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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Leveraging Automated Fiber Placement Computer Aided Process Planning Framework for Defect Validation and Dynamic Layup Strategies

Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. The current state of process planning requires a high degree of interaction from the process planner and could greatly benefit from increased automation. Therefore, a list of key steps and functions are created to identify the more difficult and time-consuming phases of process planning. Additionally, a set of metrics must exist by which to evaluate the effectiveness of the manufactured laminate from the machine code created during the Process Planning stage. Layup strategies, in addition to dog ears, stagger shifts, steering constraints, and starting points, represented the group of functions labeled as process optimization and ranked the highest in terms of priority for automation. The laminates resulting from the selected parameters are evaluated through the occurrences of principal defect metrics such as fiber gaps, overlaps, angle deviation and steering violations. This document presents an automated software solution to the layup strategy and starting point selection phase of process planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.

HiCAM↗
DOE OSTI2022

Sensor selection and tool wear prediction with data‐driven models for precision machining

Abstract Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency‐domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data‐driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time‐domain signal statistics, frequency‐domain analysis, and time‐frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least‐Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time‐frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

Han, Seulki↗
NASA NTRS2003

Alternate Communications Spectrum Study (ACSS) for Aviation Data Links (ADL)

The aim of the work was to identify the key factors involved in the use of alternate spectrum in various bands for a future integrated CNS data link. The study focused on systems and spectral bands that can deliver VDL-or-higher data rates in a two-way communication setting (including air-ground, ground-air, and air-air modes of operation), with multiple platforms (aircraft) operating in the same local environment. We begin with a review of the initial task list, and the final task list. The final task list contained a focus upon spectral availability and related systems that could be affected by the deployment of a new aviation data link (ADL) system. Most of this addresses the lower few layers of the communications protocol stack. A brief review of current related efforts in the aeronautical community is then provided, in which we describe several systems and programs of interest. Participation in some of these efforts is recommended. We also delineate several of the advantages and disadvantages of these system/efforts, in view of anticipated requirements of a new ADL. Desired attributes of a new ADL system are then discussed, and a connection with existing systems is made. The need to consider a wider set of alternative systems and technologies is described, and the beneficial aspects of a particular transmission technique- spread spectrum-are discussed. We then discuss in more detail several potential spectral regions, in terms of propagation conditions, available technology, spectrum availability, and waveform selection. Some comments on the need for standardization are also provided. We note that none of the existing systems described will likely meet the full range of desired features of a new ADL, but that several systems and spectral regions offer promise in terms of one or more characteristics. A system design and analysis approach is then provided. In this, we again focus on the lower few layers of the protocol stack, and aim to capture the main features and parameters that must be selected in the design. Two appendices show example versions and initial results of the first few technical steps in the design approach. Some conclusions are then drawn, and in the final section, recommendations are provided, the most important of which are repeated here: 1. Continue the effort begun here. As detailed in this report, we have only uncovered much of the work that needs to be done in order to provide the foundation for a flexible, high- performance, robust ADL. 2. Seize the opportunity to begin testing in the MLS band. The wide bandwidths and low level of usage of this band make it an ideal one for proof-of-concept type testing. Other (non- aeronautical) organizations are likely to make claims on the band if it is not being used. The primary conclusion is that there is a real and pressing need for a new aviation data link. vi

Matolak, David W.↗
DOE OSTI2021

Tunable and scalable fabrication of plasmonic dimer arrays with sub-10nm nanogaps by area-selective atomic layer deposition

Nanogaps in metallic nanostructures produce local field enhancements with potential applications in surface enhanced spectroscopy, solar energy conversion, and photocatalysis. Atomic layer deposition is applied as a conformal coating to modify nanogap sizes and tune the optical properties of plasmonic dimer arrays with sub-10 nm nanogaps. Nanostructures are fabricated using layers of gold and palladium to combine features of plasmonics and area-selective atomic layer deposition, where copper metal is deposited on palladium-covered surfaces. Direct measurements of optical extinction for successive smaller nanogaps and thicker copper coatings show that spectral features become broadened at first due to heating-induced shape changes but subsequently sharpen as copper coatings form on palladium structures. Furthermore, longitudinal resonances of plasmonic dimers blue shift for thin coatings due to heating and decreasing aspect ratio, but thicker coatings lead to red shifts due to narrowing nanogaps. Together, these results show that area-selective atomic layer deposition is a promising tool for achieving large area arrays of plasmonic dimers with sub-10 nm nanogaps.

Engineering↗
DOE OSTI2023

Data-driven analysis and prediction of stable phases for high-entropy alloy design

High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.

36 MATERIALS SCIENCE↗
NASA NTRS1987

IUE observations of the broad continuum feature at 1400 A in the silicon and related stars

The upper main sequence chemically peculiar stars have previously been studied spectrophotometrically using low-resolution TD-1 spectra. These show that the broad continuum feature at 1400 A is a useful indicator of Si anomaly. In this paper the observations are extended to IUE low-resolution spectra and it is shown that this feature is indeed well correlated with Si and also with optical chemical peculiarity indices. Its utility in selecting silicon stars is further demonstrated. This feature is likely due to autoionization of Si II and is the best available discriminator of the overabundance of a key element in the Ap stars.

Shore, S. N.↗
DOE OSTI2022

Metal‐Organic Framework (MOF) Morphology Control by Design

Abstract Exerting morphological control over metal‐organic frameworks (MOFs) is critical for determining their catalytic performance and to optimize their packing behavior in areas from separations to fuel gas storage. A mechanism‐based approach to tailor the morphology of MOFs is introduced and experimentally demonstrated for five cubic Zn 4 O‐based MOFs. This methodology provides three key features: 1) computational screening for selection of appropriate additives to change crystal morphology based on knowledge of the crystal structure alone; 2) use of additive to metal cluster geometric relationships to achieve morphologies expressing desired crystallographic facets; 3) potential for suppression of interpenetration for certain phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs a multilayer feedforward artificial neural network with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. Here, we detail the hyperparameters optimization of these models using the grid search algorithm.

42 ENGINEERING↗
DOE OSTI2021

Energy and power quality measurement for electrical distribution in AC and DC microgrid buildings

Today's selection of DC microgrid buildings features a diverse set of electrical topologies and turnkey solutions, each with specific design trade-offs and optimizations. Designers desperately need standardized metrics and procedures for measurement and verification (M&V) to analyze and compare the advantages of each DC solution to traditional AC building networks. This work develops M&V procedures for quantifying and comparing the energy efficiency and power quality in buildings. To calculate full-building efficiency, this work introduces the measurement-informed modeling method, a procedure that develops and refines a building's energy model with metered data. To quantify power quality, this work defines a new voltage quality index that applies to both AC and DC buildings. This article describes the equipment, instrumentation, and operation necessary to calculate the efficiency and power quality. It then demonstrates these methods with a set of field tests. We report these M&V procedures can ultimately be used to compare and improve the efficiency and power quality of various DC topologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Exploiting Intersubband Plasmons in Vertically Aligned Carbon Nanotubes for Near-Infrared Electrochromic Windows

Optically transparent materials with switchable near-infrared (NIR) transmissivity are of significant interest for energy-saving smart window technologies. To this end, we demonstrate that semitransparent films of vertically aligned carbon nanotubes (CNTs) incorporated into electrochemically gated devices exhibit NIR transmittance changes up to 47% and bistable optical states that are appealing for low-power, large-area operation. The tunable NIR electrochromic response is driven by a doping-induced intersubband plasmon (ISBP) absorption, an optical feature in CNTs that is selective to light polarized perpendicular to the CNT axis. Vertically aligned CNT films (as opposed to more conventional planar CNT mats) thus allow us to isolate and study the ISBP resonance changes with applied voltage, electrode material, and film thickness.

absorption↗
DOE OSTI2022

Finding predictive models for singlet fission by machine learning

Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Three-Terminal Bipolar Junction Bottom Cell as Simple as PERC: Towards Lean Tandem Cell Processing

Silicon-based tandem cells are a potential upgrade for the dominating PERC technology and can greatly increase the conversion efficiency. A novel approach, the three-terminal (3T) tandem using interdigitated back-contact (IBC) cells, was shown to combine the advantages of four- and two-terminal tandem cells at the expense of a more complex bottom cell fabrication process. In this paper, we propose a simplified and lean PERC-like 3T bipolar junction bottom cell featuring two minority-carrier selective contacts and a single majority-carrier selective contact, and thus resembling a bipolar junction transistor architecture. We present the lean PERC-like fabrication process and explain the working principle by using current-voltage measurements of illuminated 3T IBC cells.

14 SOLAR ENERGY↗
DOE OSTI2024

Anti-idiotype isolation of a broad and potent influenza A virus-neutralizing human antibody

The VH6-1 class of antibodies includes some of the broadest and most potent antibodies that neutralize influenza A virus. Here, we elicit and isolate anti-idiotype antibodies against germline versions of VH6-1 antibodies, use these to sort human leukocytes, and isolate a new VH6-1-class member, antibody L5A7, which potently neutralized diverse group 1 and group 2 influenza A strains. While its heavy chain derived from the canonical IGHV6-1 heavy chain gene used by the class, L5A7 utilized a light chain gene, IGKV1-9, which had not been previously observed in other VH6-1-class antibodies. The cryo-EM structure of L5A7 in complex with Indonesia 2005 hemagglutinin revealed a nearly identical binding mode to other VH6-1-class members. The structure of L5A7 bound to the isolating anti-idiotype antibody, 28H6E11, revealed a shared surface for binding anti-idiotype and hemagglutinin that included two critical L5A7 regions: an FG motif in the third heavy chain-complementary determining region (CDR H3) and the CDR L1 loop. Surprisingly, the chemistries of L5A7 interactions with hemagglutinin and with anti-idiotype were substantially different. Overall, we demonstrate anti-idiotype-based isolation of a broad and potent influenza A virus-neutralizing antibody, revealing that anti-idiotypic selection of antibodies can involve features other than chemical mimicry of the target antigen.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1975

Application of remote sensor data to geologic analysis of the Bonanza test site Colorado

Selected samples of anomalous surface features commonly associated with the various types of uranium deposits are presented and recommendations for sensor applications are given. The features studied include: epigenetic uranium ore roll type; precambrian basal conglomerate type; vein-type uranium deposits; pipe-structure or diatreme deposits; evaporitic uranium deposits. The hydrogeology of the Mosquito Range and the San Luis Valley is also examined.

Lee, K.↗
NASA NTRS1981

Extracting spatial information from large aperture exposures of diffuse sources

The spatial properties of large aperture exposures of diffuse emission can be used both to investigate spatial variations in the emission and to filter out camera noise in exposures of weak emission sources. Spatial imaging can be accomplished both parallel and perpendicular to dispersion with a resolution of 5-6 arc sec, and a narrow median filter running perpendicular to dispersion across a diffuse image selectively filters out point source features, such as reseaux marks and fast particle hits. Spatial information derived from observations of solar system objects is presented.

Clarke, J. T.↗
NASA NTRS1982

Multilevel optimum design of structures with fiber-composite stiffened-panel components

The multilevel approach to minimum weight structural design is extended to wing box structures with fiber-composite stiffened-panel components. Strength, deflection, and panel buckling constraints are treated at the system level with equivalent-thickness-type design variables. Local buckling and panel buckling constraints are guarded against at the component level, employing detailed component dimensions as design variables. A key feature of the method is selection of change in stiffness as the component level objective function to be minimized. Numerical results are given for wing box structures with sandwich and hat-stiffened fiber-composite panels.

Schmit, L. A.↗
NASA NTRS1983

Comparison of scientific and administrative database management systems

Some characteristics found to be different for scientific and administrative data bases are identified and some of the corresponding generic requirements for data base management systems (DBMS) are discussed. The requirements discussed are especially stringent for either the scientific or administrative data bases. For some, no commercial DBMS is fully satisfactory, and the data base designer must invent a suitable approach. For others, commercial systems are available with elegant solutions, and a wrong choice would mean an expensive work-around to provide the missing features. It is concluded that selection of a DBMS must be based on the requirements for the information system. There is no unique distinction between scientific and administrative data bases or DBMS. The distinction comes from the logical structure of the data, and understanding the data and their relationships is the key to defining the requirements and selecting an appropriate DBMS for a given set of applications.

Stoltzfus, J. C.↗