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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ASSESSING FUEL PROPERTY EFFECTS ON CAVITATION AND EROSION PROPENSITY USING A COMPUTATIONAL FUEL SCREENING TOOL

To advance compression ignition combustion strategies, researchers have evaluated fuel property effects and their impact on achieving higher efficiencies and lower emissions levels relative to current capabilities. Within the Department of Energy's Co-Optima initiative, there has been a recent focus on understanding the influence of fuel properties on fuel injection performance. To help identify candidate fuels that can meet desired injector performance metrics, a computational fuel screening tool is under development that can link fuel properties with the tendency of a given fuel to cavitate and lead to cavitation-induced erosion. In the initial development of this tool, five liquid fuel properties were selected to represent candidate fuels, namely density, viscosity, vapor pressure, surface tension, and heat of vaporization. A design of experiments methodology was employed to generate a set of pseudo fuel cases, which can represent the main effects and interactions among the fuel properties and be related to cavitation erosion predictions. Large eddy simulations were performed using a mixture modeling approach to predict the cavitation and erosion propensity of these pseudo-fuels in terms of the mean fuel vapor mole fraction and stored impact energy from repeated cloud collapse events. The low order regression models generated from this study revealed the importance of liquid fuel density on cavitation formation, whereas liquid viscosity was found to have a strong negative correlation with erosion severity. The surrogate models were then used in the fuel screening tool to rank the cavitation and erosion tendency of four candidate single-component fuels: methyl decanoate, isooctane, ethanol and n-dodecane. While the fuel screening tool was not able to quantitatively predict the cavitation and erosion response metrics, the tool was able to accurately rank the relative cavitation and erosion propensity of the four fuels. Overall, ethanol and iso-octane were observed to produce the highest levels of cavitation and erosion, respectively.

cavitation-induced erosion↗
DOE OSTI2018

Controlling the State of Machine Tools

Globally, manufacturers make great efforts to maintain the performance of industrial equipment. Developers and researchers are interested in automated approaches to maintaining the performance of equipment, especially at enterprises with high degrees of computerization and comprehensive information systems. In the present work, a new approach is outlined: an autonomous control system for maintaining the condition of metal-cutting machines. This approach is based on prior work in the field and permits maintenance on the basis of autonomous control of the state of machine tools. The structure of a system with the following generalized control functions is presented: decision making; and the issuing of commands on the basis of built-in resources. The formulation of control decisions employs the theory of fuzzy sets and fuzzy logic. By means of the ANFIS fuzzy network system, the autonomy of state control of the machine tool may be assessed. Criticality assessment of the condition of machine tools and their components is important in making decisions as to the setup of systems controlling the condition of equipment at enterprises, with assessment of their efficiency. In the monitoring subsystem, provision is made for assessment of the diagnostic results, prediction, and the generation of control decisions so as to prevent disruptions of machine-tool operation.

42 ENGINEERING↗
NASA NTRS2017

Greased Lightning (GL-10) Performance Flight Research: Flight Data Report

Modern aircraft design methods have produced acceptable designs for large conventional aircraft performance. With revolutionary electronic propulsion technologies fueled by the growth in the small UAS (Unmanned Aerial Systems) industry, these same prediction models are being applied to new smaller, and experimental design concepts requiring a VTOL (Vertical Take Off and Landing) capability for ODM (On Demand Mobility). A 50% sub-scale GL-10 flight model was built and tested to demonstrate the transition from hover to forward flight utilizing DEP (Distributed Electric Propulsion)[1][2]. In 2016 plans were put in place to conduct performance flight testing on the 50% sub-scale GL-10 flight model to support a NASA project called DELIVER (Design Environment for Novel Vertical Lift Vehicles). DELIVER was investigating the feasibility of including smaller and more experimental aircraft configurations into a NASA design tool called NDARC (NASA Design and Analysis of Rotorcraft)[3]. This report covers the performance flight data collected during flight testing of the GL-10 50% sub-scale flight model conducted at Beaver Dam Airpark, VA. Overall the flight test data provides great insight into how well our existing conceptual design tools predict the performance of small scale experimental DEP concepts. Low fidelity conceptual design tools estimated the (L/D)( sub max)of the GL-10 50% sub-scale flight model to be 16. Experimentally measured (L/D)( sub max) for the GL-10 50% scale flight model was 7.2. The aerodynamic performance predicted versus measured highlights the complexity of wing and nacelle interactions which is not currently accounted for in existing low fidelity tools.

McSwain, Robert G.↗
DOE OSTI2023

A Guideline to Mitigate Interfacial Degradation Processes in Solid‐State Batteries Caused by Cross Diffusion

Abstract Diffusion of transition metals across the cathode–electrolyte interface is identified as a key challenge for the practical realization of solid‐state batteries. This is related to the formation of highly resistive interphases impeding the charge transport across the materials. Herein, the hypothesis that formation of interphases is associated with the incorporation of Co into the Li 7 La 3 Zr 2 O 12 lattice representing the starting point of a cascade of degradation processes is investigated. It is shown that Co incorporates into the garnet structure preferably four‐fold coordinated as Co 2+ or Co 3+ depending on oxygen fugacity. The solubility limit of Co is determined to be around 0.16 per formula unit, whereby concentrations beyond this limit causes a cubic‐to‐tetragonal phase transition. Moreover, the temperature‐dependent Co diffusion coefficient is determined, for example, D 700 °C = 9.46 × 10 −14 cm 2 s −1 and an activation energy E a = 1.65 eV, suggesting that detrimental cross diffusion will take place at any relevant process condition. Additionally, the optimal protective Al 2 O 3 coating thickness for relevant temperatures is studied, which allows to create a process diagram to mitigate any degradation with a minimum compromise on electrochemical performance. This study provides a tool to optimize processing conditions toward developing high energy density solid‐state batteries.

Chemistry↗
DOE OSTI2017

TC2015: Life cycle analysis of co-formed coal fines and hydrochar produced in twin-screw extruder (TSE)

Life cycle analysis (LCA) is a quantitative tool that evaluates the environmental performance of a process or system. The main objective of this LCA study is to assess the greenhouse gas (GHG) emissions and life cycle energy use associated with solid fuels produced by co-forming coal fines and hydrochar. The hydrochar is derived from loblolly pine wood via hydrothermal carbonization (HTC) conducted in a reactive twin-screw extruder (TSE). The final product is a drop-in replacement for coal. The life cycle impacts of the co-formed end products are quantified from ‘cradle-to-grave’. Model input parameters are collected from life cycle databases, literature, experimental measurements and simulations by engineering software. Results show that electricity generated from the co-formed products has significantly lower GHG intensity and slightly higher life cycle energy use than coal-generated electricity. The most carbon-intensive component in the overall hydrochar system is the HTC plant. However, this could be improved in the future by plant re-design to recover and recycle thermal energy for more efficient operation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine learning enables national assessment of wind plant controls with implications for land use

Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

17 WIND ENERGY↗
DOE OSTI2019

The Quest for High Critical Current in Applied High-Temperature Superconductors

In this work, we present a perspective on a new critical-current-by-design paradigm to tailor and enhance the current-carrying capacity of applied superconductors. Critical-current-by-design is based on large-scale simulations of vortex matter pinning in high-temperature superconductors and has qualitative and quantitative predictive powers to elucidate vortex dynamics under realistic conditions and to propose vortex pinning defects that could enhance the critical current, particularly at high magnetic fields. The simulations are validated with controlled experiments and demonstrate a powerful tool for designing high-performance superconductors for targeted applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Development of a conduction-based model for analyzing frozen startup of alkali-metal heat pipes

One key area of interest in heat pipe modeling/simulation is to analyze the startup behavior of the liquid-metal heat pipes (LMHPs) from a frozen state. This so-called ‘frozen startup’ process involves a complex set of nonlinear mass and heat transport phenomena, including phase transitions from solid to liquid and vapor, multiphase interactions, microporous wick flow, and compressible vapor dynamics. The complexity of these processes makes it challenging to simulate LMHP’s frozen startup using conventional numerical methods or commercial computational fluid dynamics (CFD) software. This paper presents a simplified conduction-based modeling approach that can provide practical insights into the entire LMHP frozen startup process, while alleviating the challenges of modeling its complex physics. The theoretical foundation and physical assumptions of the proposed model are based solely on heat-conduction equation, allowing for a more tractable simulation without sacrificing essential physical accuracy. The proposed model was implemented in a commercial CFD software, and its prediction was compared with the experimental data obtained from sodium heat-pipe startup experiments. The comparison highlights the proposed model's ability to capture the transient thermal behavior of LMHP during frozen startup. This study not only validates the conduction-based frozen startup modeling method but also shows its potential as a practical and efficient tool for understanding the startup performance of the LMHP systems.

Microreactor↗
DOE OSTI2016

A thematic review of life cycle assessment (LCA) applied to pig production

Commercial livestock production is known to have significant impacts on the environment. Pig production is a complex system which involves the production of animal feed, transportation, animal rearing and waste management. One tool for assessing the environmental performance of such complex systems is life cycle assessment (LCA). LCA has been applied to pig production considerably to date. This paper provides a chronological review of state-of-the-art pig production LCAs under three themes: feed production; entire-system livestock rearing; and waste management. The study considers how LCA applications have addressed technological improvements in animal husbandry, and highlights methodological limitations, particularly related to cross-study comparisons. Recent research demonstrates crude protein reduction in feed and anaerobic treatment of pig excreta resulting in bioenergy production are the key targets for environmental performance improvements related to pig production. - Highlights: • An extensive review of LCA applied to pig production is provided chronologically over the past decade. • Individual studies have been categorised into feed, whole-system pig production and waste management themes. • We consider how LCAs have addressed state-of-the-art pig husbandry. • We offer a discussion on key findings, limitations and future research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A multiphase flow model of water droplets dielectrophoretic-induced air dehumidification phenomena

Air humidity in indoor spaces plays a critical role in human comfort and health. Dehumidification systems are used for building humidity controls, but they can take significant energy consumption, especially in geographic locations with high outdoor humidity and warm climates. Consequently, there is a growing demand for innovative dehumidification processes that consume minimal energy. Dielectrophoretic air dehumidification represents one such promising approach. However, it has not garnered significant attention due to the absence of engineering models and simulation tools capable of evaluating its performance and limitations at large-scale airflows. A new numerical multiphase CFD model, which is also experimentally validated, is developed in a customized Reacting Foam solver based on OpenFOAM® version 9. The newly developed model seeks to decrease substantial energy consumption and lower costs by leveraging the dielectrophoretic phenomenon to regulate moisture levels in the air. The solver integrates a hybrid Eulerian-Lagrangian framework to track the droplet's trajectory and growth rate while solving the continuum equations for the moist air. An electrospray produces electrically charged droplets, which grow during their in-flight trajectories as water vapor condenses onto their surfaces. The role of electrostatic forces in promoting vapor condensation within a high-gradient electrical field is investigated, and the dielectrophoretic vapor nucleation process on charged water droplets is discussed. The CFD model was validated against results from the literature and from proof-of-concept experiments conducted by the authors, which showed a 2 % air dehumidification with a single electrospray and airflow rate of 5 cubic feet per minute. The simulation results indicated that augmenting the number of electrically charged spray droplets increased the dehumidification of the air to 25 %. The initial mean droplet diameter, the orientation of the injector and relative humidity significantly influence the assessment of dehumidification. As a result, scaling up this approach to larger airflow volumes is identified as a potential future research direction.

42 ENGINEERING↗
DOE OSTI2022

A review of computational methods for studying oscillating water columns – the Navier-Stokes based equation approach

This review evaluates the state-of-the-practice numerical tools used to predict the performance of Oscillating Water Column (OWC). The OWC is a widely studied Wave Energy Converter that provides a reliable form of renewable form of electricity that can potentially meet global energy needs. However, the fluid-flow phenomena affecting its hydrodynamic performance are not fully understood. While there has been the successful full-scale deployment of OWCs, various computational methods are being explored to optimize this technology. Potential flow theory is commonly used to evaluate the efficiency of OWCs; however, this assumption tends to over-predict the hydrodynamic performance. Recently, numerical studies using a diverse set of commercial, open-source, or in-house Computational Fluid Dynamics (CFD) using Reynolds Averaged Navier Stokes (RANS) and Large-Eddy Simulation codes show a better comparison to available experimental results but are computationally expensive. ANSYS Fluent was found to be the most widely used CFD code applied to the study of the OWCs, with a high degree of accuracy in terms of experimental validation of numerical results.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO 2 . Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. In conclusion, a strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems such as cell signaling and epigenetic regulation. Precisely defining the stoichiometry of PTMs has been challenging because, in the widely used bottom-up proteomics methods, the detection occurs at the peptide level and thus the link between peptides and their specific modification site is lost, resulting in proteoform ambiguity. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Proteins with positional isomers refers to proteoforms with identical total mass and set of modifications but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for relative quantification of PI within a single tool. We benchmarked IsoForma’s performance against two existing workflows and highlight the similarity of the results and improvements in speed. Overall, IsoForma provides a streamlined process, reduces the time of conducting isoform-based analyses, and offers an essential framework for developing customized proteoform analysis workflows. Finally, the software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Quantifying Progress Across Different CMIP Phases With the ESMValTool

Abstract More than 40 model groups worldwide are participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), providing a new and rich source of information to better understand past, present, and future climate change. Here, we use the Earth System Model Evaluation Tool (ESMValTool) to assess the performance of the CMIP6 ensemble compared to the previous generations CMIP3 and CMIP5. While CMIP5 models did not capture the observed pause in the increase in global mean surface temperature between 1998 and 2013, the historical CMIP6 simulations agree well with the observed recent temperature increase, but some models have difficulties in reproducing the observed global mean surface temperature record of the second half of the twentieth century. While systematic biases in annual mean surface temperature and precipitation remain in the CMIP6 multimodel mean, individual models and high‐resolution versions of the models show significant reductions in many long‐standing biases. Some improvements are also found in the vertical temperature, water vapor, and zonal wind speed distributions, and root‐mean‐square errors for selected fields are generally smaller with reduced intermodel spread and higher average skill in the correlation patterns relative to observations. An emerging property of the CMIP6 ensemble is a higher effective climate sensitivity with an increased range between 2.3 and 5.6 K. A possible reason for this increase in some models is improvements in cloud representation resulting in stronger shortwave cloud feedbacks than in their predecessor versions.

Bock, L.↗
DOE OSTI2021

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients

Abstract The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

New developments in cost modeling for the LHC computing

The increase in the scale of LHC computing during Run 3 and Run 4 (HL-LHC) will certainly require radical changes to the computing models and the data processing of the LHC experiments. The working group established by WLCG and the HEP Software Foundation to investigate all aspects of the cost of computing and how to optimise them has continued producing results and improving our understanding of this process. In particular, experiments have developed more sophisticated ways to calculate their resource needs, we have a much more detailed process to calculate infrastructure costs. This includes stud-ies on the impact of HPC and GPU based resources on meeting the computing demands. We have also developed and perfected tools to quantitatively study the performance of experiments workloads and we are actively collaborating with other activities related to data access, benchmarking and technology cost evolution. In this contribution we expose our recent developments and results and outline the directions of future work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Enhanced thermoelectric figure of merit in strained Tl-doped Bi{sub 2}Se{sub 3}

We explain recent experimental findings on Tl-doped Bi{sub 2}Se{sub 3} by determining the electronic and transport properties by first-principles calculations and semi-classical Boltzmann theory. Though Tl-doping introduces a momentum-dependent spin-orbit splitting, the effective mass of the carriers is essentially not modified, while the band gap is reduced. Tl is found to be exceptional in this respect as other dopants modify the dispersion, which compromises thermoelectricity. Moreover, we demonstrate that only after Tl-doping strain becomes an efficient tool for enhancing the thermoelectric performance. A high figure of merit of 0.86 is obtained for strong p-doping (7 × 10{sup 20} cm{sup −3}, maximal power factor) at 500 K under 2% tensile strain.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

THE NASA AMES PAH IR SPECTROSCOPIC DATABASE VERSION 2.00: UPDATED CONTENT, WEB SITE, AND ON(OFF)LINE TOOLS

A significantly updated version of the NASA Ames PAH IR Spectroscopic Database, the first major revision since its release in 2010, is presented. The current version, version 2.00, contains 700 computational and 75 experimental spectra compared, respectively, with 583 and 60 in the initial release. The spectra span the 2.5-4000 μm (4000-2.5 cm{sup -1}) range. New tools are available on the site that allow one to analyze spectra in the database and compare them with imported astronomical spectra as well as a suite of IDL object classes (a collection of programs utilizing IDL's object-oriented programming capabilities) that permit offline analysis called the AmesPAHdbIDLSuite. Most noteworthy among the additions are the extension of the computational spectroscopic database to include a number of significantly larger polycyclic aromatic hydrocarbons (PAHs), the ability to visualize the molecular atomic motions corresponding to each vibrational mode, and a new tool that allows one to perform a non-negative least-squares fit of an imported astronomical spectrum with PAH spectra in the computational database. Finally, a methodology is described in the Appendix, and implemented using the AmesPAHdbIDLSuite, that allows the user to enforce charge balance during the fitting procedure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗