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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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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Quantifying Pilot Contribution to Flight Safety During an In-Flight Airspeed Failure

Accident statistics cite the flight crew as a causal factor in over 60% of large transport fatal accidents. Yet a well-trained and well-qualified crew is acknowledged as the critical center point of aircraft systems safety and an integral component of the entire commercial aviation system. A human-in-the-loop test was conducted using a Level D certified Boeing 737-800 simulator to evaluate the pilot's contribution to safety-of-flight during routine air carrier flight operations and in response to system failures. To quantify the human's contribution, crew complement was used as an independent variable in a between-subjects design. This paper details the crew's actions and responses while dealing with an in-flight airspeed failure. Accident statistics often cite flight crew error (Baker, 2001) as the primary contributor in accidents and incidents in transport category aircraft. However, the Air Line Pilots Association (2011) suggests "a well-trained and well-qualified pilot is acknowledged as the critical center point of the aircraft systems safety and an integral safety component of the entire commercial aviation system." This is generally acknowledged but cannot be verified because little or no quantitative data exists on how or how many accidents/incidents are averted by crew actions. Anecdotal evidence suggest crews handle failures on a daily basis and Aviation Safety Action Program data generally supports this assertion, even if the data is not released to the public. However without hard evidence, the contribution and means by which pilots achieve safety of flight is difficult to define. Thus, ways to improve the human ability to contribute or overcome deficiencies are ill-defined.

Etherington, Timothy J.↗
NASA NTRS2023

Spacesuit Fit and Mobility Assessments by Digital Human Modeling

Spacesuit Fit and Mobility Assessments by Digital Human Modeling K. Han Kim (Leidos, Inc.) Elizabeth A. Benson (KBR, Inc.) Sudhakar L. Rajulu (NASA Johnson Space Center) Spacesuits are required to accommodate safe operations for astronauts across gender and a wide variety of body shapes and sizes. This goal has been of particular importance given the increasing diversity of NASA crewmembers for upcoming Missions. While testing with design prototypes is a critical step for spacesuit development, iterative mockup design, fabrication, and human subject tests can be extremely costly and time consuming. Moreover, testing with a limited subject pool has often raised questions for validity, as test subjects need to represent the entire astronaut population, not only of the past or current, but also the future. This study is aimed at demonstrating how digital human modeling (DHM) tools have been built and directly supported NASA spacesuit developments. With DHM, the computer aided design (CAD) model of a spacesuit was integrated with human body models. Two use scenarios are presented in this paper, namely fit and mobility. For fit assessments, the suit-to-body contact and compression patterns were estimated using 3D human body scan models virtually wearing a spacesuit model. A statistical fit classifier was made from the contact patterns and applied against a large database of body scans (N=2,500). With this technique, the NASA reference design spacesuit Exploration EVA Mobility Unit (xEMU)was verified to accommodate 90% of the astronaut-like population, with the critical dimensions covering 1stto 99thpercentiles of the target body measurements. A similar technique assessed XEMU mobility. The maximum reach envelopes were considered, within which the work objects and critical hardware interfaces should be located for safe and ergonomic operations. While the reach envelope geometry varies significantly with the suit wearer’s body size and strength, the existing test data did not include the subjects critically required to define suit mobility requirements, such as very small females or large males. Using the xEMU virtual model kinematically simulated and permuted for a hypothetical wearer, however, the existing data were statistically transformed and scaled. This method enabled for a parametric estimation of the reach envelopes from the 1stpercentile female or 99thpercentile male. The outcome was successfully incorporated for requirement developments. With the new DHM tools, human integration of the spacesuit was structurally simulated and predictively assessed, which would have been otherwise impossible. Also, the needs for iterative mockup and subject tests were significantly reduced, which resulted in time and cost saving. Additional work is in progress to integrate additional vehicles, tools, and hardware with DHM framework.

K Han Kim↗
DOE OSTI2022

The straw tracking detector for the Fermilab Muon g-2 Experiment

The Muon g-2 Experiment at Fermilab uses a gaseous straw tracking detector to make detailed measurements of the stored muon beam profile, which are essential for the experiment to achieve its uncertainty goals. Positrons from muon decays spiral inward and pass through the tracking detector before striking an electromagnetic calorimeter. The tracking detector is therefore located inside the vacuum chamber in a region where the magnetic field is large and non-uniform. As such, the tracking detector must have a low leak rate to maintain a high-quality vacuum, must be non-magnetic so as not to perturb the magnetic field and, to minimize energy loss, must have a low radiation length. The performance of the tracking detector has met or surpassed the design requirements, with adequate electronic noise levels, an average straw hit resolution of (110 ± 20) μm, a detection efficiency of 97% or higher, and no performance degradation or signs of aging. The tracking detector's measurements result in an otherwise unachievable understanding of the muon's beam motion, particularly at early times in the experiment's measurement period when there are a significantly greater number of muons decaying. This is vital to the statistical power of the experiment, as well as facilitating the precise extraction of several systematic corrections and uncertainties. This paper describes the design, construction, testing, commissioning, and performance of the tracking detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

CASM — A software package for first-principles based study of multicomponent crystalline solids

CASM is a software package that enables first-principles based studies of crystalline materials. It has been designed to treat coupled chemical, mechanical, vibrational, and magnetic degrees of freedom to determine ground state and finite temperature properties of crystals. The symmetry of the underlying parent crystal structure is used to enumerate perturbations of the parent crystal structure and generate derivative structures which can be input to first-principles calculations in order to explore the ground state energy landscape. CASM algorithmically constructs cluster expansions that fully couple discrete and continuous degrees of freedom, and generates highly efficient code to evaluate the cluster expansion basis functions. Widely used machine learning methods are integrated for fitting expansion coefficients to first-principle calculations. The fully parameterized cluster expansions can be combined with (kinetic) Monte Carlo methods to calculate finite temperature thermodynamic and kinetic properties. CASM Alloy Manager identifies distinct parent crystal structures in an alloy system, creating individual projects for each, and integrating the results. The integrated infrastructure facilitates the linkage between first-principles statistical mechanics predictions with higher length scale computational methods, such as phase field simulations. In conclusion, CASM projects are designed to be easy to share in repositories, to re-use, and to extend to include additional chemical species or types of degrees of freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning

Hybrid composites combine two or more different fillers to achieve multifunctional or advanced material properties, such as lightweight and enhanced mechanical properties. The properties of the composites significantly depend on their microstructures, which can be tailored via advanced 3D printing processes. Understanding the process-structure-property relationships is critical to enable the design and engineering of novel hybrid composites for applications in aerospace, automotive, and protective coatings. Here, for this work, we develop 3D printable and lightweight hybrid composites and leverage the conventional design of experiments, a theoretical hybrid model, and an image-driven machine learning (ML) method to investigate their mechanical behaviors. The hybrid composites are formulated with elastomer matrix, microfillers, and thin-shell particles, enabling a significant degree of design freedom of microstructures with densities and mechanical properties varying up to 70% and 91%, respectively. Our statistical analysis indicates that the 3D printing path direction and the microfibers fraction are dominating process parameters with contribution percentages of 45.3% and 57.7% on the specific stiffness and strength, respectively. A hybrid mechanics model is developed based on a simple Weibull distribution function and classical single-filler models to effectively capture the variations in mechanical properties, however, it overestimates the values due to its statistical constraints and idealization of experimental uncertainty. The image-driven ML model leverages the microscale images directly without losing the structural details, shows more accurate predictions with experimental data, and has 48.6% lower root mean square error than the theoretical model.

3D printing↗
DOE OSTI2023

High-Level Reverse Intersystem Crossing and Molecular Rigidity Improve Spin Statistics for Triplet–Triplet Annihilation Upconversion

The structural factors affecting triplet−triplet annihilation (TTA) at the molecular level are not well-understood. Here, our steady-state photoluminescence and transient absorption results demonstrate that the spin statistical factor, η, decreases from 0.60 to 0.46 and 0.14 going from 9,10-diphenylanthracene (DPA) to the 1,5-DPA and 2,6-DPA isomers, respectively, during photon upconversion with a platinum octaethylporphyrin sensitizer. Density functional theory (DFT) shows that η depends on the energetics of hot triplet states and molecular rigidity. The significantly high conical intersection energy between the S 0 and T 1 states for 9,10-DPA gives its longer triplet lifetime. Time-dependent DFT calculations show that 9,10-DPA and 1,5-DPA can undergo high-level reverse intersystem crossing from their T 2 and T 3 states, respectively, to the bright S 1 state, increasing the limit of the spin statistical factor. Both factors ultimately serve to enhance the TTA efficiency. Furthermore, this work provides insight into designing molecules for efficient light-emitting and photon upconversion applications.

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

High-Throughput Discovery Illuminates Design Principles and Limits for Long-Lived Charged Species in Organic Electrolytes

The chemical stability of charged molecules in all-organic redox flow batteries (RFBs) is required for the prolonged operation of these devices. Molecular engineering and electrolyte optimization are used to mitigate parasitic reactions and extend the lifetimes of the charge carriers. However, how much can structural variation extend the lifetime? To probe this query, we designed a high-throughput kinetic study of the radical cation of N-methylphenothiazinium, guided by statistical sampling and learning algorithms. Using Argonne’s autonomous discovery facility, we conducted over 6,000 kinetic experiments with robotic sample preparation, parallel kinetic measurements, and machine learning inputs, testing 188 solvent molecules selected from a space of over 540 candidates from 11 chemical classes. Algorithmic selections guided us to stable solvent candidates, which were further tested in high concentration with and without supporting electrolyte. Our findings reveal the inherent difficulty of exceeding the current state of the art through solvent variation. The desired stability is statistically rare and poorly predictable. Among the many tested, only three solvents significantly outperformed our baseline, acetonitrile─and none by more than a factor of 3─suggesting a general challenge in achieving the necessary techno-economic targets. Furthermore, we suggest that self-discharge through solvent homolysis is the cause of the observed limitations. Several structural motifs contribute to >1,000 h half-life stability including molecular simplicity, symmetry, oxidation complement, and strategic fluorination. Importantly, this workflow establishes effective assays for diagnosing and predicting oxidative stress for highly stable liquid electrolytes in all batteries.

Batteries↗
DOE OSTI2021

A deep generative model enables automated structure elucidation of novel psychoactive substances

Over the past decade, the illicit drug market has been reshaped by the proliferation of clandestinely produced designer drugs. These agents, referred to as new psychoactive substances (NPSs), are designed to mimic the physiological actions of better-known drugs of abuse while skirting drug control laws. The public health burden of NPS abuse obliges toxicological, police and customs laboratories to screen for them in law enforcement seizures and biological samples. However, the identification of emerging NPSs is challenging due to the chemical diversity of these substances and the fleeting nature of their appearance on the illicit market. Here, in this study, we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51% and a top-10 accuracy of 86%. Our generative approach has the potential to enable de novo structure elucidation for other types of small molecules that are routinely analysed by mass spectrometry.

Cheminformatics↗
DOE OSTI2021

Serpent and MCNP Calculations of the Energy Deposition in the Transformational Challenge Reactor

This paper focuses on the calculation of the energy deposition in the Transformational Challenge Reactor by two major Monte Carlo codes: Serpent and MCNP. The first software computation relies on Kinetic Energy Released per unit Mass (KERMA) factors while the second one relies on Q-values. The results from these two independent computation methodologies are in very good agreement; however, Serpent runs much faster than MCNP (for the same computational model) and allows for a detailed energy deposition distribution from a 1-mm-side square mesh with a relative statistical error between 0.5% and 1%. This detailed energy deposition is suitable for multiphysics analyses aimed at design optimizations. In order to calculate the energy deposition, Serpent needs enhanced ACE files (distributed by the software developers). Unlike other Monte Carlo software that uses inputs based on Python or Java languages, the Serpent input syntax is very similar to that of MCNP; a Python script can convert a MCNP input to a Serpent input in seconds. For simulations not requiring the calculation of the energy deposition, Serpent can also read nuclear data from MCNP ACE files, which eventually improves the comparison of the results of the two codes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

2023 Roadmap on molecular modelling of electrochemical energy materials

New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as empowered by machine learning techniques can help us to understand, control and design electrochemical energy materials at atomistic precision. Therefore, this roadmap, which is a collection of authoritative opinions, serves as a gateway for both the experts and the beginners to have a quick overview of the current status and corresponding challenges in molecular modelling of electrochemical energy materials for batteries, supercapacitors, CO 2 reduction reaction, and fuel cell applications.

25 ENERGY STORAGE↗
DOE OSTI2025

Experimental investigation of longitudinal scraping of H- bunches via photo-detachment

Longitudinal emittance growth is a significant challenge in RF linacs, especially for poorly bunched beams. This stems from particles occupying outer synchrotron oscillation orbits in the LBET, causing unwanted bunch-bunch interactions and degraded beam quality. To address this, we proposed using temporally spaced laser pulses to selectively photo-detach electrons from the longitudinal head and tail regions of H- ion bunches. This approach aims to reduce particle density in extreme orbits, enhancing beam uniformity and limiting emittance growth. Our experiments employed Fermilab's 'LaserNotcher' system at the font end of the linac, delivering 1.6 MW peak power with sub-nanosecond precision. By neutralizing the first and last half-nanosecond of several H- bunches, we measured their propagation injection into the booster. Measurements of pulse width, average height, and temporal spacing over booster cycles were compared between the scraped and unscraped bunches. Statistical analysis evaluated the results’ significance, highlighting the feasibility of laser-based scraping for future linac designs to achieve higher beam energies with improved emittance control.

Landon, Parker [Boston U.]↗
DOE OSTI2021

Monte Carlo Hauser-Feshbach computer code system to model nuclear reactions: YAHFC

A computer program framework, YAHFC, to model low-energy nuclear reactions is presented. The framework allows for reactions with incident particles ranging from protons/neutrons to alphas and is designed to address reactions that ultimately lead to the formation of compound nuclear systems that then decay statistically as outlined in concepts of Hauser and Feshbach. Additionally, instead of a reaction, it is also possible to model the decay of a nuclear system with an initial excitation and population. The code models nuclear decays with a Monte Carlo process that tracks the decay of each state. This allows for an exact representation of the spectra for all emitted particles in each of the final exit channels and the possibility of generating reaction data for simulation purposes. The program is interfaced with the optical model code system FRESCOX to calculate transmission coefficients as well as the effects of coupled channels and other direct excitations via the distorted wave Born approximation (DWBA). Modules are included to account for nuclear processes such as width corrections, pre-equilibrium emission, and fission. The program is controlled by a series of input commands and while a set of input parameters exists for each projectile and target, the input commands allow for complete control over each input parameter. Extensive data files are produced and a program is provided that converts YAHFC data files into nuclear data library entries in the generalized nuclear data structure (GNDS).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Experimental investigation of longitudinal scraping of $H-$ bunches via photo-detachment

Longitudinal emittance growth is a significant challenge in RF linacs, especially for poorly bunched beams. This stems from particles occupying outer synchrotron oscillation orbits in the LBET, causing unwanted bunch-bunch interactions and degraded beam quality. To address this, we proposed using temporally spaced laser pulses to selectively photo-detach electrons from the longitudinal head and tail regions of H- ion bunches. This approach aims to reduce particle density in extreme orbits, enhancing beam uniformity and limiting emittance growth. Our experiments employed Fermilab's 'LaserNotcher' system at the font end of the linac, delivering 1.6 MW peak power with sub-nanosecond precision. By neutralizing the first and last half-nanosecond of several H- bunches, we measured their propagation injection into the booster. Measurements of pulse width, average height, and temporal spacing over booster cycles were compared between the scraped and unscraped bunches. Statistical analysis evaluated the results’ significance, highlighting the feasibility of laser-based scraping for future linac designs to achieve higher beam energies with improved emittance control.

Landon, P. [Boston U.; Fermilab]↗
DOE OSTI2026

Multimodal Instrument Concepts for Chemical Sciences at Second Target Station

The development of advanced chemical and energy systems is inherently complex, relying heavily on their unique pathways. Because no two catalysts or batteries follow exactly the same kinetic trajectories, conventional sequential measurements often obscure crucial relationships between structure and dynamics through statistical averaging. This report introduces two neutron scattering instrument concepts for the Second Target Station (STS) specifically designed to overcome this barrier: the Extended Q-range Diffractometer (EQD) and the Multimodal Chemical Spectrometer (MCS).

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

Status and prospects of Muon g-2 experiment

This article reviews the muon g-2 experiment, a cornerstone in precision tests of the Standard Model of particle physics. The experiment measures the anomalous magnetic moment of the muon with unprecedented accuracy, seeking potential discrepancies between theoretical predictions and experimental results that might indicate physics beyond the Standard Model. We trace the evolution of this measurement from its beginnings at CERN in the 1960s to the current state-of-the-art experiment at Fermilab, highlighting the remarkable engineering achievements required to achieve parts-per-billion precision. Recent results from Runs 1-3 have achieved a systematic uncertainty of 70 ppb, exceeding design goals, while ongoing theoretical calculations continue to refine predictions. Despite these advances, the analysis remains statistics-limited, with continued data collection and novel experimental approaches at MUonE and J-PARC promising further insights into this fundamental physical quantity.

Karuza, Marin [INFN, Trieste; Rijeka U.] (ORCID:00↗
NASA NTRS1975

A Monte Carlo analysis of the Viking lander dynamics at touchdown

The performance of the Viking lander has been evaluated by using a Monte Carlo simulation, and all results are presented in statistical form. The primary objectives of this analysis were as follows: (1) to determine the three sigma design values of maximum rigid body accelerations and the minimum clearance of the lander body during landing; (2) to determine the probability of an unstable landing; and (3) to determine the probability of the lander body striking a rock. Two configurations were analyzed with the only difference being in the ability of the primary landing gear struts to carry tension loads.

Muraca, R. J.↗
NASA NTRS1976

Linear and nonlinear analysis of orbital telescope/space shuttle dynamics and control

Work completed on the design and study of an annular suspension and pointing (ASP) system for the space shuttle was presented. This system makes use of a magnetically suspended vernier pointing assembly. The following objectives were pursued in this study: (1) development of a detailed mathematical model of the Space Shuttle/ASP system, (2) design of control laws in order to obtain the desired pointing performance, and (3) prediction of the statistical pointing accuracies in the presence of stochastic disturbances such as crew-motion, and sensor and actuator noise. The first two of these objectives are documented in this report.

Roberts, A. S., Jr.↗
NASA NTRS1977

Cycle life test

Statistical information concerning cell performance characteristics and limitations of secondary spacecraft cells is presented. Weaknesses in cell design as well as battery weaknesses encountered in various satellite programs are reported. Emphasis is placed on improving the reliability of space batteries.

Harkness, J. D.↗