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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 NTRS2017

NExSS/NAI Joint ExoPAG SAG 16 Report on Remote Biosignatures for Exoplanets

Future exoplanet observations will soon focus on the search for life beyond the Solar System. Exoplanet biosignatures to be sought are those with global, potentially detectable, impacts on a planet. Biosignatures occur in an environmental context in which geological, atmospheric, and stellar processes and interactions may work to enhance, suppress or mimic these biosignatures. Thus biosignature scienceis inherently interdisciplinary. Its advance is necessary to inform the design of the next flagship missions that will obtain spectra of habitable extrasolar planets. The NExSS NAI Joint Exoplanet Biosignatures Workshop Without Walls brought together the astrobiology, exoplanet, and mission concept communities to review, discuss, debate, and advance the science of remote detection of planetary biosignatures. The multi-meeting workshop began in June 2016, and was a process that engaged a broad range of experts across the interdisciplinary reaches of NASA's Nexus for Exoplanet System Science (NExSS) program, the NASA Astrobiology Institute (NAI), NASAs Exoplanet Exploration Program (ExEP), and international partners, such as the European Astrobiology Network Association (EANA) and Japans Earth Life Science Institute (ELSI). These groups spanned expertise in astronomy, planetary science, Earth sciences, heliophysics, biology, instrument mission development, and engineering.

Kiang, Nancy Y.↗
NASA NTRS2000

Discriminability measures for predicting readability of text on textured backgrounds

Several discriminability measures were examined for their ability to predict reading search times for three levels of text contrast and a range of backgrounds (plain, a periodic texture, and four spatial-frequency-filtered textures created from the periodic texture). Search times indicate that these background variations only affect readability when the text contrast is low, and that spatial frequency content of the background affects readability. These results were not well predicted by the single variables of text contrast (Spearman rank correlation = -0.64) and background RMS contrast (0.08), but a global masking index and a spatial-frequency-selective masking index led to better predictions (-0.84 and -0.81, respectively). c2000 Optical Society of America.

NASA Discipline Space Human Factors↗
DOE OSTI2026

Multi-omic characterization of a soil microbial consortium reveals critical role of succinate and glutamate metabolism during calcium carbonate precipitation

Microbially induced calcium carbonate precipitation (MICP) holds potential for use in soil stabilization and carbon sequestration, with the overall efficiency of the process being a major determinant for use in many environmental and civil engineering applications. While the biogeochemical pathways and enzymes driving MICP are known, the microbial metabolic networks and community dynamics underlying such precipitation remain poorly characterized. To address this gap, we developed a four-member consortium of soil bacteria (Curtobacterium flaccumfaciens, Rhodococcus qingshengii, Microbacterium sp., and Bacillus toyonensis), termed carbon storing consortium - A (CSC-A), that is capable of MICP. Prior work shows that MICP production is higher in CSC-A compared to the sum of carbonate produced by each member, suggesting carbonate production is driven by consortium dynamics. To that end we used a multi-omic integration approach of genomics, transcriptomics, and metabolomics to investigate potential inter-species interactions that may influence the MICP phenotype. Genomic life history characterizations identified evidence of niche specialization by B. toyonensis and Microbacterium, while metatranscriptomic analysis suggests R. qingshengii is a keystone species during growth in urea. By comparing individual species’ metabolomes to the metabolic profile of a shared well of precipitated metabolites, we identified over 200 metabolites predicted to be produced or consumed by CSC-A members. Integrating both data types to search the KEGG reactome highlighted a network centered around glutamine metabolism and branched chain amino acid biosynthesis under regulation during CSC-A growth in urea. Succinate metabolism was also a major node in this network and laboratory assays confirmed that increasing the amount of succinate in the growth medium leads to increased carbonate precipitation by CSC-A, a critical confirmation of our modeling approach. By isolating and identifying the interconnected metabolic components underlying MICP in CSC-A, we identified keystone taxa, metabolites, and pathways important for future optimization of the application of this consortia to carbonate precipitation.

carbon storing consortium - A (CSC-A)↗
NASA NTRS2000

Optimization of Car Body under Constraints of Noise, Vibration, and Harshness (NVH), and Crash

To be competitive on the today's market, cars have to be as light as possible while meeting the Noise, Vibration, and Harshness (NVH) requirements and conforming to Government-man dated crash survival regulations. The latter are difficult to meet because they involve very compute-intensive, nonlinear analysis, e.g., the code RADIOSS capable of simulation of the dynamics, and the geometrical and material nonlinearities of a thin-walled car structure in crash, would require over 12 days of elapsed time for a single design of a 390K elastic degrees of freedom model, if executed on a single processor of the state-of-the-art SGI Origin2000 computer. Of course, in optimization that crash analysis would have to be invoked many times. Needless to say, that has rendered such optimization intractable until now. The car finite element model is shown. The advent of computers that comprise large numbers of concurrently operating processors has created a new environment wherein the above optimization, and other engineering problems heretofore regarded as intractable may be solved. The procedure, shown, is a piecewise approximation based method and involves using a sensitivity based Taylor series approximation model for NVH and a polynomial response surface model for Crash. In that method the NVH constraints are evaluated using a finite element code (MSC/NASTRAN) that yields the constraint values and their derivatives with respect to design variables. The crash constraints are evaluated using the explicit code RADIOSS on the Origin 2000 operating on 256 processors simultaneously to generate data for a polynomial response surface in the design variable domain. The NVH constraints and their derivatives combined with the response surface for the crash constraints form an approximation to the system analysis (surrogate analysis) that enables a cycle of multidisciplinary optimization within move limits. In the inner loop, the NVH sensitivities are recomputed to update the NVH approximation model while keeping the Crash response surface constant. In every outer loop, the Crash response surface approximation is updated, including a gradual increase in the order of the response surface and the response surface extension in the direction of the search. In this optimization task, the NVH discipline has 30 design variables while the crash discipline has 20 design variables. A subset of these design variables (10) are common to both the NVH and crash disciplines. In order to construct a linear response surface for the Crash discipline constraints, a minimum of 21 design points would have to be analyzed using the RADIOSS code. On a single processor in Origin 2000 that amount of computing would require over 9 months! In this work, these runs were carried out concurrently on the Origin 2000 using multiple processors, ranging from 8 to 16, for each crash (RADIOSS) analysis. Another figure shows the wall time required for a single RADIOSS analysis using varying number of processors, as well as provides a comparison of 2 different common data placement procedures within the allotted memories for each analysis. The initial design is an infeasible design with NVH discipline Static Torsion constraint violations of over 10%. The final optimized design is a feasible design with a weight reduction of 15 kg compared to the initial design. This work demonstrates how advanced methodology for optimization combined with the technology of concurrent processing enables applications that until now were out of reach because of very long time-to-solution.

Kodiyalam, Srinivas↗
DOE OSTI2022

Hypothesis testing via AI: Generating physically interpretable models of scientific data with machine learning (Full Technical Report)

Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Cas9 interrogates DNA in discrete steps modulated by mismatches and supercoiling

The CRISPR-Cas9 nuclease has been widely repurposed as a molecular and cell biology tool for its ability to programmably target and cleave DNA. Cas9 recognizes its target site by unwinding the DNA double helix and hybridizing a 20-nucleotide section of its associated guide RNA to one DNA strand, forming an R-loop structure. A dynamic and mechanical description of R-loop formation is needed to understand the biophysics of target searching and develop rational approaches for mitigating off-target activity while accounting for the influence of torsional strain in the genome. Here we investigate the dynamics of Cas9 R-loop formation and collapse using rotor bead tracking (RBT), a single-molecule technique that can simultaneously monitor DNA unwinding with base-pair resolution and binding of fluorescently labeled macromolecules in real time. By measuring changes in torque upon unwinding of the double helix, we find that R-loop formation and collapse proceed via a transient discrete intermediate, consistent with DNA:RNA hybridization within an initial seed region. Using systematic measurements of target and off-target sequences under controlled mechanical perturbations, we characterize position-dependent effects of sequence mismatches and show how DNA supercoiling modulates the energy landscape of R-loop formation and dictates access to states competent for stable binding and cleavage. Consistent with this energy landscape model, in bulk experiments we observe promiscuous cleavage under physiological negative supercoiling. The detailed description of DNA interrogation presented here suggests strategies for improving the specificity and kinetics of Cas9 as a genome engineering tool and may inspire expanded applications that exploit sensitivity to DNA supercoiling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Aftershock Identification Using a Paired Neural Network Applied to Constructed Data

This report is intended to detail the findings of our investigation of the applicability of machine learning to the task of aftershock identification. The ability to automatically identify nuisance aftershock events to reduce analyst workload when searching for events of interest is an important step in improving nuclear monitoring capabilities and while waveform cross - correlation methods have proven successful, they have limitations (e.g., difficulties with spike artifacts, multiple aftershocks in the same window) that machine learning may be able to overcome. Here we apply a Paired Neural Network (PNN) to a dataset consisting of real, high quality signals added to real seismic noises in order to work with controlled, labeled data and establish a baseline of the PNN's capability to identify aftershocks. We compare to waveform cross - correlation and find that the PNN performs well, outperforming waveform cross - correlation when classifying similar waveform pairs, i.e., aftershocks.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1994

Unique strategies for technical information management at Johnson Space Center

In addition to the current NASA manned programs, the maturation of Space Station and the introduction of the Space Exploration programs are anticipated to add substantially to the number and variety of data and documentation at NASA Johnson Space Center (JSC). This growth in the next decade has been estimated at five to ten fold compared to the current numbers. There will be an increased requirement for the tracking and currency of space program data and documents with National pressures to realize economic benefits from the research and technological developments of space programs. From a global perspective the demand for NASA's technical data and documentation is anticipated to increase at local, national, and international levels. The primary users will be government, industry, and academia. In our present national strategy, NASA's research and technology will assume a great role in the revitalization of the economy and gaining international competitiveness. Thus, greater demand will be placed on NASA's data and documentation resources. In this paper the strategies and procedures developed by DDMS, Inc., to accommodate the present and future information utilization needs are presented. The DDMS, Inc., strategies and procedures rely on understanding user requirements, library management issues, and technological applications for acquiring, searching, storing, and retrieving specific information accurately and quickly. The proposed approach responds to changing customer requirements and product deliveries. The unique features of the proposed strategy include: (1) To establish customer driven data and documentation management through an innovative and unique methods to identify needs and requirements. (2) To implement a structured process which responds to user needs, aimed at minimizing costs and maximizing services, resulting in increased productivity. (3) To provide a process of standardization of services and procedures. This standardization is the central theme of the strategic approach. It will allow Division level Data and Documentation Libraries (DDL's) to function independently and optimize efficiency at the Directorate level. This process also facilitates interconnectivity between Division level DDL's and makes them transparent to the users. (4) To implement the process of 'cost savings', and at the same time the objective is to gain substantial improvement in the organization, categorization, and preservation of JSC-generated data and documentation, and (5) To find, locate, retrace, restore, and preserve the Center-generated crucial scientific and technical information that has been and is being provided by the engineers and scientists of JSC. This is important to the preservation of 'lessons learned'. Preliminary estimates of the possible cost savings which will result from the implementation of this process will also be discussed in this paper.

Krishen, Vijay↗
DOE OSTI2020

Addressing qubits with a software-defined radio FPGA (Full Technical Final Report)

Superconducting transmons can be configured as qubits and can also be used for weak signal axion searches. During a prior LDRD (17-ERD-006), a robust capability for system simulation and analysis, algorithm development, algorithm to FPGA workflow and experimental measurements was developed. During that project it was determined that a new software-defined radio FPGA would be a significant improvement in cost, simplicity, and software maintainability over the X6-1000M FPGA plus RF front-end system that had been used. In this Feasibility Study, we successfully developed the interface to the new NI USRP-2954R SDR platform, generated and optimized the VHDL of the existing algorithms, and experimentally tested the new FPGA system on real qubit in the laboratory. These tests showed it had the same SNR on weak measurements as the prior FPGA.

42 ENGINEERING↗
DOE OSTI2026

Numerical Modeling of a Two-Stage Ocean Current Turbine

The Equinox Ocean Turbines (EQOT) current energy converter has a unique design with power generation in two small-diameter turbines attached to the tips of a large-diameter passive rotor. This configuration offers some key advantages for capturing ocean currents. With no centrally placed generator, almost no reaction torque is required at the nacelle of the main large-diameter rotor, and the small-diameter tip turbine generators operate at a higher speed and lower torque. The physics that determine the performance and loads on the turbine are also unique. The interactions of the flow field between the two stages and the general architecture of the system cannot be captured with traditional mid-fidelity modeling tools. For design iterations and large sets of load cases, it is important to have mid-fidelity models that can capture the important phenomenon with enough accuracy to identify global trends. This work uses a limited set of high-fidelity computational fluid dynamics (CFD) simulations to help inform the selection of and construction of a custom mid-fidelity model. Mid-fidelity modeling approaches were verified by comparing key turbine performance quantities to those found with the CFD model. Hydrodynamic interactions of the two-stage rotor were identified through high-fidelity CFD modeling. This highlighted the impact of the main rotor tip vortex and wake on the secondary rotor apparent inflow. This results in a relative flow rotation and sharp deficit, that change the optimal secondary rotor rotation speed and adds unsteadiness to the blade loading respectively. Multiple mid-fidelity approaches were evaluated for their ability to capture these effects. A simple approximation of the combined-stage performance based on single-stage BEM provides a reasonable rough prediction, especially near the peak TSR values, with some larger discrepancy at higher TSRs. Predicting the combined-stage performance based on single-stage CFD data improves this prediction across the TSR range. Although the combined-stage modeling in OLAF was not successful in this stage of the project, it showed promise as a mid-fidelity method, assuming the parameters can be tuned to account for the significant differences in time and length scales between the main and secondary rotors. This may be addressed through code changes in future work. A significant finding from the OLAF work was the agreement between the vortex core radius values found independently via a parameter space search and via CFD. The technique of using single-stage secondary rotor BEM, with a custom inflow taken from single-stage main rotor CFD or OLAF, provides an efficient method to capture one-way coupled flow interactions. This method provided generally good predictions of the impact of the flow rotation on the secondary rotor but struggled to accurately predict the peaks of the unsteady load progression. Future work could include some superposition of a tuned main rotor trailing edge viscous wake into the custom inflow to better predict this interaction.

16 TIDAL AND WAVE POWER↗
NASA NTRS2018

Assessment and Verification of SLS Block 1-B Exploration Upper Stage State and Stage Disposal Performance

One of the SLS Navigation System's key performance requirements is a constraint on the payload system's delta-v allocation to correct for insertion errors due to vehicle state uncertainty at payload separation. The SLS navigation team has developed a Delta-Delta-V analysis approach to assess the effect on trajectory correction maneuver (TCM) design needed to correct for navigation errors. This approach differs from traditional covariance analysis based methods and makes no assumptions with regard to the propagation of the state dynamics. This allows for consideration of non-linearity in the propagation of state uncertainties. The Delta-Delta-V analysis approach re-optimizes perturbed SLS mission trajectories by varying key mission states in accordance with an assumed state error. The state error is developed from detailed vehicle 6-DOF Monte Carlo analysis or generated using covariance analysis. These perturbed trajectories are compared to a nominal trajectory to determine necessary TCM design. To implement this analysis approach, a tool set was developed which combines the functionality of a 3-DOF trajectory optimization tool, Copernicus, and a detailed 6-DOF vehicle simulation tool, Marshall Aerospace Vehicle Representation in C (MAVERIC). In addition to delta-v allocation constraints on SLS navigation performance, SLS mission requirement dictate successful upper stage disposal. Due to engine and propellant constraints, the SLS Exploration Upper Stage (EUS) must dispose into heliocentric space by means of a lunar fly-by maneuver. As with payload delta-v allocation, upper stage disposal maneuvers must place the EUS on a trajectory that maximizes the probability of achieving a heliocentric orbit post Lunar fly-by considering all sources of vehicle state uncertainty prior to the maneuver. To ensure disposal, the SLS navigation team has developed an analysis approach to derive optimal disposal guidance targets. This approach maximizes the state error covariance prior to the maneuver to develop and re-optimize a nominal disposal maneuver (DM) target that, if achieved, would maximize the potential for successful upper stage disposal. For EUS disposal analysis, a set of two tools was developed. The first considers only the nominal pre-disposal maneuver state, vehicle constraints, and an a priori estimate of the state error covariance. In the analysis, the optimal nominal disposal target is determined. This is performed by re-formulating the trajectory optimization to consider constraints on the eigenvectors of the error ellipse applied to the nominal trajectory. A bisection search methodology is implemented in the tool to refine these dispersions resulting in the maximum dispersion feasible for successful disposal via lunar fly-by. Success is defined based on the probability that the vehicle will not impact the lunar surface and will achieve a characteristic energy (C3) relative to the Earth such that it is no longer in the Earth-Moon system. The second tool propagates post-disposal maneuver states to determine the success of disposal for provided trajectory achieved states. This is performed using the optimized nominal target within the 6-DOF vehicle simulation. This paper will discuss the application of the Delta-Delta-V analysis approach for performance evaluation as well as trajectory re-optimization so as to demonstrate the system's capability in meeting performance constraints. Additionally, further discussion of the implementation of assessing disposal analysis will be provided.

Patrick, Sean↗
DOE OSTI2024

Constitutive model development of aluminum alloy 1100 for elevated temperature forming process

Commercially pure aluminum alloy, AA1100, presents good electrical and thermal conductivity, high formability, and low cost. Those favorable characteristics have the potential to enable bipolar plates with improved economics and enhanced performance compared to current stainless steel bipolar plates for proton exchange membrane fuel cells. An accurate constitutive model is essential to develop and optimize processing parameters and effectively control the forming process. Here, the objective of this work is to develop a constitutive model of AA1100 that is able to simulate stress-strain relation, formed geometry, and predict the onset of fracture strain to avoid forming failure. Initially, a set of tensile tests at temperature between 300 and 500°C and strain rate between 0.005 and 1.0/s were conducted to examine the deformation behavior. Then, a set of damage-based unified visco-plastic constitutive equations is proposed and calibrated based on the results of stress-strain data. A genetic algorithm optimization method is applied to search for best fitting material constants in constitutive equations. The proposed model shows good predictability of both the stress-strain relation and fracture strain at low strain rate and high temperature conditions. The accuracy of proposed model is also evaluated statistically. A comparison of the proposed model with three popular models (Arrhenius-type mode, Johnson-Cook model and Zerilli-Armstrong model) was made. The proposed model shows the best experimental agreement with correlation coefficient of 0.96 in contrast to 0.25, 0.38 and 0.75 for the popular models, respectively. The proposed model can help to optimize the elevated temperature forming process and guide die design to enable optimal geometric features in the formed components.

08 HYDROGEN↗
DOE OSTI2022

Intact, Commercial Lithium-Polymer Batteries: Spatially Resolved Grating-Based Interferometry Imaging, Bragg Edge Imaging, and Neutron Diffraction

We survey several neutron imaging and diffraction methods for non-destructive testing and evaluation of intact, commercial lithium-ion batteries. Specifically, far-field interferometry was explored as an option to probe a wide range of autocorrelation lengths within the batteries via neutron imaging. The dark-field interferometry images change remarkably from fresh to worn batteries, and from charged to discharged batteries. When attempting to search for visual evidence of battery degradation, neutron Talbot-Lau grating interferometry exposed battery layering and particle scattering through dark-field imaging. Bragg edge imaging also reveals battery wear and state of charge. Neutron diffraction observed chemical changes between fresh and worn, charged and discharged batteries. However, the utility of these methods, for commercial batteries, is dependent upon battery size and shape, with 19 to 43 mAh prismatic batteries proving most convenient for these experimental methods. This study reports some of the first spatially resolved, small angle scattering (dark-field) images showing battery degradation.

36 MATERIALS SCIENCE↗
NASA NTRS2007

Lunar e-Library: A Research Tool Focused on the Lunar Environment

As NASA plans and implements the Vision for Space Exploration, managers, engineers, and scientists need lunar environment information that is readily available and easily accessed. For this effort, lunar environment data was compiled from a variety of missions from Apollo to more recent remote sensing missions, such as Clementine. This valuable information comes not only in the form of measurements and images but also from the observations of astronauts who have visited the Moon and people who have designed spacecraft for lunar missions. To provide a research tool that makes the voluminous lunar data more accessible, the Space Environments and Effects (SEE) Program, managed at NASA's Marshall Space Flight Center (MSFC) in Huntsville, AL, organized the data into a DVD knowledgebase: the Lunar e-Library. This searchable collection of 1100 electronic (.PDF) documents and abstracts makes it easy to find critical technical data and lessons learned from past lunar missions and exploration studies. The SEE Program began distributing the Lunar e-Library DVD in 2006. This paper describes the Lunar e-Library development process (including a description of the databases and resources used to acquire the documents) and the contents of the DVD product, demonstrates its usefulness with focused searches, and provides information on how to obtain this free resource.

McMahan, Tracy A.↗
DOE OSTI2020

Additive Manufacturing Technologies Survey

A literature search of the most prominent and widely available additive manufacturing technologies was done to understand the current developments in this area. The first section provides the introduction and scope for this report, followed by a very detailed second section on the different types of additive manufacturing technologies, how they work, the materials used, advantages and disadvantages of each technology, and manufacturer information. For comparative purposes, a third section on the most widely used subtractive technology, Computer Numerical Control (CNC) machining, is presented with information about the parameters used and the benefits and limitations of this technology. A final section with a summary and conclusions is presented with information comparing the power and utility of the different additive manufacturing technologies compared to traditional manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Construction status of the Mu2e crystal calorimeter

The Mu2e experiment at Fermilab will search for the charged-lepton flavor violating neutrino-less conversion of a negative muon into an electron in the field of an aluminum nucleus. The Mu2e detector is composed of a tracker, an electromagnetic calorimeter and an external veto for cosmic rays. The calorimeter plays an important role in providing excellent particle identification capabilities and a fast online trigger filter, while aiding the track reconstruction capabilities. Calorimeter requirements are to provide a large acceptance for 0~10 MeV electrons and reach: i) a time resolution better than 0.5 ns; ii) an energy resolution better than 10%; and iii) a position resolution of 1 cm. The calorimeter consists of two disks, each one made of 674 pure CsI crystals. Each crystal is readout by two large area 2×3 arrays of UV-extended SiPMs of 6×6 mm2 dimensions. A large scale prototype (Module-0) has been tested at an electron beam. We report here the tests done to finalize the calorimeter design, the results obtained with Module-0 and the status of production. At this time, the performance characteristics of 85% of the crystals and all of the SiPMs have been measured. The calorimeter engineering drawings have been completed and the large mechanical components are under fabrication. Analog and digital electronics have been prototyped and tested with irradiation dose. Their serial production is being organized. The calorimeter assembly phase is planned for mid-2020.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Influence of the Anions on the Interaction Energy between Water and Ionic Liquids

It is essential to understand the interaction energy between water and ionic liquids (ILs) in search of ILs for dehumidification applications. In this paper, the intermolecular interaction energy between water and 1-ethyl-3-methylimidazolium ([EMIM])-based ILs was investigated. The vapor-liquid equilibrium data were first measured for solutions of [EMIM][BF 4 ], [EMIM][N(CN) 2 ], [EMIM][CF 3 SO 3 ], and [EMIM][CH 3 SO 3 ] with water molar fractions of 8–95 % at temperatures of 303–343 K. The IL-IL and water-IL intermolecular interaction energies were determined by using the correlated non-random two-liquid model. The results revealed that fluorine atoms in anions reduce the attraction energy between IL and water, and thus, decrease the water affinity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Investigating performance and variability of NIF ICF experiments with deep learning

The parameter space involved in designing an inertial confinement fusion shot at the National Ignition Facility (NIF) is massively multi-dimensional and the cost of a single shot makes a comprehensive set of sensitivity studies in the laboratory impractical. The use of machine learning to overcome these challenges has gained popularity and has had several successful applications by the scientific community. We extend on these efforts by training a neural network (NN) on information about the experimental design, engineering elements, and drive asymmetry to predict with uncertainty the neutron yield of an experiment. We find the measured and model predicted values are in good agreement, with an R 2 value of 0.91 for a randomly selected test dataset. Almost all the predicted 95% credible intervals contain the corresponding measured value for both training and test datasets. We identify correlations picked up by the NN between the shot design, yield, and variability and use them to motivate shot sensitivity studies. The first shot to exceed the Lawson-like ignition criteria (N210808) was conducted at the NIF and subsequent shots studied the design’s robustness. In a follow-up shot to N210808, our model predicts capsule quality to be the main performance degradation mechanism that prevented the shot from repeating previous performance levels. Shot N221204 was the first shot to exceed a target energy gain of 1. Our model predicts increased yield with reduced coast time for a N221204 study and greater variability for designs with lower peak powers at constant yield. The model’s fast prediction speed and uncertainty prediction are useful for identifying interesting design paths that could warrant further investigation with conventional simulations to search for robust high yield designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗