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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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This report presents analysis of high-temporal-resolution whole-house electrical consumption data obtained from 17 residential buildings located in the Denver metro and Aspen regions of Colorado as well as survey data collected from the participants related to building characteristics and equipment types.
Excessive entry loads for abort trajectories from nominal AS-205/101 launch profile
Wind profiles and velocity distributions, and ground roughness effect on boundary layer.
The response of STS ascent structural load indicators to the wind profile is analyzed. The load indicator values versus Mach numbers are calculated with algorithms using trajectory information. The ascent load minimum margin concept is used to show that the detailed wind profile structure measured by the Jimsphere wind system is not needed to assess the STS rigid body structural wind loads.
High penetration of photovoltaics (PV) in distribution feeders can cause problems, such as overvoltage, reverse power flow, and large net load changes. Traditional voltage regulation devices, such as capacitors and voltage regulators, can solve some of these problems but might have some delays. Today, smart inverters are gradually being used to provide voltage regulation and frequency support in distribution systems. Different smart inverter settings have been recommended in various rules and standards; however, the potential benefits and their impacts on distribution system operation are not well compared and studied. This paper presents a comparison of different smart inverter settings as applied to a distribution system. An actual feeder model from San Diego Gas & Electric Company is used to conduct the simulation. Additionally, a load disaggregation method is proposed to disaggregate the load and PV profile for each load location using advanced metering infrastructure net load measurements. Then, different smart inverter settings are applied to the PV systems in the feeder, and the simulation results are compared. The results show that the implementation of specific functions of smart inverters can reduce voltage exceedances, and the utility can determine the specific inverter setting based on its operational requirements.
Description of a single unified theory to account generally for the discrete radiation from the whole family of heavily loaded axial flow rotors, including helicopter rotors, propellers, and compressors. The approach used is to analyze, in a Fourier way, the periodic blade loading variation (azimuth profile) into blade loading harmonics (BLH) and then sum the total radiation from each BLH. The basic differences between high and low solidity rotor spectrums are considered, emphasizing the difference between subsonic and supersonic rotor noise. The special effect of helicopter blade slap and compressor rotor-stator interaction is discussed, and measured rotor spectrums have been included to help illustrate the theory.
Hybrid-RL-MPC4CLR was developed as a hybrid controller for active distribution grid critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. The RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while the MPC models grid operations incorporating the RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. The developers formulated the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The software is developed using various software packages in Python. The MPC's optimal power flow (OPF) model is implemented using the Pyomo package, the RL simulation environment is implemented using the MPC simulation with various scenarios of renewable energy and load demand profiles and power outage beginning times, based on the OpenAI Gym framework. The RL agent training is performed using the RLlib Ray package. The RL algorithm is trained offline using historical forecasts of renewable generation and load demand profiles. Simulation analysis and performance tests are conducted using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery.
A complete look at the near-field development and subsequent role-up of a wingtip vortex from a NACA 0015 wing section is investigated. Two separate but equally important surveys of the vortex structure in the region adjacent to the wingtip and approximately one chord length downstream of the trailing edge are performed. The two surveys provide qualitative flow-visualization an quantitative velocity measurement data. The near-field development and subsequent role-up of the vortex structures is strongly influenced by the angle-of-attack and the end-cap treatment of the wing section. The velocity field near the wingtip of the NACA 0015 wing section was measured with a triple-sensor hot wire probe and compared to flow visualization images produced with titanium tetrachloride smoke injection and laser illumination. The flat end-cap results indicate the formation of multiple, relatively strong vortex structures as opposed to the formation of a single vortex produced with the round end-cap. The multiple vortices generated by the flat end-cap are seen to rotate around a common ce te in a helical pattern until they eventually merge into a single vortex. Compared to a non-dimensional loading parameter, the results of the velocity and flow visualization data shows a "jetlike" axial velocity profile for loading parameter values on the order of 0.1 and a "wakelike" profile for much lower loading parameter values.
Here this paper presents a novel Iterative Bidirectional Gradient Boosting Model (IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR) programs. In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem. The approach involves dividing the load and its corresponding temperature profiles into three periods: pre-CVR, CVR, and post-CVR. To restore the missing load profile during the CVR period, the method employs a three-step process. First, a forward-pass GBM is executed using data from the pre-CVR period as inputs. Subsequently, a backward-pass GBM is applied using data from the post-CVR period. The two restored load profiles are reconciled, considering pre-calculated weights derived from forecasting accuracy, and only the leftmost and rightmost points are retained. The newly restored points are then included as inputs for the subsequent iteration. This iterative procedure continues until the original load data in the CVR period is fully restored. We develop IBi-GBM using actual smart meter and Supervisory Control and Data Acquisition (SCADA) data. Our results demonstrate that IBi-GBM exhibits robust performance across various data resolutions and in different seasons and outperforms existing methods by achieving a 1-2% reduction in normalized Root Mean Square Error (nRMSE).
Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting using past data from multiple buildings, data heterogeneity from diverse buildings can reduce model performance. The impact of increasing dataset heterogeneity in time series forecasting, while keeping size and model constant, is understudied. We tackle this issue using the ComStock dataset, which provides synthetic energy consumption data for U.S. commercial buildings. Two curated subsets, identical in size and region but differing in building type diversity, are used to assess the performance of various time series forecasting models, including finetuned open-source foundation models (FMs). The results show that dataset heterogeneity and model architecture have a greater impact on post-training forecasting performance than the parameter count. Moreover, despite the higher computational cost, finetuned FMs demonstrate competitive performance compared to base models trained from scratch.
As probably the most complicated and critical infrastructure system, U.S. power grids become increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as well as higher DER penetrations and growing information mismatch among system operators, utilities (transmission or generation owners), and end-users. This paper proposes a data-driven democratized control architecture considering two democratization pathways to assist transmission system operators, with a targeted use case of developing online proactive islanding strategies. Detailed discussions on load capability profiling at transmission buses and disaggregation of DER generations are provided and illustrated with real-world utility data. By Combining network and operational constraints, transmission system operators can be equipped with new tools built on top of this architecture, to derive accurate, proactive, and strategic islanding decisions to incorporate the wide range of dynamic portfolios and needs when facing extreme events or unseen grid contingencies.
In this study a large scale digitized approach is used for an uninterrupted static and dynamic analysis of spur gearing. An interactive method was developed to calculate directly the variable gear mesh stiffness as a function of transmitted load, gear profile errors, gear tooth deflections and gear hub torsional deformation, and position of contacting profile points. The developed methods are applicable to both the normal and high contact ratio gearing. Certain types of simulated sinusoidal profile errors and pitting can cause interruptions of the normal gear mesh stiffness function and, thus, increase the dynamic loads in gearing.
Dynamometer testing of full-scale wind turbine drivetrains involves subjecting integrated components to extreme static loads, fatigue loads, or dynamic loads to replicate field conditions. This paper provides a detailed evaluation of the uncertainty and errors in applied loads and measured responses, and their magnitude relative to established repeatability bounds for a drivetrain subjected to static and dynamic loads on the 7.5 MW test bench at the Clemson University Wind Turbine Drivetrain Testing Facility. An ideal drivetrain simulation model is utilized to isolate the influence of load tracking errors on test article responses. It is shown that the test article response variations are mainly driven by run out and clearances inherent to the drivetrain. For the dynamic profiles, the load tracking errors are within acceptable limits. Furthermore, a frequency analysis is used to show that the test bench controller tracking performance is acceptable for profiles with frequency content up to 1.5 Hz.
An analytical computer simulation program for dynamic modeling of low-contact-ratio spur gear systems is presented. The procedure computes the static transmission error of the gears operating under load and uses a fast Fourier transform to generate the frequency spectrum of the static transmission error at various tooth profile modifications. The dynamic loading response of an unmodified (perfect involute) gear pair was compared with that of gears with various profile modifications. Correlations were found between various profile modifications and the resulting dynamic loads. An effective error, obtained from frequency domain analysis of the static transmission error of the gears, gave a very good indication of the optimum profile modification to reduce gear dynamic loading. Design curves generated by dynamic simulation at various profile modifications are given for gear systems operated at various loads. Optimum profile modifications can be determined from these design curves for improved gear design.
An analytical computer simulation program for dynamic modeling of low-contact-ratio spur gear systems is presented. The procedure computes the static transmission error of the gears operating under load and uses a fast Fourier transform to generate the frequency spectrum of the static transmission error at various tooth profile modifications. The dynamic loading response of an unmodified (perfect involute) gear pair was compared with that of gears with various profile modifications. Correlations were found between various profile modifications and the resulting dynamic loads. An effective error, obtained from frequency domain analysis of the static transmission error of the gears, gave a very good indication of the optimum profile modification to reduce gear dynamic loading. Design curves generated by dynamic simulation at various profile modifications are given for gear systems operated at various loads. Optimum profile modifications can be determined from these design curves for improved gear design.
The ascent opeational flight profile for the space transportation system 1 flight is designed (1) to limit the maximum undispersed dynamic pressure to 580 lb/sq ft, (2) to follow the design load indicator profiles where q alpha is a specified profile and q beta is desired to be as close to zero as passible, and (3) to maximize nominal and abort performance. Significant trajectory parameters achieved are presented. A maximum dynamic pressure of 575 lb/sq ft was achieved, a minimum q alpha of -2187 lb-deg/sq ft was achieved, and q beta was limited to approximately + or - 100 lb-deg/sq ft in the high q region of the trajectory. The trajectory performance allows a press to main engine cutoff capability with one space shuttle main engine out at 262 seconds ground elapsed time. The orbital maneuvering system burns achieve a final orbit of 150.9 x 149.9 x 149.8 n. mi. and the desired inclination of 40.3 degrees.
In order for microreactors to be economically competitive, operation costs will need to be minimized through some degree of autonomous control. Previous work has demonstrated the effectiveness of reinforcement learning (RL) for load-following control in a drum-controlled microreactor. This study extends that work by exploring the potential of RL to independently control each of the reactor’s drums. We compare a single-agent RL approach with a multi-agent RL (MARL) framework, testing them for generalization across different load-following power profiles and control timescales, and for robustness in cases of randomly disabled control drums. Since the point kinetics simulation environment used in this study cannot resolve spatial effects, we assume that in the absence of spatially localized disturbances, optimal drum movements should be symmetrical. We demonstrate that single-agent RL is able to achieve accurate performance only when symmetric actions are ignored; otherwise, it fails to train a useful controller. Meanwhile, the MARL framework performs symmetric actions by design and trains a robust, accurate agent, as evidenced by mean absolute errors in power matching of 0.41% for the training power profile, 0.68% for a profile with half the drums disabled, and 0.21% for a profile on a realistic load-following time horizon.
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).