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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Sensitivity Analysis of Numerical Modeling Input Parameters on Wind Turbine Loads in Deterministic Transient Load Cases

Aero-hydro-elastic-servo numerical models used to design and analyze wind turbines are based on thousands of variable input parameters that dictate the inflow, aerodynamic, structural, and control characteristics of the system as well as sea state, hydrodynamic, and mooring characteristics for fixed-bottom and floating offshore wind turbines. Each of these parameters has some level of uncertainty, which can significantly impact the predicted loads. Understanding the uncertainty in the inputs is critical to understanding the uncertainty in the outputs. This work demonstrates a screening technique to identify which parameters ultimate loads are most sensitive to so that more focus can be given to quantifying the possible range of those parameters. This technique has been demonstrated previously for different turbine and load case types and is extended here for a floating offshore wind turbine in design load cases with transient events both in the inflow and operations. Each load case features a deterministic gust, including variations in wind speed, direction, and shear. Load cases are considered with an operating turbine as well as with prescribed fault, startup, and shutdown procedures. The study found that key input parameters with a large impact on loads include the length of the gust, the magnitude of direction change and speed in the gust, the initial wind speed, and the shape of the gust profile.

17 WIND ENERGY↗
DOE OSTI2018

The Demand-Side Grid (dsgrid) Model Documentation

Electrical load is the backdrop for all power systems analysis. As a coequal partner in the supply-demand balance that must be maintained on electrical grids at all times and for all timescales, load is a major source of variability and uncertainty in grid operations. Despite this, utility load forecasting is typically done in a top-down manner that lacks the granularity in time, geography, end use, and technology that is needed to explore the potential impact of technological shifts; and many studies of future grid systems employ relatively simple scaling of historical loads, placing more emphasis on supply-side resources such as generation and transmission that are fewer in number and are better understood in terms of cost and performance. The primary purpose of the demand-side grid model (dsgrid) is to fill these gaps by creating comprehensive load data sets at a sufficient temporal, geographic, sectoral, and end-use resolution to enable detailed analyses of current patterns and future projections of end-use load. Furthermore, the dsgrid platform uses a bottom-up methodology that allows highly resolved analysis of 'what-if' scenarios. dsgrid leverages detailed sectoral energy models to provide hourly time series of load by subsector, end use, and county covering a full year. Although dsgrid currently emphasizes electricity load data, its component sector models for residential buildings, commercial buildings, and industry provide information on other fuel use, including natural gas. The data sets can thus be leveraged to support analysis of numerous demand-side technology-driven changes, such as energy efficiency, electrification, and operational flexibility (i.e., demand response). The electricity use data are time-synchronized with solar and wind data sets so as to be suitable for use in power systems analysis. This report documents dsgrid and its initial data set, which covers electricity demand in the contiguous United States for the historical year of 2012. We start with a historical year to enable model calibration and validation, as well as the development of a mathematical description of our model residuals that can be applied to future-year 'what-if' scenarios to remove known errors between our bottom-up modeling and historical load shapes. This work is part of the Electrification Futures Study (EFS), for which the dsgrid team will be developing future load snapshots that describe projected year 2050 under baseline and a range of electrification assumptions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Bottom Fixed Platform Dynamics Models Assessing Surface Ice Interactions for Transitional Depth Structures in the Great Lakes: FAST8 – IceDyn

To create long-term solutions for offshore wind turbines in a variety of environmental conditions, CAE tools are needed to model the design-driving loads that interact with an offshore wind turbine system during operation. This report describes our efforts in augmenting existing CAE tools used for offshore wind turbine analysis with a new module that can provide simulation capabilities for ice loading on the system. This augmentation was accomplished by creating an ice-loading module coupled to FAST8, the CAE tool maintained by the NREL for simulating land-based and offshore wind turbine dynamics. The new module includes both static and dynamic ice loading that can be applied during a dynamic simulation of the response of an offshore wind turbine. The ice forces can be prescribed, or influenced by the structure’s compliant response, or by the dynamics of both the structure and the ice floe. The new module covers ice failure modes of spalling, buckling, crushing, splitting, and bending. The supporting structure of wind turbines can be modeled as a vertical or sloping form at the waterline. The Inward Battered Guide Structure (IBGS) foundation designed by Keystone Engineering for the Great Lakes was used to study the ice models coupled to FAST8. The IBGS foundation ice loading simulations in FAST8 were compared to the baseline simulation case without ice loading. The ice conditions reflecting those from Lake Huron at Port Huron and Lake Michigan at North Manitou were studied under near rated wind speed of 12 m/s for the NREL 5-MW reference turbine. Simulations were performed on ice loading models 1 through 4 and ice model 6 with their respective sub-models. The purpose of ice model 5 is to investigate ice loading on sloping structures such as ice-cones on a monopile and is not suitable for multi-membered jacketed structures like the IBGS foundation. The key response parameters from the simulations, shear forces and moments from the tower base and IBGS foundation base, were compared. Ice models 1 and 6 do not significantly affect the tower fore-aft shear and moment. However, ice model 2 (dynamic analyses), model 3 (random ice loading), and model 4 (multiple ice failure zone loading) show increased effect on the tower fore-aft shear and moment with significant effect from ice model 3.1. In general ice loading creates large reaction forces and moments at the base of the IBGS foundation; the largest occurred in model 1.1 (steady creep ice indentation loading) followed by model 3.1 (random creep ice indentation loading). In general the power production from the ice loading cases had little deviation from the baseline case without ice loading. For ultimate limit state (ULS), ice model 1.1 ice and 3.1 appear to be the ice most critical models to consider at an early stage of design. Ice model 4 is an important tool for assessing structural fatigue.

17 WIND ENERGY↗
DOE OSTI2022

Modeling District Heating and Cooling Systems with URBANopt, GeoJSON to Modelica Translator, and the Modelica Buildings Library

The URBANopt project has successfully leveraged OpenStudio/EnergyPlus to model buildings and electrical systems at an urban scale; however, URBANopt has lacked the ability to model district thermal energy systems until recently. This paper will present the modeling infrastructure that was developed specifically for the analysis of district heating and cooling systems, and how it is integrated into the existing URBANopt framework. The paper also discusses the development of new models added to the Modelica Buildings Library to model various district energy system components including loads, energy transfer stations (ETS), distribution networks, and central plants. The paper describes how different building loads can be modeled including time series, TEASER reduced-order models, or Spawn of EnergyPlus models. URBANopt District Energy Systems allows the user to switch between the various configurations.

district heating and cooling↗
DOE OSTI2012

A Hybrid Demand Response Simulator Version 1.0

A hybrid demand response simulator is developed to test different control algorithms for centralized and distributed demand response (DR) programs in a small distribution power grid. The HDRS is designed to model a wide variety of DR services such as peak having, load shifting, arbitrage, spinning reserves, load following, regulation, emergency load shedding, etc. The HDRS does not model the dynamic behaviors of the loads, rather, it simulates the load scheduling and dispatch process. The load models include TCAs (water heaters, air conditioners, refrigerators, freezers, etc) and non-TCAs (lighting, washer, dishwasher, etc.) The ambient temperature changes, thermal resistance, capacitance, and the unit control logics can be modeled for TCA loads. The use patterns of the non-TCA can be modeled by probability of use and probabilistic durations. Some of the communication network characteristics, such as delays and errors, can also be modeled. Most importantly, because the simulator is modular and greatly simplified the thermal models for TCA loads, it is very easy and fast to be used to test and validate different control algorithms in a simulated environment.

Lu, Ning↗
DOE OSTI2012

Aggregate Model for Heterogeneous Thermostatically Controlled Loads with Demand Response

Due to the potentially large number of Distributed Energy Resources (DERs) – demand response, distributed generation, distributed storage - that are expected to be deployed, it is impractical to use detailed models of these resources when integrated with the transmission system. Being able to accurately estimate the fast transients caused by demand response is especially important to analyze the stability of the system under different demand response strategies. On the other hand, a less complex model is more amenable to design feedback control strategies for the population of devices to provide ancillary services. The main contribution of this paper is to develop aggregated models for a heterogeneous population of Thermostatic Controlled Loads (TCLs) to accurately capture their collective behavior under demand response and other time varying effects of the system. The aggregated model efficiently includes statistical information of the population and accounts for a second order effect necessary to accurately capture the collective dynamic behavior. The developed aggregated models are validated against simulations of thousands of detailed building models using GridLAB-D (an open source distribution simulation software) under both steady state and severe dynamic conditions caused due to temperature set point changes.

Zhang, Wei↗
DOE OSTI2018

A phase model approach for thermostatically controlled load demand response

A significant portion of electricity consumed worldwide is used to power thermostatically controlled loads (TCLs) such as air conditioners, refrigerators, and water heaters. Because the short-term timing of operation of such systems is inconsequential as long as their long-run average power consumption is maintained, they are increasingly used in demand response (DR) programs to balance supply and demand on the power grid. Here, we present an ab initio phase model for general TCLs, and use the concept to develop a continuous oscillator model of a TCL and compute its phase response to changes in temperature and applied power. This yields a simple control system model that can be used to evaluate control policies for modulating the power consumption of aggregated loads with parameter heterogeneity and stochastic drift. We demonstrate this concept by comparing simulations of ensembles of heterogeneous loads using the continuous state model and an established hybrid state model. The developed phase model approach is a novel means of evaluating DR provision using TCLs, and is instrumental in estimating the capacity of ancillary services or DR on different time scales. We further propose a novel phase response based open-loop control policy that effectively modulates the aggregate power of a heterogeneous TCL population while maintaining load diversity and minimizing power overshoots. This is demonstrated by low-error tracking of a regulation signal by filtering it into frequency bands and using TCL sub-ensembles with duty cycles in corresponding ranges. As a result, control policies that can maintain a uniform distribution of power consumption by aggregated heterogeneous loads will enable distribution system management (DSM) approaches that maintain stability as well as power quality, and further allow more integration of renewable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Model Fidelity Study of Dynamic Transient Loads in a Wind Turbine Gearbox: Preprint

Transient events cause high loads in the drivetrain components so measuring and calculating these loads can improve confidence in drivetrain design. This paper studies the Gearbox Reliability Collaborative 750kW wind turbine gearbox response during transient events using a combined experimental and modeling approach. The transient events include emergency shut-downs and start-ups measured during a field testing period in 2009. The drivetrain model is established in the multibody simulation tool Simpack. A detailed study of modeling fidelity required for accurate load prediction is performed and results are compared against measured loads. A high fidelity model that includes shaft and housing flexibility and accurate bearing stiffnesses is important for the higher-speed stage bearing loads. Each of the transient events has different modeling requirements.

17 WIND ENERGY↗
DOE OSTI2016

Modeling and Depletion Simulations for a High Flux Isotope Reactor Cycle with a Representative Experiment Loading

The purpose of this report is to document a high-fidelity VESTA/MCNP High Flux Isotope Reactor (HFIR) core model that features a new, representative experiment loading. This model, which represents the current, high-enriched uranium fuel core, will serve as a reference for low-enriched uranium conversion studies, safety-basis calculations, and other research activities. A new experiment loading model was developed to better represent current, typical experiment loadings, in comparison to the experiment loading included in the model for Cycle 400 (operated in 2004). The new experiment loading model for the flux trap target region includes full length 252 Cf production targets, 75 Se production capsules, 63 Ni production capsules, a 188 W production capsule, and various materials irradiation targets. Fully loaded 238 Pu production targets are modeled in eleven vertical experiment facilities located in the beryllium reflector. Other changes compared to the Cycle 400 model are the high-fidelity modeling of the fuel element side plates and the material composition of the control elements. Results obtained from the depletion simulations with the new model are presented, with a focus on time-dependent isotopic composition of irradiated fuel and single cycle isotope production metrics.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Dynamic Modeling and Simulation of a Subcritical Coal-Fired Power Plant under Load-Following Conditions

Dynamic models for power plants that capture realistic general process trends and effects of manipulated variables are needed to improve load-following, while minimizing carbon footprint. In this work, a dynamic modeling approach and simulation results for subcritical coal-fired power plant components are presented. These encompass simulation of the dynamics in the fireside, including the effects of fuel, air combustion, and the dynamics of the entire waterside and power generation sections. This model development enables the simulation and analysis of the important short and long-time scale dynamics of components such as heaters, evaporative loop, and power generation units. Furthermore, additional variables in the power generation section are introduced to improve model accuracy, extending the prediction capability of subcritical power plant models and opening new opportunities for research in operator training, optimization, and advanced model-based controller design that are based on these models. The change in process gain for different ramp rates associated with disturbance signals that affect process variables is also explored and a correlation developed. This provides opportunities to study disturbance rejection control implementation and adaptation for scenarios with such variations in ramp rates. The prediction capabilities of selected components are compared to data available in literature, with the obtained root mean squared error ranges that reflect the model performance and quality of predictions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Generating realistic building electrical load profiles through the Generative Adversarial Network (GAN)

Building electrical load profiles can improve understanding of building energy efficiency, demand flexibility, and building-grid interactions. Current approaches to generating load profiles are time-consuming and not capable of reflecting the dynamic and stochastic behaviors of real buildings; some approaches also trigger data privacy concerns. In this study, we proposed a novel approach for generating realistic electrical load profiles of buildings through the Generative Adversarial Network (GAN), a machine learning technique that is capable of revealing an unknown probability distribution purely from data. The proposed approach has three main steps: (1) normalizing the daily 24-hour load profiles, (2) clustering the daily load profiles with the k-means algorithm, and (3) using GAN to generate daily load profiles for each cluster. The approach was tested with an open-source database – the Building Data Genome Project. We validated the proposed method by comparing the mean, standard deviation, and distribution of key parameters of the generated load profiles with those of the real ones. The KL divergence of the generated and real load profiles are within 0.3 for majority of parameters and clusters. Additionally, results showed the load profiles generated by GAN can capture not only the general trend but also the random variations of the actual electrical loads in buildings. We report the proposed GAN approach can be used to generate building electrical load profiles, verify other load profile generation models, detect changes to load profiles, and more importantly, anonymize smart meter data for sharing, to support research and applications of grid-interactive efficient buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A tool for assessing demand side management and operating strategies for isolated microgrids

Globally, power system operators are exploring ways to leverage the capabilities of smart meters to implement fine-grained demand side management (DSM). Here this paper presents a new simulation tool to evaluate operating strategies for smart-meter-enabled (SME) islanded microgrids being advanced for sustainable rural electrification in emerging economies. Based on MATLAB/Simulink, the tool's component-level models of small microgrids can be easily configured. Alternative rule-based operating strategies are implemented with controllable supply and storage components as well as other components driven by external factors (e.g., weather and time of day). To account for the wide range of possible operating conditions in real-world applications, statistical metrics can be evaluated using Monte Carlo (MC) methods based on user-defined, time-dependent probability distributions for demands, supplies, and environmental variables. Features of the tool are demonstrated with case studies for a representative microgrid in rural Rwanda.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

COMPASS-U Global Heat Balance Calculations

COMPASS-U is a medium size, high magnetic field experimental tokamak (R = 0.9 m, B t = 5 T, and I p = 2 MA), built at the Institute of Plasma Physics, Czech Academy of Sciences (IPP-CAS). This global heat balance calculation for COMPASS-U was done at Princeton Plasma Physics Laboratory (PPPL). Based on our previous experience of building global thermal model for National Spherical Torus Experiment-Upgrade (NSTX-U) at PPPL, this 2-D global thermal model geometry represents a typical cross section of COMPASS-U machine. The model includes thermal radiation, conduction, and convection among components and also between the machine and outer environment. Helium gas heating/cooling was modeled with fluid element and surface element. This model was used to calculate component temperatures and heat distribution during heat up, cryogenic cool down, normal operation, and fault operation scenarios. Definition of 14 main thermal scenarios was provided by IPP-CAS. First nine are normal operation scenarios. Scenarios #10–#14 are fault scenarios. Results of thermal scenario #5 will be given and discussed in this article, including peak temperatures, temperature ratcheting, energy distribution, and required cooling power.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Fusion RF Modeling Machine Learning (FusionML_RF) v1.0

FusionML_RF consists of multiple codes and trained machine learning (ML) models that perform low-cost output modeling from the Genray-CQL3D. Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. For example, completing a single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time. On the other hand, these ML models achieve ~ms of inference time with high accuracy across the input parameter space. This software collection consists of multiple components. (1) codes that use ML methods and precomputed Genray-CQL3D simulation output to build regression models that enable approximate computations of Genray-CLQ3D outputs from arbitrary but physically meaningful input parameters (surrogate modeling); (2) three trained models created by the team, using a database of 16,000+ GENRAY/CQL3D simulations, to study the performance of ML models for surrogate modeling; (3) codes that load the trained models and simulation data, and then compute mean squared error between the models' predictions and the ground truth of simulation output data. This collection is being made available in conjunction with a scientific publication about the work to promote reusability and provide an artifact of the scientific work.

Bai, Zhe↗
DOE OSTI2019

A closure model for predicting crack growth under creep-fatigue loading

A strip-yield model was formulated here to simulate creep-fatigue crack growth and to quantify the influence of hold time on plasticity-induced crack closure during creep-fatigue crack growth. Creep-fatigue experiments have shown that longer creep hold times result in faster crack growth rates in subsequent fatigue cycles. This model advances the idea that a decrease of plasticity-induced crack closure is experienced by the crack during fatigue loading when a longer hold time is applied each creep-fatigue cycle. Consequently, the crack tip experiences an increase in the effective stress intensity factor range causing faster growth rate during the fatigue loading. The weight function method was used to compute stress intensity factors and surface displacements for cracks embedded in a material experiencing elastic, plastic and creep deformations at elevated temperatures. It is shown that the longer the hold time, the larger the creep deformation and crack opening displacements in the near crack-tip region. In turn, this leads to a decrease in the crack-tip opening stress/load and faster crack growth rates during the subsequent fatigue cycle. The model was used to perform simulations of creep-fatigue crack growth at elevated temperatures in a nickel-base superalloy and AISI 316 austenitic steel.

42 ENGINEERING↗
DOE OSTI2023

Evaluation of enzymatic depolymerization of PET, PTT, and PBT polyesters

Millions of tons of waste polyester plastics, including polyethylene terephthalate (PET), polytrimethylene terephthalate (PTT), and polybutylene terephthalate (PBT), end up in the environment as soil and water contaminants. Recent advances in enzymatic polyester degradation have motivated researchers toward biorecycling of plastic wastes. Leaf-branch compost cutinase (LCC) enzymes have been proven to be effective in the biodegradation of PET. This study focuses on enzymatic depolymerization of waste PET, PTT, and PBT materials by using an ICCG variant of LCC (LCC ICCG ) produced from Escherichia coli BL21(DE3). The degradation efficiency of the polyesters was determined by the monomer terephthalic acid (TPA) released from the depolymerization reaction. It was found that the most efficient depolymerization was achieved for PET, followed by PTT and PBT. A kinetic model based on Langmuir adsorption and the Michaelis-Menten equation was developed to describe the enzymatic depolymerization of PET, PTT, and PBT with various enzyme and substrate loadings. The model simulation results revealed that the LCCICCG enzyme loading should be linearly increased as the work capacity of the polyester substrate increases. A specific enzyme loading of 0.91 mg/g PET is suggested to achieve 90% depolymerization of PET within three days. Furthermore, the experimental data and model simulation results can be used to help further engineer the enzyme and process to achieve a complete biodegradation of polyester wastes at large scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Modeling Terrestrial Dissolved Organic Carbon Loading to Western Arctic Rivers

The mobilization and land-to-ocean transfer of dissolved organic carbon (DOC) in Arctic watersheds is intricately linked with the region's climate and water cycle, and furthermore at risk of changes from climate warming and associated impacts. This study quantifies model-simulated estimates of runoff, surface and active layer leachate DOC concentrations and loadings to western Arctic rivers, specifically for basins that drain into coastal waters between and including the Yukon and Mackenzie Rivers. Model validation leverages data from other field measurements, synthesis studies, and modeling efforts. The simulations effectively quantify DOC leaching in surface and subsurface runoff and broadly capture the seasonal cycle in DOC concentration and mass loadings reported from other studies that use river-based measurements. A marked east-west gradient in simulated spring and summer DOC concentrations of 24 drainage basins on the North Slope of Alaska is captured by the modeling, consistent with independent data derived from river sampling. Simulated loadings for the Mackenzie and Yukon show reasonable agreement with estimates of DOC export for annual totals and four of the six seasonal comparisons. Nearly equivalent loading occurs to rivers which drain north to the Beaufort Sea and west to the Bering and Chukchi Seas. The modeling framework provides a basis for understanding carbon export to coastal waters and for assessing impacts of hydrological cycle intensification and permafrost thaw with ongoing warming in the Arctic.

54 ENVIRONMENTAL SCIENCES↗