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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 NTRS2013

Aerodynamic Characterization of a Thin, High-Performance Airfoil for Use in Ground Fluids Testing

The FAA has worked with Transport Canada and others to develop allowance times for aircraft operating in ice-pellet precipitation. Wind-tunnel testing has been carried out to better understand the flowoff characteristics and resulting aerodynamic effects of anti-icing fluids contaminated with ice pellets using a thin, high-performance wing section at the National Research Council of Canada Propulsion and Icing Wind Tunnel. The objective of this paper is to characterize the aerodynamic behavior of this wing section in order to better understand the adverse aerodynamic effects of anti-icing fluids and ice-pellet contamination. Aerodynamic performance data, boundary-layer surveys and flow visualization were conducted at a Reynolds number of approximately 6.0 x 10(exp 6) and a Mach number of 0.12. The clean, baseline model exhibited leading-edge stall characteristics including a leading-edge laminar separation bubble and minimal or no separation on the trailing edge of the main element or flap. These results were consistent with expected 2-D aerodynamics and showed no anomalies that could adversely affect the evaluation of anti-icing fluids and ice-pellet contamination on the wing. Tests conducted with roughness and leading-edge flow disturbances helped to explain the aerodynamic impact of the anti-icing fluids and contamination. The stalling characteristics of the wing section with fluid and contamination appear to be driven at least partially by the effects of a secondary wave of fluid that forms near the leading edge as the wing is rotated in the simulated takeoff profile. These results have provided a much more complete understanding of the adverse aerodynamic effects of anti-icing fluids and ice-pellet contamination on this wing section. This is important since these results are used, in part, to develop the ice-pellet allowance times that are applicable to many different airplanes.

Broeren, Andy P.↗
NASA NTRS2013

Controlling Tensegrity Robots Through Evolution

Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball-shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400 percent better than a hand-coded solution, while the multi-agent evolution performs 800 percent better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.

Robot↗
NASA NTRS2016

Boeing Smart Rotor Full-scale Wind Tunnel Test Data Report

A full-scale helicopter smart material actuated rotor technology (SMART) rotor test was conducted in the USAF National Full-Scale Aerodynamics Complex 40- by 80-Foot Wind Tunnel at NASA Ames. The SMART rotor system is a five-bladed MD 902 bearingless rotor with active trailing-edge flaps. The flaps are actuated using piezoelectric actuators. Rotor performance, structural loads, and acoustic data were obtained over a wide range of rotor shaft angles of attack, thrust, and airspeeds. The primary test objective was to acquire unique validation data for the high-performance computing analyses developed under the Defense Advanced Research Project Agency (DARPA) Helicopter Quieting Program (HQP). Other research objectives included quantifying the ability of the on-blade flaps to achieve vibration reduction, rotor smoothing, and performance improvements. This data set of rotor performance and structural loads can be used for analytical and experimental comparison studies with other full-scale rotor systems and for analytical validation of computer simulation models. The purpose of this final data report is to document a comprehensive, highquality data set that includes only data points where the flap was actively controlled and each of the five flaps behaved in a similar manner.

Full-Scale Wind Tunnel↗
NASA NTRS2015

High-Power, High-Thrust Ion Thruster (HPHTion)

Advances in high-power photovoltaic technology have enabled the possibility of reasonably sized, high-specific power solar arrays. At high specific powers, power levels ranging from 50 to several hundred kilowatts are feasible. Ion thrusters offer long life and overall high efficiency (typically greater than 70 percent efficiency). In Phase I, the team at ElectroDynamic Applications, Inc., built a 25-kW, 50-cm ion thruster discharge chamber and fabricated a laboratory model. This was in response to the need for a single, high-powered engine to fill the gulf between the 7-kW NASA's Evolutionary Xenon Thruster (NEXT) system and a notional 25-kW engine. The Phase II project matured the laboratory model into a protoengineering model ion thruster. This involved the evolution of the discharge chamber to a high-performance thruster by performance testing and characterization via simulated and full beam extraction testing. Through such testing, the team optimized the design and built a protoengineering model thruster. Coupled with gridded ion thruster technology, this technology can enable a wide range of missions, including ambitious near-Earth NASA missions, Department of Defense missions, and commercial satellite activities.

Peterson, Peter Y.↗
NASA NTRS2016

Levitation Technology in International Space Station Research

The International Space Station (ISS) is a unique multidisciplinary orbiting laboratory for science and technology research, enabling discoveries that benefit life on Earth and exploration of the universe. ISS facilities for containerless sample processing in Materials Science experiments include levitation devices with specimen positioning control while reducing containment vessel contamination. For example, ESA's EML (ElectroMagnetic Levitator), is used for melting and solidification of conductive metals, alloys, or semiconductors in ultra-high vacuum, or in high-purity gaseous atmospheres. Sample heating and positioning are accomplished through electromagnetic fields generated by a coil system. EML applications cover investigation of solidification and microstructural formation, evaluation of thermophysical properties of highly reactive metals (whose properties can be very sensitive to contamination), and examination of undercooled liquid metals to understand metastable phase convection and influence convection on structural changes. MSL utilization includes development of novel light-weight, high-performance materials. Another facility, JAXA's ELF (Electrostatic Levitation Furnace), is used to perform high temperature melting while avoiding chemical reactions with crucibles by levitating a sample through Coulomb force. ELF is capable of measuring density, surface tension, and viscosity of samples at high temperatures. One of the initial ELF investigations, Interfacial Energy-1, is aimed at clarification of interfacial phenomena between molten steels and oxide melts with industrial applications in control processes for liquid mixing. In addition to these Materials Science facilities, other ISS investigations that involve levitation employ it for biological research. For example, NASA's "Magnetic 3D Culturing and Bioprinting" investigation uses magnetic levitation for three-dimensional culturing and positioning of magnetized cells to generate spheroid assemblies for biomedical applications. Levitation is also used as a modeled microgravity ground analog in the NASA OsteoOmics ISS investigation, which tests whether magnetic levitation accurately simulates microgravity conditions by studying gravitational regulation of osteoblast and osteoclast genomics and metabolism. Elucidating the cellular mechanisms of bone loss in microgravity contributes to the understanding of bone loss in medical disorders on Earth, which may lead to development of preventive or therapeutic countermeasures. Thus, the ISS state-of-the-art laboratory offers various levitation capability platforms with applications for innovative research in Materials and Life Sciences disciplines, with benefits for humanity.

Guinart-Ramirez, Y.↗
NASA NTRS2018

Miniature Exercise Device-2 (MED-2): Preliminary ISS Evaluation Results for a Compact Motorized Resistive and Aerobic Rowing Exercise Device

Future human missions beyond Low Earth Orbit (LEO) will require onboard equipment to provide exercise capabilities for the crew to counter the adverse physiological effects of long-duration microgravity. To accomplish this within the physical constraints of a space vehicle or transit module, a single miniature device that provides both resistive and aerobic exercise modalities is required. To meet this need, Johnson Space Center’s (JSC) Software, Robotics, and Simulation Division (ER) developed the Miniature Exercise Device-2 (MED-2). MED-2 integrates a torque-controlled servomotor and a series-elastic actuator to provide highly-controllable load profiles and a large magnitude output performance in a very small package. This innovative technology is derived from years of JSC/ER design, development and operational experience with cutting-edge robotics, motor controllers, software and actuator/sensor miniaturization, including Robonaut 2 and MED-1. MED-2 was presented at the 2016 ISS R&D Conference. This is an update now that the last of six crewmembers will have completed planned MED-2 sessions on the International Space Station (ISS) in May 2018.Current state-of-the-art ISS exercise equipment consists of two treadmills, a resistive exercise device and two cycle ergometers with a total mass of several thousand pounds and a total volume of several cubic yards. This equipment has proven vital to mitigate the musculoskeletal and cardiovascular degradation effects of microgravity. However, due to the large operational volume and mass of these ISS devices, tailoring them for smaller vehicles, such as Orion, is not possible. In addition, each of the current ISS devices targets a single specific modality. Compared to the existing spaceflight (and even terrestrial) exercise equipment, MED-2 is a new archetype altogether. The combined features of compact size, multi-modality and high-performance is attributable to its innovative series elastic actuator and motor controller. Following its arrival on ISS in 2016, MED-2 was evaluated in two parts. The first and shorter evaluation was an engineering functional checkout of the hardware. As this was a novel exercise device previously never used on ISS, the initial checkout assessed the operation of the hardware and ensured the motion and dynamic range of the crew did not present any collision or other hazards. The second portion of the study collected the heart rates, kinematics and utilized operational volumes of six astronauts to determine the quality of both the resistive and aerobic exercise modalities as delivered by MED-2. Investigators from JSC Biomedical Research and Environmental Science Division (SK) and Glenn Research Center are currently evaluating the data and preparing preliminary results. For the resistive exercise modality, MED-2 demonstrated a range of constant resistive loads from 10-150 lbf. With a displacement range of 84 inches, the MED-2 accommodates users from 5th percentile Japanese female through 95th percentile American male for all of its certified exercises. The displacement measurement accuracy has also been verified within 2.5 percent full range. The crew was able to successfully perform all prescribed resistive exercises, except Goblet Squats which were not feasible with a constant load profile. For the aerobic exercise modality, MED-2 simulated a rowing motion with prescribed and user-selected resistance levels. It has demonstrated rates up to 60 strokes per minute on the ground. MED-2 loads and displacements performance are the same as those cited for the resistive modality. Although each of the crew was able to perform the prescribed aerobic rowing sets, there was considerable variability in the rowing motion among different crewmembers. Also, as expected, the crew was unable to get the full benefits of a typical terrestrial rowing stroke because the current configuration does not allow the user to reach past their feet. These observations have already informed the requirements for other microgravity rowing devices currently in development. One of the unique features of the MED-2 device is the intuitive touch-screen control system. This One Portal graphical user interface (GUI) was developed based on JSC/ER’s heritage knowledge and experience of developing and sustaining the current ISS exercise equipment. Through this interface, the crew easily performed prepared prescriptions as well as had the ability to adjust exercise modality, load and other exercise details such as number of repetitions and number of sets. This touch-screen and GUI fulfilled the MED-2 project goal to simplify the interaction between the user and the device. Furthermore, the extent to which MED-2 utilizes a touchscreen and GUI to control exercise equipment is unmatched among the existing ISS exercise devices. As a motorized device, MED-2 technology can provide a customizable force profile that can be varied as a function of strap displacement, strap velocity or a combination of these and other variables. During 2017, JSC/ER developed and flight-certified a resistive exercise algorithm that mimics the 1-G inertial effects of free-weights and enables adjustable eccentric-to-concentric loading ratios. Subsequent development will explore varying the load profiles and incorporating additional exercises beyond the current list of certified movements.

Exercise↗
NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.

Yamakov, Vesselin I.↗
NASA NTRSDate not supplied

Solar White Thermal Coating for Cryogenic Propulsion Systems

NASA is currently conducting research into the potential of storing cryogenic fluid in low Earth orbit (LEO). Having cryogenic propellant readily available for high-performance propulsion systems can be very beneficial for deep space missions in the near future. One of the key challenges to storing cryogenic fluid in LEO is minimizing boil-off. To address the challenge, NASA is evaluating new concepts in thermal insulation. One recent experimental study evaluated the feasibility of using Yttrium Oxide (Y2O3), formed into tiles or spray coating that can potentially be used as a thermal coating for cryogenic propellant storage applications in deep space. Due to its temperature and wavelength dependent optical properties, this “solar white” material can reflect a vast majority of the Sun’s radiative energy while having a very high infrared emissivity for rejecting heat to deep space. As a part of the material development and proof of concept testing, multiple tests have been run at KSC and GRC to demonstrate the performance of the material. At GRC, the tests were run using the Deep Space Solar Simulator (DS3) which contains a thermal vacuum chamber in which the solar white sample was exposed to a deep space environment (< 10 K, optically dense walls) while under full illumination via solar lamp. In order to improve the use of the test results and apply them to spacecraft, there is a need to be able to model the material properties within NASA’s standard thermal modeling tools. As such, it was set out to verify a thermal model of one of the experiments using Thermal Desktop with Sinda. The thermal model of the DS3 test setup includes the solar white sample along with a solar lamp simulator capable of outputting heat at varying wavelengths. The model was developed in order to validate the test results and also to help predict results that will be obtained in future tests. This paper will review the modeling methods and thermal analysis results for various test cases that were run within the DS3 facility.

Cryogenic↗
NASA NTRSDate not supplied

Autonomous Spacecraft Attitude Control Using Deep Reinforcement Learning

While machine learning and spacecraft autonomy continue to gain research interest, significant work remains to be done in efficiently applying modern machine learning techniques to problems in space ight. This study presents a framework for deriving a discrete neural spacecraft attitude controller using reinforcement learning, a paradigm of machine learning, without the need for high-performance computing. The developed attitude controller is an approximately time-optimal solution to a highly constrained control problem, able to achieve well above industry-standard pointing accuracies. Control examples are also presented of the agent performing large-angle spacecraft slews in the developed simulation environment and future extensions of this work are discussed.

ATAP↗
NASA NTRSDate not supplied

NASA and Blue Origin Collaborative Assessment of Precision Landing Algorithms and Computing

NASA’s Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project is developing sensor, algorithm, and compute technologies for precision landing and hazard avoidance. These technologies are being tested as an integrated Precision Landing and Hazard Avoidance (PL&HA) system on Blue Origin’s New Shephard suborbital vehicle. A key goal for the computing element of this technology development is to characterize the performance of the SPLICE software workloads on the project’s Descent and Landing Computer (DLC). The DLC is a multi-core processor designed as a surrogate for NASA’s High-Performance Space Computer (HPSC). Measurements of the SPLICE workload performance on the DLC provides NASA insight on how PL&HA capabilities will perform on the HPSC, and guidance on how the SPLICE algorithms can be implemented to best utilize the DLC platform. This insight can also be used to derive requirements to guide trade studies on candidate computing architectures, for use on platforms like Blue Moon. NASA and Blue Origin are collaborating under an agreement to pursue this mutual benefit. Performance metrics collected are based on measurement of common compute resources such as percentage used of memory bandwidth, I/O utilization, interrupt latency, and kernel vs. user space code residency. Where possible existing performance counters and metrics that are part of the operating system kernel are used. As the design has a significant FPGA component, performance counters are identified and instantiated in the fabric to measure DMA performance and interface metrics. Collection of metrics is performed on the DLC with a representative workload that simulates a full landing cycle of the Blue Origin New Shepard vehicle. Consideration is given to the other compute implementations and whether they can run SPLICE algorithms at the same rate and with the same latency as the DLC. One option being considered is the use of a RISC-V soft core instantiated in a radiation resilient FPGA fabric such as the Xilinx KU60. Select algorithms from the SPLICE code will be run for comparison with the DLC. This paper describes how the DLC is instrumented to collect performance measurements of the SPLICE workloads, preliminary results from these measurements, and their implications on SPLICE algorithm implementation. The results of experimentation to derive candidate requirements for architecture trades on a PL&HA computing system are also presented.

computer performance↗
NASA NTRS2017

Design and testing of high-performance mini-channel graphite heat exchangers in thermoelectric energy recovery systems

Recent national energy usage studies by Lawrence Livermore National Laboratory in 2015 [1] show that there is approximately 59 Quads (1015 Btu’s) of waste thermal energy throughout various industrial, residential, power generation, and transportation sectors of the U.S. economy. Thermoelectric energy recovery is one important technology for recovering this waste thermal energy in high-temperature industrial, transportation and military energy systems. Thermoelectric generator (TEG) systems in these applications require high performance hot-side and cold-side heat exchangers to provide the critical temperature differential and transfer the required thermal energy. High performance hot-side heat exchangers in these systems are often metal-based due to requirements for high-temperature operation, strength at temperature, corrosion resistance, and chemical stability. However, the generally selected metal-based hot-side heat exchangers (i.e., Inconels, Stainless Steels) suffer from low thermal conductivity, high thermal expansion, and high density, which degrades their thermal performance, leads to high thermal-expansion-driven stresses, and creates relatively high mass/high volume (i.e., low power density) TEG systems that are then difficult to fabricate and integrate into viable energy recovery systems. This paper describes the design and testing of a new, high-temperature minichannel graphite heat exchanger designed for operation up to 500C that is a critical element of a high-power-density TEG power system for aircraft energy recovery. This high-performance graphite heat exchanger represents a new state-of-the-art standard in high-temperature heat exchangers for TEG systems, which provides higher thermal transport, less thermal expansion at operation, lower system level stresses on TE components, and a lighter weight TEG system. This new heat exchanger creates a new design paradigm in TEG system design for terrestrial energy recovery and potential NASA technology infusion into terrestrial energy system applications. This paper will present and discuss the key heat transfer, pressure drop, pumping power analyses and design tradeoffs that created this unique design. Heat transfer and pressure drop modeling was performed with both empirical models based on known heat transfer and friction factor correlations and COMSOL thermal/fluid dynamic modeling of the graphite heat exchanger structure. We will also discuss resulting thermal transport and heat fluxes predicted at the TEG interface level. Heat exchanger performance testing was performed under simulated operating conditions and correlation with test data at the anticipated operating temperature conditions will be presented and discussed.

Shevade, Abhijit↗
NASA NTRS2022

Predicting Unreinforced Fabric Mechanical Behavior with Recurrent Neural Networks

Unreinforced woven fabrics are widely employed in various high-performance applications, including parachute deployment systems, airbags, and ballistic armor. The analysis of such materials is inherently complex due to the multiscale structure of these materials, and the dependence of macroscale behavior on changes that occur at lower scales. Previously, NASA’s Multiscale Analysis Tool (NASMAT) showed its capability in predicting unreinforced fabric behavior at the macroscale by capturing finite rotations that occur at the mesoscale. Though effective, the tool can face high computational cost for large, complex problems, motivating the need for the development of a surrogate model that can capture the same behavior. A recurrent neural network (RNN) was developed and trained on virtual NASMAT data to mimic the physics-based solutions while improving the computational runtime. The architecture of the RNN to best simulate the fabric behavior was carefully crafted based on heuristic knowledge of predicting physics-based temporal data, manual hyperparameter case studies, and Hyperband optimization.. The resultant model was able to predict a variety of stress-strain curves for fabrics with different mesoscale geometries, and was further validated by comparing to experimental data for the K706 style Kevlar plain-weave fabric, demonstrating the ability of the model to effectively capture the geometric changes in the fabric without explicitly calculating them, as is done in NASMAT. Furthermore, the tool showed its ability to improve on the runtime by a factor of 10 for fabric solutions compared to the multiscale tool, which would further enable the simulation of complex loading scenarios on unreinforced fabrics.

Fabric↗
NASA NTRSDate not supplied

NASA and Blue Origin’s Flight Assessment of Precision Landing Algorithms Computing Performance

NASA’s Safe and Precise Landing - Integrated Capabilities Evolution (SPLICE) project continues NASA’s work in the development and testing of technologies for Precision Landing and Hazard Avoidance (PL&HA). This paper presents results characterizing how SPLICE flight software utilizes the shared computing resources of the Descent Landing Computer (DLC), one of the PL&HA technologies under development. The SPLICE technologies are being tested as an integrated payload on Blue Origin’s New Shepard suborbital vehicle. The results presented in this paper are measured by applications running in and with the flight software both in flight, and in a high-fidelity Hardware-in-the-Loop (HWIL) simulation environment. Linux utilities to measure performance are also executed from the command line in the HWIL configuration. Performance measurements of the SPLICE workloads executing on the DLC provide insight on how efficiently the software is utilizing the DLC resources. Examples of how these measurements have guided improvements in the flight code are presented. In addition, the DLC uses a commercial processor as a surrogate for NASA’s High-Performance Spaceflight Computing (HPSC) processor. This work provides insight on how an HPSC system may perform delivering PL&HA capabilities on a future mission. The measurements also can be used to infer architectural requirements for PL&HA capabilities, informing the HPSC project and other flight computer development efforts. Examples of the measurements collected include processor utilization, I/O bandwidth, cache and branch misses, and application profiles.

Precision Landing↗
NASA NTRSDate not supplied

Autonomous In-space Construction, Maintenance, and Reconfiguration Using Programmable Meta-Material

NASA ARC's Coded Structures Laboratory (CSL) is developing autonomous construction, maintenance, and reconfiguration technologies to meet long-duration and deep space infrastructure needs, in accordance with long-term NASA goals of "in-space reliance" and "mass-less exploration." We seek to achieve these capabilities by utilizing a "programmable meta-material" approach that integrates emerging advances in materials (mechanical meta-materials), manufacturing (cooperative mobile robotics), and autonomy (multi-agent planning algorithms). Through the ARMADAS project, we have shown assembly of high-performance engineered cellular materials using multiple cooperating mobile robotic assemblers. In this paper, we describe how such a programmable meta-material architecture may shift the paradigm of how we design, build, manufacture, and operate future space infrastructure and assets. The core of a programmable meta-material architecture consists of 3 main technology sub-areas: the structure, the assembly agents, and the assembly algorithms. We co-design these systems to ensure an adaptable system that can create and reconfigure structures from a base set of building block components. From this core technology, we can branch out and expand the capability of the system through additional secondary component types and robotic agents to perform activities such as inspections, maintenance, repair, payload installation, or perform power and communications interconnect. As these technologies mature, future designers will be able to utilize the system to rapidly integrate and operate assets in space or on planetary surfaces from a set of well-tested part library, or create their own modules to integrate into the system. A core trait to the development of this system is the automation approach. Because of the modular and functional discrete (pixel-like) nature of the structural system, a diverse set of powerful algorithms for analysis, planning, and simulation can be adapted and leveraged to optimize construction, maintenance, and dynamic reorganization (as hardware with programmable form and function). With an ability to free the design space from launch vehicle constraints and fundamentally shift how a mission is designed and conducted, we discuss the influence of a programmable meta-material architecture on mission design, build, and operations. For the "design phase", we discuss project lifecycle effects, costs, time, and performance. For the "build phase", we discuss reusability, ISRU, manufacturing, material logistics, and scalability. And for "operations", we discuss autonomy, maintenance and upgrades, reliability, and reconfiguration. Autonomy and modularity are the primary enabling traits of this system. Engineering systems that utilize a modular and reconfiguration building block approach such as digital communication and computation systems, currently lead all other areas of technology in size and complexity scalability. NASA is extending the benefits and flexibility of digital systems to hardware systems, to optimize materials lifecycle management and expand our space exploration mission capabilities.

in space assembly↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
DOE OSTI2024

Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems

Active learning algorithms, integrating machine learning, quantum computing and optics simulation in an iterative loop, offer a promising approach to optimizing metamaterials. However, these algorithms can face difficulties in optimizing highly complex structures due to computational limitations. High-performance computing (HPC) and quantum computing (QC) integrated systems can address these issues by enabling parallel computing. In this study, we develop an active learning algorithm working on HPC-QC integrated systems. We evaluate the performance of optimization processes within active learning (i.e., training a machine learning model, problem-solving with quantum computing, and evaluating optical properties through wave-optics simulation) for highly complex metamaterial cases. Our results showcase that utilizing multiple cores on the integrated system can significantly reduce computational time, thereby enhancing the efficiency of optimization processes. Therefore, we expect that leveraging HPC-QC integrated systems helps effectively tackle large-scale optimization challenges in general.

Kim, Seongmin↗
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2025

Development of Innovative Non-Concentrated Alloy (NCA) Fuel Cladding for Advanced Nuclear Reactors

As the nuclear energy sector advances toward next-generation reactors, the need for high-performance fuel cladding materials has become increasingly urgent. Traditional alloys like zirconium and stainless steel are reaching their performance limits under higher temperatures, more corrosive coolants, and extended irradiation. This report presents the development of a new class of fuel cladding materials based on Non-Concentrated Alloys (NCAs) ? multi-element systems designed to deliver enhanced mechanical strength, corrosion resistance, and radiation tolerance. Through a combination of computational modeling (Computer Coupling of Phase Diagrams and Thermochemistry: CALPHAD), simulation-guided alloy selection, and experimental fabrication via arc melting and spark plasma sintering (SPS), three strategic alloy design paths were explored: (1) FeCrAl-based NCAs, (2) refractory-lean neutron-efficient alloys, and (3) equimolar high-entropy compositions. Microstructural analysis confirmed the formation of stable body-centered cubic BCC_A2 phases, while mechanical testing demonstrated hardness values significantly exceeding those of conventional cladding materials. The results highlight the tunability of NCA systems and their potential for balancing strength and ductility ? a critical consideration for in-reactor performance. Looking forward, future work will focus on thermomechanical optimization, CALPHAD refinement, and benchmarking against industry standards to enable scalable deployment. This work not only advances the science of nuclear materials but also supports broader goals in nuclear safety, performance, and nuclear energy innovation.

36 - MATERIALS SCIENCE↗