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DOE OSTI2024

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 σ point-source depth of g = 24.3, r = 23 . 9 , i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of − 0 . 0013 , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2003

Novel High Efficient Organic Photovoltaic Materials

Solar energy is a renewable, nonpolluting, and most abundant energy source for human exploration of a remote site or outer space. In order to generate appreciable electrical power in space or on the earth, it is necessary to collect sunlight from large areas and with high efficiency due to the low density of sunlight. Future organic or polymer (plastic) solar cells appear very attractive due to their unique features such as light weight, flexible shape, tunability of energy band-gaps via versatile molecular or supramolecular design, synthesis, processing and device fabrication schemes, and much lower cost on large scale industrial production. It has been predicted that supramolecular and nano-phase separated block copolymer systems containing electron rich donor blocks and electron deficient acceptor blocks may facilitate the charge carrier separation and migration due to improved electronic ultrastructure and morphology in comparison to polymer composite system. This presentation will describe our recent progress in the design, synthesis and characterization of a novel block copolymer system containing donor and acceptor blocks covalently attached. Specifically, the donor block contains an electron donating alkyloxy derivatized polyphenylenevinylene (RO-PPV), the acceptor block contains an electron withdrawing alkyl-sulfone derivatized polyphenylenevinylene (SF-PPV). The key synthetic strategy includes the synthesis of each individual block first, then couple the blocks together. While the donor block has a strong PL emission at around 560 nm, and acceptor block has a strong PL emission at around 520 nm, the PL emissions of final block copolymers are severely quenched. This verifies the expected electron transfer and charge separation due to interfaces of donor and acceptor nano phase separated blocks. The system therefore has potential for variety light harvesting applications, including high efficient photovoltaic applications.

Sun, Sam↗

NASA NTRS2014

Innovation in Deep Space Habitat Interior Design: Lessons Learned From Small Space Design in Terrestrial Architecture

Increased public awareness of carbon footprints, crowding in urban areas, and rising housing costs have spawned a 'small house movement' in the housing industry. Members of this movement desire small, yet highly functional residences which are both affordable and sensitive to consumer comfort standards. In order to create comfortable, minimum-volume interiors, recent advances have been made in furniture design and approaches to interior layout that improve both space utilization and encourage multi-functional design for small homes, apartments, naval, and recreational vehicles. Design efforts in this evolving niche of terrestrial architecture can provide useful insights leading to innovation and efficiency in the design of space habitats for future human space exploration missions. This paper highlights many of the cross-cutting architectural solutions used in small space design which are applicable to the spacecraft interior design problem. Specific solutions discussed include reconfigurable, multi-purpose spaces; collapsible or transformable furniture; multi-purpose accommodations; efficient, space saving appliances; stowable and mobile workstations; and the miniaturization of electronics and computing hardware. For each of these design features, descriptions of how they save interior volume or mitigate other small space issues such as confinement stress or crowding are discussed. Finally, recommendations are provided to provide guidance for future designs and identify potential collaborations with the small spaces design community.

Simon, Matthew A.↗

DOE OSTI2026

Gradient-informed Hamiltonian Monte Carlo for multicomponent CALPHAD model optimization and uncertainty quantification

CALPHAD model parameter optimization is inherently challenging due to non-smooth objective functions, high-dimensional parameter spaces, and the need for uncertainty quantification (UQ). Traditional weighted nonlinear least squares approaches are computationally efficient but local, whereas black-box global optimizers and ensemble Markov Chain Monte Carlo (MCMC) methods provide broader exploration at substantial computational cost. The objective of this work is to combine the global exploration capability of gradient-informed Hamiltonian Monte Carlo – specifically the No-U-Turn Sampler (NUTS) – with local deterministic refinement using BFGS to efficiently optimize multicomponent CALPHAD models with minimal manual intervention. Analytic gradients are computed via the Jansson derivative framework. The methodology is demonstrated on the Cr—Fe binary system and extended to the Cr—Fe—Ni ternary system with 32 degrees of freedom. For Cr—Fe, NUTS achieves comparable or superior optimality relative to ensemble MCMC while requiring over an order-of-magnitude fewer likelihood evaluations. Parameter uncertainties are quantified through NUTS sampling and propagated to thermodynamic observables using local expansion, demonstrating a novel modular approach that combines binary and ternary parameter subsets without requiring global relaxation. These results establish gradient-informed exploration as a scalable strategy for multicomponent CALPHAD optimization and provide a practical route towards efficient higher-order database development with quantified uncertainty.

36 MATERIALS SCIENCE↗

NASA NTRS2002

Efficient Design in a DC to DC Converter Unit

Space Flight hardware requires high power conversion efficiencies due to limited power availability and weight penalties of cooling systems. The International Space Station (ISS) Electric Power System (EPS) DC-DC Converter Unit (DDCU) power converter is no exception. This paper explores the design methods and tradeoffs that were utilized to accomplish high efficiency in the DDCU. An isolating DC to DC converter was selected for the ISS power system because of requirements for separate primary and secondary grounds and for a well-regulated secondary output voltage derived from a widely varying input voltage. A flyback-current-fed push-pull topology or improved Weinberg circuit was chosen for this converter because of its potential for high efficiency and reliability. To enhance efficiency, a non-dissipative snubber circuit for the very-low-Rds-on Field Effect Transistors (FETs) was utilized, redistributing the energy that could be wasted during the switching cycle of the power FETs. A unique, low-impedance connection system was utilized to improve contact resistance over a bolted connection. For improved consistency in performance and to lower internal wiring inductance and losses a planar bus system is employed. All of these choices contributed to the design of a 6.25 KW regulated dc to dc converter that is 95 percent efficient. The methodology used in the design of this DC to DC Converter Unit may be directly applicable to other systems that require a conservative approach to efficient power conversion and distribution.

Bruemmer, Joel E.↗

NASA NTRS2008

Electronic Components for use in Extreme Temperature Aerospace Applications

Electrical power management and control systems designed for use in planetary exploration missions and deep space probes require electronics that are capable of efficient and reliable operation under extreme temperature conditions. Space-based infra-red satellites, all-electric ships, jet engines, electromagnetic launchers, magnetic levitation transport systems, and power facilities are also typical examples where the electronics are expected to be exposed to harsh temperatures and to operate under severe thermal swings. Most commercial-off-the-shelf (COTS) devices are not designed to function under such extreme conditions and, therefore, new parts must be developed or the conventional devices need to be modified. For example, spacecraft operating in the cold environment of deep space carry a large number of radioisotope heating units in order to maintain the surrounding temperature of the on-board electronics at approximately 20 C. At the other end, built-in radiators and coolers render the operation of electronics possible under hot conditions. These thermal measures lead to design complexity, affect development costs, and increase size and weight. Electronics capable of operation at extreme temperatures, thus, will not only tolerate the hostile operational environment, but also make the overall system efficient, more reliable, and less expensive. The Extreme Temperature Electronics Program at the NASA Glenn Research Center focuses on research and development of electronics suitable for applications in the aerospace environment and deep space exploration missions. Research is being conducted on devices, including COTS parts, for potential use under extreme temperatures. These components include semiconductor switching devices, passive devices, DC/DC converters, operational amplifiers, and oscillators. An overview of the program will be presented along with some experimental findings.

Patterson, Richard L.↗

NASA NTRS2000

Getting Involved with the Discovery Program

NASA's Discovery Program represents the implementation of NASA Administrator Daniel Goldin's vision of 'faster, better, cheaper' planetary missions; encompasses a series of low-cost solar system exploration missions intended to accomplish high quality, focused planetary science investigations using innovative, streamlined, and efficient approaches to assure the highest science value for the cost; and aims to enhance our understanding of the solar system by exploring the planets, their moons and other small bodies, either by traveling to them or remotely from the vicinity of Earth. The objectives of this program include the following: (1) Provide exciting and important scientific data to the global community; (2) Pursue new and innovative ways of doing business; (3) Encourage technological development by designing and testing new technologies and transferring them to the private sector; (4) Increase public awareness of, and appreciation for, solar system exploration through exciting education and public outreach activities; (5) Support national education initiatives through mission-specific programs; and (6) Ensure participation of small disadvantaged businesses, women-owned businesses, HBCUs, and other minority educational institutions in procurements.

Asplund, Shari↗

NASA NTRS2024

Hybrid Thermally Efficient Core (HyTEC) HyTEC Phase 1 – Advanced Aerodynamics Final Report

The objective of the Hybrid Thermally Efficient Core (HyTEC) – Advanced High Pressure Turbine (HPT) Aerodynamics project is to develop technology for a compact core that contributes to significant fuel burn reductions of 5-10% over current generation technologies. To accomplish this, the HPT is incorporating a range of aerodynamic features and technologies to improve component efficiency and provide favorable systems level trades. In particular, this project explored low solidity airfoils, advanced tip treatments, platform contouring, and advanced ceramic matrix composite (CMC) Stage 2 Nozzle (S2N) airfoils to eliminate the need for post throat cooling in a compact core environment. The maturation of these technology areas is expected to provide a significant improvement in component efficiencies, and consequently reductions in fuel burn, over the current state of the art (SoA). To mature these technologies to Technology Readiness Level (TRL) 4, a test campaign was performed that consisted of four tests at three facilities. TRL 3 testing was performed in the CW22 linear cascade at NASA Glenn for blade and nozzle technologies, TRL 4 nozzle testing was performed at GE Aerospace (GEA) Test Cell A8, and TRL 4 blade testing was performed at the Notre Dame Turbomachinery Laboratory (NDTL) using the Transonic Research Turbine (TRT) rig. Low solidity was successfully demonstrated to a TRL 4 level. Low solidity nozzles showed benefits in line with pre-project expectations, while low solidity blades were shown to have an aerodynamic penalty. Crucially, this program only considered the aerodynamic losses, and systems trades such as reductions in cooling flows are expected to continue to make low solidity blades a net positive. By successfully quantifying the aerodynamic performance in this project, these trades can be conducted to determine where in the engine architecture low solidity blades will contribute positively to system operation. Platform contouring was demonstrated to a TRL 4 level, with performance in line with the lower end of the pre-project expected range. Advanced tip treatments performance levels were indeterminate, showing the expected improvements to flow physics but with a performance level confounded by several rig issues including whirl mode induced variation in tip clearance. The elimination of post-throat cooling on the S2N was successfully demonstrated to provide a performance benefit, however that benefit was approximately half the level that was expected in pre-project predictions. Overall, the technology maturation plan for HyTEC Phase 1 was successful, bringing the suite of technologies to TRL 4.

High pressure turbine↗

NASA NTRS2013

Human Exploration of Near-Earth Asteroids

A major goal for NASA's human spaceflight program is to send astronauts to near-Earth asteroids (NEA) in the coming decades. Missions to NEAs would undoubtedly provide a great deal of technical and engineering data on spacecraft operations for future human space exploration while conducting in-depth scientific examinations of these primitive objects. However, before sending human explorers to NEAs, robotic investigations of these bodies would be required to maximize operational efficiency and reduce mission risk. These precursor missions to NEAs would fill crucial strategic knowledge gaps concerning their physical characteristics that are relevant for human exploration of these relatively unknown destinations. Dr. Paul Abell discussed some of the physical characteristics of NEOs that will be relevant for EVA considerations, reviewed the current data from previous NEA missions (e.g., Near-Earth Asteroid Rendezvous (NEAR) Shoemaker and Hayabusa), and discussed why future robotic and human missions to NEAs are important from space exploration and planetary defense perspectives.

Abell, Paul↗

NASA NTRS2013

Human Exploration of Near-Earth Asteroids

Abell, Paul↗

DOE OSTI2024

Semi-automatic image annotation using 3D LiDAR projections and depth camera data

Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

Direct writing of PVBVA/Ti 3 C 2 T x (MXene) triboelectric nanogenerators for energy harvesting and sensing applications

Triboelectric nanogenerators (TENGs) have gained recognition for their potential to convert mechanical energy into electrical energy, making them attractive for applications in healthcare, robotics, and human-device interfaces. However, many TENG devices rely on fluorinated polymers for high charge generation and involve complex fabrication processes, which limit their practicality and environmental sustainability. Here, we developed an eco-friendly composite of poly (vinyl butyral-co-vinyl alcohol-co-vinyl acetate) (PVBVA) and Ti 3 C 2 T x MXene for extrusion printing onto aluminum foil substrates, enabling the additive manufacturing of TENGs. Experimental results indicate that integrating 5.5 mg mL -1 of MXene (P-MX 5.5) into PVBVA resulted in a power density of 760 mW·m -2 , with simultaneous improvements in open-circuit voltage (129 %) and short-circuit current (250 %), demonstrating enhanced charge transfer efficiency. Beyond aluminum foil-based devices, we further explored the fully printed P-MX 5.5 TENG by utilizing silver ink electrodes, eliminating the need for aluminum foil. This fully printed, flexible TENG was successfully used for real-time human motion sensing, demonstrating its ability to detect activities such as walking, running, knee bending, and jumping. Collectively, our additively manufactured and sustainable PVBVA-MXene TENG composites, including both aluminum-based and fully printed versions, show promise for future energy harvesters, sensors, wearable electronics, healthcare, and robotic applications.

Additive manufacturing↗

DOE OSTI2024

The cluster decomposition of the configurational energy of multicomponent alloys

Abstract The cluster expansion method (CEM) is a widely used lattice-based technique in the study of multicomponent alloys. Despite its prevalent use, a clear understanding of expansion terms is lacking. We present a modern mathematical formalism of the CEM and introduce thecluster decomposition—a unique and basis-independent decomposition for functions of the atomic configuration in a crystal. We identify the cluster decomposition as an invariant ANOVA decomposition; and demonstrate how functional analysis of variance and sensitivity analysis can be used to interpret interactions among species. Furthermore, we show how the mathematical structure of the cluster decomposition enables numerical evaluation that scales with the number of clusters and is independent of the number of species. Overall, our work enables rigorous interpretations of interactions among species, provides opportunities to explore parameter estimation beyond linear regression, introduces a numerical efficient implementation, and enables analysis of cluster expansions based on established mathematical and statistical principles.

Chemistry↗

DOE OSTI2024

Sparse Linear Solvers for Large-scale Electromagnetic Transient Simulations

Linear solvers form the basis for electromagnetic transient (EMT) simulations. There is a need to speed up EMT simulations as larger regions are analyzed using EMT simulations. For the same, the performance of linear solvers plays an important role. Exploiting the sparsity of the matrices generated in EMT simulations could assist with speed-up. Scalability is also crucial as power grids expand, demanding solutions capable of accommodating the increasing system size. Recent studies from the North American Electric Reliability Corporation (NERC) increasingly emphasize that EMT simulation models of the power grid will grow larger with the inclusion of power electronics components. Parallelisms in sparsity patterns exploit modern central processing units (CPUs), multi-core CPUs, and graphics processing units (GPUs) architectures in sparse solver designs. Therefore, this paper explores publicly available existing linear solvers and investigates their efficiency in large-scale power grid simulations. A large-scale power grid is developed by increasing the size of the IEEE 39 bus test system to up to 39000 bus systems.

Hsu, Kuan-Chieh↗

DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗

NASA NTRS1973

The NASA-Lewis terrestrial photovoltaics program

Research and technology efforts on solar cells and arrays having relevance to terrestrial uses are outline. These include raising cell efficiency, developing the FEP-covered module concept, and exploring low cost cell concepts. Solar cell-battery power systems for remote weather stations have been built to demonstrate the capabilities of solar cells for terrestrial applications.

Bernatowicz, D. T.↗

NASA NTRS1974

The NASA-Lewis terrestrial photovoltaics program

Those parts of the present NASA-Lewis research and technology effort on solar cells and arrays having relevance to terrestrial uses are outlined. These include raising cell efficiency, developing the FEP-covered module concept, and exploring low-cost cell concepts. Solar cell-battery power systems for remote weather stations have been built to demonstrate the capabilities of solar cells for terrestrial applications.

Bernatowicz, D. T.↗

NASA NTRS1984

Advanced Data Structure and Geographic Information Systems

The current state of the art in specified areas of Geographic Information Systems GIS technology is examined. Study of the question of very large, efficient, heterogeneous spatial databases is required in order to explore the potential application of remotely sensed data for studying the long term habitability of the Earth. Research includes a review of spatial data structures and storage, development of operations required by GIS, and preparation of a testbed system to compare Vaster data structure with NASA's Topological Raster Structure.

Peuquet, D.↗