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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A parallel evolutionary multiple-try metropolis Markov chain Monte Carlo algorithm for sampling spatial partitions

We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm for sampling spatial partitions that lie within a large, complex, and constrained spatial state space. Our algorithm combines the advantages of evolutionary algorithms (EAs) as optimization heuristics for state space traversal and the theoretical convergence properties of Markov Chain Monte Carlo algorithms for sampling from unknown distributions. Local optimality information that is identified via a directed search by our optimization heuristic is used to adaptively update a Markov chain in a promising direction within the framework of a Multiple-Try Metropolis Markov Chain model that incorporates a generalized Metropolis-Hastings ratio. We further expand the reach of our EMCMC algorithm by harnessing the computational power afforded by massively parallel computing architecture through the integration of a parallel EA framework that guides Markov chains running in parallel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Effect of oxidation on the release of multiple metals from industrially polluted sediments and synchrotron-based evidence of Cu–S dynamic association

Disposal operations for industrially polluted sediments are usually accompanied by disturbance and resuspension, which can induce metal remobilization and secondary pollution. Evaluating the risk of metal release under various redox conditions is fundamental for predicting contaminant mobilization and guiding remediation measures. An abandoned oxidation pond, Yanjia Lake, China, was selected as a typical industrially polluted site. Re-suspension experiments were carried out by mixing polluted sediments with lake water under oxic or anoxic conditions, then investigating the effect of oxidation conditions on the release of multiple metals. Metal concentrations and aqueous chemistry in the overlying water were monitored. Synchrotron-based methods, including X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS), were used to characterize oxidation states and coordination conditions of metals in sediments. The release of metals, including Cr, Co, Ni, Cu, Zn, Se, Mo, Sn, Cd, and Pb, was enhanced under oxic vs. anoxic conditions. The XANES analysis revealed that elevated Cr and Zn concentrations under oxic conditions likely resulted from the oxidation of Cr(III) and oxidizing dissolution of ZnS, respectively. K-edge Cu XANES, S XANES, and Cu EXAFS analyses reconstructed the Cu–S association, indicating that S-related oxidation promoted Cu release and Cu–O partly replaced Cu–S in the sediment after a 7-day oxic treatment. The release of most metals was promoted under oxic conditions, resulting from the oxidation of sulfides and metals as indicated by aqueous and synchrotron-based evidence. The risk of secondary pollution is greatly enhanced under oxic conditions, which suggests that measures should be taken to minimize the redox disturbance during sediment remediation. This information can guide the management of sediments in Yanjia Lake and other contaminated sites with similar properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Heterogeneities at multiple length scales in 2D layered materials: From localized defects and dopants to mesoscopic heterostructures

Two-dimensional (2D) materials hold great promise for applications in optoelectronics, quantum information science, and energy conversion due to their remarkable properties imbued by their physical characteristics. Although heterogeneities in their intrinsic structure are the major challenges limiting their synthesis and predictable properties, they also provide a pathway to controllably tune the properties and broaden the potential of 2D materials. Heterogeneities that can be tailored, including defects, dopants, strain, edges, and layer stackings offer transformative opportunities in heterogeneous 2D materials through the introduction of novel properties for technological applications. This article provides a review of recent progress in studying heterogeneities in 2D materials. The review uses examples from our work to develop a strategy to understand the heterogeneities across multiple length scales to link the effect of heterogeneity at the nanoscale with the macroscale properties of 2D materials. We describe specific types of heterogeneities and explore novel synthesis and processing methods for their controlled production with example of the potential impact and applications enabled by their intriguing properties. Finally, we provide a perspective on how to extend the range of tunable properties through further engineering the heterogeneities in 2D materials.

2D materials↗
DOE OSTI2025

High-performance n-Type Polymers Based on Multiple Electron-withdrawing Groups Decorated ( E )-1,2-Di(thiophen-2-yl)ethene Building Blocks

The performance of n-type conjugated polymers lags far behind that of p-type polymers, which significantly restricts the development of organic electronics. The (E)-1,2-di(thiophen-2-yl)ethene (TVT) unit, owing to its unique advantages, has been widely applied in the design of p-type polymer semiconductors. Previous studies have demonstrated that introducing electron-withdrawing groups can lower the frontier orbital energy levels of polymers and enhance electron injection/transporting capabilities. Based on this, we proposed incorporating multiple electronwithdrawing groups, such as amide groups, fluorine atoms, and cyano groups, into the polymer backbones of TVT-based polymer to facilitate the electron transport. Here, we successfully designed and synthesized the polymers TVTDA-4FTVT and TVTDA-2F2CNTVT. Both polymers exhibited low frontier orbital energy levels. Due to its significantly higher crystallization tendency and favorable intermolecular packing structure, the organic field-effect transistor (OFET) device based on TVTDA-4FTVT demonstrated an electron mobility one order of magnitude higher than that of TVTDA-2F2CNTVT. TVTDA-4FTVT showed the highest electron mobility of 0.87 cm 2 ·V −1 ·s −1 , while TVTDA-2F2CNTVT exhibited the highest electron mobility of 0.049 cm 2 ·V −1 ·s −1 . Owing to its deeper lowest unoccupied molecular orbital (LUMO) level, the OFET devices based on TVTDA-2F2CNTVT showed good air stability after being placed in a natural environment for 15 d.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Oxidation mechanism in a refractory multiple-principal-element alloy at high temperature

Refractory multiple-principal-element alloys (RMPEAs) are promising structural materials to enable increased power efficiency in high-temperature oxidation environments, but the oxidation behavior and microstructures of the oxides, especially at the beginning of the oxidation, have received limited attention. Here, the oxidation mechanism in an equimolar W-Mo-Ta-Nb-V was investigated at 1300 °C and compared with the equimolar W-Mo-Ta-Nb alloy without V. The oxide scale on WMoTaNbV after 1 min exposure is shown to be composed of a degradation layer at the interface of the alloy/oxide, an initial oxide transition layer, followed by the main phase aggregate oxide layer, and an outermost oxide layer. At the early stage of oxidation, the absorption of oxygen by the RMPEA substrate forms a solid solution. Vanadium accelerates the initial degradation process as it forms VO. The initial oxidation-induced degradation of RMPEA follows a sequence governed by the free energy change accompanied by local element segregation. Liquid V-Mo oxides aggregate in the intermediate oxide layer. The inward growth of the oxide scale is controlled by the local composition changes, the orientation of the substrate, the crystal structure, and physical properties such as melting points of the oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

The expansion of commercial building demand response as a demand-side management resource for the electric grid necessitates new decision support resources for customers seeking to assess the benefit–risk tradeoffs of possible strategies for energy flexible building operations. To address this need, we, in this study, develop surrogate models that predict the impacts of several load flexibility strategies on commercial building electricity demand and indoor temperature, focusing on offices and retail buildings at multiple scales. The surrogate models are fit to a synthetic database generated via whole building simulations, which establish the relationships between the key operational features of a given strategy and potential changes in building demand and temperature across a variety of contexts. The surrogate models are translated to a Bayesian framework to allow straightforward communication of uncertainty and parameter updating given new evidence. We find strong predictive performance across the suite of models, underscoring the usefulness of the approach in guiding decisions about implementing load flexibility strategies under a particular set of operational and environmental conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Quantitative evaluations of subtropical westerly jet simulations over East Asia based on multiple CMIP5 and CMIP6 GCMs

As a salient feature of the Asian monsoon system, the East Asian subtropical westerly jet (EASWJ) exerts significant impacts on weather and climate changes in China and even throughout East Asia. In this paper, we applied a new self-adaptive algorithm to detect the EASWJ, identify its boundaries, and then represent its characteristics by defining three indices: the intensity index, meridional displacement index, and width index. Compared to the reanalysis data, we carried out a comprehensive, objective, and quantitative EASWJ evaluation using historical experiments from multiple global climate models (GCMs) in the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6). The results show that the multimodel ensemble mean (MME) of both CMIP5 and CMIP6 can simulate the characteristics of winter EASWJ well. While for the other three seasons, the MME of both phase models underestimate the 200-hPa zonal wind (U200) strength in the jet coverage area and overestimate the U200 outside the jet area, such simulation results weaken the meridional shear of the wind field. The EASWJ simulations from the CMIP5 GCMs had no consistent intensity or location characteristic tendencies, and most CMIP5 GCMs tended to simulate relatively wide-coverage jets. In contrast, most CMIP6 GCMs are inclined to simulate significantly weaker, wider, and more-northward jets. Compared to the predecessors in CMIP5, about half of CMIP6 GCMs significantly minimized the jet intensity bias, but remarkable errors were still observed in their jet location and coverage representations. Furthermore, the comparative analysis performed by classifying models based on their evaluated simulation results suggested that the simulated performance of the meridional temperature gradient was important for capturing the EASWJ characteristics. Further in-depth study of the causes of model differences is warranted to improve simulation results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Chemical-free production of multiple high-value bioproducts from metabolically engineered transgenic sugarcane ‘oilcane’ bagasse and their recovery using nanofiltration

Bioenergy crops have potential for being a sustainable and renewable feedstock for biofuels and various value- added bioproducts. The study utilizes recently developed transgenic sugarcane (‘oilcane’) bagasse for chemical- free coproduction of high-value bioproducts, i.e., furfurals, HMF, acetic acid, cellulosic sugars, and vegetative lipids. Hydrothermal pretreatment was optimized at 210°C for 5 min to coproduce 6.91%, 2.67%, 5.07%, 2.42% and 37.82% (w/w) furfurals, HMF, acetic acid, vegetative lipids, and cellulosic sugars, respectively from lignocellulosic biomass. Additionally, nanofiltration system in-series was successfully established to recover sugars, furfurals, HMF, and acetic acid from the pretreatment liquor. 1st nanofiltration with Duracid NF mem- brane rejected ~99% sugars. Concentrated sugars with significantly reduced inhibitory products were obtained in retentate for fermentation. 2nd nanofiltration with NF90 membrane used permeate of 1st nanofiltration as feed and rejected ~ 86% furfurals. Furthermore, the work demonstrates the feasibility of coproducing and recovering multiple biochemicals from lignocellulosic biomass.

09 BIOMASS FUELS↗
DOE OSTI2021

Multiple charging and chemical stability of tripodal catholyte redoxmers

Macromolecular catholyte materials store multiple positive charges in all-organic redox flow cells. Presently, little is known about the electronic structure and chemical stability of these macromolecules in different states of charge. Here we demonstrate that in propeller-like catholyte trimers (tripods), chemical stability increases when they become fully charged.

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

Approximating a linear multiplicative objective in watershed management optimization

Implementing management practices in a cost-efficient manner is critical for regional efforts to reduce the amount of pollutants entering the Chesapeake Bay. We study the problem of selecting a subset of practices that minimizes pollutant load—subject to budgetary and environmental constraints—as simulated in a widely used regulatory watershed model. Mimicking the computation of pollutant load in the regulatory model, we formulate this problem as a continuous optimization model with a linear multiplicative objective function and linear constraints. To lay the groundwork for incorporating additional stakeholder requirements in the future, especially those that would require integer variables, we present and study a continuous linear optimization model that approximates the nonlinear model. The linear model, which requires an exponential number of variables, arises naturally as an alternative model for the same underlying physical process. We examine the theoretical behavior of these optimization models and investigate restrictions of the linear model to handle its large number of variables. Through extensive computational tests on real and randomly generated instances, we demonstrate that the linear model and its restrictions provide optimal solutions close to those of the nonlinear model in practice, despite poor approximation properties in the worst case. We conclude that the linear model—together with our approach to handling its large number of variables—provides a viable framework from which to extend the optimization model to better meet the needs of the Chesapeake Bay watershed management stakeholders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A techno-economic assessment framework for hydrogen energy storage toward multiple energy delivery pathways and grid services

Hydrogen energy storage (HES) transforms and stores electric energy from the grid into hydrogen, and supplements other energy storage and demand response resources in addressing challenges in renewable-intensive power systems. Understanding how to optimally utilize an HES system to maximize its economic benefits from stacked value streams is highly important to its development and deployment. Here, in this paper, we present a techno-economic assessment framework for an HES system considering three common energy delivery pathways and multiple grid and end-user services. Models are developed to capture the operational capability, flexibility, and constraints associated with hydrogen production, compression, storage, and utilization as well as different grid services in an economic assessment. To define the technically achievable benefits, an optimal dispatch formulation is proposed to maximize the economic benefits over a representative year with an hourly time step considering the trade-offs among different value streams. Representative case studies are designed and carried out to show how system configuration, energy delivery pathways, and grid services may affect economic benefits. It was found that value streams from bundling grid services account for up to 76% of the total benefits and are critical for an HES project to be financially viable.

25 ENERGY STORAGE↗
DOE OSTI2022

Joint estimation of biogeochemical model parameters from multiple experiments: A bayesian approach applied to mercury methylation

Here, to characterize complex biogeochemical systems, results from multiple experiments, where each targets a specific subprocess, are commonly combined. The resulting datasets are interpreted through the calibration of biogeochemical models for process inference and predictions. Commonly used calibration approaches of fitting datasets from individual experiments to subprocess models one at a time is prone to missing information shared between datasets and incomplete uncertainty propagation. We propose a Bayesian joint-fitting scheme addressing the above-mentioned concerns by jointly fitting all the available datasets, thus calibrating the entire biogeochemical model in one go using Markov Chain Monte Carlo (MCMC). The identification of null spaces in the parameter distributions from MCMC guided the simplification of certain subprocess models. For example, fast kinetic sorption was replaced by equilibrium sorption, and Monod demethylation was replaced by first-order demethylation. Joint fitting of datasets resulted in complete uncertainty propagation with parameter estimates informed by all available data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing

Resource constraints on the computing continuum require that we make smart decisions for serving AI-based services at the network edge. AI-based services typically have multiple implementations (e.g., image classification implementations include SqueezeNet, DenseNet, and others) with varying trade-offs (e.g., latency and accuracy). The question then is how should AI-based services be placed across Function-as-a-Service (FaaS) based edge computing systems in order to maximize total Quality-of-Service (QoS). To address this question, we propose a problem that jointly aims to solve (i) edge AI service placement and (ii) request scheduling. These are done across two time-scales (one for placement and one for scheduling). Here we first cast the problem as an integer linear program. We then decompose the problem into separate placement and scheduling subproblems and prove that both are NP-hard. We then propose a novel placement algorithm that places services while considering device-to-device communication across edge clouds to offload requests to one another. Our results show that the proposed placement algorithm is able to outperform a state-of-the-art placement algorithm for AI-based services, and other baseline heuristics, with regard to maximizing total QoS. Additionally, we present a federated learning-based framework, FLIES, to predict the future incoming service requests and their QoS requirements. Our results also show that our FLIES algorithm is able to outperform a standard decentralized learning baseline for predicting incoming requests and show comparable predictive performance when compared to centralized training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Calibrating Synchronous-Generator-Interfaced DG Models in Microgrids Using Multiple Event Data

Microgrids, consisting of distributed generators (DGs), loads, and energy storage systems, play an important role in future smart grids. In order to evaluate how microgrids operate and their dynamic response, it is necessary to develop accurate dynamic models of DGs in microgrids. This paper proposes a systematic method to calibrate synchronous-generator-interfaced DG models. Challenges associated with model validation in microgrids are analyzed. Underlying parameters are categorized into two groups, i.e., the steady-state parameters and time constants, which are estimated in two successive stages using multiple event data. This methodology ensures that the validated model with the estimated parameters are applicable for all events. A modified unscented Kalman filter (UKF) is proposed to deal with the cases in which measurement of power angle is unavailable. The effectiveness of the proposed method is validated using field test data of a 2.6 MVA diesel generator in a real microgrid.

Wang, Zhiwen↗
DOE OSTI2021

Do Coordinated Knowledge Translation Campaigns Persuade Radiation Oncologists to Use Single-Fraction Radiation Therapy Compared With Multiple-Fraction Radiation Therapy for Bone Metastases?

Although level 1 evidence supports the use of single-fraction radiation therapy (SFRT) compared with multiple-fraction radiation therapy (MFRT) for the palliative management of bone metastases, SFRT is underused. In early 2017, the Canadian Partnership Against Cancer and CancerCare Manitoba undertook a comprehensive knowledge translation campaign in Manitoba, Canada featuring educational outreach visits, local consensus meetings, and audit and feedback interventions to encourage greater use of SFRT. This study assessed the impact of this campaign on SFRT use and identified variables associated with MFRT usage.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Efficient bi-directional coupling of 3D computational fluid-particle dynamics and 1D Multiple Path Particle Dosimetry lung models for multiscale modeling of aerosol dosimetry

The development of predictive aerosol dosimetry models has been a major focus of environmental toxicology and pharmaceutical health research for decades. One-dimensional (1D) models successfully predict overall deposition averages but fail to accurately predict local deposition. Computational fluid-particle dynamics (CFPD) models provide site-specific predictions but at a computational cost that prohibits whole lung predictions. Thus, there is a need for developing multiscale strategies to provide a realistic subject-specific picture of the fate of inhaled aerosol in the lungs. CT-based 3D/CFPD models of the large airways were bidirectionally coupled with individualized 1D Navier-Stokes airflow and particle transport based upon the widely used Multiple Path Particle Dosimetry Model (MPPD). Distribution of airflows among lobes was adjusted by measured lobar volume changes observed in CT images between FRC and FRC + 1.5 L. Additionally, as a test of the effectiveness of the coupling procedures, deposition modeling of previous 1 µm aerosol exposure studies was performed. The complete coupled model was run for 3 breaths, with the computation-intense portion being the 3D CFD Lagrangian particle tracking calculation. The average deposition per breath was 11% in the combined multiscale model with site-specific doses available in the CFPD portion of the model and airway- or region-specific deposition available for the MPPD portion. In conclusion, the key methods developed in this study enable predictions of ventilation heterogeneities and aerosol deposition across the lungs that are not captured by 3D or 1D models alone. Overall, these methods can be used as the foundation for multi-scale modeling of the full respiratory system.

60 APPLIED LIFE SCIENCES↗