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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Maps of plant functional type (PFT), PFT fractional cover, and uncertainty derived from AVIRIS-NG data, 2019, Seward Peninsula

Remote sensing maps of plant functional type (PFT), PFT fractional cover (fCover), and fCover uncertainty maps derived from NASA’s Airborne Visible / Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG). The AVIRIS-NG imaging spectroscopy data (380 ~ 2510 nm) was collected as a part of the collaboration between NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE; Miller et al., 2019) and DOE’s Next Generation Ecosystem Experiment in the Arctic (NGEE-Arctic). This package includes maps for flights over Teller, Kougarok, and Council watersheds created using eight AVIRIS-NG imagery collected in July, 2019. The map data and metadata are provided as image (ENVI, *.png) and text (*.txt, *hdr) formats. Additional supporting map quicklooks are provided as GIS *.kml files. Detailed description of the methods for each map are provided in Yang et al., (2023). These datasets are provided in support of Yang et al., (In revision) “Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra”.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Characterization of Thermal, Hydraulic, and Gas Diffusion Properties in Variably Saturated Sand Grades

Core Ideas This study provides physical, hydraulic, and thermal properties of five sand grades. An extended three‐region thermal conductivity model is proposed. A gas diffusivity‐based variable tortuosity parameter is examined. Useful parametric functions and descriptive models were tested. Detailed characterization of partially saturated porous media is important for understanding and predicting vadose zone transport processes. While basic properties (e.g., particle‐ and pore‐size distributions and soil‐water retention) are, in general, essential prerequisites for characterizing most porous media transport properties, key transport parameters such as thermal conductivity and gas diffusivity are particularly important to describe temperature‐induced heat transport and diffusion‐controlled gas transport processes, respectively. Despite many experimental and numerical studies focusing on a specific porous media characteristic, a single study presenting a wide range of important characteristics, together with the best‐performing functional relationships, can seldom be found. This study characterized five differently textured sand grades (Accusand no. 12/20, 20/30, 30/40, 40/50, and 50/70) in relation to physical properties, water retention, hydraulic conductivity, thermal conductivity, and gas diffusivity. We used measured basic properties and transport data to accurately parameterize the characteristic functions (particle‐ and pore‐size distributions and water retention) and descriptive transport models (thermal conductivity, saturated hydraulic conductivity, and gas diffusivity). An existing thermal conductivity model was improved to describe the distinct three‐region behavior in observed thermal conductivity–water saturation relations. Applying widely used parametric models for saturated hydraulic conductivity and soil‐gas diffusivity, we characterized porous media tortuosity in relation to grain size. Strong relations among average particle diameter, characteristic pore diameter from soil‐water retention measurements, and saturated hydraulic conductivity were found. Thus, the results of this work are useful toward better describing, linking, and predicting mass transfer and pore network properties in variably saturated porous media.

Chamindu Deepagoda, T. K. K.↗
DOE OSTI2014

Simplified continuum solvent model with a smooth cavity based on volumetric data

We present a continuum solvent model (CSM) with a smooth cavity for the application in grid-based electronic structure methods. The cavity is identified with the inherently smooth distribution function of a binary mixture at infinite dilution. We obtain a cavity model based on atomic van der Waals radii and one free parameter controlling the overall size. This single parameter is sufficient to adequately reproduce experimental partial molar volumes. The CSM based on this cavity is of similar accuracy in the prediction of aqueous solvation Gibbs energies of small neutral molecules and ions as other CSMs with a smooth cavity. We apply the model to systems in non-aqueous solution, i.e., spiropyran/merocyanin energetics, a proton transfer reaction in dimethyl sulfoxide, and the electrostatic screening of charged gold clusters in an ionic liquid.

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

Surface-mediated spontaneous emulsification of the acylated peptide, semaglutide

Acylated peptides composed of glucagon-like peptide-1 receptor agonists modified with a fatty acid side chain are an important class of therapeutics for type 2 diabetes and obesity but are susceptible to an unusual physical instability in the presence of hydrophobic surfaces, i.e., spontaneous emulsification, also known as ouzo formation in practice. In this work, light scattering, small-angle X-ray scattering, and circular dichroism measurements are used to characterize the physical properties of the semaglutide colloidal phase, including size distribution, shape, secondary structure, internal structure, and internal composition, as a function of solution physico-chemical conditions. The existence and size of the colloids formed are successfully predicted by a classical Rayleigh model, which identifies the parameters controlling their size and formation. Colloid formation is found to be catalyzed by hydrophobic surfaces, and formation rates are modeled as an autocatalytic reaction, enabling the formation of a master curve for various surfaces that elucidates the mechanism. Surfaces differ due to differences in surface wettability, which can be correlated with Hansen solubility parameters. This work provides insights into this unusual colloidal phenomenon and guides the peptide synthesis process and drug product formulation in the pharmaceutical industry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Fuzzy-Logic Subsumption Controller for Home Energy Management Systems

Home Energy Management Systems (HEMS) are controllers that manage and coordinate the generation, storage, and loads in a home. These controllers are increasingly necessary to ensure that increasing penetrations of distributed energy resources are used effectively and do not disrupt the operation of the grid. In this paper, we propose a novel approach to HEMS design based on behavioral control methods, which do not require accurate models or predictions and are very responsive to changing conditions. We develop a proof-of-concept behavioral HEMS controller and show by simulation on an example home energy system that it capable of making context-dependent tradeoffs between goals under challenging conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

A Fuzzy-Logic Subsumption Controller for Home Energy Management Systems

Presentation for NAPS 2015 associated with conference publication CP-64392. Home Energy Management Systems (HEMS) are controllers that manage and coordinate the generation, storage, and loads in a home. These controllers are increasingly necessary to ensure that increasing penetrations of distributed energy resources are used effectively and do not disrupt the operation of the grid. In this paper, we propose a novel approach to HEMS design based on behavioral control methods, which do not require accurate models or predictions and are very responsive to changing conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Integrated optical and electrical modeling of plasmon-enhanced thin film photovoltaics: A case-study on organic devices

The nanoscale light control for absorption enhancement of organic photovoltaic (OPV) devices inevitably produces strongly non-uniform optical fields. These non-uniformities due to the localized optical modes are a primary route toward absorption enhancement in OPV devices. Therefore, a rigorous modeling tool taking into account the spatial distribution of optical field and carrier generation is necessary. Presented here is a comprehensive numerical model to describe the coupled optical and electrical behavior of plasmon-enhanced polymer:fullerene bulk heterojunction (BHJ) solar cells. In this model, a position-dependent electron-hole pair generation rate that could become highly non-uniform due to photonic nanostructures is directly calculated from the optical simulations. By considering the absorption and plasmonic properties of nanophotonic gratings included in two different popular device architectures, and applying the Poisson, current continuity, and drift/diffusion equations, the model predicts quantum efficiency, short-circuit current density, and desired carrier mobility ratios for bulk heterojunction devices incorporating nanostructures for light management. In particular, the model predicts a significant degradation of device performance when the carrier species with lower mobility are generated far from the collecting electrode. Consequently, an inverted device architecture is preferred for materials with low hole mobility. This is especially true for devices that include plasmonic nanostructures. Additionally, due to the incorporation of a plasmonic nanostructure, we use simulations to theoretically predict absorption band broadening of a BHJ into energies below the band gap, resulting in a 4.8% increase in generated photocurrent.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A new dynamic zOnal model with air-diffuser (DOMA) - Application to thermal comfort prediction

A new Dynamic zOnal Model with Air-diffuser (DOMA) was developed. Several case studies were investigated and tested to evaluate and validate this program using measurement data. This new model was integrated into a TRaNsient SYstems Simulation program library and coupled with the multi-zone thermal model. The DOMA/TRNSYS coupled model was then used to predict room temperature distribution over an entire day of a single-zone building. The results show that increasing the heating outputs of the electric floor system, for example, from 75 to 200 W/m 2 , would not effectively improve the indoor thermal comfort, since the thermostat will reach the set point first and then turn off the system before the room gets enough heat and reach a comfortable level. This indicates the importance of selecting an appropriate location and set point for the thermostat when using a floor heating system. This potential thermal comfort issue can only be identified through the two-node model with a dynamic zonal model rather than the conventional PMV model, which thus suggests that for optimizing indoor thermal comfort of a building equipped with a time-sensitive control strategy and/or HVAC system, the TSENS results obtained from the two-node model integrated with DOMA are more appropriate than PMVs.

Construction & Building Technology↗
DOE OSTI2018

Prediction of Cooling Curves for Controlled Unidirectional Solidification Under the Influence of Shrinkage: A Semi-analytical Approach

Quality of casting products can be significantly improved by controlling the crystal growth rate. Both dendritic and equiaxed grain structures depend strongly on crystal growth rate along the crystallographic direction. Uncontrolled solidification processes may lead to the formation of porous and columnar material with an extremely non-homogeneous composition distribution. These undesirable micro-structures can be avoided by adopting an optimal cooling rate (K/s) during the crystal growth. The present work focuses on determining suitable cooling curves to obtain desired unidirectional crystal growth rates, by implementing a semi-analytical model. Here, we defined cooling curve as the time history of temperature evolution at the cold boundary. The proposed semi-analytical heat transfer model is diffusion driven and accounts for shrinkage effect during solidification. The proposed model is found capable of predicting cooling curves for prescribed interface velocities. The model is self-sufficient in validating itself by means of solving inverse and forward problems. The cooling curve prediction by the model is validated by using it as the transient boundary condition for an existing enthalpy updating scheme-based numerical model. The proposed model is also applied to empirical data reported in literature, to validate the model against experimental results. All the validations showcased excellent fit. The proposed model will facilitate controlled unidirectional crystal growth rate by providing cooling rate (K/s) as an input for the experiments involving directional solidification.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The influence of cooling rate on condensation of iron, aluminum, and uranium oxide nanoparticles

Fundamental observations of particle size distributions are needed to develop models that predict the fate and transport of radioactive materials in the atmosphere following a nuclear incident. The extent of material transport is influenced by the time scales of particle formation processes (e.g., condensation, coagulation). In this study, we investigated the influence of cooling time scales on size distributions of uranium, aluminum, and iron oxide particles that are synthesized separately under identical run conditions inside the controlled environment of an argon plasma flow reactor. Two distinct temperature distributions are imposed along the flow reactor by varying the argon flow rate downstream of the plasma torch. The vaporized reactants of uranium, aluminum, and iron are cooled from about 5000K to 1000K before they are collected on silicon wafers for ex situ scanning electron microscope analysis. The microscope images show that the sizes of the largest aluminum and iron oxide particles heavily depend on the cooling time scales, whereas significant size variation with cooling rate is not observed for uranium oxide particles. In addition, the size distribution of aluminum oxide particles exhibits the broadest range among all three metal oxides studied. We performed simulations of particle size distributions using a kinetic model that couples gas phase oxidation chemistry with particle formation processes, including nucleation, condensation, and coagulation. The model results demonstrate the strong sensitivity of particle size distribution to different cooling histories (i.e., temperature vs residence time) along the flow reactor. In conclusion, the kinetic model also helps identify directions for future research to improve the predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Prosumer-Based Distributed Autonomous Cyber-Physical Architecture for Ultra-reliable Green Electricity Internetworks

This project, the first of its kind, proposes a new architecture for decentralized management and control of future electricity grids with millions of decision-makers. These economically motivated decision-makers are called prosumers (producer-consumers). Prosumers can produce, consume, and/or store electricity and make strategic decisions enabled by communication networks. Prosumers can be as large as a utility or as small as an electric vehicle or a smart house with a home energy management system. The report first provides an impression of the functionality envisioned in prosumer-based smart grids and the philosophy behind their design. The architecture is intended to provide a platform for many possibilities in service definition and trading. The functionality of the architecture is identified by functional and non-functional requirements related to decentralized control and optimization. In this report, three key technologies are proposed for enabling distributed operation and control in prosumer-based smart grids. The first technology is a cyber-infrastructure model, which provides an underlying framework for distributed communication and computation in smart grids. The model is based on service-oriented computing paradigm and is compatible with the existing service-oriented technologies, used in enterprise computing, such as Web Services. However, it extends these technologies for handling the hard-to-predict real-time aspects of smart grids by introducing resource-aware service deployment and quality-of-service (QoS)-aware service monitoring phases. The second technology is a distributed frequency regulation (DFR) framework, which can ensure midterm frequency stability while avoiding inter-area oscillations and using a limited control effort. DFR can complement today’s Automatic Generation Control (AGC) systems by providing an advanced protocol for coordination between AGC systems of different prosumers. The main advantage of the DFR technology is that each frequency regulator only needs to communicate with its neighbors in the network. The efficacy of DFR is shown through simulations on real-world electric energy systems of different scale and complexity. The third technology introduced in this project is decentralized unit commitment (DUC) for heterogeneous prosumers. The DUC technology is a new scalable method for formulating and solving a distributed energy scheduling problem. The algorithm extends the alternating direction method of multipliers (ADMM) and includes several heuristics to mitigate oscillations and traps in local optimality that result from the nonconvexity of unit commitment. The main advantages of DUC is that it can solve the unit commitment problem for large-scale power systems of more than 3,000 nodes faster than the state-of-the-art, centralized methods, and that it is scalable to energy scheduling problems of prosumer systems of any size. In summary, this report illustrates that the distributed architecture for prosumer-based power grids is an enabler of transforming aging power grids into sustainable, resilient, reliable, and efficient smart grids with some desirable functionalities similar to the Internet.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

HVAC control fine-grained occupancy pattern estimation

A thermal state of a plurality of zones of the building is updated according to a building thermal model and information received from temperature sensors of the building. Predicted occupant counts for an upcoming plurality of time slots for each of the plurality of zones are updated using actual occupancy counts for each of the plurality of zones. A misprediction type distribution for the upcoming plurality of time slots for each of the plurality of zones is updated, the misprediction type distribution indicating misprediction for true negatives, false positives, false negatives, and true positives. A total misprediction cost expectation is updated according to the predicted occupant counts and the misprediction type distribution. HVAC power for each of the plurality of zones is determined to optimize occupant thermal comfort weighted according to the predicted occupant counts while minimizing the total misprediction cost expectation. HVAC operation is controlled per the HVAC power.

Lin, Shan↗
DOE OSTI2018

Technical Advances for Sustainable Remediation: In Situ Monitoring and Climate Resiliency - 18436

Recent advances in sustainable remediation techniques show promise as solutions for contaminant removal in soil and groundwater media. Although passive remediation (or natural attenuation) has gained momentum as an acceptable technique, there are still challenges in regulatory frameworks as well as in public acceptance. Leaving contaminants in subsurface requires increased burden of proof to show that plumes are stable and residual contaminants do not pose a significant health risk. Climate changes and frequent extreme climate events contribute another level of complexity and make it difficult to demonstrate system stability and predict future plume conditions. In this study, we show two coupled approaches to tackle this challenge: (1) in situ groundwater monitoring based on advanced sensor and data analytics technologies (i.e., Big Data), and (2) state-of-art modeling for optimizing monitoring and site closure strategies. In the new monitoring approach, in situ sensors are used to continuously measure the key variables that control contaminant plume mobility and its spatial and temporal distribution (such as pH, redox potential, electrical conductivity, and groundwater level). Coupled with a limited number of groundwater sampling, the data analytics methods - data mining and machine learning - allow us to estimate contaminant concentrations, and also to detect any significant changes associated with the plume mobility. In parallel, the state-of-art numerical flow and reactive transport model - ASCEM software under DOE Office of Environmental Management - provides a physical and mechanistic understanding of the system behavior, and predicts the long-term plume migration for optimizing the monitoring and closure strategies. In particular, we focus on the effects of extreme precipitation or drought on the site closure as well as monitoring strategies. We demonstrate this approach at the Savannah River Site (SRS) F-Area, where groundwater is contaminated by various radionuclides, including uranium, tritium and technetium. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Understanding and control of Zener pinning via phase field and ensemble learning

Zener pinning refers to the dispersion of fine particles which influences grain size distribution via movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution via Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control via Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. The developed method is a highly versatile and generalizable approach that can be used to assess the combined effects of individual features in the presence of multiple variables.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Sci—Fri PM: Topics — 04: What if bystander effects influence cell kill within a target volume? Potential consequences of dose heterogeneity on TCP and EUD on intermediate risk prostate patients

In vitro evidence has suggested that radiation induced bystander effects may enhance non-local cell killing which may influence radiotherapy treatment planning paradigms. This work applies a bystander effect model, which has been derived from published in vitro data, to calculate equivalent uniform dose (EUD) and tumour control probability (TCP) and compare them with predictions from standard linear quadratic (LQ) models that assume a response due only to local absorbed dose. Comparisons between the models were made under increasing dose heterogeneity scenarios. Dose throughout the CTV was modeled with normal distributions, where the degree of heterogeneity was then dictated by changing the standard deviation (SD). The broad assumptions applied in the bystander effect model are intended to place an upper limit on the extent of the results in a clinical context. The bystander model suggests a moderate degree of dose heterogeneity yields as good or better outcome compared to a uniform dose in terms of EUD and TCP. Intermediate risk prostate prescriptions of 78 Gy over 39 fractions had maximum EUD and TCP values at SD of around 5Gy. The plots only dropped below the uniform dose values for SD ∼ 10 Gy, almost 13% of the prescribed dose. The bystander model demonstrates the potential to deviate from the common local LQ model predictions as dose heterogeneity through a prostate CTV is varies. The results suggest the potential for allowing some degree of dose heterogeneity within a CTV, although further investigations of the assumptions of the bystander model are warranted.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra: Supporting Data

High-resolution classification maps derived from occupied aerial systems (UASs). The UAS data were collected in August 2021 using a Skydio 2+ drone equipped with a 4K resolution red-green-blue (RGB) camera (2024 Skydio Inc) and a 3DR SOLO Quadcopter carried a Parrot Sequoia+ Multispectral Sensor (2023 Parrot Drone SAS). This package includes vegetation classification maps at four locations around Next Generation Ecosystem Experiment Arctic (NGEE Arctic) Council watershed study site on the Seward Peninsula, Alaska. The classification maps were generated using a combination of RGB and canopy height information. The map data and metadata are provided as ENVI image (.dat) and text (.txt, *hdr) formats. Additional map quicklooks are provided as GIS *.kml files. These datasets are provided in support of Yang et al., (In review), “Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra”.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Design and analysis of a wake steering controller with wind direction variability

Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

17 WIND ENERGY↗