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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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NASA NTRS2004

Microelectronic DNA assay for the detection of BRCA1 gene mutations

Mutations in BRCA1 are characterized by predisposition to breast cancer, ovarian cancer and prostate cancer as well as colon cancer. Prognosis for this cancer survival depends upon the stage at which cancer is diagnosed. Reliable and rapid mutation detection is crucial for the early diagnosis and treatment. We developed an electronic assay for the detection of a representative single nucleotide polymorphism (SNP), deletion and insertion in BRCA1 gene by the microelectronics microarray instrumentation. The assay is rapid, and it takes 30 minutes for the immobilization of target DNA samples, hybridization, washing and readout. The assay is multiplexing since it is carried out at the same temperature and buffer conditions for each step. The assay is also highly specific, as the signal-to-noise ratio is much larger than recommended value (72.86 to 321.05 vs. 5) for homozygotes genotyping, and signal ratio close to the perfect value 1 for heterozygotes genotyping (1.04).

Validation Studies↗

NASA NTRS2009

Advanced Fuel Cell System Thermal Management for NASA Exploration Missions

The NASA Glenn Research Center is developing advanced passive thermal management technology to reduce the mass and improve the reliability of space fuel cell systems for the NASA exploration program. An analysis of a state-of-the-art fuel cell cooling systems was done to benchmark the portion of a fuel cell system s mass that is dedicated to thermal management. Additional analysis was done to determine the key performance targets of the advanced passive thermal management technology that would substantially reduce fuel cell system mass.

Burke, Kenneth A.↗

NASA NTRS2015

Midlatitude Ice-Rich Ground on Mars: An Important Target for Science and In Situ Resource Utilization on Human Missions

The region of ROI is characterized by proven presence of near surface ground ice and numerous periglacial features. Midlatitude ground ice on Mars is of significant scientific interest for understanding the history and evolution of ice stability on Mars, the impact that changes in insolation produced by variations in Mars’ orbital parameters has on the regions climate, and could provide human exploration with a reliable and plentiful in situ resource. For both science and exploration, assessing the astrobiological potential of the ice is important in terms of (1) understanding the potential for life on Mars and (2) evaluating the presence of possible biohazards in advance of human exploration. Heldmann et al. (2014) studied locations on Mars in the Amazonis Planitia region where near surface ground ice was exposed by new impact craters (Byrne et al. 2009). The study examined whether sites in this region were suitable for human exploration including reviewing the evidence for midlatitude ground ice, discussing the possible explanations for its occurrence, assessing its potential habitability for modern life, and evaluating the resource potential. They systematically analyzed remote-sensing data sets to identify a viable landing site. Five sites where ground ice was exposed were examined with HiRise imaging and were classified according to (1) presence of polygons as a proxy for subsurface ice, (2) presence and abundance of rough topographic obstacles (e.g., large cracks, cliffs, uneven topography), (3) rock density, (4) presence and abundance of large boulders, and (5) presence of craters. A suitable landing site was found having ground ice at only 0.15m depth, and no landing site hazards within a 25 km landing ellipse. This paper presents results of that study and examines the relevance of this ROI to the workshop goals.

Human Missions↗

NASA NTRS2021

Compact Augmented Spark Igniters for Liquid Rocket Engines

The objective of this dissertation is to characterize augmented spark impinging (torch) igniters. Torch igniters inject propellants into a small prechamber where a spark igniter imparts the activation energy to initiate combustion. The flame stabilizes within the prechamber and exits into the main combustion chamber where it ignites the main flow directly or by mixing with a coaxial flow to create a secondary flame. The underlying phenomena that promote reliable ignition are multifaceted and required a broad scope of investigations. Initial efforts focused on spark discharges across an annular geometry. Gaseous propellent projects the electrical arc toward the prechamber, where the arc and the thermal energy is imparted to the surrounding fluid then meets with a combustible mixture. Discharges are parametrically examined against pressure, spark-gap size, and exciter types to emulate transient engine startup. The energy imparted to the flow, approximate velocity, and distance traveled by the exhaust plume are determined. Spark quenching (no energy deposition) and its effects on perceived versus actual measurements are shown. Results outline the prechamber target zone where a combustible mixture is necessary for ignition. Next, three-dimensional, time-accurate, and nonreacting computational fluid dynamics simulations assess effects of geometric and mass flow differences on the size, location, and composition of a combustible mixture. Fluid dynamic phenomena are elucidated. Correlations between nondimensional variables are found. Results are coupled with a conservation-law analysis to produce novel objective functions. Lastly, a full-scale, modular torch igniter is tested. Injector sizes, mixture ratios, and injector momentum ratios are systematically varied to map ignition probability and ignition delay. Results show the importance of local mixture ratios, provide injector momentum ratios guidelines, and demonstrate reliable ignition of core mixture ratios typically outside flammability limits. The deliverable of this project is not a torch igniter or derivative thereof. Instead, this dissertation produces novel objective-functions for igniter design. These equations are functions of variables that may be constrained by engine-level requirements, physics, and experimental results from this work. They provide a foundation for future igniter design and will minimize iterative processes from the design cycle for next-generation ignition hardware.

Torch Igniter↗

DOE OSTI2022

Optimizing the accuracy of viscoelastic characterization with AFM force–distance experiments in the time and frequency domains

Atomic Force Microscopy (AFM) force-distance (FD) experiments have emerged as an attractive alternative to traditional micro-rheology measurement techniques owing to their versatility of use in materials of a wide range of mechanical properties. Here, we show that the range of time dependent behaviour which can reliably be resolved from the typical method of FD inversion (fitting constitutive FD relations to FD data) is inherently restricted by the experimental parameters: sampling frequency, experiment length, and strain rate. Specifically, we demonstrate that violating these restrictions can result in errors in the values of the parameters of the complex modulus. In the case of complex materials, such as cells, whose behaviour is not specifically understood a priori, the physical sensibility of these parameters cannot be assessed and may lead to falsely attributing a physical phenomenon to an artifact of the violation of these restrictions. We use arguments from information theory to understand the nature of these inconsistencies as well as devise limits on the range of mechanical parameters which can be reliably obtained from FD experiments. The results further demonstrate that the nature of these restrictions depends on the domain (time or frequency) used in the inversion process, with the time domain being far more restrictive than the frequency domain. Lastly, we demonstrate how to use these restrictions to better design FD experiments to target specific timescales of a material's behaviour through our analysis of a polydimethylsiloxane (PDMS) polymer sample.

information theory↗

DOE OSTI2026

Accelerated Selectrion of Optimal Perovskite Alloys for Solar PV using a Combined Quantum and Machine Learning Hierachiral Approach

The project aims to: (i) accelerate the discovery of “Missing HP alloys” by combining quantum mechanics and artificial intelligence machine learning approaches, and (ii) analyze the stabilities of candidate alloys, including those that do not pass selection filters (and are thus expected to degrade over time) to decipher the nature of the instabilities to guide the development of durable solar cell materials. Successful candidates will be subjected to validation experiments at NREL's state-of-the-art facilities. The discoveries this effort will provide will be directly testable and implementable and will greatly impact U.S. progress in HP PV as they will provide clear direction and motivation for experimental studies including specific material synthetic targets, device optimization, and device stability protocols. A key advantage of this effort is the feedback and guidance provided by the Industry Collaborative Work Group that we established to coordinate academic and national lab research with industry needs. The proposed work will provide a basis for and direct the development of robust and reliable HP PV. It will also provide a timely, valuable and extensive roadmap to the experimental HP PV community to enable it to focus its efforts on improving and fine-tuning promising HP compositions that this effort predicts will likely be the best performers rather than wandering in the vast chemical space for decades spending enormous resources mostly evaluating unpromising candidate materials.

14 SOLAR ENERGY↗

NASA NTRS1984

Research pressure instrumentation for NASA Space Shuttle main engine, modification no. 5

The purpose of Modification No. 5 of this contract is to expand the scope of work (Task C) of this research study effort to develop pressure instrumentation for the SSME. The objective of this contract (Task C) is to direct Honeywell's Solid State Electronics Division's (SSED) extensive experience and expertise in solid state sensor technology to develop prototype pressure transducers which are targeted to meet the SSME performance design goals and to fabricate, test and deliver a total of 10 prototype units. SSED's basic approach is to effectively utilize the many advantages of silicon piezoresistive strain sensing technology to achieve the objectives of advanced state-of-the-art pressure sensors in terms of reliability, accuracy and ease of manufacture. More specifically, integration of multiple functions on a single chip is the key attribute of this technology which will be exploited during this research study.

Anderson, P. J.↗

NASA NTRS2015

Development of an Exploration-Class Cascade Distillation System: Flight Like Prototype Preliminary Design

The ability to recover and purify water through physiochemical processes is crucial for realizing long-term human space missions, including both planetary habitation and space travel. Because of their robust nature, distillation systems have been actively pursued as one of the technologies for water recovery. One such technology is the Cascade Distillation System (CDS) a multi-stage vacuum rotary distiller system designed to recover water in a microgravity environment. Its rotating cascading distiller operates similarly to the state of the art (SOA) vapor compressor distiller (VCD), but its control scheme and ancillary components are judged to be straightforward and simpler to implement into a successful design. Through the Advanced Exploration Systems (AES) Life Support Systems (LSS) Project, the NASA Johnson Space Center (JSC) in collaboration with Honeywell International is developing a second generation flight forward prototype (CDS 2.0). The key objectives for the CDS 2.0 design task is to provide a flight forward ground prototype that demonstrates improvements over the SOA system in the areas of increased reliability and robustness, and reduced mass, power and volume. It will also incorporate exploration-class automation. The products of this task are a preliminary flight system design and a high fidelity prototype of an exploration class CDS. These products will inform the design and development of the third generation CDS which is targeted for on-orbit DTO. This paper details the preliminary design of the CDS 2.0.

Callahan, Michael R.↗

DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is that the amount of equipment reliability (ER) data being continuously generated are extremely large. These data elements come in different forms: textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) and they provide system engineers with valuable insights and information regarding the discovery of anomalous behaviors or degradation trends, the identification of the possible causes behind such behaviors and trends, and the prediction of their direct consequences. This paper directly targets the generation of knowledge from ER data by putting “data into context”. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by identifying first which elements of the developed MBSE elements they are referring to. This task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process “knowledge extraction” where our methods to extract knowledge from textual data. Lastly, once numeric and textual ER data elements have been processed and “understood”, we discover possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if there is a temporal relation among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Elastic proton scattering off nonzero spin nuclei

In recent years, we constructed a microscopic optical potential (OP) for elastic nucleon-nucleus ( NA ) scattering using modern approaches based on chiral theories for the nucleon-nucleon ( NN ) interaction. The OP was derived at first order of the spectator expansion in Watson multiple scattering theory and its final expression was a folding integral between the NN t matrix and the nuclear density of the target. Two- and three-body forces are consistently included both in the target and in the projectile description. The purpose of this work is to apply our microscopic OP to nuclei characterized by a ground state of spin-parity quantum numbers J π ≠ 0 + . We extended our formalism to include the spin of the target nucleus. The full amplitudes of the NN reaction matrix are retained in the calculations starting from two- and three-body chiral forces. The microscopic OP can be applied in the energy range 100 ≤ E ≤ 350 MeV. Finally, we show a remarkable agreement with experimental data for the available observables and, simultaneously, provide reliable estimates for the theoretical uncertainties. This work paves the way toward a full microscopic approach to inelastic NA scattering, showing that the derivation of optical potentials between states with J π ≠ 0 + is completely under control.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Human-Centered and Explainable Artificial Intelligence in Nuclear Operations

Nuclear power plants in the United States are critical to the nation’s energy security, accounting for 20% of all electricity produced for the power grid. As energy needs grow, 100 gigawatts of additional nuclear power will be necessary by 2050, more than double the current capacity. Realizing this target requires cutting-edge technology like artificial intelligence (AI) and machine learning (ML) that can bring about significant increases in the level of automation. Human-centered AI (HCAI) is a combination of human-centered design (human factors, human-in-the-loop, etc.) with AI/ML to help produce an efficient and reliable system with full consideration for human engagement. This paper provides a comprehensive and novel discussion of HCAI considerations in nuclear power, introducing unique applications for the existing fleet as well as new advanced reactor designs. We include real-life use cases of AI applications to work management processes at nuclear power sites and highlight lessons learned for HCAI.

Hall, Anna↗

DOE OSTI2023

Target Selection and Validation of DESI Emission Line Galaxies

The Dark Energy Spectroscopic Instrument (DESI) will precisely constrain cosmic expansion and the growth of structure by collecting ~40 million extragalactic redshifts across ~80% of cosmic history and one-third of the sky. The Emission Line galaxy (ELG) sample, which will comprise about one-third of all DESI tracers, will be used to probe the universe over the 0.6 < z < 1.6 range, including the 1.1 < z < 1.6 range, which is expected to provide the tightest constraints. We present the target selection for the DESI Survey Validation (SV) and Main Survey ELG samples, which relies on the imaging of the Legacy Surveys. The Main ELG selection consists of a g-band magnitude cut and a (g – r) versus (r – z) color box, while the SV selection explores extensions of the Main selection boundaries. The Main ELG sample is composed of two disjoint subsamples, which have target densities of about 1940 deg –2 and 460 deg –2 , respectively. We first characterize their photometric properties and density variations across the footprint. We then analyze the DESI spectroscopic data that have been obtained from 2020 December to 2021 December in the SV and Main Survey. We establish a preliminary criterion for selecting reliable redshifts, based on the [O ιι] flux measurement, and assess its performance. Using this criterion, we are able to present the spectroscopic efficiency of the Main ELG selection, along with its redshift distribution. We thus demonstrate that the Main selection 1940 deg –2 subsample alone should provide 400 deg –2 and 460 deg –2 reliable redshifts in the 0.6 < z < 1.1 and the 1.1 < z < 1.6 ranges, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1987

Operation of a multi-year, multi-agency project

Organizational and technological aspects of the operation by NASA and ESA of the IUE satellite observatory since its launch in 1978 are discussed. Topics addressed include the division of responsibilities among the ground stations, the IUE orbit and its evolution, the IUE spacecraft, normal operations (proposal selection and observation planning, shift handover, and spacecraft operations), and a typical observation (target identification and acquisition, telescope focus, spectrograph modes, camera operations, calibration, and data reduction). Consideration is given to constraints on IUE operation due to the sun-earth-moon configuration (eclipses), spacecraft power, radiation, temperature, and momentum-wheel speed; problems encountered with gyros, onboard computers, the fine sun sensor, and cameras; and the reliable high-efficiency overall performance of the IUE. Diagrams, drawings, and graphs are provided.

Faelker, Juergen↗

NASA NTRS1992

Study of optical techniques for the Ames unitary wind tunnels. Part 4: Model deformation

A survey of systems capable of model deformation measurements was conducted. The survey included stereo-cameras, scanners, and digitizers. Moire, holographic, and heterodyne interferometry techniques were also looked at. Stereo-cameras with passive or active targets are currently being deployed for model deformation measurements at NASA Ames and LaRC, Boeing, and ONERA. Scanners and digitizers are widely used in robotics, motion analysis, medicine, etc., and some of the scanner and digitizers can meet the model deformation requirements. Commercial stereo-cameras, scanners, and digitizers are being improved in accuracy, reliability, and ease of operation. A number of new systems are coming onto the market.

Lee, George↗

DOE OSTI2023

Investigating Net-Zero Carbon Microgrids for DOE’s National Laboratory Facilities: A Case Study for Idaho National Laboratory

The concept of net-zero carbon microgrids (NZMs) has received significant interest in recent years considering its promises to provide both energy resilience and carbon reduction. This paper investigates the practical constraints of NZM deployment in a government facility, exploring various cases revolving around the objectives of cost, emission, and energy resilience. The technoeconomic results for one of the Idaho National Laboratory (INL) facilities are discussed to identify a practical and economic approach to achieve the net-zero target considering both present and future scenarios. The study shows the carbon reduction, resilience, and economic success of net-zero initiatives are tied directly to the energy portfolio of its electricity provider. The NZMs typically required a hybrid mix of renewable generation and storage assets to maximize onsite clean generation and to provide reliable power during grid outages. Besides supporting the ongoing net-zero effort at INL, this work also provides a framework that can be directly used in other facilities aspiring to become a net zero in the future.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

25 ENERGY STORAGE↗

DOE OSTI2021

Quenching of single-particle strengths in direct reactions

A discrepancy in the asymmetry dependence of spectroscopic factors extracted with different reaction probes calls into question whether the corresponding reaction models are properly understood. In this work, we present extracted spectroscopic factors from the Ar 46 , 34 ( p , d ) Ar 45 , 33 transfer reactions in inverse kinematics at a beam energy of 70 MeV/nucleon. The results are consistent with previous measurements of these reactions at a lower beam energy [Lee et al. , Phys. Rev. Lett. 104 , 112701 (2010) ], indicating that the transfer reaction is a reliable probe for the nuclear structure of exotic nuclei across a wide energy range. Results from a large body of transfer reaction measurements, ( p , p N ) measurements, and theoretical nuclear structure studies make a compelling case for much weaker asymmetry dependence than what is observed with single-nucleon knockout reactions on beryllium or carbon targets.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Uncertainty-Driven Rapid Thermodynamic Assessment of Nb-Ta-Zr System and Effects of C impurities (L25GF9298S): Annual Progress Report

An integrated computational materials engineering (ICME) method is in development for rapid thermodynamic experimental investigation and high-fidelity computational modeling of refractory multi-principal element alloys (RMPEAs). These ultra-high-temperature (UHT) alloys are of interest for structural applications in extreme environments, but deficiency of reliable data, especially melting temperatures, impedes the prediction of alloys with favorable properties. The method leverages UHT capabilities and computational expertise of LLNL’s Materials Science Division and the McCormack Lab’s UHT conical nozzle levitation (CNL) system to iteratively map the Nb-Ta-Zr phase space, with focus on the liquidus surface, through targeted experiments selected by quantifying uncertainty in the thermodynamic model fitting parameters. This method will reduce the time to map uncharted RMPEA phase space and thereby accelerate discovery and development of advanced materials for applications in extreme environments.

36 MATERIALS SCIENCE↗