Supporting energy policy research with large language models: A case study in wind energy siting ordinances
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Crystalline silicon carbide (SiC) substrates and epilayers, undoped as well as n- and p-doped, have been electrically characterized by performing Hall effect and resistivity measurements (van der Pauw) over the temperature range of approximately 85 K to 650 K (200 K to 500 K for p-type sample). By fitting the measured temperature dependent carrier concentration data to the single activation energy theoretical model: (1) the activation energy for the nitrogen donor ranged from 0.078 eV to 0.101 eV for a doping concentration range of 10(exp 17) cm(exp -3) to 10(exp 18) cm(exp -3) and (2) the activation energy for the aluminum acceptor was 0.252 eV for a doping concentration of 4.6 x 10(exp 18) cm(exp -3). By fitting the measured temperature dependent carrier concentration data to the double activation energy level theoretical model for the nitrogen donor: (1) the activation energy for the hexagonal site was 0.056 eV and 0.093 eV corresponding to doping concentrations of 3.33 x 10 (exp 17) cm(exp -3) and 1.6 x 10(exp 18) cm(exp -3) and (2) the activation energy for the cubic site was 0.113 and 0.126 eV corresponding to doping concentrations of 4.2 x 10(exp 17) cm(exp -3) and 5.4 x 10(exp 18) cm(exp -3).
Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.
As the NOvA long-baseline neutrino oscillation experiment takes more data, systematic uncertainty begins to more heavily impact the oscillation analyses. The largest effects are calibration on calorimetric energy, detector modeling on muon energy scale, and near-detector pileup on normalization of the far-detector expectation. Cross-section systematics will be discussed in another poster. Detailed studies have justified previous systematics as too conservative and suggested smaller values. Focused study of detector modeling using dE/dx and external measurements constrain the calibration and energy scale uncertainties. A novel technique is used to assess the impact of activity from neutrino activity piling up over signal, where a simulated neutrino is overlaid into both data and simulation. The difference in efficiency of selecting that single neutrino among data and simulated spills then dominates the normalization systematic.
Affordability, sustainability, and efficiency are primary motivators driving the future of NASA aeronautics research. These factors are realized, in part, through the development and implementation of new technologies and strategies enabling efficient, affordable, and safe hybrid-electric aircraft. Research supporting electrified aircraft propulsion control systems exemplifies such new methodologies, offering varied opportunities to integrate electric machines with gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures seek to introduce energy storage and exploit electrical power system components to assist gas-based system components. Capitalizing on the electric machines in hybridized engines, Turbine Electrified Energy Management (TEEM) is a control approach that enhances transient operability to improve overall propulsion and vehicle efficiency by injecting or extracting power from engine shafts. Traditionally implemented with proportional-integral (PI) control, this study expands the application of TEEM by presenting model predictive control (MPC) schemes to execute the TEEM concept. Via cost function design and constraint selection, the transient operability goals for TEEM are considered in the controller designs. The proposed MPCs are simulated on a nonlinear turbofan engine model at two environmental conditions, with comparisons drawn to a baseline PI. Performance is evaluated using compressor maps and two TEEM-specific metrics: transient stack usage and transient excursion integral. Simulation results reveal the developed schemes perform comparably to the benchmark controller and can be implemented in two distinct configurations. Potential modifications for future investigations include cost function measures that optimize energy use, additional performance effectiveness measures, and battery storage capabilities.
The engineering manual provides a complete companion documentation about the structure of the main program and subroutines, the preparation of input data, the interpretation of output results, access and use of the program, and the detailed description of all the analytic, logical expressions and flow charts used in computations and program structure. A numerical example is provided and solved completely to show the sequence of computations followed. The program is carefully structured to reduce both user's time and costs without sacrificing accuracy. The user would expect a cost of CPU time of approximately $5.00 per building zone excluding printing costs. The accuracy, on the other hand, measured by deviation of simulated consumption from watt-hour meter readings, was found by many simulation tests not to exceed + or - 10 percent margin.
We have modeled two mid-infrared imaging photometry data sets to determine the spatial distribution of physical conditions in the BN/KL (Becklin-Neugebauer / Kleinmann-Low) infrared complex. We observed the BN/KL region using the 10-meter Keck I telescope and the LWS (Living With a Star) in the direct imaging mode, over a 13 inch by 19 inch field . We also modeled images obtained with COMICS (Cooled Mid-Infrared Camera and Spectrometer, Kataza et al. 2000) at the 8.2-meter SUBARU telescope, over a total field of view [which] is 31 inches by 41 inches in a total of nine bands: 7.8, 8.8, 9.7, 10.5, 11.7, 12.4, 18.5, 20.8 and 24.8 microns with 1-micron bandwidth interference filters.
For the first time, measurements of the cosmic microwave background radiation (CMB) alone favor cosmologies with w = -1 dark energy over models without dark energy at a 3.2-sigma level. We demonstrate this by combining the CMB lensing deflection power spectrum from the Atacama Cosmology Telescope with temperature and polarization power spectra from the "Wilkinson Microwave Anisotropy Probe. The lensing data break the geometric degeneracy of different cosmological models with similar CMB temperature power spectra. Our CMB-only measurement of the dark energy density Omega(delta) confirms other measurements from supernovae, galaxy clusters and baryon acoustic oscillations, and demonstrates the power of CMB lensing as a new cosmological tool.
High energy diffraction microscopy is used to measure the crystallographic orientation and evolution of lattice strain in an Al Li alloy. The relative spatial arrangement of the several pancake-shaped grains in a tensile sample is determined through in situ and ex situ techniques. A model for crystal plasticity with continuity of lattice spin is posed, where grains are represented by layers in a finite element mesh following the arrangement indicated by experiment. Comparison is drawn between experiment and simulation.
This study investigates the efficiency of a new method of powering remote sensors by the means of wireless energy transfer. The increased use of sensors for data collection comes with the inherent cost of supplying power from sources such as power cables or batteries. Wireless energy transfer technology eliminates the need for power cables or periodic battery replacement. The time and cost of setting up or expanding a sensor network will be reduced while allowing sensors to be placed in areas where running power cables or battery replacement is not feasible. This paper models wireless channels for power and data separately. Smart scheduling for the data channel is proposed to avoid transmitting data on a noisy channel where the probability of data loss is high to improve power efficiency. Analytical models have been developed and verified using simulations.
Elastic collisions and rotation inelastic collisions involving quadrupole molecular transitions are important in energy relaxation of electrons in molecular gases. It has been customary in studying the cooling of electrons in a plasma heated by high-frequency waves to characterize this relaxation by the ionospheric G factor. The G factor is first defined in a fundamental way by use of kinetic theory. Then present models for theoretical cross sections for electron collision with O2 and N2 are used to derive expressions for the G factor in the lower ionosphere. Two models are considered: one uses Gerjuoy-Stein models for both N2 and O2; the other uses a Gerjuoy-Stein model for N2 and a Geltman-Takayanaki model for O2. Neither O2 model has been definitely established experimentally. It is found that temperature dependence of G is critically dependent on the O2 model.
The XRS on Astro-E2 consists of a microcalorimeter array with 31 active pixels. The XRS energy scale, which converts the measured pulse heights to the energies of the photons incident on the array, is a non-linear function of the thermal properties of the pixels and the power and noise conditions on the array. Each of the pixels operates independently and requires an individual energy scale. Analytic models of the XRS pulses are currently being developed that will be used to model the energy scale. However, because of the inherent complexity, a full empirical calibration of the energy scale is being conducted. The XRS energy scale has been calibrated for a range of operating conditions including variations in the detector temperature, bath temperatures, and photon flux. This allows us to characterize the functional dependence of the energy scale on these variables and to then generate a semi-empirical model that can be used to account for variations in operating conditions in space. We will present the results of the empirical characterization and comparisons to the analytic models.
A simple climatic model is developed for a Mars atmosphere containing CO2 and sufficient liquid water to account for the observed hydrologic surface features by the existence of a CO2/H2O greenhouse effect. A latitude-resolved climate model originally devised for terrestrial climate studies is applied to Martian conditions, with the difference between absorbed solar flux and emitted long-wave flux to space per unit area attributed to the divergence of the meridional heat flux and the poleward heat flux assumed to equal the atmospheric eddy heat flux. The global mean energy balance is calculated as a function of atmospheric pressure to assess the CO2/H2O greenhouse liquid water hypothesis, and some latitude-resolved cases are examined in detail in order to clarify the role of atmospheric transport and temperature-albedo feedback. It is shown that the combined CO2/H2O greenhouse at plausible early surface pressures may account for climates hot enough to support a hydrological cycle and running water at present-day insolation and visible albedo levels.
Detailed reviews of a past advanced nuclear reactor based integrated energy system, as well as other nuclear reactor and fossil fuel based integrated energy systems have been performed for this work. Review of the utilization of heat from nuclear reactors for various applications and cogeneration has been done. The heat can be utilized by extraction of the steam from the turbine while the steam is still at a desired temperature. While use of nuclear process heat for district heating in countries like Finland, France, China, Poland, and elsewhere is discussed, more focus of the review has been given on nuclear desalination processes. Integrated energy systems (IES) where distinct types of reactors like PWR, BWR, sodium cooled fast reactor, heavy water reactor and other advanced reactors are coupled with various nuclear desalination processes like multi-effect distillation (MED), multi-stage flashing (MSF) and reverse osmosis (RO) methods have been discussed. The nuclear desalination plant at Aktau has been discussed in more detail due to its decades of successful operation. The IES of the Aktau plant coupled with 5-effect MED desalination plant has been taken as a reference for modeling the Open Modelica (OM) based IES of this work. Here, the OM IES model shows good agreement with the MED plant output of Aktau and can be extended for future applications of IES.
This project will assess the performance of an innovative high efficiency air conditioner integrated with a composite phase change material (PCM). The research will address critical electric grid challenges by evaluating the thermal efficiency and load-shifting capabilities in NREL’s HVAC characterization lab.
Our work seeks to introduce a computational tool tailored to the physics of inertial fusion energy chambers, in particular, those concepts based on thick liquid walls. In this approach, the structural materials are protected by several neutron mean-free-paths of renewable liquid and thus will be able to survive much longer than un-shielded walls, with virtually all structures lasting for the life of the plant and enabling the use of commercially available and qualified materials. The OpenFOAM-based solver named rhoCentralFoam has been used as a starting point. rhoCentralFoam belongs to the standard OpenFOAM solver toolset. It is a high-speed, explicit compressible flow solver with shock-capturing capability. While the main features have been retained, the solver had to be restructured to make use of tabular data for equations of states, a necessary addition to model the complex thermo-physical properties of ionized gasses. This entailed the need to change the independent state variables used by the solver, resulting in a new thermodynamic library and slightly different solution algorithm. Moreover, a radiation heat transfer model based on the P-1 approximation was added to the solver. The solver is verified against an analytical solution from the Sedov-Taylor-Neumann test problem to showcase the ability of the hydrodynamic solvers to handle strong shocks, whereas the P-1 model was verified using a simple one-dimensional problem with an analytical solution. Additionally, a validation case involving shock-wave propagation through jet array is presented, and the results are compared with experimental data from the open literature. Lastly, in order to showcase the utility of the solver for practical cases, we applied the refined solver to two representative scenarios: gas venting within the HYLIFE-II chamber and the compression of the gas following the partial ablation of the liquid wall.
Energy load models in the process of hydrogen reduction of ilmenite for lunar oxygen production are being developed. The load models will be used as a first step to ultimately determine the optimal energy system needed to supply the power requirements for the process. The goal is to determine the energy requirements in the process of hydrogen reduction of ilmenite to produce oxygen. The general approach is shown, and the objectives are to determine the energy loads of the processes in the system. Subsequent energy management studies will be made to minimize the system losses (irreversibilities) and to design optimal energy system power requirements. A number of processes are being proposed as possible candidates for lunar application and some detailed experimental efforts are being conducted within this project at the University of Arizona. Priorities are directed toward developing the energy models for each of the proposed processes being considered. The immediate goals are to identify the variables that would impact energy requirements and energy sources of supply.
The demand for new energy infrastructure is increasing across the United States, but heterogenous permitting processes and embedded requirements across different local jurisdictions can cause project delays, increase “soft costs,” and hinder developer expansion. This study analyzes the variability in local permitting requirements across the U.S. and develops a quantitative approach to describe their clarity and effectiveness in enabling infrastructure project development. By using an Energy Language Model (ELM), a large language model (LLM) for energy technologies, we systematically gathered permitting information from nearly 300 state-, county-, and city-level documents, creating a structured dataset of requirements and procedures on an unprecedented scale and speed. Our analysis revealed that local (city and county) permitting requirement documents are underrepresented compared to state-level guidance documents, which can impede timely and cost-effective installation of new electric infrastructure. Our validation process showed that the final database has an accuracy of approximately 95%. We, further, created a new quantitative method to score permitting requirements for clarity and efficiency, with electric vehicle supply equipment as an initial use case. The average local permitting document scored a 1.8 out of 5, which we interpret as meaning that half of the requirements developers face when installing electric infrastructure are ambiguous, increasing both cost and time. We also created a “Generalized Permit Process”, highlighting common procedural steps and identifying specific opportunities for municipalities to improve their documentation. This research establishes a systematic and scalable framework for evaluating the complexities of local infrastructure permitting processes by combining LLM-powered data collection and quantitative scoring. The framework enables policymakers and developers to identify and mitigate procedural bottlenecks, with the expectation that these improvements can accelerate application review and approval, reduce project costs, and expedite connection to utility distribution grids. As a foundational approach for streamlining local project development processes, this study’s methods are intended to be extended to a wide range of energy applications.