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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Trajectory prediction dimensionality reduction for low-cost connected automated vehicle systems

Here, to facilitate low-cost connected automated vehicle (CAV) system development, this study proposes two interpretable dimensionality reduction techniques in vehicle trajectory prediction, i.e., the piecewise Taylor series approximation (PTA) and the piecewise Fourier series approximation (PFA), to lower computation complexity, reduce device investment, and decrease computation energy consumption. Two benchmarks are developed, the long short-term memory (LSTM)-based model without dimensionality reduction and the LSTM-based model with encoder-decoder (a widely used dimensionality reduction technique). Results show that the four predictions have similar accuracy, and the training time (proportional to computation energy consumption) of models with dimensionality reduction techniques is greatly reduced. The reduction is even more significant when PTA/PFA is used. Sensitivity analysis advises PFA/PTA parameter selections to reduce computation complexity without significant loss of prediction accuracy. Further, the robustness of the LSTM PTA/PFA is proven by the investigation of data noises.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Electro-reduction of organics on metal cathodes: A multiscale-modeling study of benzaldehyde on Au (111)

Here, we present a multiscale modeling study on the electro-reduction of benzaldehyde (BZY) on Au(111) under realistic electrochemical conditions. To model the electrochemical cell, we adopt a capacitor model in which complex solvents are confined between a Au cathode and a carbon anode. Classical molecular dynamics simulations reveal that electrode charge density and the presence of alcohol show strong effects on the density, adsorption geometry and dynamics of benzaldehyde on the Au electrode. Under charging conditions, the surface concentration of benzaldehyde on the Au electrode decreases, while the content of other species increases. Finally, we proposed a scheme that correlates the electric current running through the Au/solvent interface with the applied bias. This study provides a molecular level understanding of how solvent composition, in this case water/alcohol content, controls the activity of electrocatalytic hydrogenation

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

Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal

Machine learning models were developed for intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2–5 s over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2 and Average Absolute Relative Error (AARE) of 0.78–0.95 and 2.96–6.16 %, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control.

Intermittent RO operation↗
DOE OSTI2018

The Semireduced Mechanism for Nitric Oxide Reduction by Non-Heme Diiron Complexes: Modeling Flavodiiron Nitric Oxide Reductases

Flavodiiron nitric oxide reductases (FNORs) are a subclass of Favodiiron proteins (FDPs) capable of preferential binding and subsequent reduction of NO to N 2 O. FNORs are found in certain pathogenic bacteria, equipping them with resistance to nitrosative stress, generated as a part of the immune defense in humans, and allowing them to proliferate. Here, we report the spectroscopic characterization and detailed reactivity studies of the diiron dinitrosyl model complex [Fe 2 (BPMP)(OPr)(NO)2](OTf) 2 for the FNOR active site that is capable of reducing NO to N 2 O [Zheng et al., J. Am. Chem. Soc. 2013, 135, 4902-4905]. Using UV-vis spectroscopy, cyclic voltammetry, and spectro-electrochemis- try, we show that one reductive equivalent is in fact sufficient for the quantitative generation of N 2 O, following a semi-reduced reaction mechanism. This reaction is very efficient and produces N 2 O with a first-order rate constant k > 10 2 s -1 . Further isotope labeling studies confirm an intramolecular N-N coupling mechanism, consistent with the rapid time scale of the reduction and a very low barrier for N-N bond formation. Accordingly, the reaction proceeds at -80 °C, allowing for the direct observation of the mixed-valent product of the reaction. At higher temperatures, the initial reaction product is unstable and decays, ultimately generating the diferrous complex [Fe 2 (BPMP)(OPr) 2 ](OTf) and an unidentified ferric product. Finally, these results combined offer deep insight into the mechanism of NO reduction by the relevant model complex [Fe 2 (BPMP)(OPr)(NO) 2 ] 2+ and provide direct evidence that the semireduced mechanism would constitute a highly efficient pathway to accomplish NO reduction to N 2 O in FNORs and in synthetic catalysts.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2012

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Diallo, Ousmane H.↗
DOE OSTI2025

Decoding diffraction and spectroscopy data with machine learning: A tutorial

This Tutorial provides a step-by-step guide on how to apply supervised machine-learning techniques to analyze diffraction and spectroscopy data. This Tutorial details four models—a reconstruction-focused model, a regression-focused model, a hybrid reconstruction/regression model, and a multimodal model—that use x-ray diffraction profiles and vibrational density of states spectra to predict various microstructural descriptors. In this Tutorial, we cover data pre-processing steps, constructions of the models via dimensionality reduction and regression, training, and analysis of these models. Comparisons of the model’s performance are provided, highlighting the strength and weakness of the various approaches utilized.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A modeling comparison of deep greenhouse gas emissions reduction scenarios by 2030 in California

California aims to reduce greenhouse gas (GHG) emissions to 40% below 1990 levels by 2030. We compare six energy models that have played various roles in informing the state policymakers in setting climate policy goals and targets. These models adopt a range of modeling structures, including stock-turnover back-casting models, a least-cost optimization model, macroeconomic/macro-econometric models, and an electricity dispatch model. Results from these models provide useful insights in terms of the transformations in the energy system required, including efficiency improvements in cars, trucks, and buildings, electrification of end-uses, low- or zero-carbon electricity and fuels, aggressive adoptions of zero-emission vehicles (ZEVs), demand reduction, and large reductions of non-energy GHG emissions. Some of these studies also suggest that the direct economic costs can be fairly modest or even generate net savings, while the indirect macroeconomic benefits are large, as shifts in employment and capital investments could have higher economic returns than conventional energy expenditures. These models, however, often assume perfect markets, perfect competition, and zero transaction costs. They also do not provide specific policy guidance on how these transformative changes can be achieved. Greater emphasis on modeling uncertainty, consumer behaviors, heterogeneity of impacts, and spatial modeling would further enhance policymakers' ability to design more effective and targeted policies. Here, this paper presents an example of how policymakers, energy system modelers and stakeholders interact and work together to develop and evaluate long-term state climate policy targets. Lastly, even though this paper focuses on California, the process of dialogue and interactions, modeling results, and lessons learned can be generally adopted across different regions and scales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Regional oxygen reduction and denitrification rates in groundwater from multi-model residence time distributions, San Joaquin Valley, USA

Rates of oxygen and nitrate reduction are key factors in determining the chemical evolution of groundwater. Little is known about how these rates vary and covary in regional groundwater settings, as few studies have focused on regional datasets with multiple tracers and methods of analysis that account for effects of mixed residence times on apparent reaction rates. This study provides insight into the characteristics of residence times and rates of O 2 reduction and denitrification (NO 3 – reduction) by comparing reaction rates using multi-model analytical residence time distributions (RTDs) applied to a data set of atmospheric tracers of groundwater age and geochemical data from 141 well samples in the Central Eastern San Joaquin Valley, CA. The RTD approach accounts for mixtures of residence times in a single sample to provide estimates of in-situ rates. Tracers included SF 6 , CFCs, 3 H, He from 3 H (tritiogenic He), 14 C, and terrigenic He. Parameter estimation and multi-model averaging were used to establish RTDs with lower error variances than those produced by individual RTD models. The set of multi-model RTDs was used in combination with NO 3 – and dissolved gas data to estimate zero order and first order rates of O 2 reduction and denitrification. Results indicated that O 2 reduction and denitrification rates followed approximately log-normal distributions. Rates of O 2 and NO 3 – reduction were correlated and, on an electron milliequivalent basis, denitrification rates tended to exceed O 2 reduction rates. Estimated historical NO 3 – trends were similar to historical measurements. Here, results show that the multi-model approach can improve estimation of age distributions, and that relatively easily measured O 2 rates can provide information about trends in denitrification rates, which are more difficult to estimate.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

Noise reduction tests of large-scale-model externally blown flap using trailing-edge blowing and partial flap slot covering

Noise data were obtained with a large-scale cold-flow model of a two-flap, under-the-wing, externally blown flap proposed for use on future STOL aircraft. The noise suppression effectiveness of locating a slot conical nozzle at the trailing edge of the second flap and of applying partial covers to the slots between the wing and flaps was evaluated. Overall-sound-pressure-level reductions of 5 db occurred below the wing in the flyover plane. Existing models of several noise sources were applied to the test results. The resulting analytical relation compares favorably with the test data. The noise source mechanisms were analyzed and are discussed.

Mckinzie, D. J., Jr.↗
DOE OSTI2019

Performance of Polymer Electrolyte Fuel Cell Electrodes with Atomically Dispersed (AD) Fe-C-N ORR Catalyst

Activity of (AD)Fe-N-C catalyst with low Fe content is investigated in differential cells prepared by hot pressing anode gas diffusion electrodes (0.2 mgPt/cm2) to N211 membrane and brush painting the cathode catalyst ink. Polarization curves obtained in H2/O2 show double Tafel slopes which, in conjunction with the redox potential observed in cyclic voltammetry traces, forms the basis for a distributed ORR (oxygen reduction reaction) kinetic model with potential-dependent available sites. Application of this model to polarization data in H2/air provides the basis for formulating an oxygen transport model and leads to 7.7 ± 0.6 mA/cm2 catalyst activity at 0.9 V, 31.5-34.3 mA/cm2 cell performance at 0.8 V, and 0.8-2 s/cm O2 transport resistance. A coupled kinetic, O2 transport and proton transport illustrates the projected improvements needed in catalyst activity and electrode structure to approach the automotive target of 1000 mW/cm2 stack power density while meeting the 1.45 kW/oC heat rejection constraint at 1.5 cathode stoichiometry. The model indicates that we need twelve-fold higher mass activity for reducing the kinetic losses, doubling of active site density and an engineered electrode structure for 50% lower proton transport resistance, as well as a 50% reduction in electrode thickness to limit the O2 transport losses

ACTIVE-SITES↗
DOE OSTI2024

Gray-Box Modeling for Distribution Systems With Inverter-Based Resources: Integrating Physics-Based and Data-Driven Approaches

Here, in this paper, we develop a novel gray-box modeling approach for distribution systems with inverter-based resources (IBRs). The proposed gray-box modeling method aims to improve estimation accuracy by taking advantages of both physics-based (white-box) and data-driven (black-box) modeling approaches. To this end, we utilize partial physical knowledge of the system, including the inverters’ structures and control diagrams, as well as the equivalent network model simplified through Kron reduction. The white-box model containing unknown parameters is then constructed with mathematical equations and an optimization-based method is subsequently employed to identify these unknown parameters within the white-box model. Next, the graybox modeling framework is then constructed by embedding the output variables of the white-box model into the input vector of a black-box model (represented using a neural network). Finally, the black-box section is trained using the collected input-output datasets and the gray-box model is then obtained. Furthermore, case studies demonstrate that our gray-box modeling approach effectively improves estimation accuracy compared to purely physics-based or data-driven methods.

42 ENGINEERING↗
DOE OSTI2019

XPCA - R Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2019-10591 M

Kincher-Winoto, Kina↗
DOE OSTI2019

XPCA -Python Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. SAND2019-10591 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kincher-Winoto, Kina↗
DOE OSTI2018

Regional power system modeling for evaluating renewable energy development and CO2 emissions reduction in China

Highlights: • We introduce a capacity expansion and operation model for China's power system. • Reference carbon emissions of China's power sector exceed 5.1 billion tons in 2050. • Flexible district heating marginally increases the integration of renewable energy. • Ultra-high voltage transmission does not improve the integration by itself. • A complete spot market as a policy change could be crucial for the integration. - Abstract: This study demonstrates how a regional power system modeling tool could be applied to evaluate renewable energy development and emissions reduction with technology change and market reform. We introduce a capacity expansion and operation model with provincial details for China's power system – the Renewable Electricity Planning and Operation (REPO) Model. We compare the spatial and temporal variation of renewable energy development under three alternative scenarios that depict three potentially important technology and reform directions – flexible district heating technology, ultra-high voltage transmission technology and complete spot market to the reference scenario. Under the reference scenario, the share of non-hydro renewable energy in China's power sector will reach 11.3% and carbon emissions of the power sector will increase to 5.1 billion metric tons in 2050. With assumptions made in the flexible district heating technology, ultra-high voltage transmission technology and complete spot market scenario, the share of non-hydro renewable energy in the power sector could increase to 14.3%, 11.5%, and 23.7%, and carbon emissions of power sector will change by −0.3, +0.0, and −1.0 billion metric tons in 2050, respectively. Similar analyses could be extended to other developing countries that plan to deregulate and decarbonize their power systems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Modeling the Prodromal Effects and Performance Reduction of Astronauts from Exposure to Large Solar Particle Events

In space exploration outside the Earth's geomagnetic field, radiation exposure from solar particle events (SPE) presents a health concern for astronauts, that could impair their performance and result in possibility of failure of the mission. Acute risks are especially of concern during spacewalks on the lunar surface because of the rapid onset of SPE's and science goals that involve long distances to crew habitats. Thus assessing the potential of early radiation effect under such adverse conditions is of prime importance. Here we present a biologic based mathematical model which describes the dose and time-dependent early human responses to ionizing radiation. We examine the possible early effects on crew behind various shielding materials from exposure to some historical large SPEs on the lunar and Mars surfaces. The doses and dose rates were calculated using the BRYNTRN code (Kim, M.Y, Hu, X, and Cucinotta, F.A, Effect of Shielding Materials from SPEs on the Lunar and Mars Surface, AIAA Space 2005, paper number AIAA-2005-6653, Long Beach, CA, August 30-September 1, 2005) and the hazard of the early radiation effects and performance reduction were calculated using the RIPD code (Anno, G.H, McClellan, G.E., Dore, M.A, Protracted Radiation-Induced Performance Decrement, Volume 1 Model Development,1996, Defense Nuclear Agency: Alexandria VA). Based on model assumptions we show that exposure to these historical SPEs do cause early effects to crew members and impair their performance if effective shielding and medical countermeasure tactics are not provided. The calculations show multiple occurrence of large SPEs in a short period of time significantly increase the severity of early illness, however early death from failure of the hematopoietic system is very unlikely because of the dose-rate and dose heterogeneity of SPEs. Results from these types of calculations will be a guide in design of protection systems and medical response strategy for astronauts in case of exposure to high dose irradiation during future space missions.

Hu, S.↗
DOE OSTI2015

Study of Eu{sup 3+} → Eu{sup 2+} reduction in BaAl{sub 2}O{sub 4}:Eu prepared in different gas atmospheres

Highlights: • The effect of different gas atmospheres on the Eu reduction process was studied. • The Eu reduction was monitored analyzing XANES region at the Eu L{sub III}-edge. • Hydrogen reducing agent are the most appropriate gas for Eu{sup 2+} stabilization. • Only a part of the Eu ions can be stabilized in the divalent state. • A model of Eu reduction process is proposed. - Abstract: The effect of different gas atmospheres such as H{sub 2}(g), synthetic air, carbon monoxide (CO) and nitrogen (N{sub 2}) on the Eu{sup 3+} → Eu{sup 2+} reduction process during the synthesis of Eu-doped BaAl{sub 2}O{sub 4} was studied using synchrotron radiation. The Eu{sup 3+} → Eu{sup 2+} reduction was monitored analyzing XANES region when the sample are excited at the Eu L{sub III}-edge. The results show that the hydrogen reducing agent are the most appropriate gas for Eu{sup 2+} stabilization in BaAl{sub 2}O{sub 4} and that only a part of the Eu ions can be stabilized in the divalent state. A model of Eu reduction process, based on the incorporation of charge compensation defects, is proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nucleophilic Thiols Reductively Cleave Ether Linkages in Lignin Model Polymers and Lignin

Lignin may serve as a renewable feedstock for the production of chemicals and fuels if mild, scalable processes for its depolymerization can be devised. The use of small organic thiols represents a bioinspired strategy to cleave the β–O–4 bond, the most common linkage in lignin. In the present study, synthetic β–O–4 linked polymers were treated with organic thiols, yielding up to 90 % cleaved monomer products. Lignin extracted from poplar was also treated with organic thiols resulting in molecular weight reductions as high as 65 % (Mn) in oxidized lignin. Thiol–based cleavage of other lignin linkages was also explored in small–molecule model systems to uncover additional potential pathways by which thiols might depolymerize lignin. The success of thiol–mediated cleavage on model dimers, polymers, and biomass–derived lignin illustrates the potential utility of small redox–active molecules to penetrate complex polymer matrices for depolymerization and subsequent valorization of lignin into fuels and chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Mechanisms of Furfural Reduction on Metal Electrodes: Distinguishing Pathways for Selective Hydrogenation of Bioderived Oxygenates

Electrochemical reduction of biomass-derived platform molecules is an emerging route for the sustainable production of fuels and chemicals. Understanding gaps between reaction conditions, underlying mechanisms, and product selectivity have limited the rational design of active, stable, and selective catalyst systems. Here, the mechanisms of electrochemical reduction of furfural, an important biobased platform molecule and model for aldehyde reduction, are explored through a combination of voltammetry, preparative electrolysis, thiol-electrode modifications, and kinetic isotope studies. It is demonstrated that two distinct mechanisms are operable on metallic Cu electrodes in acidic electrolytes: (i) electrocatalytic hydrogenation (ECH) and (ii) direct electroreduction. The contributions of each mechanism to the observed product distribution are clarified by evaluating the requirement for direct chemical interactions with the electrode surface and the role of adsorbed hydrogen. Further analysis reveals that hydrogenation and hydrogenolysis products are generated by parallel ECH pathways. By understanding the underlying mechanisms it enables the manipulation of furfural reduction by rationally tuning the electrode potential, electrolyte pH, and furfural concentration to promote selective formation of important biobased polymer precursors and fuels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗