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Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS)

To meet the needs of future deep space exploration, NASA is interested in large-scale hardware systems in the agency’s thrust areas of solar power, communications, habitats and science interests. Scalable in-space assembly of physical systems is critical to massless exploration and in-space reliance goals. The ARMADAS project demonstrates the autonomous assembly of digital materials and structures. This provides automation technologies with potential for meeting long duration and deep space infrastructure needs, such as construction and maintenance of long duration spaceport, surface infrastructure, and habitat scale systems. Project demonstrations to date include a system that can fit into a small satellite-sized payload, which automatically assembles into primary structures, such as a small habitat module or array/antenna, using onboard robotic assemblers.

In Space Assembly↗

NASA NTRS2005

System concepts for transmit arrays of parabolic antennas for deep space uplinks

Phased arrays of parabolic antennas are a potentially lower-cost way to provide uplink transmission to distant spacecraft, compared to the 34-m and 70-m antennas now used by the NASA Deep Space Network. A large transmit array could provide very high EIRP when needed for spacecraft emergencies, such as the equivalent of 1 MW radiated from a 70-m antenna. Cost-effectiveness is realized by dividing the array into smaller arrays to provide routine support to many spacecraft simultaneously. The antennas might be as small as 12-m in diameter, with as many as 100 antennas covering an area of 0.5 km to 1 km in extent. Such arrays present significant technical challenges in phase alignment, which must be maintained at close to 1 mm. The concept requires a very stable system with accurately known antenna phase center locations. The system is first calibrated by transmitting from all antennas, and observing the signals at a target located in the far fields of the individual antennas. The antennas are then pointed to the operational targets, with the signal phases and time delays set to reinforce in the target directions. This requires accurate knowledge of the target directions and calculation of the required phases. The system must be phase-stable for all directions and over the time between calibrations, which is desired to be at least one day. In this paper, a system concept is presented, the major error sources are identified, a rough error budget is established, and key elements of the system are discussed. A calibration method is recommended which uses satellites as radar targets. The performance goal is to achieve a combining loss of less than 0.2 dB in good weather, and of less than 1 dB in all but extremely bad weather.

Effective Isotropic Radiated Power (EIRP)↗

DOE OSTI2022

Data-Centric Development of Lignin Structure–Solubility Relationships in Deep Eutectic Solvents Using Molecular Simulations

Lignin is a natural source of aromatic chemicals with significant potential as an abundant, renewable feedstock for value-added products. Deep eutectic solvents (DES)–solvents composed of a hydrogen bond donor (HBD) and acceptor (HBA) in varying ratios–have emerged as a highly tunable class of solvents for lignin solubilization. However, the variety of possible DES compositions and limited molecular-scale understanding of lignin solubility makes solvent selection a challenge without laborious trial-and-error experimentation. To address these challenges, we use classical molecular dynamics (MD) simulations to study the interactions of lignin model compounds with various DES–water systems. Quantitative parameters (descriptors) were calculated by postprocessing the MD results and used to train a regression model that predicts experimentally determined solubilities of lignin model compounds. This approach revealed that the most important descriptors of solubility are the system temperature, solute hydrophilicity, and metrics quantifying hydrogen bonding. Maximizing the interactions between solute–HBD (hydrophobic group), water–HBD (hydrophilic group), and water–HBA molecules led to the highest model compound solubility. Our results support a hydrotropic mechanism in which extensive DES–water hydrogen bonding and favorable HBD interactions with the solute promote high solubility. We applied the regression model derived using model compounds to predict the solubility of representative lignin oligomers. The model predicted lignin oligomers’ solubilities in good agreement with experiments, indicating that the simulations of model compounds can be extended to predict the solubility of larger lignin compounds across a range of solvent compositions and temperatures. Furthermore, these findings provide new molecular-scale insight into lignin solubilization mechanisms and a new method for computationally screening potential solvent systems for lignin valorization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Borehole Disposal: Update on the Science, Technology, and Potentially Suitable Radioactive Wastes - 20170

A recent Special Issue of the Energies Journal on Deep Borehole Disposal of Nuclear Waste has delivered a timely update on the science and technology of borehole disposal and the types of radioactive wastes it could potentially accommodate. The Special Issue papers discuss (i) circumstances under which a national waste management programme might wish to consider deep borehole disposal [1], (ii) a status report of deep borehole disposal options in Germany [2], (iii) the use of deep horizontal drill-holes in sedimentary, metamorphic or igneous rocks for disposal of spent nuclear fuel and high-level radioactive waste [3], (iv) the analysis of corrosion performance of engineered barrier systems [4], (v) a review of the potential cementing systems suitable for deep borehole disposal [5], (vi) the thermal evolution around heat-generating waste for a wide range of material properties and disposal configurations [6], (vii) a geochemical analysis of deep brines focussed on fluid-rock interactions [7], (viii) post-closure performance assessment calculations for deep borehole disposal of Cs/Sr capsules [8], and (ix) an example safety case for deep borehole disposal of nuclear wastes [9]. This paper provides an overview of the main findings from these publications and highlights some of the technological and scientific challenges that need to be overcome when developing a deep borehole disposal concept. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Deep Isolation-Development of the Safety Case for Disposal of Radioactive Wastes in Horizontal Boreholes - 20028

Deep Isolation has developed a safe, secure, and permanent geological disposal method for high-level waste, including spent nuclear fuel as well as sealed sources and other highly radioactive materials. The method leverages well established directional drilling technology to create horizontal repositories deep underground. The combination of great depth (1-3 km) and the ability to precisely position repositories in a horizontal orientation provides access to geologic strata that are inaccessible for typical mined and deep vertical borehole repositories. A number of potential direct and indirect safety benefits accrue including:1) greater depth below regional freshwater aquifers; 2) increased flexibility and more geologic options for siting; 3) access to deep formations with persistent and sustained reducing conditions. In addition there are a number of safety elements related specifically to the horizontal repository geometry including: 1) passive direction of thermally driven fluid and radionuclide movement away from the vertical access hole and toward the 'dead end' portion of the repository; 2) mitigation of seismic hazards by orienting repositories parallel to local and regional fault structures. In this paper we explore and discuss some of the key technologic, geologic and hydrologic elements that support the deep horizontal borehole safety case. The stalemate seen across the globe on the disposal of spent nuclear fuel and high-level waste can be broken. Deep Isolation offers a novel option for safe, secure, and permanent deep geological disposal of nuclear waste that can be developed as centralized repositories or adapted to smaller regional or site-specific repositories located near waste sources. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2012

The Near-Earth Object Human Space Flight Accessible Targets Study (NHATS) List of Near-Earth Asteroids: Identifying Potential Targets for Future Exploration

Over the past several years, much attention has been focused on the human exploration of near-Earth asteroids (NEAs). Two independent NASA studies examined the feasibility of sending piloted missions to NEAs [1, 2], and in 2009, the Augustine Commission identified NEAs as high profile destinations for human exploration missions beyond the Earth-Moon system [3]. More recently the current U.S. presidential administration directed NASA to include NEAs as destinations for future human exploration with the goal of sending astronauts to a NEA in the mid to late 2020s. This directive became part of the official National Space Policy of the United States of America as of June 28, 2010 [4]. Detailed planning for such deep space exploration missions and identifying potential NEAs as targets for human spaceflight requires selecting objects from the ever growing list of newly discovered NEAs. Hence NASA developed and implemented the Near-Earth Object (NEO) Human Space Flight (HSF) Accessible Target Study (NHATS), which identifies potential candidate objects on the basis of defined dynamical trajectory performance constraints.

Abell, Paul A.↗

NASA NTRS2009

Ultra High Power and Efficiency Space Traveling-Wave Tube Amplifier Power Combiner with Reduced Size and Mass for NASA Missions

In the 2008 International Microwave Symposium (IMS) Digest version of our paper, recent advances in high power and efficiency space traveling-wave tube amplifiers (TWTAs) for NASA s space-to-Earth communications are presented. The RF power and efficiency of a new K-Band amplifier are 40 W and 50 percent and that of a new Ka-Band amplifier are 200 W and 60 percent. An important figure-of-merit, which is defined as the ratio of the RF power output to the mass (W/kg) of a TWT, has improved by a factor of ten over the previous generation Ka-Band devices. In this extended paper, a high power, high efficiency Ka-band combiner for multiple TWTs, based on a novel hybrid magic-T waveguide circuit design, is presented. The measured combiner efficiency is as high as 90 percent. In addition, at the design frequency of 32.05 GHz, error-free uncoded BPSK/QPSK data transmission at 8 megabits per second (Mbps), which is typical for deep space communications is demonstrated. Furthermore, QPSK data transmission at 622 Mbps is demonstrated with a low bit error rate of 2.4x10(exp -8), which exceeds the deep space state-of-the-art data rate transmission capability by more than two orders of magnitude. A potential application of the TWT combiner is in deep space communication systems for planetary exploration requiring transmitter power on the order of a kilowatt or higher.

Simons, Rainee N.↗

NASA NTRS2017

The Opportunity in Commercial Approaches for Future NASA Deep Space Exploration Elements

This work joins two events, showing the potential for commercial, public private partnerships, modeled on programs like COTS, to reduce the cost to NASA significantly for other required deep space exploration capabilities. These other capabilities include landers, stages and more. We mature the concept of costed baseball cards, adding cost estimates to NASAs space systems baseball cards.

cost modeling estimation↗

NASA NTRS1990

Development of realtime connected element interferometry at the Goldstone Deep Space Communications Complex

Connected-element interferometry (CEI) has the potential to provide high-accuracy angular spacecraft tracking on short baselines by making use of the very precise phase delay observable. Within the Goldstone Deep Space Communications Complex (DSCC), one of three tracking complexes in the NASA Deep Space Network, baselines of up to 21 km in length are available. Analysis of data from a series of short-baseline phase-delay interferometry experiments are presented to demonstrate the potential tracking accuracy on these baselines. Repeated differential observations of pairs of angularly close extragalactic radio sources were made to simulate differential spacecraft-quasar measurements. Fiber-optic data links and a correlation processor are currently being developed and installed at Goldstone for a demonstration of real-time CEI in 1990.

Edwards, C. D.↗

DOE OSTI2021

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗

NASA NTRS2019

Using Machine Learning to Develop a Predictive Model for Future Fire Seasons

The deep learning model shows promise for predicting areas of high wildfire potential. Full evaluation of the model performance is ongoing. Currently, the developed deep learning model is better overall at predicting the number of fires over the acres burned. Acres burned is dependent on location, suppression plan, and current conditions. Antecedent conditions are only one piece of the equation. In-season changes are not accounted for. An ignition source is required, which further complicates the model training and prediction.

White, Andrew T.↗

DOE OSTI2021

Short- and medium-range orders in Al90Tb10 glass and their relation to the structures of competing crystalline phases

Molecular dynamics simulations using an interatomic potential developed by artificial neural network deep machine learning are performed to study the local structural order in Al90Tb10 metallic glass. We show that more than 80% of the Tb-centered clusters in Al90Tb10 glass have short-range order (SRO) with their 17 first coordination shell atoms stacked in a ‘3661’ or ‘15551’ sequence. Medium-range order (MRO) in Bergman-type packing extended out to the second and third coordination shells is also clearly observed. Analysis of the network formed by the ‘3661’ and ‘15551’ clusters show that ~82% of such SRO units share their faces or vertexes, while only ~6% of neighboring SRO pairs are interpenetrating. Such a network topology is consistent with the Bergman-type MRO around the Tb-centers. Moreover, crystal structure searches using genetic algorithm and the neural network interatomic potential reveal several low-energy metastable crystalline structures in the composition range close to Al 90 Tb 10 . Some of these crystalline structures have the ‘3661’ SRO while others have the ‘15551’ SRO. While the crystalline structures with the ‘3661’ SRO also exhibit the MRO very similar to that observed in the glass, the ones with the ‘15551’ SRO have very different atomic packing in the second and third shells around the Tb centers from that of the Bergman-type MRO observed in the glassy phase.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Dark Energy Survey Year 3 results: Simulation-based 𝑤CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. Here, this work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological 𝑤CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving 2−3× higher figures of merit in the 𝛺 𝑚 − 𝑆 8 plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.

Thomsen, A. [Zurich, ETH] (ORCID:0000000203099021)↗

NASA NTRS2004

Epoxy Adhesives for Stator Magnet Assembly in Stirling Radioisotope Generators (SRG)

As NASA seeks to fulfill its goals of exploration and understanding through missions planned to visit the moons of Saturn and beyond, a number of challenges arise from the idea of deep space flight. One of the first problems associated with deep space travel is electrical power production for systems on the spacecraft. Conventional methods such as solar power are not practical because efficiency decreases substantially as the craft moves away from the Sun. The criterion for power generation during deep space missions are very specific, the main points requiring high reliability, low mass, minimal vibration and a long lifespan. A Stirling generator, although fairly old in concept, is considered to be a potential solution for electrical power generation for deep space flight. A Stirling generator works on the same electromagnetic principles of a standard generator, using the linear motion of the alternator through the stationary stator which produces electric induction. The motion of the alternator, however, is produced by the heating and cooling dynamics of pressurized gases. Essentially heating one end and cooling another of a contained gas will cause a periodic expansion and compression of the gas from one side to the other, which a displacer translates into linear mechanical motion. NASA needs to confirm that the materials used in the generator will be able to withstand the rigors of space and the life expectancy of the mission. I am working on the verification of the epoxy adhesives used to bond magnets to the steel lamination stack to complete the stator; in terms of in-service performance and durability under various space environments. Understanding the proper curing conditions, high temperature properties, and degassing problems as well as production difficulties are crucial to the long term success of the generator. system and steel substrate used in the stator. To optimize the curing conditions of the epoxies, modulated differential scanning calorimetry analysis was done as a function of cure time and temperatures. Adhesion bond strength was tested at various temperatures with lap shear samples using Hiperco 50 substrate to ensure that the proper adhesive is being used. To try and solve the problem of bondline thickness, micro glass beads of 0.0017" in diameter were investigated to see if any other physical properties of the epoxy were affected. Efforts will be made to develop a standard, optimized, fabrication process/procedure of sub-scale magnet-stator assemblies for various adhesive performance evaluation studies under simulated generator conditions. Also, accelerated aging testing will be done in a pressurized canister with stator assembly samples for three years to verify if any degassing or thermal degradation of the epoxy occurs. The necessity of verifying the correct epoxy adhesive system for the stator magnet in the SRG is crucial because failure of the stator assembly would jeopardize the electrical system, and thereby the entire mission itself. My work involves specimen fabrications, testing, and data analyses of the epoxy adhesive system for the stator magnet in the SRG is crucial because failure of the stator assembly would jeopardize the electrical system, and thereby the entire mission itself.

Cater, George M.↗

DOE OSTI2021

Rapid failure mode classification and quantification in batteries: A deep learning modeling framework

Unique, rapid identification and quantification of the dominant aging modes in lithium-ion batteries (LiBs) with early and non-specialized test data is a significant scientific challenge. Leveraging synthetic-data, deep-learning (DL) techniques have great potential to enable fast and robust classification and quantification of battery aging modes that produce different patterns of cell aging. This study, for the first time, presents a synthetic–data-based DL modeling framework for rapid and automatic classification and quantification of battery-aging modes and resultant aging with experimental validation. Availing synthetic dQ.dV -1 curves for ~26000 initial conditions and aging modes, the framework classified the dominant aging modes, for cells undergoing fast charge, in fewer than 100 cycles. Upon classification, the framework quantified the evolution of the aging modes, which were often nonuniform with cycling, for 22 gr/NMC532 pouch cells tested up to 600 cycles at different charging rates (1C–9C).

25 ENERGY STORAGE↗

DOE OSTI2022

Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Exploring the Origin of Anionic Redox Activity in Super Li-Rich Iron Oxide-Based High-Energy-Density Cathode Materials

Super alkali-rich materials (alkali:transition metal ratio of ≥2:1), such as Li 5 FeO 4 , exhibit the potential to realize anionic redox upon deep delithiation. Li 5 FeO 4 was shown to undergo reversible cycling between Li 4 FeO 3.5 and Li 3 FeO 3.5 with combined cation/anion redox, a remarkable capacity of 189 mAh/g, and no O 2 release. However, the impact of phase transformations on the reaction thermodynamics and the correlation between the structural changes and reaction reversibility remain unclear. In this study, we use first-principles calculations to examine the delithiation and (re)lithiation reactions of Li 5 FeO 4 . We show that the experimentally observed charge and discharge processes go through non-equilibrium pathways. Upon delithiation, the compound undergoes a phase transformation from Li 5 FeO 4 , with tetrahedrally coordinated (T d ) Fe ions, to a delithiated disordered rocksalt structure, with octahedral (O h ) Fe ions. Fe-ion migration has an asymmetric kinetic barrier that makes T d → O h migration facile, whereas the reverse process has a much larger barrier, explaining the difficulties in reaction reversibility. We further elucidate the transition metal and O redox sequences during the charge cycle and identify the complex electrochemistry associated with the dual participation of cationic redox (Fe 3+ /Fe 4+ ) and anionic redox (O 2– /O – /O o ). Armed with this knowledge, we conduct high-throughput screening of known alkali-rich transition metal oxides by evaluating their potential to enable reversible anionic redox, with multiple candidates proposed for further experimental trials. Furthermore, our work provides a useful guide for the further development of super alkali-rich anionic-redox-active electrodes for high-energy-density batteries.

25 ENERGY STORAGE↗