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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 OSTI2015

Building America Case Study: Low-Load Space-Conditioning Needs Assessment, Northeast and Mid-Atlantic; Technology Solutions for New and Existing Homes, Energy Efficiency & Renewable Energy (EERE)

With limited low-load options in the HVAC market, many new-construction housing units are being fitted with oversized equipment - thus facing penalties in system efficiency, comfort, and cost. To bridge the gap between currently available HVAC equipment and the rising demand for low-load HVAC equipment in the marketplace, HVAC equipment manufacturers need to be fully aware of multifamily buildings and single-family homes market needs. Over the past decade, Steven Winter Associates, Inc. (SWA) has provided certification and consulting services on hundreds of housing projects and has accrued a large pool of data. CARB compiled and analyzed these data to see what the thermal load ranges are in various multifamily apartments and attached single-family home types (duplex and townhouse). In total, design loads from 941 dwellings from SWA's recent multifamily and attached single-family work across the Northeast and Mid-Atlantic were analyzed. Information on the dwelling characteristics, design loads, and the specifications of installed mechanical equipment were analyzed to determine any trends that exist within the dataset. Of the 941 dwellings, CARB found that only 1% had right-sized heating equipment and 6% of the dwellings had right-sized cooling equipment (within 25% or less of design load).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Technology Solutions Case Study: Low-Load Space-Conditioning Needs Assessment

Low-load options in the heating, ventilating, and air-conditioning (HVAC) market are limited, so many new-construction housing units are being fitted with oversized equipment that results in penalties in system efficiency, comfort, and cost. To bridge the gap between currently available HVAC equipment and the rising demand for low-load HVAC equipment in the marketplace, HVAC equipment manufacturers need to be fully aware of the needs of the multifamily building and attached single-family home markets. Over the past decade, Steven Winter Associates, Inc. has provided certification and consulting services for hundreds of housing projects and has accrued a large pool of data that describe multifamily and attached single-family home characteristics. In this project, the research team Consortium for Advanced Residential Buildings (CARB) compiled and analyzed the data from 941 low-load buildings in the Northeast and Mid-Atlantic regions to outline the heating and cooling design load characteristics of low-load dwellings. Within this data set, CARB found that only 1% of the dwellings had right-sized (within 25% of design load) heating equipment and 6% had right-sized cooling equipment.

Air-source heat pumps↗
DOE OSTI2017

Stress Models of the Annual Hydrospheric, Atmospheric, Thermal, and Tidal Loading Cycles on California Faults: Perturbation of Background Stress and Changes in Seismicity

Stresses in the lithosphere arise from multiple natural loading sources that include both surface and body forces. The largest surface loads include near-surface water storage, snow and ice, atmosphere pressure, ocean loading, and temperature changes. The solid Earth also deforms from celestial body interactions and variations in Earth's rotation. We model the seasonal stress changes in California from 2006 through 2014 for seven different loading sources with annual periods to produce an aggregate stressing history for faults in the study area. Our modeling shows that the annual water loading, atmosphere, temperature, and Earth pole tides are the largest loading sources and should each be evaluated to fully describe seasonal stress changes. In California we find that the hydrological loads are the largest source of seasonal stresses. We explore the seasonal stresses with respect to the background principal stress orientation constrained with regional focal mechanisms and analyze the modulation of seismicity. Our results do not suggest a resolvable seasonal variation for the ambient stress orientation in the shallow crust. When projecting the seasonal stresses into the background stress orientation we find that the timing of microseismicity modestly increases from an ~8 kPa seasonal mean-normal-stress perturbation. The results suggest that faults in California are optimally oriented with the background stress field and respond to subsurface pressure changes, possibly due to processes we have not considered in this study. At any time a population of faults are near failure as evident from earthquakes triggered by these slight seasonal stress perturbations.

58 GEOSCIENCES↗
DOE OSTI2021

Short-term nodal load forecasting based on machine learning techniques

This paper introduces an advanced Short-term Nodal Load Forecasting (STNLF) method that forecasts nodal load profiles for the next day in power systems, based on the combined use of three machine learning techniques. Least Absolute Shrinkage and Selection Operator (LASSO) is employed to reduce the number of features for a single nodal load forecasting. Principal Component Analysis (PCA) is used to capture the features of historical loads in low-dimensional space compared to the original high-dimensional load space where features are barely possible to depict. Additionally, Bayesian Ridge Regression (BRR) is utilized to decide the parameters of the prediction model from a statistics perspective. Tests based on modified PJM load data demonstrate the effectiveness of the proposed STNLF method compared to the state-of-the-art General Regression Neural Network (GRNN) method. Moreover, the reliability of the day-ahead Unit Commitment (UC) solution is shown to have been improved, based on the forecasted load data using the proposed STNLF method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Synthesizing Carbon‐Supported, High‐Loading, Ultra‐Small Pt 3 Ni Nanoparticles via Tuning the Surface Electrostatic Effect

Carbon‐supported nanoparticles (NPs) are widely used as catalysts in fuel cells and electrolyzers. While it is well known that NPs with smaller size and higher loading often lead to better catalytic activity, they remain challenging to synthesize due to the weak control over the surface properties of the support. Herein, a facile approach to synthesize carbon‐supported, high‐loading, and ultra‐small Pt 3 Ni NPs via applying thermal shock on strongly interacted carbon support with metal salt is reported. Specifically, sodium citrate is introduced into the precursor solution and substrate mixture, which induces strong electrostatic effect between metal salts and carbon particles that markedly improves precursor anchoring and dispersion, thereby achieving high particle loading as well as small size and distribution. As a proof‐of‐concept, the synthesis of Pt 3 Ni NPs supported on carbon black with particle size of 1.56 ± 0.36 nm at 30 wt% loading and 1.66 ± 0.56 nm at 40 wt% loading is reported, where the sizes are among the smallest while the loadings are among the highest in the literature. This approach can be readily extended to many compositions and substrates, with tunable particle size and loading, thereby substantially expanding the synthesis space for NP catalysts in various electrochemical applications.

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

Dynamic load balancing with enhanced shared-memory parallelism for particle-in-cell codes

Furthering our understanding of many of today’s interesting problems in plasma physics – including plasma based acceleration and magnetic reconnection with pair production due to quantum electrodynamic effects – requires large-scale kinetic simulations using particle-in-cell (PIC) codes. However, these simulations are extremely demanding, requiring that contemporary PIC codes be designed to efficiently use a new fleet of exascale computing architectures. To this end, the key issue of parallel load balance across computational nodes must be addressed. We discuss the implementation of dynamic load balancing by dividing the simulation space into many small, self-contained regions or ‘‘tiles,’’ along with shared-memory (e.g., OpenMP) parallelism both over many tiles and within single tiles. The load balancing algorithm can be used with three different topologies, including two space-filling curves. Here, we tested this implementation in the code Osiris and show low overhead and improved scalability with OpenMP thread number on simulations with both uniform load and severe load imbalance. Compared to other load-balancing techniques, our algorithm gives order-of-magnitude improvement in parallel scalability for simulations with severe load imbalance issues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Cooling load forecasting-based predictive optimisation for chiller plants

Extensive electric power is required to maintain indoor thermal comfort using heating, ventilation and air conditioning (HVAC) systems, of which, water-cooled chiller plants consume more than 50% of the total electric power. To improve energy efficiency, supervisory optimisation control can be adopted. The controlled variables are usually optimised according to instant building cooling load and ambient wet bulb air temperature at regular time intervals. In this way, the energy efficiency of chiller plants has been improved. However, with an inherent assumption that the instant building cooling load and ambient wet bulb temperature remain constant in the coming time interval, the energy efficiency potential has not been fully realised, especially when cooling loads vary suddenly and extremely. To solve this problem, a cooling load forecasting-based predictive optimisation method is proposed. Rather than minimising the instant system power according to the instant building cooling load and ambient wet bulb temperature, the controlled variables are derived to minimise the sum of the instant system power and one-time-step-ahead future system power according to both instant and forecasted future building cooling loads. With this method, the energy efficiency potential of a chiller plant can be further improved without shortening the operation time interval. 80% redundant energy consumption has been reduced for the sample chiller plant; energy can be saved for chiller plants that work for years. The evaluation on the effect of cooling load forecasting accuracy turns out that the more accurate the forecasts are, the more redundant energy consumption can be reduced.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study

Time-frequency analysis that disaggregates a signal in both time and frequency domain is an important supporting technique for building energy analysis such as noise cancellation in data-driven building load forecasting. There is a gap in the literature related to comparing various time–frequency-analysis techniques, especially discrete wavelet transform (DWT) and empirical mode decomposition (EMD), to guide the selection and tuning of time–frequency-analysis techniques in data-driven building load forecasting. This article provides a framework to conduct a comprehensive comparison among thirteen DWT/EMD techniques with various parameters in a load forecasting modeling task. A real campus building is used as a case study for illustration. The DWT and EMD techniques are also compared under various data-driven modeling algorithms for building load forecasting. The results in the case study show that the load forecasting models trained with noise-cancelled energy data have increased their accuracy to 9.6% on average tested under unseen data. This study also shows that the effectiveness of DWT/EMD techniques depends on the data-driven algorithms used for load forecasting modeling and the training data. Hence, DWT/EMD-based noise cancellation needs customized selection and tuning to optimize their performance for data-driven building load forecasting modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

Building energy models are used to simulate heat and mass transfer and estimate end-use load in buildings. With the proliferation of solar photovoltaics on residential and commercial buildings, increasingly, buildings are expected to provide grid services, for which accurate and computationally efficient building energy simulations and end-use load prediction are imperative. Existing building energy simulation tools, however, have significant computational overhead that make them less practical in real-time deployment for optimization, design, uncertainty quantification and control in building energy management systems. Here this article presents a data-driven machine learning model based on light gradient boosting method (LightGBM) as a surrogate for a physics-based simulator for residential buildings to predict end-use load. The machine learning based surrogate model accounts for time-series related variables, seasonality and trend component of end-use load, and history of end-use load. The accuracy of the surrogate model is assessed on the prediction of the load profiles of 100 different houses in Cook County, Illinois, USA. The LightGBM surrogate model is shown to reduce the root-mean-squared error by 53% relative to a reference decision tree (DT) based model reported previously in the literature. Moreover, the model predicts the load spikes and high-ramp rate events throughout the year which are often the Achilles heel of other models in the literature. The machine learning based surrogate model is demonstrated to be computationally efficient, with a ten-fold reduction in the computational time compared to a physics-based building energy simulation, and suitable for uncertainty analysis and real-time control of building characteristics in response to uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Grain size dependence of dynamic mechanical behavior of AZ31B magnesium alloy sheet under compressive shock loading

The effects of grain size on the dynamic deformation behavior of rolled AZ31B alloy at high strain rates were investigated. Rolled AZ31B alloy samples with grain sizes of 6, 18 and 37 μm, were subjected to shock loading tests using Split Hopkinson Pressure Bar at room temperature and at a strain rate of 1100 s{sup −} {sup 1}. It was found that a double-peak basal texture formed in the shock loaded samples. The strength and ductility of the alloy under the high strain-rate compressive loading increased with decreasing grain size. However, twinning fraction and strain hardening rate were found to decrease with decreasing grain size. In addition, orientation imaging microscopy showed a higher contribution of double and contraction twins in the deformation process of the coarse-grained samples. Using transmission electron microscopy, pyramidal dislocations were detected in the shock loaded sample, proving the activation of pyramidal slip system under dynamic impact loading. - Highlights: • A double-peak basal texture developed in all shock loaded samples. • Both strength and ductility increased with decreasing grain size. • Twinning fraction and strain hardening rate decreased with decreasing grain size. • ‘g.b’ analysis confirmed the presence of dislocations in shock loaded alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effect of the integral length scales of turbulent inflows on wind turbine loads

As wind turbines become larger, the fluctuations in the inflow become increasingly influential in the turbine structural loading. These fluctuations are characterized by the integral length scale, which represents the average size of the largest energy-containing turbulent eddies. Current design standards neglect the varying integral length scales that characterize inflows of wind turbines in operation. Using large-eddy simulations, we generate turbulent inflows of varying integral length scales and quantify the loads of the IEA 15-MW reference wind turbine. Results illustrate that the impact of turbulence on rotor and tower loads is up to 10 times greater than the impact of the mean shear profile. Increasing integral length scales from 0.3x to 0.5x the rotor diameter reduces blade root flapwise moments and rotor and tower loads. Increasing integral length scales from 0.5x to 1.7x the rotor diameter increases the rotor aerodynamic thrust force and the blade root flapwise shear loads and decreases the tower base torsional moment and the tilting and yawing rotor aerodynamic moments. Additionally, turbulence intensity has a greater impact on wind turbine loads than integral length scales. In conclusion, findings indicate that design standards should consider varying integral length scales for accurate wind turbine loading characterization in turbulent inflow conditions.

17 WIND ENERGY↗
DOE OSTI2024

Impact of wake steering on loads of downstream wind turbines at an above-rated condition

Wake steering strategies often seek to gain power at the expense of increased fatigue loads. Here, we investigate the feasibility of applying wake steering at an above-rated condition. In such a condition, the farm is operating at rated power, and thus, increased power output is not the goal. Instead, wake steering is considered in the context of load reduction. We perform a sweep of wind directions and yaw misalignment angles, ranging from negative to positive values. This approach allows us to obtain trends and identify asymmetries in turbine response for symmetric scenarios. We use a wind farm consisting of five aligned IEA Wind 15-MW reference wind turbines, and analyze trends related to the blade-root, low-speed shaft, and tower-base moments, both in terms of standard deviation and damage equivalent loads. We show that for any given fixed wind direction, the turbines can be yawed such that the fatigue loads are reduced. Reductions of up to 5% (depending on the component) in terms of standard deviation and damage equivalent loads can be achieved by negatively yawing the turbine. A negative yaw misalignment has shown to be the direction of larger improvements. Such results contrast those found for below-rated conditions, where a positive yaw misalignment is typically preferred. However, since load reduction is not uniform across all component loads, more study and consideration is required before operational recommendations can be made.

17 WIND ENERGY↗
DOE OSTI2022

Behavioral and Population Data-Driven Distribution System Load Modeling

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.

American Time Use Survey↗
DOE OSTI2018

Load-Flow in Multiphase Distribution Networks: Existence, Uniqueness, Non-Singularity, and Linear Models

This paper considers unbalanced multiphase distribution systems with generic topology and different load models, and extends the Z-bus iterative load-flow algorithm based on a fixed-point interpretation of the AC load-flow equations. Explicit conditions for existence and uniqueness of load-flow solutions are presented. These conditions also guarantee convergence of the load-flow algorithm to the unique solution. The proposed methodology is applicable to generic systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. Further, a sufficient condition for the non-singularity of the load-flow Jacobian is proposed. Lastly, linear load-flow models are derived, and their approximation accuracy is analyzed. Theoretical results are corroborated through experiments on IEEE test feeders.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

A Fast Load Control System Based on Mobile Distribution-Level Phasor Measurement Unit

The increase of renewable penetration in power grids calls for loads to participate in frequency regulation to avoid under-frequency load shedding after large resource contingencies. The motivation of our work is to fulfill a fast residential load control for frequency stability enhancement. For this purpose, a novel Mobile Distribution-level Phasor Measurement Unit (MDPMU) is developed for disturbance event detection, power mismatch estimation and fast load control. First, the systematic design of the MDPMU introduced. The proposed MDPMU has the major advantages of low cost and high frequency measurement accuracy. Second, the distribution-level measurement is utilized for the Rate Of Change Of Frequency (ROCOF) calculation. Upon detection of a power system frequency event, power mismatch is estimated at the early stage of the event. Details of a robust approach to calculate ROCOF and determine the event starting point are presented. Practical issues including the measurement reporting rate and estimation time are also examined. Finally, with the proposed control system, residential load responses can be controlled coordinately based on load availability and compensation prices offered by customers, providing adaptive frequency regulation service after large resource contingencies. The accuracy of frequency measurement and power mismatch estimation are evaluated via experimental analysis. The frequency stability improvement by load control is validated via PSS/E simulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads that are served by the same distribution transformer. This enables the generation of a large amount of correlated SLPs required for microgrid and distribution system studies. Here, the novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, to the best of our knowledge, this is the first method for generating a group of load profiles bearing realistic spatial- temporal correlations simultaneously. Second, two complementary realisticness metrics for evaluating generated load profiles are developed: computing statistics based on domain knowledge and comparing high-level features via a deep-learning classifier. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad- GAN can generate more realistic load profiles than existing approaches, especially in group level characteristics. With little finetuning, MultiLoad-GAN can be readily extended to generate a group of load or PV profiles for a feeder or a service area.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Developing Load Cell Monitoring on the Mock UF 6 Feed and Withdrawal System

A proof-of-concept load cell monitoring system has been developed for the Mock Feed and Withdrawal (F&W) System at Oak Ridge National Laboratory (ORNL). This system leverages industry-standard commercial off-the-shelf (COTS) hardware and software in a modular and scalable design. The Mock F&W System was designed to simulate operations at a uranium hexafluoride (UF 6 ) gas centrifuge enrichment plant F&W area. It provides a test bed for process-monitoring technologies. Water, used as a surrogate for UF 6 , is pumped from three feed tanks, through a process surge tank, and is then cut between two tails tanks and three product tanks. The weight of the each tank is continuously monitored using a load cell monitoring system. In addition to the F&W stations, large and small accountancy stations are monitored. The Mock F&W System allows ORNL to test and evaluate capabilities of industry-standard process monitoring hardware and software. A variety of commonly used weighing indicators are connected to the load cells to monitor the weight at each station. These weighing indicators are representative of the types likely found in operating facilities. A data center modeled after those currently used for international safeguards was installed to collect and manage the weight data. This data center provides a robust and flexible test bed for testing and developing software solutions. COTS software packages that leverage industry-standard communication protocols provide a data flow architecture that is modular, interoperable, and scalable. One software package provides communication drivers to collect the raw weight data from the weighing indicators. A data historian is used to store the raw data. Custom in-house algorithms have been developed using MATLAB to analyze the raw historical data and to automatically identify process information. This information includes the number of cycles completed and the mass of material transferred from stations to the process. The process information identified by the analysis is stored in a Microsoft SQL Server database. Visualization concepts have been developed that display the analyzed process information alongside visually compelling plots and animations of the raw data. The load cell monitoring system developed on the Mock F&W System was designed as a proof of concept that could be scaled to a field trial at an operating facility. This system, built with industry standard hardware and software, has allowed ORNL to develop capabilities that provide confidence in the ability to leverage existing operator load cell monitoring equipment with minimal impact on operations. The international safeguards community is evaluating the use of load cell monitoring as an additional safeguards measure; this system demonstrates a practical solution to collect, store, analyze, and visualize load cell weight data.

commercial off the shelf↗
DOE OSTI2018

Characterizing the Subsurface Using Deformation from Pumping and Surface Loading Tests

Changes in pore pressure or applied load cause deformation that can be used to characterize the subsurface. A series of field tests were conducted by measuring the deformation and pressure response from a variety of stresses at our test site in South Carolina. The site is underlain by saprolite to a depth of 30 m, and the water table in the unconfined aquifer occurs at a depth of 9 m. The types of tests performed included constant-rate pumping tests and surface point loading tests using the weight of a person or vehicle. Vertical displacement of the saprolite in response to these stresses was measured at depths of 3, 6, 9 and 11 meters in both saturated and unsaturated conditions using an extensometer with a resolution of +/- 10 nm. Results from 2.5-hr-long constant-rate pumping tests indicate a repeatable, compressive vertical strain of roughly 0.1 microstrain in the vadose zone and 1-10 microstrain in the saturated zone. Drawdown was 5 m in the pumping well and 0.1 m in a nearby (r = 9m) observation well. Under saturated conditions, the strain rate gradually decreased throughout the test in a pattern that resembles the drawdown curve in the monitoring well. In contrast, the strain in the vadose zone increased quickly early in the test, but the strain rate diminished and was negligible after approximately 30 minutes of pumping. Interestingly, drawdown in the pumping well also increased quickly over 30 minutes and remained roughly constant after that. Additional tests were conducted by applying a load (weight of a person or vehicle) at the ground surface above the extensometer and allowing the signal to stabilize before removing the load. In the vadose zone, the soil compressed rapidly and then stabilized. In the saturated zone, sudden compression was followed by additional compression at a much slower rate. A similar response was observed upon removal of the load. This transient signal in the saturated zone is interpreted to be a poroelastic effect. When a load is applied, the solid skeleton compresses and pressurizes the pore fluid, causing flow. The strain rate is then a function of pressure diffusion. Simulation of the deformation from both pumping and loading tests using an unsaturated, poroelastic finite-element model indicates that strains are sensitive to the Young’s modulus and hydraulic conductivity as functions of saturation.

well tests, strain, surface loading, hydromechanic↗