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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 talk goes into the algorithmic work done under the Forest project in order to solve expensive power grid models. We explore multiple fidelities of models that balance accuracy and computational expense. We use bundling strategies and progressive hedging in order to parallelize large stochastic programs.
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We introduce rank-limiting strategies to optimize tensor-train decompositions for three-dimensional finite-difference time-domain simulations using the relationship between the tensors and their specific dimensionality. These include the use of hard caps on the inner ranks of the tensor train decomposition and the use of a group rounding algorithm taking into account all field components simultaneously. Here, several numerical examples are considered to verify the efficacy of the proposed optimization strategies.
Weather data plays a critical role in renewable energy analysis. Compared to using multiple Actual Meteorological Years, simulations using a single typical year require significantly fewer computational resources. Previous efforts to create typical weather datasets for renewable energy analysis either lack justified or optimized strategies for selecting and weighting different weather parameters or are limited to a few specific locations. Here, in this study, we developed a dataset comprising Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for over 2000 locations across the U.S., based on data from NASA's POWER project. The strategies for creating TSYs and TWYs were optimized based on the simulated outputs of various PV systems and wind turbines in 16 representative cities. This dataset provides an efficient means for the rapid evaluation and optimization of renewable energy systems throughout the entire U.S. Additionally, the optimal strategies identified in this study can be directly applied to create near-optimal TSYs and TWYs for most locations worldwide. However, readers can also employ the optimization approach presented in this work to develop optimal strategies tailored for particular regions.
Heliostat Aimpoint and Layout Optimization Software (HALOS) is an open-source software package that allows users to explore solar field layout optimization, aimpoint strategy optimization, and performance characterization of concentrating solar power tower plants. Users interface with the tool through python, and results are reported in time series tables, plots, runtime logs, and flat-file outputs. Users choose from a list of variables such as tower height, receiver capacity, flux limits, design-point irradiance, etc., and specify information about the system using a small collection of flat files. The software can then optimize the specified variables (e.g., aimpoints for each heliostat) to maximize the thermal energy delivered to the receiver while adhering to flux limits. HALOS is implemented to be flexible with respect to flux characterization methods, but includes a direct connection to NREL's SolarPILOT™ software via its python API so that users can utilize high-fidelity flux simulation methods that have already been developed.
Offshore microgrids, due to their remote location and lack of external energy support, face significant challenges in wide-range load operation and maintenance. Consequently, efficiency and reliability are critical concerns for converters in offshore dc microgrids. This article presents an optimal control strategy aimed at enhancing both efficiency and reliability. A normalized nonlinear relationship between power loss and thermal stress of a paralleled converter is first established. Based on this, a dual-objective optimization function with an active weight function as well as a system overall performance index is established. The active weight function dynamically adjusts the control priority based on converter efficiency and switching device thermal stress. Then, the optimal power-sharing strategy is derived by the Lagrange multiplier method with the proposed optimal function. Additionally, to accommodate a wide load range, an optimal selection strategy for operating converter combinations is proposed, requiring only low-bandwidth communication. Experiment verification is given to validate the effectiveness of the proposed control strategy. The experiment results demonstrate that the proposed control strategy can improve the overall performance of offshore microgrids by optimizing efficiency and reliability.
This paper aims at developing a data-driven optimal control strategy for virtual synchronous generator (VSG) in the scenario where no expert knowledge or requirement for system model is available. Firstly, the optimal and adaptive control problem for VSG is transformed into a reinforcement learning task. Specifically, the control variables, i.e., virtual inertia and damping factor, are defined as the actions. Meanwhile, the active power output, angular frequency and its derivative are considered as the observations. Moreover, the reward mechanism is designed based on three preset characteristic functions to quantify the control targets: (1) maintaining the deviation of angular frequency within special limits; (2) preserving well-damped oscillations for both the angular frequency and active power output; (3) obtaining slow frequency drop in the transient process. Next, to maximize the cumulative rewards, a decentralized deep policy gradient algorithm, which features model-free and faster convergence, is developed and employed to find the optimal control policy. With this effort, a data-driven adaptive VSG controller can be obtained. By using the proposed controller, the inverter-based distributed generator can adaptively adjust its control variables based on current observations to fulfill the expected targets in model-free fashion. Finally, simulation results validate the feasibility and effectiveness of the proposed approach.
This report is a draft white paper on the factors determining commercially optimal development strategies for CO 2 storage with and without CO 2 -EOR. The draft report provides a summary of accomplishments during the first budget period of the Southeast Offshore Storage Resource Assessment for the Gulf of Mexico (SECARB Offshore GOM) project. It also identifies “Next Steps” to be undertaken during Budget Period II. A final white paper will be prepared at the end of Budget Period II. The goal of this draft white paper is to expand the knowledge base required for commercially viable, secure, longterm, large-scale carbon dioxide (CO 2 ) subsea storage in the U.S. Gulf of Mexico (GOM), both with and without enhanced hydrocarbon recovery. This effort supports the U.S. Department of Energy’s (DOE) long-term objective of ensuring a comprehensive assessment of the potential for offshore CO 2 subsea storage in the GOM. The groundwork that has been completed in the draft white paper advanced by expanding the membership of the Southern States Energy Board’s (SSEB) existing Southeast Offshore Storage Resource Assessment (DE-FE0026082) GOM government-industry partnership during the next budget period. Further, the team plans to consult with U.S. federal and state agencies to develop recommendations to remove barriers and streamline the regulatory process to encourage subsea CO 2 storage with or without enhanced hydrocarbon recovery.
This paper presents a lossless regenerative dV/dt snubber circuit for PWM converters to achieve high-efficiency high power-density without significant cost and reliability penalties. The dV/dt snubber employs lossless capacitors for each MOSFET device in a converter to provide an additional path to store the switching energy during turn-off transient and decrease the actual turn-off loss of the MOSFET channel. A novel mathematical turn-off loss model is built to separate the actual turn-off loss of the MOSFET channel and the recycling switching energy stored in the total equivalent output capacitance including Coss, the dV/dt snubber, and etc. Necessary snubber optimization strategy is proposed in order to balance the turn-off loss with ZVS operation range and deadtime conduction loss, and thereby achieving an optimal efficiency performance. To prove the validity and accuracy of the novel turn-off loss model and optimization strategy, the dV/dt snubber has been incorporated into a 1.3kV/200kW DC/DC and a 1kV/70kVA DC/AC PWM converters. Experimental results are given to demonstrate the validity of the proposed dV/dt snubber capacitors and optimization strategy.
This report outlines strategies for Weatherization and Intergovernmental Programs Office (WIP) investment aimed at improving state energy office programs. The project was funded by WIP within the U.S. Department of Energy (DOE) office of Energy Efficiency and Renewable Energy (EERE). WIP aims "to enable strategic investments in energy efficiency and renewable energy technologies through the use of innovative practices across the United States in partnership with a wide range of stakeholders, including state and local organizations and community-based nonprofits." Across its programs, WIP has created thousands of jobs, maintains an investment ratio of 4.1 to 1, and increases U.S. economic output by 1.2 billion a year. This project was funded in the FY21 budget cycle and originally focused on improving WIP energy efficiency funding and programs. The project gathered information from various state energy offices to understand how WIP funding and processes can be optimized to improve state-level energy efficiency programs. We propose that the conclusions are also broadly applicable to weatherization and renewable energy deployment programs.
Numerous particle models for the cosmological dark matter feature a pair-annihilation rate that scales with powers of the relative velocity between the annihilating particles. As a result, the annihilation rate in the central regions of a dark matter halo can be significantly lower than at the halo’s periphery for particular ambient gravitational potentials. While this might be offset by an increasing dark matter pair number density in the inner halo, it raises the question; what angular region for dark matter models with velocity-suppressed annihilation rates optimizes the signal to noise ratio? We consider simplified background models for galactic and extragalactic targets and demonstrate that the optimal observing strategy varies greatly case by case. Generally, a bright central source warrants an annular region of interest, while a flatter background warrants as large as possible an angular region, possibly including the central regions.
Achieving success with grid-interactive efficient buildings (GEBs) is closely tied to the utilization of flexible loads. A valuable strategy involves the implementation of precooling techniques before high-demand events, such as peak hours, by adjusting zone air temperature setpoints. This leads to a reduction in thermal loads and peak electricity demand during these times, as the building’s thermal mass stores and subsequently releases thermal energy. However, the effectiveness of the pre-cooling optimization is highly contingent on specific conditions such as building thermal properties, weather conditions, utility rate structure, HVAC equipment sizing, etc. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Here, the optimal thermostat schedule was parameterized using three optimization variables: the precooling start time, the precooling end time, and the precooling temperature setpoint. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. In addition, we investigated the impact of different influencing factors on the optimal precooling strategy. These factors include building thermal mass, outdoor air conditions, and energy price profiles. Using high-performance computing, we simulated a total of 225 scenarios, consisting of three levels of thermal mass, five typical outdoor air temperature profiles, and fifteen time-of-use price plans. The results demonstrate that optimal thermostat scheduling could save substantial energy cost in medium-sized office buildings with heavy thermal mass but with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. The study also highlights the need to account for zone diversity and recognize that a one-size-fits-all-zone setpoint schedule may not be suitable for all zones and can lead to unnecessary energy wastage. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. Although cost savings are comparable under certain conditions with the same cooling load, observed deviations in energy penalty indicate potential disparities in the efficiency of the HVAC system during the load-shifting process. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings.
Abstract A statistical framework is presented enabling optimal sampling and analysis of constant life fatigue data. Protocols using Bayesian maximum entropy sampling are built based on conventional staircase and stress step methods, reducing the requirement of prior knowledge for data collection. The Bayesian Staircase method shows improved parameter estimation efficiency, and the Bayesian Stress Step method shows equal accuracy to the standard method at larger step size allowing experimentalists to lessen concerns of loading history. Statistical methods for determining model suitability are shown, highlighting the influence of protocol. Experimental validation is performed, showing the applicability of the methods in laboratory testing.
Phase 3 of this project uses the knowledge and techniques gained from Phase 1 and Phase 2. The first outcome of Phase 3 is a new test section which can be manufactured and tested in the Helium Flow Loop Experiment to verify the results of the optimization study. The second outcome of Phase 3 is analyzing the features of this design and determining its performance under simulated TBM conditions: heat flux, pressure, mass flow rate, one-sided and volumetric heating. This will be accomplished by first using the results from the Phase 1 analyses and then applying the optimization strategy from Phase This phase uses the STAR-CCM+ Design Manager tool to apply the optimization strategy to the Helium Flow Loop Experiment (HFLE). The optimization algorithm used by the Design Manager tool is Simultaneous Hybrid Exploration that is Robust, Progressive, and Adaptive (SHERPA search algorithm). In addition to demonstrating the optimization features, a simple parameter sweep is also included in this phase. This report details the applied single objective optimization.
Here, the problem of engine idling for heavy-duty trucks has been under study for decades with Auxiliary Power Units (APUs) and Truck Stop Electrification (TSE) as the most compelling solutions. With the electrification of trucks approaching feasibility in terms of cost-effective technology, hybridization offers another “degree of freedom” to tackle the problem. Here, this work aims at exploiting a battery pack of a 48 V mild-hybrid heavy-duty truck to store sufficient onboard energy for powering the auxiliary loads during the hoteling. This problem is not trivial, as the battery packs typically cannot recover the entire energy required through regeneration alone; hence an optimal energy management strategy needs to be employed to charge the battery through the engine during drive operation. This strategy optimizes powertrain performances among the four modes: (i) Engine of Coasting (EOC), (ii) Regeneration by braking, (iii) Regeneration by engine, and (iv) engine idling. This paper presents the development of a Dynamic Programming (DP) framework that employs a multi-objective cost function to minimize the fuel consumption and maximize the regeneration using the above-mentioned four modes. A typical heavy-duty truck drive cycle is used to represent the drive phase, with mandatory hoteling stops as per regulations. A comprehensive powertrain model is developed using validated components’ model. The DP employs two state variables: battery State-of-Charge (SOC) and engine mode, and three control inputs: (i) the engine ON–OFF state, (ii) clutch engagement state, and (iii) power request at the Electric Machine (EM) for calculating optimal SOC trajectory. The framework also tackles rapid engine ON–OFF scenarios to avoid the challenges associated with DP and the compromises in fuel cost with those approaches. Finally, the effectiveness of the proposed framework is tested for potential fuel savings on two different battery packs by performing the full cycle simulations. The results show 6.47% of fuel consumption reduction as compared to traditional APU-based heavy-duty truck.
Detecting anisotropic screening of the cosmic microwave background (CMB) holds the promise of revealing the distribution of gas in the Universe, characterizing the complex processes of galaxy formation and feedback, and studying the epoch of reionization. Estimators for inhomogeneous screening, including some recently proposed small-scale (stacked) estimators, are quadratic or higher order in the CMB temperature or polarization fields and are therefore subject to contamination from CMB lensing. We review the origin of this lensing bias and show that, when stacking on unWISE galaxies, the expected lensing bias dominates the signal if left unmitigated. Hardening techniques that null the lensing bias have been proposed for standard quadratic estimators, whereas only approximate methods have been proposed for stacked estimators. In conclusion, we review these techniques and apply the former to stacked estimators, presenting several strategies (including the optimal strategy) to null lensing contamination when stacking on any large-scale structure tracer.
This project developed and evaluated physics-informed Bayesian learning and machine learning (ML)-based optimization methods for improving beam polarization preservation in the BNL hadron injector chain. The work focused on uncertainty-aware digital twin modeling, Bayesian calibration of accelerator simulations using beam measurements, and data-efficient optimization strategies including Bayesian optimization and reinforcement learning. These methods were applied to injector tuning and RF control problems in realistic accelerator settings to support improved operational robustness and readiness for RHIC operations and future Electron–Ion Collider facilities. No subject inventions were disclosed under this award.