply into the specific technological innovations that have occurred in the past and the policies that encouraged them, and also opportunities for future cost reduction and widespread deployment. The project contributes new fundamental insight on the determinants of technological innovation by developing novel methods and insights that are generalizable and can therefore be applied to other technologies. The results will allow policy makers, engineers, and other stakeholders to better prioritize their efforts and investments in the future. The project is organized around four journal articles as described below. The first article [1] identifies ‘low-level’ (e.g. conversion efficiency improvement) and ‘high- level’ (e.g. R&D efforts) mechanisms of cost reduction in PV systems (Tasks 1-4). This work builds on a previous DOE grant, where we developed a framework for technological innovation leading to PV module cost reduction [2]. We advance a method to disentangle the contributions of physical (‘hardware’) and non-physical (‘soft technology’) changes. Our results uncover reasons behind the relatively slow evolution of soft technology and can inform new innovation approaches to these technologies. The second article [3] identifies specific engineering or institutional innovations that enabled the low-level mechanisms of cost reduction in PV module and balance-of-systems (BOS) (Tasks 5, 9, 10). We identify 85 innovations and connect them to the cost variables they affected. By developing an innovations typology, this study shows the differences between the types of innovations affecting PV modules and BOS components. Finally, by analyzing the industry origins of innovations, this study also finds that PV was well-positioned within an ecosystem of continuously advancing technologies. The third article [4] studies prospective cost reduction opportunities (Task 10). We explore how design approaches that emphasize standardization and automation, such as plug-and-play PV systems, can create cost reduction opportunities by reducing interactions and speeding up activities with high process costs. We show that this can lead to cost reduction in cost components with the most untapped opportunity for improvement such as installation labor, overhead, electrical BOS, and customer acquisition. The fourth article [5] analyzes how various policies supporting PV deployment and R&D contributed to PV’s cost improvement by enabling high-level mechanisms, specific innovations, and ultimately low-level mechanisms of cost reduction (Tasks 8, 13, 14). We investigate examples from different countries and connect these policies to quantifiable cost change mechanisms. Our study sheds light on the roles that different nations played over time, through a diverse set of policy approaches.

14 SOLAR ENERGY↗

DOE OSTI2025

HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs

Scientific applications produce vast amounts of data, posing grand challenges in the underlying data management and analytic tasks. Progressive compression is a promising way to address this problem, as it allows for on-demand data retrieval with significantly reduced data movement cost. However, most existing progressive methods are designed for CPUs, leaving a gap for them to unleash the power of today’s heterogeneous computing systems with GPUs.In this work, we propose HP-MDR, a high-performance and portable data refactoring and progressive retrieval framework for GPUs. Our contributions are four-fold: (1) We carefully optimize the bitplane encoding and lossless encoding, two key stages in progressive methods, to achieve high performance on GPUs; (2) We propose pipeline optimization and incorporate it with data refactoring and progressive retrieval workflows to further enhance the performance for large data process; (3) We leverage our framework to enable high-performance data retrieval with guaranteed error control for common Quantities of Interest; (4) We evaluate HP-MDR and compare it with state of the arts using five real-world datasets. Experimental results demonstrate that HP-MDR delivers an average 13.68 × and 6.31 × throughput in data refactoring and progressive retrieval tasks, respectively. It also leads to 11.22 × throughput for recomposing required data representations under Quantity-of-Interest error control and 6.04 × performance for the corresponding end-to-end data retrieval, when compared with state-of-the-art solutions.

Li, Yanliang [University of Oregon]↗

DOE OSTI2018

Glovebox Handling of High-Consequence Materials with Super Baxter and Gesture-Based Programming - 18598

The handling of high-consequence materials is a difficult task requiring safe object manipulation while avoiding the risk of contamination or spillage. Specific operations including opening containers, segregating the wastes, and repackaging are required to be safely executed inside contained spaces such as gloveboxes. However, the workers' ability and dexterity to manipulate objects through thick protective gloves are compromised in many aspects. The fixed position of the glovebox arm ports restricts the movements of the workers' arms, which also makes it hard to lift heavy objects. Further, the operational workspace inside the glovebox is restricted by the gloves' reachability. Safety of workers is the paramount concern in glovebox operations and the very reason for their existence. Sharp edges and tools increases the risk of glove punctures, which may expose the operator to chemicals and radiation and risks contamination in the vicinity outside the glovebox. The operators are also affected by ergonomic stressors due to prolonged and repetitive operations. To achieve a high degree of human safety, robotic solutions to handle high-consequence materials inside the glovebox are desirable, as they remove the operators from the hazards listed above. However, robots, in general, lack the degree of adaptability necessary for most high-consequence material handling tasks as these tasks are often unstructured or highly variable. Likewise, most human operators, while highly skilled in the tasks at hand (task expertise), lack the robot programming skills necessary to adapt the robot's motions to the variations (programming expertise). A way to bridge this divide is to make robot programming more intuitive to human operators. Since humans naturally teach one another through demonstration and learning, robotic 'programming by demonstration' paradigms have begun to appear to reduce the burden of robot reprogramming. Gesture-Based Programming attempts to achieve that by enabling a robot to observe the normal actions and affordances of a human performing a task to learn to map those onto the skills of the robot. In the end, this approach permits benefits from both the human and the robot: the human's adaptive decision making and the robot's resilience to high-consequence material. Robotic solutions assuringly provide protection for the operators, however their autonomy brings about other potential risks. Artificial Intelligence is not perfect; it is a black box, which can produce results, but the full extent of those results may not be known precisely. In self-driving cars, a hypothetical AI problem is the 'green man detector'. Since self-driving cars have presumably never seen a green man, how can we know it won't veer toward him the first time it sees a green man? Likewise, even though GbP provides an open method for safe task execution replicating a human, incorrect task inferences can lead to potential collisions or unintended operations. Thus, a hardware safety measure is proposed to provide reliable operation inside the glovebox, and true safe operations are achieved by mechanically limiting the operational range of each arm link. The operational range is computed and validated through exhaustive offline simulations, which can be streamlined by affordable computing power. Super Baxter is one such robot which incorporates these capabilities. It is a human-like, bimanual robot, being developed at the Collaborative Robotics Lab (CRL) in conjunction with the Dept. of Energy/Environmental Management, Rethink Robotics, Barrett Technology, and the NSF Center for Robots and Sensors for the Human Well-Being (RoSe-HUB). Super Baxter is envisioned to represent the next generation of collaborative robots that will be intrinsically human-safe, as well as exceptionally human-intuitive. In summary, Super Baxter will enable safe glovebox operations through low-shot Gesture based Programming incorporated with hardware safety measures. Two aspects of safety will be discussed in the paper. First, the safety of the operator achieved by allowing him to program Super Baxter remotely through Gesture based Programming. Second, the safety of the glovebox, achieved by incorporating hardware safety measures using joint limits. Further, the capability of Super Baxter on Gesture based Programming is demonstrated through typical object manipulation tasks in glovebox operations. (authors)

42 ENGINEERING↗

DOE OSTI2019

Performance and Reliability Advancements in a Durable Low Temperature Tubular SOFC

The key objective of this program was to develop, build and test an ultra-low degradation, high efficiency SOFC stack with a novel capability for energy storage. The demonstration would be a step toward MW systems but a successful prototype leads to revolutionize our future system designs, significantly reducing the cost and extending the capability of the SOFC in energy harvesting projects. To achieve these innovative goals, advancements in cell and stack technology as well as manufacturing technology and automation were undertaken. The major tasks in cell and stack technology included: 1. The incorporation of an inexpensive solid fuel element in each cell to provide protection against overloading and enable storage inside the fuel cell 2. Increasing efficiency without water recirculation using novel integral POX reforming 3. Reduced stack degradation through tried and true electrode material augmentations for intermediate temperature cell operation In order to ensure that the technology can be accepted by the largest market, manufacturing costs were also addressed for targeting cost and reliability as outlined below: 1. Thinner tube by isostatic pressing automation 2. Electrode impregnation automation 3. Stack manufacturing improvements Atrex Energy has successfully developed and demonstrated the advanced Solid Oxide Fuel Cell (SOFC) technology through this program. R&D efforts have demonstrated the ability to operate SOFCs under sub-stoichiometric partial oxidation air-to-fuel ratios while showing significant resiliency against high fuel utilizations approaching 120%. Core fuel cell innovations including manufacturing advancements enabled Atrex to develop and demonstrate a cost effective, durable 5KW SOFC stack operating on natural gas at net fuel-to-DC electricity efficiencies of ~50 % while operating at a nominal temperature of 650 deg C.

03 NATURAL GAS↗

DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Human and Robotic Testing in Gloveboxes to Reduce Worker Fatigue - 19542

A DOE-sponsored study conducted by the Los Alamos National Laboratory reported that the incidence rate of repetitive stress injuries (RSI) during manipulation tasks in glovebox and hot cell workspaces increased from 22% after 1-2 hours of work per day to 50% after 3 hours of work per day due to fatigue, repetitive motion, and hyperextension. Robotics can be a viable solution to this issue as robotic manipulators and wearable robotic devices can provide effective assistance to alleviate the workload. In order to use the robotic devices, an initial study to detect the amount of fatigue experienced by the glovebox operators is essential. Hence, in the present research two objectives have been specified and investigated. These include the use of electromyography (EMG) sensors to precisely detect the fatigue in various hands, arm and shoulder muscles and; the use of dexterous robotic arm and hand combinations to effectively perform certain repetitive and monotonous tasks conducted inside an in-house glovebox constructed for the research. A UR5 robotic arm and an i-limb artificial hand have been used. Human tests included eight subjects and two customized glovebox tasks. Pilot scale test results indicate variation in the mean frequency which is an indication of the level of fatigue that occurred. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2005

GPS World, Innovation: Autonomous Navigation at High Earth Orbits

Calculating a spacecraft's precise location at high orbital altitudes-22,000 miles (35,800 km) and beyond-is an important and challenging problem. New and exciting opportunities become possible if satellites are able to autonomously determine their own orbits. First, the repetitive task of periodically collecting range measurements from terrestrial antennas to high altitude spacecraft becomes less important-this lessens competition for control facilities and saves money by reducing operational costs. Also, autonomous navigation at high orbital altitudes introduces the possibility of autonomous station keeping. For example, if a geostationary satellite begins to drift outside of its designated slot it can make orbit adjustments without requiring commands from the ground. Finally, precise onboard orbit determination opens the door to satellites flying in formation-an emerging concept for many scientific space applications. The realization of these benefits is not a trivial task. While the navigation signals broadcast by GPS satellites are well suited for orbit and attitude determination at lower altitudes, acquiring and using these signals at geostationary (GEO) and highly elliptical orbits is much more difficult. The light blue trace describes the GPS orbit at approximately 12,550 miles (20,200 km) altitude. GPS satellites were designed to provide navigation signals to terrestrial users-consequently the antenna array points directly toward the earth. GEO and HE0 orbits, however, are well above the operational GPS constellation, making signal reception at these altitudes more challenging. The nominal beamwidth of a Block II/IIA GPS satellite antenna array is approximately 42.6 degrees. At GEO and HE0 altitudes, most of these primary beam transmissions are blocked by the Earth, leaving only a narrow region of nominal signal visibility near opposing limbs of the earth. This region is highlighted in gray. If GPS receivers at GEO and HE0 orbits were designed to use these higher power signals only, precise orbit determination would not be practical. Fortunately, the GPS satellite antenna array also produces side lobe signals at much lower power levels. NASA has designed and tested the Navigator, a new GPS receiver that can acquire and track these weaker signals, thereby dramatically increasing the signal visibility at these altitudes. While using much weaker signals is a fundamental requirement for a high orbital altitude GPS receiver, it is certainly not the only challenge. There are other unique characteristics of this application that must also be considered. For example, Position Dilution of Precision (PDOP) figures are much higher at GEO and HE0 altitudes because visible GPS satellites are concentrated in a much smaller area with respect to the spacecraft antenna. These poor PDOP values contribute considerable error to the point solutions calculated by the spacecraft GPS receiver. Finally, spacecraft GPS receivers must be designed to withstand a variety of extreme environmental conditions. Variations in acceleration between launch and booster separation are extreme. Temperature gradients in the space environment are also severe. Furthermore, radiation effects are a major concern-spacecraft-borne GPS receivers must be designed with radiation-hardened electronics to guard against this phenomenon, otherwise they simply will not work. Perhaps most importantly, there are no opportunities to repair or modify any space-borne GPS receiver after it has been launched. Great care must be taken to ensure all performance characteristics have been analyzed prior to liftoff.

Bamford, William↗

NASA NTRS1993

Adaptive function allocation reduces performance costs of static automation

Adaptive automation offers the option of flexible function allocation between the pilot and on-board computer systems. One of the important claims for the superiority of adaptive over static automation is that such systems do not suffer from some of the drawbacks associated with conventional function allocation. Several experiments designed to test this claim are reported in this article. The efficacy of adaptive function allocation was examined using a laboratory flight-simulation task involving multiple functions of tracking, fuel-management, and systems monitoring. The results show that monitoring inefficiency represents one of the performance costs of static automation. Adaptive function allocation can reduce the performance cost associated with long-term static automation.

Parasuraman, Raja↗

NASA NTRS2003

Methods Developed by the Tools for Engine Diagnostics Task to Monitor and Predict Rotor Damage in Real Time

Tools for Engine Diagnostics is a major task in the Propulsion System Health Management area of the Single Aircraft Accident Prevention project under NASA s Aviation Safety Program. The major goal of the Aviation Safety Program is to reduce fatal aircraft accidents by 80 percent within 10 years and by 90 percent within 25 years. The goal of the Propulsion System Health Management area is to eliminate propulsion system malfunctions as a primary or contributing factor to the cause of aircraft accidents. The purpose of Tools for Engine Diagnostics, a 2-yr-old task, is to establish and improve tools for engine diagnostics and prognostics that measure the deformation and damage of rotating engine components at the ground level and that perform intermittent or continuous monitoring on the engine wing. In this work, nondestructive-evaluation- (NDE-) based technology is combined with model-dependent disk spin experimental simulation systems, like finite element modeling (FEM) and modal norms, to monitor and predict rotor damage in real time. Fracture mechanics time-dependent fatigue crack growth and damage-mechanics-based life estimation are being developed, and their potential use investigated. In addition, wireless eddy current and advanced acoustics are being developed for on-wing and just-in-time NDE engine inspection to provide deeper access and higher sensitivity to extend on-wing capabilities and improve inspection readiness. In the long run, these methods could establish a base for prognostic sensing while an engine is running, without any overt actions, like inspections. This damage-detection strategy includes experimentally acquired vibration-, eddy-current- and capacitance-based displacement measurements and analytically computed FEM-, modal norms-, and conventional rotordynamics-based models of well-defined damages and critical mass imbalances in rotating disks and rotors.

Baaklini, George Y.↗

DOE OSTI2018

Quantifying Analysis of Uncertainty in Medical Reporting: Creation of User and Context-Specific Uncertainty Profiles

While uncertainty is ubiquitous in medical practice, minimal work to date has been performed to analyze the cause and effect relationship between uncertainty and patient outcomes. In medical imaging practice, uncertainty in the radiology report has been well documented to be a source of clinician dissatisfaction. Before one can effectively create intervention strategies aimed at reducing uncertainty, it must first be better understood through context- and user-specific analysis. One strategy for accomplishing this task is to characterize the source of uncertainty and create user-specific uncertainty profiles which take into account a number of provider-specific variables which may contribute to report uncertainty. The resulting data can in turn be used to create real-time report uncertainty metrics aimed at providing uncertainty analytics at the point of care, for the combined purposes of decision support, improved communication, and enhanced clinical/economic outcomes.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2017

Sensor for Measuring Hydrogen Partial Pressure in Parabolic Trough Power Plant Expansion Tanks

The National Renewable Energy Laboratory and Acciona Energy North America are working together to design and implement a process system that provides a permanent solution to the issue of hydrogen buildup at parabolic trough power plants. We are pursuing a method that selectively removes hydrogen from the expansion tanks that serve as reservoirs for the heat transfer fluid (HTF) that circulates in the collector field and power block components. Our modeling shows that removing hydrogen from the expansion tanks at a design rate reduces and maintains dissolved hydrogen in the circulating HTF to a selected target level. Our collaborative work consists of several tasks that are needed to advance this process concept to a development stage, where it is ready for implementation at a commercial power plant. Our main effort is to design and evaluate likely process-unit operations that remove hydrogen from the expansion tanks at a specified rate. Additionally, we designed and demonstrated a method and instrumentation to measure hydrogen partial pressure and concentration in the expansion-tank headspace gas. We measured hydrogen partial pressure in the headspace gas mixture using a palladium-alloy membrane, which is permeable exclusively to hydrogen. The membrane establishes a pure hydrogen gas phase that is in equilibrium with the hydrogen in the gas mixture. We designed and fabricated instrumentation, and demonstrated its effectiveness in measuring hydrogen partial pressures over a range of three orders of magnitude. Our goal is to install this instrument at the Nevada Solar One power plant and to demonstrate its effectiveness in measuring hydrogen levels in the expansion tanks under normal plant operating conditions.

14 SOLAR ENERGY↗

DOE OSTI2026

Structure-aware Initialization via Numerical Continuation and Informed Priors

Scientific machine learning (SciML) often operates in ill-conditioned, weakly identifiable regimes due to limited data or indirect observations. In such settings, optimization and inference are highly sensitive to the starting point, making initialization--often under-reported--a consequential degree of freedom. Random initialization is not a neutral default as it induces an implicit prior over candidate solutions and can systematically bias the result, producing large run-to-run variability. Here, we formalize this view by treating initialization as a hidden confounder in SciML and develop a unifying theory for structure-aware initialization via numerical continuation, constructing warm starts from related problem instances. Across representative tasks, including physics-informed neural networks, maximum likelihood estimation, and variational inference, warm starts have been shown to consistently reduce optimization effort and improve reliability.

Data integrity↗