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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Artificial Intelligence Medical Support for Long-Duration Space Missions

We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.

Artificial Intelligence↗
DOE OSTI2023

Robotic Systems integration for cleanroom SRF assembly at Fermilab

Superconducting Radio Frequency (SRF) cryomodule assembly is a critical process that demands the highest levels of contamination control in a cleanroom to ensure product quality and reliability. Within the cleanroom environment, human-driven activities remain the major source of uncontrolled particulate. Moreover, the precise manual alignment of components often exceeds expected timeframes, increasing the risk of particulate migration into the critical inner space of the SRF resonant cavity, and causing extended discomfort for workers. Automation comes as a natural solution to address these challenges. The goal of this activity is to set up and integrate a robotic system to support cavity coupler installation in the cleanroom environment. Initially, remote control of a 6-axis collaborative robot arm will be implemented as an introductory step towards automation in the cleanroom. Subsequently, the system will be further enhanced by integrating manipulation capabilities through an appropriate end-effector, and computer vision technology to enable accurate pose estimation. This comprehensive approach aims to achieve a fully automated and adaptable assembly process in the partially structured cleanroom environment, thereby enhancing efficiency and mitigating existing challenges for better product quality and workers safety.

42 ENGINEERING↗
NASA NTRS2010

NASA Tech Briefs, July 2010

Topics covered include: Wirelessly Interrogated Wear or Temperature Sensors; Processing Nanostructured Sensors Using Microfabrication Techniques; Optical Pointing Sensor; Radio-Frequency Tank Eigenmode Sensor for Propellant Quantity Gauging; High-Temperature Optical Sensor; Integral Battery Power Limiting Circuit for Intrinsically Safe Applications; Configurable Multi-Purpose Processor; Squeezing Alters Frequency Tuning of WGM Optical Resonator; Automated Computer Access Request System; Range Safety for an Autonomous Flight Safety System; Fast and Easy Searching of Files in Unisys 2200 Computers; Parachute Drag Model; Evolutionary Scheduler for the Deep Space Network; Modular Habitats Comprising Rigid and Inflatable Modules; More About N2O-Based Propulsion and Breathable-Gas Systems; Ultrasonic/Sonic Rotary-Hammer Drills; Miniature Piezoelectric Shaker for Distribution of Unconsolidated Samples to Instrument Cells; Lunar Soil Particle Separator; Advanced Aerobots for Scientific Exploration; Miniature Bioreactor System for Long-Term Cell Culture; Electrochemical Detection of Multiple Bioprocess Analytes; Fabrication and Modification of Nanoporous Silicon Particles; High-Altitude Hydration System; Photon Counting Using Edge-Detection Algorithm; Holographic Vortex Coronagraph; Optical Structural Health Monitoring Device; Fuel-Cell Power Source Based on Onboard Rocket Propellants; Polar Lunar Regions: Exploiting Natural and Augmented Thermal Environments; Simultaneous Spectral Temporal Adaptive Raman Spectrometer - SSTARS; Improved Speed and Functionality of a 580-GHz Imaging Radar; Bolometric Device Based on Fluxoid Quantization; Algorithms for Learning Preferences for Sets of Objects; Model for Simulating a Spiral Software-Development Process; Algorithm That Synthesizes Other Algorithms for Hashing; Algorithms for High-Speed Noninvasive Eye-Tracking System; and Adapting ASPEN for Orbital Express.

Source record↗
DOE OSTI2026

Generative Models for Crystalline Materials

Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery.

Metni, Houssam [Karlsruhe Inst. of Technology (KIT↗
NASA NTRS2019

Pterodactyl: An Uncoupled Range Control Approach to Fully Numerical Predictor-Corrector Entry Guidance

Entry, descent, and landing (EDL) has been identified as a core area of investment in NASA's Strategic Technology Investment Plan (NASA STIP). STIP lists the space technologies needed to help achieve NASA's science, technology, and exploration goals across the agency. Within the EDL core area, deployable hypersonic decelerators, also known as deployable entry vehicles (DEVs), have been identified as an area of investment, due to its potential to revolutionize payload delivery methods to Earth and other planets. These vehicles, which can deploy their heat shields or alter their shape before entry, exploit an increased and more effective drag ratio by using less mass than traditional blunt body vehicles with rigid aeroshells. DEVs like Adaptive Deployable Entry and Placement Technology (ADEPT) and Hypersonic Inflatable Aerodynamic Decelerator (HIAD) have demonstrated the capability of transporting the equivalent science payloads of blunt body rigid aeroshells, while using a significantly smaller diameter when stowed within a launch vehicle. While DEVs' increased energy dissipation for less mass is an attractive feature, their ability to contract and expand would require advancements in the current state-of-the-art guidance and control (G&C) architectures used by traditional rigid vehicles. Pterodactyl, a project funded by NASA's Space Technology Mission Directorate (STMD), aims to provide feasible integrated G&C solutions for DEVs, complete with optimized vehicle designs and packaging analyses. Structural and aerodynamic analyses for the explored control systems suggested a need for a bank angle guidance algorithm, a heritage guidance approach that has been used in many entry precision targeting vehicles, as well as an additional need for the development of a non-bank angle guidance. For this reason, Pterodactyl will consider four different G&C configurations during its design phase: i) a reaction control system for bank (sigma) control, ii) a mass movement system for angle of attack (alpha) sideslip (beta) control, iii) flaps for alpha - beta control, and iv) flaps for sigma control. To increase the applicability of each proposed integrated G&C architecture, an 11 km/s lunar return demonstration mission is selected to stress the developed technology capability. The Lifting Nano-ADEPT (LNA) vehicle is chosen as the DEV to demonstrate the integrated solutions. This paper will detail the trajectory design for a lunar return mission, using the validated bank control guidance algorithm Fully Numerical Predictor-Corrector Entry Guidance (FNPEG) and a newly developed guidance algorithm: FNPEG Uncoupled Range Control (URC). FNPEG-URC diverges from traditional bank angle guidances by producing alpha and beta commands to thereby decouple downrange and crossrange control. This presentation will discuss the development and overall performance of FNPEG and FNPEG-URC for each of the four G&C configurations. Successful G&C configurations are defined as those that can deliver payloads to the intended descent and landing site while abiding by trajectory constraints in the face of dispersions.

Johnson, Breanna↗
DOE OSTI2018

Novel deep learning methods for track reconstruction

For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration from computer vision applications and operated on an image-like representation of tracking detector data. While these approaches have shown some promise, image-based methods face challenges in scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In contrast, models that can operate on the spacepoint representation of track measurements ("hits") can exploit the structure of the data to solve tasks efficiently. In this paper we will show two sets of new deep learning models for reconstructing tracks using space-point data arranged as sequences or connected graphs. In the first set of models, Recurrent Neural Networks (RNNs) are used to extrapolate, build, and evaluate track candidates akin to Kalman Filter algorithms. Such models can express their own uncertainty when trained with an appropriate likelihood loss function. The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification. These models read a graph of connected hits and compute features on the nodes and edges. They adaptively learn which hit connections are important and which are spurious. The models are scaleable with simple architecture and relatively few parameters. Results for all models will be presented on ACTS generic detector simulated data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Manipulating nanoscale structure to control functionality in printed organic photovoltaic, transistor and bioelectronic devices

Printed electronics is simultaneously one of the most intensely studied emerging research areas in science and technology and one of the fastest growing commercial markets in the world today. For the past decade the potential for organic electronic (OE) materials to revolutionize this printed electronics space has been widely promoted. Such conviction in the potential of these carbon-based semiconducting materials arises from their ability to be dissolved in solution, and thus the exciting possibility of simply printing a range of multifunctional devices onto flexible substrates at high speeds for very low cost using standard roll-to-roll printing techniques. However, the transition from promising laboratory innovations to large scale prototypes requires precise control of nanoscale material and device structure across large areas during printing fabrication. Maintaining this nanoscale material control during printing presents a significant new challenge that demands the coupling of OE materials and devices with clever nanoscience fabrication approaches that are adapted to the limited thermodynamic levers available. In this review we present an update on the strategies and capabilities that are required in order to manipulate the nanoscale structure of large area printed organic photovoltaic (OPV), transistor and bioelectronics devices in order to control their device functionality. This discussion covers a range of efforts to manipulate the electroactive ink materials and their nanostructured assembly into devices, and also device processing strategies to tune the nanoscale material properties and assembly routes through printing fabrication. The review finishes by highlighting progress in printed OE devices that provide a feedback loop between laboratory nanoscience innovations and their feasibility in adapting to large scale printing fabrication. The ability to control material properties on the nanoscale whilst simultaneously printing functional devices on the square metre scale is prompting innovative developments in the targeted nanoscience required for OPV, transistor and biofunctional devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2009

Detection, Identification, Location, and Remote Sensing Using SAW RFID Sensor Tags

The Electromagnetic Systems Branch (EV4) of the Avionic Systems Division at NASA Johnson Space Center in Houston, TX is studying the utility of surface acoustic wave (SAW) radiofrequency identification (RFID) tags for multiple wireless applications including detection, identification, tracking, and remote sensing of objects on the lunar surface, monitoring of environmental test facilities, structural shape and health monitoring, and nondestructive test and evaluation of assets. For all of these applications, it is anticipated that the system utilized to interrogate the SAW RFID tags may need to operate at fairly long range and in the presence of considerable multipath and multiple-access interference. Towards that end, EV4 is developing a prototype SAW RFID wireless interrogation system for use in such environments called the Passive Adaptive RFID Sensor Equipment (PARSED) system. The system utilizes a digitally beam-formed planar receiving antenna array to extend range and provide direction-of-arrival information coupled with an approximate maximum-likelihood signal processing algorithm to provide near-optimal estimation of both range and temperature. The system is capable of forming a large number of beams within the field of view and resolving the information from several tags within each beam. The combination of both spatial and waveform discrimination provides the capability to track and monitor telemetry from a large number of objects appearing simultaneously within the field of view of the receiving array. In this paper, we will consider the application of the PARSEQ system to the problem of simultaneous detection, identification, localization, and temperature estimation for multiple objects. We will summarize the overall design of the PARSEQ system and present a detailed description of the design and performance of the signal detection and estimation algorithms incorporated in the system. The system is currently configured only to measure temperature (jointly with range and tag ID), but future versions will be revised to measure parameters other than temperature as SAW tags capable of interfacing with external sensors become available. It is anticipated that the estimation of arbitrary parameters measured using SAW-based sensors will be based on techniques very similar to the joint range and temperature estimation techniques described in this paper.

Barton, Richard J.↗
NASA NTRS2009

Implementation of an Adaptive Controller System from Concept to Flight Test

The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a "floating limiter" (FL) concept that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly "floating" and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually "hit" the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The tunable metrics for the FL are (1) window size, (2) drift rate, and (3) persistence counter. Ultimate range limits are also included in case the NN command should drift slowly to a limit value that would cause the FL to be defeated. The FL has proven to work as intended. Both high-g transients and excessive structural loads are controlled with NN hard-over commands. This presentation discusses the FL design features and presents test cases. Simulation runs are included to illustrate the dramatic improvement made to the control of NN hard-over effects. A mission control room display from a flight playback is presented to illustrate the neural net fault display representation. The FL is very adaptable to various requirements and is independent of flight condition. It should be considered as a cost-effective safety monitor to control single-string inputs in general.

Larson, Richard R.↗
NASA NTRS2012

HCIT Broadband Contrast Performance Sensitivity Studies

The High Contrast Imaging Testbed (HCIT) at the Jet Propulsion Laboratory employs a broadband wavefront correction algorithm called Electric Field Conjugation (EFC) to obtain the required 10-10 contrast. This algorithm works with one deformable mirror (DM) to estimate the electric-field to be controlled, and with one or multiple DM's to create a "darkhole" in a predefined region of the image plane where terrestrial planets would be found. We have investigated the effects of absorbing dust particles on a flat optic, absorbing spots on the occulting mask, dead actuators on the DM, and the effects of control bandwidth on the efficiency of the EFC algorithm in a Lyot coronagraph configuration. The structural design of the optical system as well as the parameters of various optical elements used in the analysis is drawn from those of the HCIT system that have been implemented with one DM. The simulation takes into account the surface errors of various optical elements. Results of some of these studies have been verified by actual measurements.

Coronagraphy↗
NASA NTRS2004

Modular, Adaptive, Reconfigurable Systems: Technology for Sustainable, Reliable, Effective, and Affordable Space Exploration

In order to execute the Vision for Space Exploration, we must find ways to reduce cost, system complexity, design, build, and test times, and at the same time increase flexibility to satisfy multiple functions. Modular, Adaptive, Reconfigurable System (MARS) technologies promise to set the stage for the delivery of system elements that form the building blocks of increasingly ambitious missions involving humans and robots. Today, space systems are largely specialized and built on a case-by-case basis. The notion of modularity however, is nothing new to NASA. The 1970's saw the development of the Multi-Mission Modular spacecraft (MMS). From 1980 to 1992 at least six satellites were built under this paradigm, and included such Goddard Space Flight Center missions as SSM, EUVE, UARS, and Landsat 4 and 5. Earlier versions consisted of standard subsystem "module" or "box" components that could be replaced within a structure based on predefined form factors. Although the primary motivation for MMS was faster/cheaper integration and test, standardization of interfaces, and ease of incorporating new subsystem technology, it lacked the technology maturity and programmatic "upgrade infrastructure" needed to satisfy varied mission requirements, and ultimately it lacked user buy-in. Consequently, it never evolved and was phased out. Such concepts as the Rapid Spacecraft Development Office (RSDO) with its regularly updated catalogue of prequalified busses became the preferred method for acquiring satellites. Notwithstanding, over the past 30 years since MMS inception, technology has advanced considerably and now modularity can be extended beyond the traditional MMS module or box to cover levels of integration, from the chip, card, box, subsystem, to the space system and to the system-of-systems. This paper will present the MARS architecture, cast within the historical context of MMS. Its application will be highlighted by comparing a state-of-the-art point design vs. a MARS-enabled lunar mission, as a representative robotic case design.

Esper, Jaime↗
DOE OSTI2024

Evaluating Material Design Principles for Calcium-Ion Mobility in Intercalation Cathodes

Multivalent-ion batteries offer an alternative to Li-based technologies, with the potential for greater sustainability, improved safety, and higher energy density, primarily due to their rechargeable system featuring a passivating metal anode. Although a system based on the Ca 2+ /Ca couple is particularly attractive given the low electrochemical plating potential of Ca 2+ , the remaining challenge for a viable rechargeable Ca battery is to identify Ca cathodes with fast ion transport. In this work, a high-throughput computational pipeline is adapted to (1) discover novel Ca cathodes in a largely unexplored space of empty intercalation hosts and (2) develop material design rules for Ca-ion mobility. One candidate from the screening, W 2 O 3 (PO 4 ) 2 , is confirmed to have a low Nudged Elastic Band (NEB) barrier of 168 meV within a one-dimensional (1D) ion percolation topology. This candidate is subsequently synthesized and electrochemically tested, achieving reversible Ca cycling with a capacity of 25 mA h/g. To further accelerate the screening for promising Ca intercalation electrodes, machine learning (ML) Random Forest (RF) and Extreme Gradient Boosting (XGB) classification models are created with local environment descriptors based on a large, structurally and chemically diverse dataset of minimum energy pathways, spanning over 5,000 density functional theory (DFT) site energy calculations. Accuracies of 92% are achieved, material design metrics are quantified, ML force-fields are leveraged in an accelerated iteration of the screening, and a total of 27 novel Ca cathode materials are highlighted for further investigation.

25 ENERGY STORAGE↗
NASA NTRS1991

US automated rendezvous and capture capabilities review. Remote umbilical system abstract

This document will describe the technology developed at Kennedy Space Center (KSC) to demonstrate automatic tracking, docking, and mating of umbilical systems. Specifically the use of a real time six degree of freedom (6DOF) target tracking vision system, (developed by Adaptive Automation, Inc. under contract to KSC), is discussed in detail. The paper describes the use of mechanical compliance in the docking, mating, and tracking-after-mating operations. The vision system computes six coordinates that define the position and orientation of a three dimensional target using data from a single CCD camera. The camera is mounted on a 6DOF robot arm. After target coordinates are computed, they are transmitted to a supervisory computer which controls the robot motion in real time. Details of the image processing algorithms, image processing hardware, and target configuration used in the vision system are discussed in the paper. The motion of the space vehicle relative to the service structure after mating led us to the development of a compliant system that allows enough displacement of the target relative to the camera so that tracking after mating may continue. This reduces internal stresses between flight and ground hardware.

Delcastillo, Eduardo Lopez↗
DOE OSTI2021

Trajectory Optimization via Unsupervised Probabilistic Learning On Manifolds

This report investigates the use of unsupervised probabilistic learning techniques for the analysis of hypersonic trajectories. The algorithm first extracts the intrinsic structure in the data via a diffusion map approach. Using the diffusion coordinates on the graph of training samples, the probabilistic framework augments the original data with samples that are statistically consistent with the original set. The augmented samples are then used to construct conditional statistics that are ultimately assembled in a path-planing algorithm. In this framework the controls are determined stage by stage during the flight to adapt to changing mission objectives in real-time. A 3DOF model was employed to generate optimal hypersonic trajectories that comprise the training datasets. The diffusion map algorithm identfied that data resides on manifolds of much lower dimensionality compared to the high-dimensional state space that describes each trajectory. In addition to the path-planing worflow we also propose an algorithm that utilizes the diffusion map coordinates along the manifold to label and possibly remove outlier samples from the training data. This algorithm can be used to both identify edge cases for further analysis as well as to remove them from the training set to create a more robust set of samples to be used for the path-planing process.

42 ENGINEERING↗
NASA NTRS2008

Aeroservoelastic Stability Analysis of the X-43A Stack

The first air launch attempt of an X-43A stack, consisting of the booster, adapter and Hyper-X research vehicle, ended in failure shortly after the successful drop from the National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) B-52B airplane and ignition of the booster. The stack was observed to begin rolling and yawing violently upon reaching transonic speeds, and the grossly oscillating fins of the booster separated shortly thereafter. The flight then had to be terminated with the stack out of control. Very careful linear flutter and aeroservoelastic analyses were subsequently performed as reported herein to numerically duplicate the observed instability. These analyses properly identified the instability mechanism and demonstrated the importance of including the flight control laws, rigid-body modes, structural flexible modes and control surface flexible modes. In spite of these efforts, however, the predicted instability speed remained more than 25 percent higher than that observed in flight. It is concluded that transonic shock phenomena, which linear analyses cannot take into account, are also important for accurate prediction of this mishap instability.

Pak, Chan-gi↗
DOE OSTI2026

Robotics for Energy Infrastructure Construction on Earth and Beyond: Pilot Study Exploring Level 3-4 Semi-Autonomous Installation of a Reduced-Scale Photovoltaic Power Plant Prototype: Preprint

There is a growing need to build energy infrastructure at massive scale on both Earth and in space. On Earth, increasing electricity demand from applications such as AI data centers is driving rapid expansion of power systems, while in space, long-duration missions such as lunar operations require reliable and resilient energy infrastructure that can be deployed quickly. These needs outpace the capabilities of conventional construction methods. Robotic construction offers a path to faster, lower-cost, and more consistent deployment, yet most existing approaches focus on automating individual tasks rather than end-to-end assembly. The Industrialized Construction Innovation (ICI) team at the National Laboratory of the Rockies (NLR), in collaboration with the Colorado School of Mines, has been investigating robotics and automation strategies for rapid end-to-end energy infrastructure construction. This paper presents a pilot study demonstrating level 3-4 semi-autonomous, end-to-end robotic assembly of a reduced-scale photovoltaic power plant prototype under terrestrial conditions. The approach combines hardware redesign of photovoltaic components for robotic compatibility, discrete-event and physics-based robotic simulations in synthetic environments, and sim-to-real deployment to execute end-to-end robotic assembly of[NM1.1][NM1.2] tracking components, structural members, photovoltaic modules, and embedded electrical connections. The pilot identifies key limitations like tolerance management, perception and localization, end effector capability, fastening methods, and multi-physics simulation fidelity, that must be addressed to enable end-to-end robotic assembly of full-scale energy infrastructure. The study highlights how these insights inform scaling strategies for terrestrial energy infrastructure and adaptation to unstructured and extreme environments, including off-world scenarios such as robotic assembly of vertical tall lunar towers.

14 SOLAR ENERGY↗
NASA NTRS1992

ROSAT data analysis with EXSAS

For the x-ray observatory ROSAT, data from survey and pointed mission phases taken with different focal plane instruments and according to a complex mission timeline have to be handled. Data analysis therefore puts high demands on appropriate software tools. With EXSAS - the Extended Scientific Analysis System developed with an effort of 20 man years by the German ROSAT Scientific Data Center - a comfortable system for the reduction of data from the ROSAT x-ray and XUV instruments has been made available. EXSAS comprises a large collection of application modules as typically required in analyzing data of this wavelength regime and runs as a specific context in the wide-spread ESO-MIDAS environment. EXSAS, completely written in FORTRAN 77, takes full advantage of all the standards used in MIDAS and therefore, reflects the same portability (different UNIX installations and VMS). If required, the FORTRAN code also enables users to adapt the software in an easy way to their specific needs. To maintain independence from the specifics of different operating systems also on the data input side, all ROSAT data redistributed in the widely accepted FITS format. Although EXSAS has been developed specifically for data analysis of the ROSAT instruments, its structural design is sufficiently general to serve equally well also data from other X-ray and XUV instruments. EXSAS analysis modules are grouped into 4 application packages dealing with Data Preparation and Instrument Correction, Spatial Analysis, Spectral Analysis and Timing Analysis. A special EXSAS header, read and updated by each application, maintains the general information transfer on the origin, the history and the parameter space of the data stored in tables and images. About 100 genuine commands (most of which offer several additional options) allow to interactively explore the functionality of the system. Up to now 40 institutes all over the world have requested the EXSAS software. Maintenance and regular updates of the software and the comprehensive documentation are provided by the ROSAT Scientific Data Center at Garching.

Zimmermann, H. U.↗