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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

"Genetically Engineered" Nanoelectronics

The quantum mechanical functionality of nanoelectronic devices such as resonant tunneling diodes (RTDs), quantum well infrared-photodetectors (QWIPs), quantum well lasers, and heterostructure field effect transistors (HFETs) is enabled by material variations on an atomic scale. The design and optimization of such devices requires a fundamental understanding of electron transport in such dimensions. The Nanoelectronic Modeling Tool (NEMO) is a general-purpose quantum device design and analysis tool based on a fundamental non-equilibrium electron transport theory. NEW was combined with a parallelized genetic algorithm package (PGAPACK) to evolve structural and material parameters to match a desired set of experimental data. A numerical experiment that evolves structural variations such as layer widths and doping concentrations is performed to analyze an experimental current voltage characteristic. The genetic algorithm is found to drive the NEMO simulation parameters close to the experimentally prescribed layer thicknesses and doping profiles. With such a quantitative agreement between theory and experiment design synthesis can be performed.

Klimeck, Gerhard↗
NASA NTRS2001

Fundamentals of Digital Engineering: Designing for Reliability

The concept of designing for reliability will be introduced along with a brief overview of reliability, redundancy and traditional methods of fault tolerance is presented, as applied to current logic devices. The fundamentals of advanced circuit design and analysis techniques will be the primary focus. The introduction will cover the definitions of key device parameters and how analysis is used to prove circuit correctness. Basic design techniques such as synchronous vs asynchronous design, metastable state resolution time/arbiter design, and finite state machine structure/implementation will be reviewed. Advanced topics will be explored such as skew-tolerant circuit design, the use of triple-modular redundancy and circuit hazards, device transients and preventative circuit design, lock-up states in finite state machines generated by logic synthesizers, device transient characteristics, radiation mitigation techniques. worst-case analysis, the use of timing analyzer and simulators, and others. Case studies and lessons learned from spaceflight designs will be given as examples

Katz, R.↗
NASA NTRS2016

The Imaging X-Ray Polarimetry Explorer (IXPE)

The Imaging X-ray Polarimetry Explorer (IXPE) is an exciting international collaboration for a scientific mission that dramatically brings together the unique talents of the partners to expand observation space by simultaneously adding polarization measurements to the array of source properties currently measured (energy, time, and location). IXPE uniquely brings to the table polarimetric imaging. IXPE will thus open new dimensions for understanding how X-ray emission is produced in astrophysical objects, especially systems under extreme physical conditions-such as neutron stars and black holes. Polarization singularly probes physical anisotropies-ordered magnetic fields, aspheric matter distributions, or general relativistic coupling to black-hole spin-that are not otherwise measurable. Hence, IXPE complements all other investigations in high-energy astrophysics by adding important and relatively unexplored information to the parameter space for studying cosmic X-ray sources and processes, as well as for using extreme astrophysical environments as laboratories for fundamental physics.

X-ray astronomy x-ray polarimetry↗
NASA NTRSDate not supplied

Machine Learning Application in Aircraft Engine Conceptual Design

In the current competitive environment, the successful creation and application of machine learning (ML) technologies have become crucial across multiple industries. This study outlines the process of creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models use supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. Key focus areas include crucial engine parameters such as thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While developing ML models is fundamental, ensuring their seamless deployment is equally important. To address this, a conversational AI chatbot is constructed using natural language processing (NLP) techniques to facilitate the deployment of these ML models. The comprehensive workflow includes several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine conceptual design.

Aircraft Engine↗
DOE OSTI2024

Parameter extraction approaches for compact modeling of thermoelectric modules

Thermoelectric (TE) cooling has experienced rapid advancements with the foundational understanding of TE materials. TE modules, compact and lightweight devices, have become the prevalent approach for implementing TE technologies. Accurately quantifying TE physical parameters (Seebeck coefficient α, thermal conductivity κ, and thermal resistance ρ) is challenging due to the dynamic temperature changes in operation. Furthermore, extracting lumped property parameters is crucial for designing energy systems using TE modules. Existing research has several limitations, such as lack of comparative analysis between prevalent formulae, reliance on potentially inaccurate vendor-supplied data, disregard for fundamental assumptions, and absence of empirical measurements. Further, this study addresses these gaps by conducting TE material characterization, comparing three existing formulae using vendor datasheets, designing a laboratory test facility for model validation and refinement, and outlining a structured data extraction procedure. The study's novelty lies in multiple key contributions: (1) a detailed comparative analysis of existing formulae for extracting TE property parameter; (2) executing experimental work in a laboratory setting to validate the model and elucidate its limitations; (3) highlighting potential risks; (4) clarifying possible assumptions from both material and engineering perspectives; and (5) considering temperature differential impacts. This comprehensive approach addresses the current research gaps and provides valuable insights into the design and application of TE modules in various energy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

How AI Predicts the Untrained and Unseen

Focus Area: Model predictability improvements (Primary); Data optimization (secondary); Data complexity insights (secondary). The Scientific Challenge: If we believe that a future under extreme conditions will look very differently from today, we can likely agree that ML/AI models trained on past and present datasets will not be adequate to make reliable predictions into the future. This is true for water cycling, as well as biogeochemistry and other Earth system components and behaviors. Additionally, ML/AI models are inherently non-physical. Despite the flourishing success of ML/AI in many applications, such as computer vision, natural language process, and gaming, even the most sophisticated AI models don’t understand the very basic physical laws. Therefore, a natural question is: Can we trust ML/AI based predictions of Earth system behaviors that are fundamentally driven by physical laws? So, are physics models with meticulous process representation a better choice? Not exactly. Physical models, when firmly rooted in first principles, work great at predicting behaviors of systems with a well-defined set of boundary conditions and variables. However, as a complex system, the number of parameters and the degree of complexity and dynamics in processes, coupling, and scale dependent emergent behaviors make the Earth system behaviors very challenging to predict with physical models composed of deterministic laws. In addition, due to the lack of fundamental understandings, physical models often implement empirical correlations derived from observations with biases from locality of data generation. Because correlation is not necessarily causation or comply with first principles, scaling of model predictions beyond locality is often invalid. Beyond the limitation of models, physics or AI, our knowledge of the Earth system is limited by the lack of observational technologies and resources. Insufficient data density, dimensionality and diversity only offer a sliced (or projected) view of the Earth system, e.g. Plato’s Cave analogy, limiting our capability to better understand and represent fundamental processes in models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Microbial Responses to Microgravity and Other Low-Shear Environments

Microbial adaptation to environmental stimuli is essential for survival. While several of these stimuli have been studied in detail, recent studies have demonstrated an important role for a novel environmental parameter in which microgravity and the low fluid shear dynamics associated with microgravity globally regulate microbial gene expression, physiology, and pathogenesis. In addition to analyzing fundamental questions about microbial responses to spaceflight, these studies have demonstrated important applications for microbial responses to a ground-based, low-shear stress environment similar to that encountered during spaceflight. Moreover, the low-shear growth environment sensed by microbes during microgravity of spaceflight and during ground-based microgravity analogue culture is relevant to those encountered during their natural life cycles on Earth. While no mechanism has been clearly defined to explain how the mechanical force of fluid shear transmits intracellular signals to microbial cells at the molecular level, the fact that cross talk exists between microbial signal transduction systems holds intriguing possibilities that future studies might reveal common mechanotransduction themes between these systems and those used to sense and respond to low-shear stress and changes in gravitation forces. The study of microbial mechanotransduction may identify common conserved mechanisms used by cells to perceive changes in mechanical and/or physical forces, and it has the potential to provide valuable insight for understanding mechanosensing mechanisms in higher organisms. This review summarizes recent and future research trends aimed at understanding the dynamic effects of changes in the mechanical forces that occur in microgravity and other low-shear environments on a wide variety of important microbial parameters.

Review↗
NASA NTRS1982

Fundamental studies in X-ray astrophysics

An analytical model calculation of the ionization structure of matter accreting onto a degenerate dwarf was carried out. Self-consistent values of the various parameters are used. The possibility of nuclear burning of the accreting matter is included. We find the blackbody radiation emitted from the stellar surface keeps hydrogen and helium ionized out to distances much larger than a typical binary separation. Except for low mass stars or high accretion rates, the assumption of complete ionization of the elements heavier than helium is a good first approximation. For low mass stars or high accretion rates the validity of assuming complete ionization depends sensitivity on the distribution of matter in the binary system.

Lamb, D. Q.↗
DOE OSTI2026

Carbon-Based Quantum Information Science with Symmetry Protected Topological States (Final Report, DOE-BES award DE-SC0023105)

This research program established the scientific foundation for the rational, bottom-up design, synthesis, isolation, and investigation of symmetry-protected topological (SPT) electron spin qubits embedded in graphene nanoribbons (GNRs). The work focused on integrating atomically precise low-dimensional carbon nanostructures with emerging quantum logic architectures, providing a pathway toward scalable quantum materials for next-generation computing and sensing technologies. A central component of the program was the elucidation of fundamental relationships between real-space molecular architecture, local spin density distributions, electronic band dispersion, and energy level alignment in atomically precise GNR systems. These correlations define key operational parameters of SPT qubits and were systematically investigated to establish quantitative benchmarks against established molecular and solid-state spin qubit platforms. Attention was given to properties critical for quantum device performance, e.g. decoherence times, spectral sharpness of energy transitions, and tunable exchange interactions between spin states. The research demonstrated that these parameters can be engineered with atomic precision through scalable bottom-up synthetic strategies. Theory-guided design played a central role in identifying candidate structures hosting topologically protected spin states. Experimental validation was performed using both ensemble measurements and single-molecule characterization. In addition to advances in quantum materials synthesis, the program developed and applied spin-sensitive scanning probe microscopy techniques capable of directly probing quantum states and dynamic processes with atomic-scale spatial resolution. These capabilities enabled direct observation and characterization of quantum structures at the single-atom level. While the research activities were primarily hypothesis-driven fundamental investigations, the program adopted a comprehensive materials-by-design framework aimed at translating scientific discoveries into technological concepts compatible with scalable and intelligent manufacturing approaches.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Effects of model deficiencies on parameter estimation

Reliable structural dynamic models will be required as a basis for deriving the reduced-order plant models used in control systems for large space structures. Ground vibration testing and model verification will play an important role in the development of these models; however, fundamental differences between the space environment and earth environment, as well as variations in structural properties due to as-built conditions, will make on-orbit identification essential. The efficiency, and perhaps even the success, of on-orbit identification will depend on having a valid model of the structure. It is envisioned that the identification process will primarily involve parametric methods. Given a correct model, a variety of estimation algorithms may be used to estimate parameter values. This paper explores the effects of modeling errors and model deficiencies on parameter estimation by reviewing previous case histories. The effects depend at least to some extent on the estimation algorithm being used. Bayesian estimation was used in the case histories presented here. It is therefore conceivable that the behavior of an estimation algorithm might be useful in detecting and possibly even diagnosing deficiencies. In practice, the task is complicated by the presence of systematic errors in experimental procedures and data processing and in the use of the estimation procedures themselves.

Hasselman, T. K.↗
DOE OSTI2022

Niobium in the clean limit: An intrinsic type-I superconductor

Niobium is one of the most researched superconductors, both theoretically and experimentally. It is enormously significant in all branches of superconducting applications, from powerful magnets to quantum computing. It is therefore imperative to understand its fundamental properties in great detail. In this work, we use the results of recent microscopic calculations of anisotropic electronic, phonon, and superconducting properties, and apply a thermodynamic criterion for the type of superconductivity, more accurate and straightforward than a conventional Ginzburg-Landau parameter κ-based delineation, to show that pure niobium is a type-I superconductor in the clean limit. However, disorder (impurities, defects, strain, stress) pushes it to become a type-II superconductor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Modulating thermal load through lightweight residential building walls using thermal energy storage and controlled precooling strategy

Precooling is a recognized technique for reducing cooling energy in buildings during peak hours by shifting load to off-peak hours. This technique is particularly effective in buildings with high thermal mass, because of their large thermal energy storage capacity, and in commercial buildings due to their variable electricity pricing based on time-of-use rates. Precooling in residential buildings has been a matter of limited interest in the past because of their low thermal mass and typically uniform electricity pricing rate. While previous studies on precooling primarily focused on cost savings, an important aspect of precooling is the thermal load modulation, which could be very effective in managing peak demand in lightweight residential buildings integrated with thermal energy storage systems. In this study, we examine different precooling strategies to manage the heat gains in lightweight building walls integrated with phase-change materials. We create nine different precooling profiles by controlling the interior temperature, and then evaluate the influence of the precooling profiles on four key building energy performance parameters: total heat gain, peak heat gain, maximum heat gain during peak hours, and time at which peak occurs. To thoroughly understand the fundamental physics, we first consider hypothetical climates and obtain the optimal precooling strategy required to achieve maximum peak shedding and shifting while minimizing the total heat gains. We then extend the model to Baltimore, Maryland, and estimate the benefits of the optimized precooling strategy under real conditions. The optimal precooling strategy proposed in this study can shift the peak heat gain by up to 14 hours, thereby reducing the heat gain during peak period by up to 95%, at the expense of a 23% increase in the total heat gains.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Novel nonwetting solid-infused surfaces for superior fouling mitigation

Fouling is a ubiquitous issue in several environmental and energy applications. Here we introduce novel nonwetting solid-infused surfaces (SIS) with superior anti-fouling characteristics that are durable than conventional nonwetting surfaces in a dynamic flow environment. A systematic study is presented to elucidate the fouling mitigation performance of SIS in comparison to lubricant-infused surface (LIS) and conventional smooth surface. Copper tubes with SIS, LIS or smooth inner walls are fabricated and subjected to accelerated calcium sulfate fouling in a flow fouling experimental setup. Fouling on the various surface types is quantified in terms of asymptotic fouling resistance, and the fundamental morphological differences in the interactions of the foulant and the various surface types are analyzed. Based on a systematic sweep of the parameter combinations using design of experiments and Taguchi analysis, an analytical dependence of asymptotic fouling resistance on the governing parameters namely, Reynolds number, foulant concentration and temperature is derived. The analytical model is shown to predict the asymptotic fouling resistance to within 20% accuracy with a 95% confidence. In addition, for the first time, the effects of shear durability on the fouling mitigation performance of LIS vis-à-vis SIS are studied. It is shown that the novel nonwetting SIS offers a robust option for superior fouling mitigation over LIS in the long run.

42 ENGINEERING↗
DOE OSTI2022

Analysis of the laser welding keyhole using inline coherent imaging

Laser beam welding is a widely used fusion welding process in many industrial applications such as automotive, aerospace, energy, defense, and medical products. Industry has a fundamental need to model the laser welding process to minimize experimental testing and improve confidence in production welds. However, computational models attempting to predict weld formation are limited by an incomplete understanding of the beam-material interactions. As a consequence, these models do not accurately predict the mechanisms associated with laser weld formation. To improve the current state of prediction capabilities, it is vital to better detect/measure the physical aspects of the weld pool during high energy density welding. In this work, a novel real-time laser weld monitoring device using inline coherent imaging (ICI) was used to provide a fundamental understanding of laser weld formation via vaporization. The objective of this work was to investigate and quantify the relationship relating laser weld parameters and the vapor capillary (keyhole) through a state-of-the-art measurement technique. Bead-on-plate laser beam welds were produced with partial penetration on 304L stainless steel, 2205 duplex stainless steel, and Ti-6Al-4V. Keyhole monitoring was performed using a commercially available ICI system to collect keyhole penetration data in real time. These measurements were reconstructed to generate the vapor capillary shape at different welding parameters. Process parameters significantly influenced the keyhole shape and the keyhole root position relative to the process beam. Finally, the keyhole geometry showed distinct differences between the stainless steel alloys and Ti-6Al-4V.

2205↗
DOE OSTI2023

Unsupervised machine learning discovery of structural units and transformation pathways from imaging data

We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. With only these postulates, we developed a machine learning method leveraging a rotationally invariant variational autoencoder (VAE) that can identify the existing molecular fragments observed within a material. The approach encodes the information contained in STEM image sequences using a small number of latent variables, allowing the exploration of chemical transformation pathways by tracing the evolution of atoms in the latent space of the system. The results suggest that atomically resolved STEM data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods in the physical sciences and will stimulate the development of more sophisticated ways to encode physical constraints in the encoder–decoder architectures and generative physical laws and causal relationships in the latent space of VAEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Shubnikov–de Haas oscillations of biaxial-strain-tuned superconductors in pulsed magnetic field up to 60 T

Two-dimensional (2D) materials have gained increasing prominence not only in fundamental research but also in daily applications. However, to fully harness their potential, it is crucial to optimize their properties with an external parameter and track the electronic structure simultaneously. Magnetotransport over a wide magnetic field range is a powerful method to probe the electronic structure and, for metallic 2D materials, quantum oscillations superimposed on the transport signals encode Fermi surface parameters. In this manuscript, we utilize biaxial strain as an external tuning parameter and investigate the effects of strain on the electronic properties of two quasi-2D superconductors, MoTe 2 and RbV 3 Sb 5 , by measuring their magnetoresistance in pulsed magnetic fields up to 60 T. With a careful selection of insulating substrates, we demonstrate the possibility of both the compressive and tensile biaxial strains imposed on MoTe 2 and RbV 3 Sb 5 , respectively. For both systems, the applied strain has led to superconducting critical temperature enhancement compared to their free-standing counterparts, proving the effectiveness of this biaxial strain method at cryogenic temperatures. Clear quantum oscillations in the magnetoresistance—the Shubnikov–de Haas (SdH) effect—are obtained in both samples. In strained MoTe 2 , the magnetoresistance exhibits a nearly quadratic dependence on the magnetic field and remains non-saturating even at the highest field, whereas in strained RbV 3 Sb 5 , two SdH frequencies showed a substantial enhancement in effective mass values, hinting at a possible enhancement of charge fluctuations. Our results demonstrate that combining biaxial strain and pulsed magnetic field paves the way for studying 2D materials under unprecedented conditions.

2D materials↗
DOE OSTI2021

A multiscale insight into the growth of h-BN: effect of the enclosure

Abstract There is a lack of knowledge on the fundamental growth mechanisms governing the characteristics of 2D materials synthesized by the chemical vapor deposition (CVD) technique and their correlation with experimentally controllable parameters, which hindered their wafer-scale synthesis. Here, we pursued an analytical and computational approach to access the system states that are not experimentally viable to address these critical needs. We developed a multiscale computational framework correlating the macroscale heat and mass flow with the mesoscale morphology of the as-grown 2D materials by solving the coupled system of heat/mass transfer and phase-field equations. We used hexagonal boron nitride (h-BN) as our model material and investigated the effect of substrate enclosure on its growth kinetics and final morphology. We revealed a lower concentration with a more uniform distribution on the substrate in an enclosed-growth than open-growth. It leads to a more uniform size distribution of the h-BN islands, consistent with existing experimental investigations.

Materials Science↗
DOE OSTI2020

Design, Optimization, and In-Depth Understanding of Front and Rear Junction Screen-Printed Double-Side Passivated Contacts Solar Cells

In this work, detailed numerical modeling is performed for front junction (FJ) and rear junction (RJ) n-type Si solar cells with screen-printed double-side poly-Si based tunnel oxide passivated contacts (TOPCon). Fundamental understanding of both structures is crucial to explore factors that limit the efficiency potential of the two designs. By investigating several key parameters such as front poly sheet resistance and thickness, bulk material properties, and carrier transport in our simulation model, we determine and explain why RJ cells outperform FJ cells. Our findings reveal that FJ suffers from present technological limitations of p-poly based passivated contacts, namely, i) large recombination observed in textured p-TOPCon emitter and ii) low solid solubility and hole mobility in boron-doped poly-Si resulting in very high sheet resistance of front p-poly layers which must be made thin to reduce absorption losses. In contrast, RJ desensitized the cell efficiency to front sheet resistance allowing application of ultra-thin front n-poly layers and is therefore ideally suited for double-side TOPCon cells. Lastly, we have developed a roadmap for efficiency projections of commercial-type screen-printed RJ and FJ topologies reaching ~24% and >22.5% efficiencies respectively, quantifying and explaining the impact of technological innovations on the performance of each design.

14 SOLAR ENERGY↗