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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Modeling Radiation Detection Using a Mixed-Effects Statistical Model for Background Variation

In this paper, we consider radiation source localization in an urban environment as an inverse problem and use Bayesian parameter estimation techniques to infer the source location and intensity. Since parameter estimation generally requires a large number of model evaluations, we employ a simplified photon transport model, as in a 2017 paper by Stefanescu, et al., to reduce computational requirements. However, the model in the 2017 paper does not account for the physical reality that background radiation varies with position within an urban locale. We incorporate statistical mixed-effects modeling into the simplified transport equation from the Stefanescu, et al. paper to reflect individual variation among the background radiation measurements at multiple detector locations. With our new model, we use a Bayesian DRAM-within-Gibbs technique, which incorporates the Delayed Rejection Adaptive Metropolis (DRAM) algorithm, to estimate the mixed-effects parameters. We note that without adequate prior information, we were unable to simultaneously estimate the set of parameters corresponding to the source intensity, source location, and individual background radiation counts. By calibrating the background in the absence of a source, we obtained narrow prior distributions, which permitted us to jointly infer the source intensity and location and the background radiation distribution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

DECENTRALIZED APPROACH TO MULTI-ZONE GREY-BOX MODELING FOR MODEL-BASED PREDICTIVE CONTROL

This study aims to improve the easiness of utilizing the grey-box model (i.e., Resistance-Capacitance circuit) for Model-based Predictive Control (MPC). The primary barrier of implementing the MPC is estimating the control-oriented building model that needs to be computationally inexpensive and quick but reasonably precise in predicting building load and indoor conditions. The estimating of the model parameters becomes more complicated when the building scale is larger; e.g., multi-zone building. In this study, a decentralized approach is introduced; each zone is split and individually estimated with measured boundary temperature from adjacent zones integrated into one single system model. The proposed decentralized method is demonstrated with experimental data from a full-scale multi-zone test cell compared with the centralized reference case.

Joe, Jaewan↗
DOE OSTI2026

Natural Language Processing to Inform Agent-Based Modeling: With Application to Modeling Adoption of Medium-Duty Electric Vehicles

Agent-based socio-technical modeling of medium- and heavy-duty (MDHD) electric vehicle (EV) adoption has the potential to provide analysis, prediction, and gui. This paper describes new applications of text analysis developed through machine learning (ML) to build and understand relevant topics and their saliency in the published discourse on adoption of MDHD EVs. This work contributes to the state of the art in topic mining models by defining a new metric of topic ranking (START) that quantifies the importance of predefined topics within the corpus using weighted results for predefined topics from two topic modeling approaches: Latent Dirichlet Allocation (LDA) and BERTopic. The START metric is then demonstrated in practice to model how academia and industry view the EV adoption process based on the respective texts published by these groups. Results show that academic literature places more emphasis on categories of interests such as norms/attitudes and adopter knowledge, while trade journals tend to emphasize long-term cost more than academia. The two bodies of literature agree on the importance of policy and incentives in MDHD EV adoption. Together these results illustrate the potential to use ML-based text analysis to populate the characteristics of agent-based socio-technical models.

Electric vehicle adoption, fleet electrification, ↗
DOE OSTI2017

Formulation and validation of a reduced order model of 2D materials exhibiting a two-phase microstructure as applied to graphene oxide [Formulation and validation of a reduced order model of 2D materials exhibiting a two-phase microstructure]

Novel 2D materials, e.g., graphene oxide (GO), are attractive building blocks in the design of advanced materials due to their reactive chemistry, which can enhance interfacial interactions while providing good in-plane mechanical properties. Recent studies have hypothesized that the randomly distributed two-phase microstructure of GO, which arises due to its oxidized chemistry, leads to differences in nano- vs meso–scale mechanical responses. However, this effect has not been carefully studied using molecular dynamics due to computational limitations. Herein, a continuum mechanics model, formulated based on density functional based tight binding (DFTB) constitutive results for GO nano-flakes, is establish for capturing the effect of oxidation patterns on the material mechanical properties. GO is idealized as a continuum heterogeneous two-phase material, where the mechanical response of each phase, graphitic and oxidized, is informed from DFTB simulations. A finite element implementation of the model is validated via MD simulations and then used to investigate the existence of GO representative volume elements (RVE). We find that for the studied GO, an RVE behavior arises for monolayer sizes in excess to 40 nm. Moreover, we reveal that the response of monolayers with two main different functional chemistries, epoxide-rich and hydroxyl–rich, present distinct differences in mechanical behavior. In addition, we explored the role of defect density in GO, and validate the applicability of the model to larger length scales by predicting membrane deflection behavior, in close agreement with previous experimental and theoretical observations. As such the work presents a reduced order modeling framework applicable in the study of mechanical properties and deformation mechanisms in 2D multiphase materials.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Modeling and Testing Miniature Torsion Specimens for SiC Joining Development Studies for Fusion

The international fusion community has designed a miniature torsion specimen for neutron irradiation studies of joined SiC and SiC/SiC composite materials. Miniature torsion joints based on this specimen design were fabricated using displacement reactions between Si and TiC to produce Ti3SiC2 + SiC joints with CVD-SiC and tested in torsion-shear prior to and after neutron irradiation. However, many of these miniature torsion specimens fail out-of-plane within the CVD-SiC specimen body, which makes it problematic to assign a shear strength value to the joints and makes it difficult to compare unirradiated and irradiated joint strengths to determine the effects of the irradiation. Finite element elastic damage and elastic-plastic damage models of miniature torsion joints are developed that indicate shear fracture is likely to occur within the body of the joined sample and cause out-of-plane failures for miniature torsion specimens when a certain modulus and strength ratio between the joint material and the joined material exists. The model results are compared and discussed with regard to unirradiated and irradiated joint test data for a variety of joint materials. The unirradiated data includes Ti3SiC2 + SiC/CVD-SiC joints with tailored joint moduli, and includes steel/epoxy and CVD-SiC/epoxy joints. The implications for joint data based on this sample design are discussed.

Joining:Fusion:Torsion:Modeling and Simulation↗
DOE OSTI2017

Modeling thermal and irradiation-induced swelling effects on the integrity of Ti 3 SiC 2 /SiC joints

Previously, results for CVD-SiC joints created using solid state displacement reactions to form a dual-phase SiC/MAX phase irradiated at 800°C and 5 dpa indicated some extent of cracking in the joint and along the CVD-SiC/joint interface. This paper elucidates the origin of cracking by thermomechanical modeling combined with irradiation-induced swelling effects using a continuum damage approach with support of micromechanical modeling. Three irradiation temperatures (400°C, 500°C and 800°C) are considered assuming experimental irradiation doses in a range leading to saturation swelling in SiC. The analyses indicate that a SiC/MAX joint heated to 400°C fails during irradiation-induced swelling at this temperature while it experiences some damage after being heated to 500°C and irradiated at the same temperature. However, it fails during cooling from 500°C to room temperature. The joint experiences minor damage when heated to and irradiated at 800°C but does not fail after cooling. The prediction agrees with the experimental findings available for this case.

damage modeling↗
DOE OSTI2017

Stochastic modeling of phosphorus transport in the Three Gorges Reservoir by incorporating variability associated with the phosphorus partition coefficient

Phosphorus (P) fate and transport plays a crucial role in the ecology of rivers and reservoirs in which eutrophication is limited by P. A key uncertainty in models used to help manage P in such systems is the partitioning of P to suspended and bed sediments. By analyzing data from field and laboratory experiments, we stochastically characterize the variability of the partition coefficient (Kd) and derive spatio-temporal solutions for P transport in the Three Gorges Reservoir (TGR). We formulate a set of stochastic partial different equations (SPDEs) to simulate P transport by randomly sampling Kd from the measured distributions, to obtain statistical descriptions of the P concentration and retention in the TGR. The correspondence between predicted and observed P concentrations and P retention in the TGR combined with the ability to effectively characterize uncertainty suggests that a model that incorporates the observed variability can better describe P dynamics and more effectively serve as a tool for P management in the system. This study highlights the importance of considering parametric uncertainty in estimating uncertainty/variability associated with simulated P transport.

Partition coefficient↗
DOE OSTI2021

A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. Here, the framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A porcine ligated loop model reveals new insight into the host immune response against Campylobacter jejuni

The symptoms of infectious diarrheal disease are mediated by a combination of a pathogen’s virulence factors and the host immune system. Campylobacter jejuni is the leading bacterial cause of diarrhea worldwide due to its near-ubiquitous zoonotic association with poultry. One of the outstanding questions is to what extent the bacteria are responsible for the diarrheal symptoms via intestinal cell necrosis versus immune cell initiated tissue damage. To determine the stepwise process of inflammation that leads to diarrhea, we used a piglet ligated intestinal loop model to study the intestinal response to C. jejuni. Pigs were chosen due to the anatomical similarity between the porcine and the human intestine. We found that the abundance of neutrophil related proteins increased in the intestinal lumen during C. jejuni infection, including proteins related to neutrophil migration (neutrophil elastase and MMP9), actin reorganization (Arp2/3), and antimicrobial proteins (lipocalin-2, myeloperoxidase, S100A8, and S100A9). The appearance of neutrophil proteins also corresponded with increases of the inflammatory cytokines IL-8 and TNF-a. Compared to infection with the C. jejuni wild-type strain, infection with the noninvasive C. jejuni ?ciaD mutant resulted in a blunted inflammatory response, with less inflammatory cytokines and neutrophil markers. These findings indicate that intestinal inflammation is driven by C. jejuni virulence and that neutrophils are the predominant cell type responding to C. jejuni infection. We propose that this model can be used as a platform to study the early immune events during infection with intestinal pathogens.

disease model, INNATE IMMUNITY, proteomics, intest↗
DOE OSTI2016

Mixed dark matter in left-right symmetric models

Motivated by the recently reported diboson and dijet excesses in Run 1 data at ATLAS and CMS, we explore models of mixed dark matter in left-right symmetric theories. In this study, we calculate the relic abundance and the elastic scattering cross section with nuclei for a number of dark matter candidates that appear within the fermionic multiplets of left-right symmetric models. In contrast to the case of pure multiplets, WIMP-nucleon scattering proceeds at tree-level, and hence the projected reach of future direct detection experiments such as LUX-ZEPLIN and XENON1T will cover large regions of parameter space for TeV-scale thermal dark matter. Decays of the heavy charged W{sup ′} boson to particles in the dark sector can potentially shift the right-handed gauge coupling to larger values when fixed to the rate of the Run 1 excesses, moving towards the theoretically attractive scenario, g{sub R}=g{sub L}. This region of parameter space may be probed by future collider searches for new Higgs bosons or electroweak fermions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Discrete Empirical Interpolation Method Based Dynamic Load Model Reduction

Dynamic load models add significant complexity to bulk power system time-domain simulations. The complexity is due to the large number of ordinary differential equations (ODEs) introduced by the dynamic load components such as induction motors. It is challenging to derive reduced-order models (ROMs) for dynamic loads due to the nonlinear functions in their governing equations. This paper applies the discrete empirical interpolation method enhanced proper orthogonal decomposition (DEIM-POD) to approximate the full dynamic load model with the ROM that minimizes the projection error of the nonlinear functions in dynamic load ODEs onto their dominant modes. This approach only requires evaluation of nonlinear functions at selected observation points. The observation points selected by DEIM also provide information for screening critical load buses where dynamic load model parameters contribute the most to the accuracy of ROM across multiple contingencies. The proposed approach is validated on IEEE 9-bus, WECC 179-bus and 2384-bus Polish systems.

bulk power system↗
DOE OSTI2015

Simplified Method for Modeling the Impact of Arbitrary Partial Shading Conditions on PV Array Performance: Preprint

It is often difficult to model the effects of partial shading conditions on PV array performance, as shade losses are nonlinear and depend heavily on a system's particular configuration. This work describes and implements a simple method for modeling shade loss: a database of shade impact results (loss percentages), generated using a validated, detailed simulation tool and encompassing a wide variety of shading scenarios. The database is intended to predict shading losses in crystalline silicon PV arrays and is accessed using basic inputs generally available in any PV simulation tool. Performance predictions using the database are within 1-2% of measured data for several partially shaded PV systems, and within 1% of those predicted by the full, detailed simulation tool on an annual basis. The shade loss database shows potential to considerably improve performance prediction for partially shaded PV systems.

14 SOLAR ENERGY↗
DOE OSTI2021

Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model

The economic potential of a behind-the-meter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM's existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.

batteries↗