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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Systematic Improvement of Quantum Monte Carlo Calculations in Transition Metal Oxides: sCI-Driven Wavefunction Optimization for Reliable Band Gap Prediction

Accurate determination of the electronic properties of correlated oxides remains a significant challenge for computational theory. Traditional Hubbard-corrected density functional theory (DFT+U) frequently encounters limitations in precisely capturing electron correlation, particularly in predicting band gaps. We introduce a systematic methodology to enhance the accuracy of diffusion Monte Carlo (DMC) simulations for both ground and excited states, focusing on LiCoO 2 as a case study. By employing a selected configuration interaction (sCI) approach, we demonstrate the capability to optimize wavefunctions beyond the constraints of single-reference DFT+U trial wavefunctions. Here, we show that the sCI framework enables accurate prediction of band gaps in LiCoO 2 , closely aligning with experimental values and substantially improving traditional computational methods. The study uncovers a nuanced mixed state of t 2g and e g orbitals at the band edges that is not captured by conventional single-reference methods, further elucidating the limitations of PBE+U in describing d-d excitations. Our findings advocate for the adoption of beyond-DFT methodologies, such as sCI, to capture the essential physics of excited-state wavefunctions in strongly correlated materials. The improved accuracy in band gap predictions and the ability to generate more reliable trial wavefunctions for DMC calculations underscore the potential of this approach for broader applications in the study of correlated oxides. This work not only provides a pathway for more accurate simulations of electronic structures in complex materials but also suggests a framework for future investigations of the excited states of other challenging systems.

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

In Silico Screening of CO 2 –Dipeptide Interactions for Bioinspired Carbon Capture

Carbon capture, sequestration and utilization offers a viable solution for reducing the total amount of atmospheric CO 2 concentrations. On an industrial scale, amine-based solvents are extensively employed for CO 2 capture through chemisorption. Nevertheless, this method is marked by the high cost associated with solvent regeneration, high vapor pressure, and the corrosive and toxic attributes of by-products, such as nitrosamines. An alternative approach is the biomimicry of sustainable materials that have strong affinity and selectivity for CO 2 . Bioinspired approaches, such as those based on naturally occurring amino acids, have been proposed for direct air capture methodologies. In this study, we present a database consisting of 960 dipeptide molecular structures, composed of the 20 naturally occurring amino acids. Furthermore, those structures were analyzed with a novel computational workflow presented in this work that considers certain interaction sites that determine CO 2 affinity. Density functional theory (DFT) and symmetry-adapted perturbation theory (SAPT) computations were performed for the calculation of CO 2 interaction energies, which allowed to limit our search space to 400 unique dipeptide structures. Using this computational workflow, we provide statistical insights into dipeptides and their affinity for CO 2 binding, as well as design principles that can further enhance CO 2 capture through cooperative binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

UrbanPop: A spatial microsimulation framework for exploring demographic influences on human dynamics

Ensuring the social equity of planning measures in social systems requires an understanding of human dynamics, particularly how individual relationships, activities, and interactions intersect with individual needs. Spatial microsimulation models (SMSMs) support planning for human security goals by representing human dynamics through realistic, georeferenced synthetic populations, that a) provide a complete representation of social systems while b) also protecting individual privacy. In this paper, we present UrbanPop, an open and reproducible SMSM framework for analysis of human dynamics with high spatial, temporal, and demographic resolution. UrbanPop creates synthetic populations of demographically detailed worker and student agents, positioning them first at probable nighttime locations (home), then moving them to probable daytime locations (work/school). Summary aggregations of these populations match the granular detail available at the census block group level in the American Community Survey Summary File (SF), providing realistic approximations of the actual population. UrbanPop users can select particular demographic traits important in their application, resulting in a highly tailored agent population. We first lay out UrbanPop's baseline methodology, including population synthesis, activity modeling, and diagnostics, then demonstrate these capabilities by developing case studies of shifting population distributions and high-risk populations in Knox County, TN during the global COVID-19 pandemic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

The role of size and structure of catalytic active sites in polyolefin hydrogenolysis

The increasing amount of plastic waste poses serious environmental problems that threaten both ecosystems and human well-being. Hydrogenolysis has been widely studied as an effective approach for converting polyolefins into high-value liquids and waxy fuels. Their multifaceted reaction mechanism, including dehydrogenation, C–C bond cleavage, and hydrogenation, highlights the need for sophisticated catalyst design. The suppression of methane production, a persistent challenge in polyolefin hydrogenolysis, requires precise control of the cleavage site and inhibition of successive C–C bond cleavage. This delicate balance is achieved by carefully tuning the size and structure of metals. In this review, we investigate the effects of the size and structure of active sites on their catalytic activity and selectivity for the hydrogenolysis of polyolefins, including polyethylene and polypropylene. In conclusion, a fundamental understanding of hydrogenolysis mechanisms, combined with strategic synthetic methodologies, is crucial for creating efficient catalysts with tailored properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Numerical investigation of the pressure-dependence of yield sooting indices for n-alkane and aromatic species

The pressure-dependence of the Yield Sooting Index (YSI), as a measure of sooting tendency, is numerically investigated for pressures ranging from 1 atm to 15 atm. Straight-chain alkanes, including ethane, propane, n-hexane, n-heptane, and n-dodecane, and aromatic species, including benzene, toluene, xylene, and ethylbenzene, are selected as test fuels. A detailed chemical mechanism is used for all test fuels considered, and a flamelet-based YSI simulation methodology is employed, where the YSI values are computed based on the Polycyclic Aromatic Hydrocarbon (PAH) dimer production rate at each pressure. It is found that the maximum flame centerline soot volume fraction increases with increasing pressure for all test compounds. However, the pressure dependence of the maximum soot volume fraction is similar for the hydrocarbons within the same class (n-alkane or aromatics). As a consequence, the YSI values computed at pressures up to 15 atm show only small deviations from their values computed and measured at atmospheric pressure. These results suggest that YSI is robust also at elevated pressures, close to those found in practical combustion devices. This study is entirely based on numerical simulations without any experimental measurements available for direct validation. Experimental YSI measurements at high pressures would be required to fully confirm the conclusions reported in this manuscript. Still, our numerical results provide some guidelines for the design of such experiments in the future.

42 ENGINEERING↗
DOE OSTI2019

Entropy-based closure for probabilistic learning on manifolds

In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffusion manifolds and the statistical sample generation is accomplished using a projected Itˆo stochastic differential equation. This probabilistic learning approach has been extended to polynomial chaos representation of databases on manifolds and to probabilistic nonconvex constrained optimization with a fixed budget of function evaluations. The methodology introduces an isotropic-diffusion kernel with hyperparameter ". Currently, " is more or less arbitrarily chosen. In this paper, we propose a selection criterion for identifying an optimal value of ", based on a maximum entropy argument. The result is a comprehensive, closed, probabilistic model for characterizing data sets with hidden constraints. This entropy argument ensures that out of all possible models, this is the one that is the most uncertain beyond any specified constraints, which is selected. Applications are presented for several databases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Capacity Design Method for Integrated Convective/Radiant Terminals to Guarantee Overall and Local Environment

To achieve energy efficiency in the operation of heating devices and ensure thermal comfort in indoor environments, the integrated convective/radiant terminals have become an important development direction for heating systems. This study selected a 14 m 2 bedroom hot summer and cold winter region in China as a case study. Computational Fluid Dynamics (CFD) methodology was used to investigate the design of integrated convective/radiant terminals to ensure thermal comfort for both the entire room and partial space. The results indicate that focusing on partial space during the steady-state stage can achieve energy savings of 31.1% compared to guaranteeing the entire room. Additionally, during the start-up stage, there is a significant reduction of convective unit capacity by 17.6% when the start-up time is 15 minutes. These findings provide data support for the design and engineering applications of products of integrated convective/radiant terminals. Besides, the excess convection ratios for the entire room and partial space are specified as 0.69 to 0.80 and 0.73 to 0.83, respectively.

Chi, Junjie↗
DOE OSTI2012

THE DEPENDENCE OF PRESTELLAR CORE MASS DISTRIBUTIONS ON THE STRUCTURE OF THE PARENTAL CLOUD

The mass distribution of prestellar cores is obtained for clouds with arbitrary internal mass distributions using a selection criterion based on the thermal and turbulent Jeans mass and applied hierarchically from small to large scales. We have checked this methodology by comparing our results for a log-normal density probability distribution function with the theoretical core mass function (CMF) derived by Hennebelle and Chabrier, namely a power law at large scales and a log-normal cutoff at low scales, but our method can be applied to any mass distributions representing a star-forming cloud. This methodology enables us to connect the parental cloud structure with the mass distribution of the cores and their spatial distribution, providing an efficient tool for investigating the physical properties of the molecular clouds that give rise to the prestellar core distributions observed. Simulated fractional Brownian motion (fBm) clouds with the Hurst exponent close to the value H = 1/3 give the best agreement with the theoretical CMF derived by Hennebelle and Chabrier and Chabrier's system initial mass function. Likewise, the spatial distribution of the cores derived from our methodology shows a surface density of companions compatible with those observed in Trapezium and Ophiucus star-forming regions. This method also allows us to analyze the properties of the mass distribution of cores for different realizations. We found that the variations in the number of cores formed in different realizations of fBm clouds (with the same Hurst exponent) are much larger than the expected root N statistical fluctuations, increasing with H.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

SU-C-16A-06: Optimum Radiation Source for Radiation Therapy of Skin Cancer

Purpose: Recently, different applicators are designed for treatment of the skin cancer such as scalp and legs, using Ir-192 HDR Brachytherapy Sources (IR-HDRS), Miniature Electronic Brachytherapy Sources (MEBXS), and External Electron Beam Radiation Therapy (EEBRT). Although, all of these methodologies may deliver the desired radiation dose to the skin, the dose to the underlying bone may become the limiting factor for selection of the optimum treatment technique. In this project the radiation dose delivered to the underlying bone has been evaluated as a function of the radiation source and thickness of the underlying bone. Methods: MC simulations were performed using MCNP5 code. In these simulations, the mono-energetic and non-divergent photon beams of 30 keV, 50 keV, and 70 keV for MEBXS, 380 keV photons for IR-HDRS, and 6 MeV mono-energetic electron beam for EEBRT were modeled. A 0.5 cm thick soft tissue (0.3 cm skin and 0.2 cm adipose) with underlying 0.5 cm cortical bone followed by 14 cm soft tissue are utilized for simulations. Results: Dose values to bone tissue as a function of beam energy and beam type, for a delivery of 5000 cGy dose to skin, were compared. These results indicate that for delivery of 5000 cGy dose to the skin surface with 30 keV, 50 keV, 70 keV of MEBXS, IR-HDRS, and EEBRT techniques, bone will receive 31750 cGy, 27450 cGy, 18550 cGy, 4875 cGy, and 10450 cGy, respectively. Conclusion: The results of these investigations indicate that, for delivery of the same skin dose, average doses received by the underlying bone are 5.2 and 2.2 times larger with a 50 keV MEBXS and EEBRT techniques than IR-HDRS, respectively.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

Characterizing Structural Controls of EGS-Candidate and Conventional Geothermal Reservoirs in the Great Basin: Developing Successful Exploration Strategies in Extended Terranes

We conducted a comprehensive analysis of the structural controls of geothermal systems within the Great Basin and adjacent regions. Our main objectives were to: 1) Produce a catalogue of favorable structural environments and models for geothermal systems. 2) Improve site-specific targeting of geothermal resources through detailed studies of representative sites, which included innovative techniques of slip tendency analysis of faults and 3D modeling. 3) Compare and contrast the structural controls and models in different tectonic settings. 4) Synthesize data and develop methodologies for enhancement of exploration strategies for conventional and EGS systems, reduction in the risk of drilling non-productive wells, and selecting the best EGS sites.

15 GEOTHERMAL ENERGY↗
NASA NTRS1990

Design optimization for cost and quality: The robust design approach

Designing reliable, low cost, and operable space systems has become the key to future space operations. Designing high quality space systems at low cost is an economic and technological challenge to the designer. A systematic and efficient way to meet this challenge is a new method of design optimization for performance, quality, and cost, called Robust Design. Robust Design is an approach for design optimization. It consists of: making system performance insensitive to material and subsystem variation, thus allowing the use of less costly materials and components; making designs less sensitive to the variations in the operating environment, thus improving reliability and reducing operating costs; and using a new structured development process so that engineering time is used most productively. The objective in Robust Design is to select the best combination of controllable design parameters so that the system is most robust to uncontrollable noise factors. The robust design methodology uses a mathematical tool called an orthogonal array, from design of experiments theory, to study a large number of decision variables with a significantly small number of experiments. Robust design also uses a statistical measure of performance, called a signal-to-noise ratio, from electrical control theory, to evaluate the level of performance and the effect of noise factors. The purpose is to investigate the Robust Design methodology for improving quality and cost, demonstrate its application by the use of an example, and suggest its use as an integral part of space system design process.

Unal, Resit↗
NASA NTRS2002

Experimental Methods in Reduced-gravity Soldering Research

The National Center for Microgravity Research, NASA Glenn Research Center, and NASA Johnson Space Center are conducting an experimental program to explore the influence of reduced gravity environments on the soldering process. An improved understanding of the effects of the acceleration environment is important to application of soldering during current and future human space missions. Solder joint characteristics that are being considered include solder fillet geometry, porosity, and microstructural features. Both through-hole and surface mounted devices are being investigated. This paper focuses on the experimental methodology employed in this project and the results of macroscopic sample examination. The specific soldering process, sample configurations, materials, and equipment were selected to be consistent with those currently on-orbit. Other apparatus was incorporated to meet requirements imposed by operation onboard NASA's KC-135 research aircraft and instrumentation was provided to monitor both the atmospheric and acceleration environments. The contingent of test operators was selected to include both highly skilled technicians and less skilled individuals to provide a population cross-section that would be representative of the skill mix that might be encountered in space mission crews.

Pettegrew, Richard D.↗
DOE OSTI2012

Autotune E+ Building Energy Models

This paper introduces a novel Autotune methodology under development for calibrating building energy models (BEM). It is aimed at developing an automated BEM tuning methodology that enables models to reproduce measured data such as utility bills, sub-meter, and/or sensor data accurately and robustly by selecting best-match E+ input parameters in a systematic, automated, and repeatable fashion. The approach is applicable to a building retrofit scenario and aims to quantify the trade-offs between tuning accuracy and the minimal amount of ground truth data required to calibrate the model. Autotune will use a suite of machine-learning algorithms developed and run on supercomputers to generate calibration functions. Specifically, the project will begin with a de-tuned model and then perform Monte Carlo simulations on the model by perturbing the uncertain parameters within permitted ranges. Machine learning algorithms will then extract minimal perturbation combinations that result in modeled results that most closely track sensor data. A large database of parametric EnergyPlus (E+) simulations has been made publicly available. Autotune is currently being applied to a heavily instrumented residential building as well as three light commercial buildings in which a de-tuned model is autotuned using faux sensor data from the corresponding target E+ model.

automatic calibration↗
DOE OSTI2013

Using Machine Learning to Create Turbine Performance Models (Presentation)

Wind turbine power output is known to be a strong function of wind speed, but is also affected by turbulence and shear. In this work, new aerostructural simulations of a generic 1.5 MW turbine are used to explore atmospheric influences on power output. Most significant is the hub height wind speed, followed by hub height turbulence intensity and then wind speed shear across the rotor disk. These simulation data are used to train regression trees that predict the turbine response for any combination of wind speed, turbulence intensity, and wind shear that might be expected at a turbine site. For a randomly selected atmospheric condition, the accuracy of the regression tree power predictions is three times higher than that of the traditional power curve methodology. The regression tree method can also be applied to turbine test data and used to predict turbine performance at a new site. No new data is required in comparison to the data that are usually collected for a wind resource assessment. Implementing the method requires turbine manufacturers to create a turbine regression tree model from test site data. Such an approach could significantly reduce bias in power predictions that arise because of different turbulence and shear at the new site, compared to the test site.

17 WIND ENERGY↗
DOE OSTI2015

Statistical Characterization of Medium-Duty Electric Vehicle Drive Cycles: Preprint

In an effort to help commercialize technologies for electric vehicles (EVs) through deployment and demonstration projects, the U.S. Department of Energy’s (DOE's) American Recovery and Reinvestment Act (ARRA) provided funding to participating U.S. companies to cover part of the cost of purchasing new EVs. Within the medium- and heavy-duty commercial vehicle segment, both Smith Electric Newton and and Navistar eStar vehicles qualified for such funding opportunities. In an effort to evaluate the performance characteristics of the new technologies deployed in these vehicles operating under real world conditions, data from Smith Electric and Navistar medium-duty EVs were collected, compiled, and analyzed by the National Renewable Energy Laboratory's (NREL) Fleet Test and Evaluation team over a period of 3 years. More than 430 Smith Newton EVs have provided data representing more than 150,000 days of operation. Similarly, data have been collected from more than 100 Navistar eStar EVs, resulting in a comparative total of more than 16,000 operating days. Combined, NREL has analyzed more than 6 million kilometers of driving and 4 million hours of charging data collected from commercially operating medium-duty electric vehicles in various configurations. In this paper, extensive duty-cycle statistical analyses are performed to examine and characterize common vehicle dynamics trends and relationships based on in-use field data. The results of these analyses statistically define the vehicle dynamic and kinematic requirements for each vehicle, aiding in the selection of representative chassis dynamometer test cycles and the development of custom drive cycles that emulate daily operation. In this paper, the methodology and accompanying results of the duty-cycle statistical analysis are presented and discussed. Results are presented in both graphical and tabular formats illustrating a number of key relationships between parameters observed within the data set that relate to medium duty EVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Statistical Characterization of Medium-Duty Electric Vehicle Drive Cycles

In an effort to help commercialize technologies for electric vehicles (EVs) through deployment and demonstration projects, the U.S. Department of Energy's (DOE's) American Recovery and Reinvestment Act (ARRA) provided funding to participating U.S. companies to cover part of the cost of purchasing new EVs. Within the medium- and heavy-duty commercial vehicle segment, both Smith Electric Newton and and Navistar eStar vehicles qualified for such funding opportunities. In an effort to evaluate the performance characteristics of the new technologies deployed in these vehicles operating under real world conditions, data from Smith Electric and Navistar medium-duty EVs were collected, compiled, and analyzed by the National Renewable Energy Laboratory's (NREL) Fleet Test and Evaluation team over a period of 3 years. More than 430 Smith Newton EVs have provided data representing more than 150,000 days of operation. Similarly, data have been collected from more than 100 Navistar eStar EVs, resulting in a comparative total of more than 16,000 operating days. Combined, NREL has analyzed more than 6 million kilometers of driving and 4 million hours of charging data collected from commercially operating medium-duty electric vehicles in various configurations. In this paper, extensive duty-cycle statistical analyses are performed to examine and characterize common vehicle dynamics trends and relationships based on in-use field data. The results of these analyses statistically define the vehicle dynamic and kinematic requirements for each vehicle, aiding in the selection of representative chassis dynamometer test cycles and the development of custom drive cycles that emulate daily operation. In this paper, the methodology and accompanying results of the duty-cycle statistical analysis are presented and discussed. Results are presented in both graphical and tabular formats illustrating a number of key relationships between parameters observed within the data set that relate to medium duty EVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

On the use of genetic algorithm to optimize industrial assets lifecycle management under safety and budget constraints

The purpose of this paper is to describe the method and tool dedicated to optimize investments planning for industrial assets. These investments may either be preventive maintenance tasks, asset enhancements or logistic investments such as spare parts purchases. The two methodological points to investigate in such an issue are: 1. The measure of the profitability of a portfolio of investments 2. The selection and planning of an optimal set of investments 3. The measure of the risk of a portfolio of investments The measure of the profitability of a set of investments in the IPOP tool is synthesised in the Net Present Value indicator. The NPV is the sum of the differences of discounted cash flows (direct costs, forced outages...) between the situations with and without a given investment. These cash flows are calculated through a pseudo-Markov reliability model representing independently the components of the industrial asset and the spare parts inventories. The component model has been widely discussed over the years but the spare part model is a new one based on some approximations that will be discussed. This model, referred as the NPV function, takes for input an investments portfolio and gives its NPV. The second issue is to optimize the NPV. If all investments were independent, this optimization would be an easy calculation, unfortunately there are two sources of dependency. The first one is introduced by the spare part model, as if components are indeed independent in their reliability model, the fact that several components use the same inventory induces a dependency. The second dependency comes from economic, technical or logistic constraints, such as a global maintenance budget limit or a safety requirement limiting the residual risk of failure of a component or group of component, making the aggregation of individual optimum not necessary feasible. The algorithm used to solve such a difficult optimization problem is a genetic algorithm. After a description of the features of the software a test case is presented showing the influence of the optimization algorithm parameters on its efficiency to find an optimal investments planning. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

59 BASIC BIOLOGICAL SCIENCES↗