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At least 235 records · Page 13

An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6

This paper documents the methodology and preliminary results from a perturbed parameter ensemble (PPE) technique, where multiple parameters are varied simultaneously and the parameter values are determined with Latin hypercube sampling. This is done with the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community Earth System Model version 2 (CESM2). We apply the PPE method to CESM2–CAM6 to understand climate sensitivity to atmospheric physics parameters. The initial simulations vary 45 parameters in the microphysics, convection, turbulence and aerosol schemes with 263 ensemble members. These atmospheric parameters are typically the most uncertain in many climate models. Control simulations and targeted simulations to understand climate forcing due to aerosols and fast climate feedbacks are analyzed. The use of various emulators is explored in the multi-dimensional space mapping input parameters to output metrics. Parameter impacts on various model outputs, such as radiation, cloud and aerosol properties, are evaluated. Machine learning is also used to probe optimal parameter values against observations. Our findings show that PPE is a valuable tool for climate uncertainty analysis. Furthermore, by varying many parameters simultaneously, we find that many different combinations of parameter values can produce results consistent with observations, and thus careful analysis of tuning is important. The CESM2–CAM6 PPE is publicly available and extensible to other configurations to address questions of other model processes in the atmosphere and other model components (e.g., coupling to the land surface).

54 ENVIRONMENTAL SCIENCES↗

Surrogate-driven Variance-based Sensitivity Analysis of Thermal Storage Tanks in Integrated Energy Systems

Sensitivity analysis and uncertainty quantification are essential steps for enhancing the accuracy of computational models by identifying and mitigating uncertainties. This study focuses on these steps for the Thermal Energy Delivery System at Idaho National Laboratory, specifically targeting the thermocline tank. Using a Modelica/Dymola simulation model, the study perturbed various design parameters and boundary conditions, including shape factor, porosity, outlet temperature, inlet mass flow rate, and system pressure, to predict and quantify uncertainty in the tank’s ax- ial temperature. A dataset of over 1,000 simulations was generated, and surrogate models were developed using the pyMAISE (Michigan Artificial Intelligence Standard Environment) library, which is an Automatic Machine Learning library for nuclear engineering applications. The optimal model, a feedforward neural network with two hidden layers, achieved an R2 score above 0.99 and a mean absolute error below 1 Kelvin. Sensitivity analyses using Sobol indices and Fourier amplitude sensitivity testing methods on this surrogate model revealed that the inlet mass flow rate at initial timestamps and porosity significantly impacts predicted temperatures across all sensors and time steps.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

fluxfinder: An R Package for Reproducible Calculation and Initial Processing of Greenhouse Gas Fluxes From Static Chamber Measurements

Fluxes of greenhouse gases are a critical component of the earth's natural climate, but anthropogenic emissions have created an imbalance and resulted in global climate change. Quantifying the emission of these gases is vital to our understanding of their sources and sinks, both natural and anthropogenic. The static chamber method, in which a system of interest is enclosed, and gas concentrations are measured over time, is widely used to estimate fluxes of greenhouse gases. With the development of instruments such as infrared gas analyzers (IRGAs) supporting high-frequency concentration data, there is a growing need for open-source workflows to calculate fluxes. Here we present fluxfinder, an R package designed to support reproducible calculations and processing of greenhouse gas fluxes measured with the static chamber method. The package includes raw data file parsing from widely used IRGAs, metadata matching, unit conversion, flux estimations, and initial quality assurance/quality control (QA/QC). Diagnostic graphical plots provide a transparent way to differentiate between measurement issues and nonlinear behavior. The package is also designed to be easily integrated with the gasfluxes package for further fitting of nonlinear concentration-time models, allowing alternative or additional flux QA/QC. The fluxfinder package offers a flexible workflow that is easily adaptable to promote open and reproducible greenhouse gas flux estimations.

Wilson, Stephanie J.↗

Second-order spectral line shift comparisons

The second-order spectral line width formulae from the projection operator and kinetic theory methods were recently compared. It was shown that a systematic expansion of the projection operator width expression including initial correlations formally agrees with the second-order kinetic theory result. It is now shown that the second-order dynamic shifts are also formally the same. The static shifts, however, differ due to an ad hoc treatment of electron-electron correlations in the projection operator method. The approximation is necessary in order to screen the radiator-electron interactions. The differences, however, are expected to be small. Finally, the results suggest using the rigorous and more compact second-order width and shift expressions from the kinetic theory method as the starting point for spectral line shape calculations. At line center, however, the projection operator second-order expression for the width and shift simplifies and reduces to the kinetic theory result.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Mechanically induced thermal runaway severity analysis of Li-ion batteries and continuous energy release monitoring

The large-scale deployment of Li-ion batteries in stationary energy storage and electrical vehicle applications demands a strong focus on safety, particularly on the thermal runaway risk and severity evaluation. A standardized single-side mechanical indentation test protocol was developed to induce an internal short-circuit (ISC) and evaluate cells' thermal runaway severity at different state of charge (SOC). The observed hazard severity (OHS in five categories) and evaluated scores in this work have a comprehensive consideration of each cell's capacity, initial voltage, SOC, temperature and voltage change, allowing a better evaluation of the cells' thermal runaway potential. This method was applied to about 200 Li-ion batteries in order to build an extensive thermal runaway database covering various SOCs, capacities and chemistries. In this study, we monitored the transitions of stored electrochemical energy and applied mechanical energy into both thermal energy and acoustic emissions (AE). The surface temperature and mechanical failures were monitored by infrared imaging and AE to capture critical events within battery cells throughout the mechanical indentation tests. Furthermore, the initial temperature maps can predict two types of follow-up events: thermal runaway or gradual heat release via conduction. Analyzing each cell's severity, AEs, and leveraging the evolving database offer insights into predicting occurrences of thermal runaway. The test method, thermal runaway severity evaluation and prediction, and the corresponding database provide battery designers, manufacturers, and end-users a clear overview of Li-ion batteries' thermal runaway potential under mechanical abuse, advancing the safety design of Li-ion batteries.

Acoustic emission↗

Integral Kernel Methods for Nonlinear Parabolic-Elliptic Systems

Nonlinear parabolic-elliptic systems arise in many physical, biological, and chemical phenomena such as chemotaxis, ion transport, self-gravitating particles, and Brownian vortices. Existing methods struggle with the strong coupling and high nonlinearity and nonlocality of some of these systems, especially the ill-conditioned, convection-dominated problems. To overcome numerical difficulties, current approaches rely on initial guesses, preconditioning, or iterative techniques with no convergence guarantees. They might suffer from poor scalability, large memory usage, and difficulty to parallelize. Inspired by the connection of parabolic-elliptic systems to stochastic processes, we introduce a novel meshless, monolithic, and fully explicit method that naturally encapsulates the elliptic and parabolic operators into a single step which updates each node deterministically with global information. By being fully quadrature-based, it avoids solving systems of discretized equations and does not utilize initial guesses or preconditioning, while requiring little memory and being easy to parallelize. We first derive the method in an integral kernel formulation with quadratic complexity in the number of integration nodes and then leverage kernel-independent fast multipole methods (FMM) to present a scalable algorithm with linear complexity. We provide numerical examples for the Poisson-Nernst-Planck equations in one, two, and three dimensions, together with the derivation of the integral kernel for each case. Furthermore, the examples demonstrate the fast convergence and scalability of the FMM-accelerated algorithm, as well as its suitability for convection-dominated problems, making it competitive against traditional PDE solvers.

PDE systems↗

Extraction of lithium from battery recycling wastewater using synergistic D2EHPA and TBP

The recycling of spent lithium-ion batteries (LIBs) poses significant challenges, including the generation of large volumes of chemically complex wastewater. The composition of this wastewater is influenced by both the intrinsic chemistry of the batteries and the specific recycling processes employed. Notably, this wastewater contains economically valuable components, such as lithium, which can be recovered. Here, in this study, a solvent extraction (SX) process was investigated as a method to recover lithium from battery recycling wastewater, especially from anode-washing stage. Initially, various commercial extractants were evaluated, including di(2-ethylhexyl)phosphoric acid (D2EHPA), mono-2-ethylhexyl (2-ethylhexyl)phosphonate (PC88A), bis(2,4,4-trimethylpentyl)phosphinic acid (Cyanex 272), 2-hydroxy-5-nonylacetophenone oxime (LIX 84-I), tri-butyl phosphate (TBP), and their combinations. Among these, D2EHPA + TBP demonstrated a synergism to advance and maximize the lithium extraction. Subsequently, the effects of key parameters, including D2EHPA concentration, TBP concentration, contact time, initial pH, and aqueous-to-organic (A/O) phase ratio, were systematically investigated and optimized. A two-stage SX approach was employed to enhance lithium recovery. Under the optimized conditions of 30.0 vol% D2EHPA, 10.0 vol% TBP, 10 min of contact time, and a 1:1 A/O phase ratio, a lithium extraction efficiency of more than 88% in a two-stage solvent extraction was achieved. Lithium was subsequently stripped from the loaded organic solution using sulfuric acid (H 2 SO 4 ). Using 2.0 M H 2 SO 4 , lithium stripping was achieved after two counter-current stripping stages at an organic-to-aqueous (O/A) phase ratio of 6:1. This stripping process enriched the lithium concentration by a factor of four compared to the original lithium concentration in the anode-washing wastewater. The recyclability of the synergistic D2EHPA + TBP system was also evaluated over four extraction-stripping cycles. The results demonstrated that the system maintained high extraction and stripping efficiencies.

D2EHPA↗

Rinse-Free, Sodium-Efficient Synthesis of O3-Type Layered Oxide Materials Enabled by Acetate Precursors

Sodium-ion batteries (SIBs) are a sustainable alternative to lithium-ion systems for global electrification, with O3-type layered oxide cathodes offering high specific capacity and feasibility of scalable synthesis. Industrial co-precipitation synthesis of these cathodes typically uses transition metal sulfates, requiring extensive water rinsing to remove Na 2 SO 4 impurities, a process that consumes significant water and risks residual inactive phases if incomplete. Here, this work introduces a rinse-free, resource-efficient approach using metal acetate precursors. Residual sodium acetate in non-rinsed precursors decomposes during sintering to generate Na 2 CO 3 in situ, partially substituting an external sodium resource (e.g., NaOH and Na 2 CO 3 ) and reducing its consumption by ∼18–20%. Phase-pure O3-Na 1.0 Ni 1/3 Fe 1/3 Mn 1/3 O 2 (NFM111) cathodes synthesized via this method exhibit microstructure and electrochemical performance comparable to rinsed sulfate-derived counterparts, with initial capacities of 141 mAh g −1 at C/20 (7.5 mA g −1 ). By eliminating rinsing and minimizing sodium reagent use, this acetate-based route enhances sustainability and scalability of layered oxide production for SIBs.

acetate vs. sulfate↗

Sputter-Coated TiO 2 Films as Passivation and Hole Transfer Layers for Improved Energy Conversion with Solar Fuel WO 3 /CuWO 4 Photoanodes

Atomic layer deposited (ALD) “leaky” TiO 2 have gained interest as charge-selective protection layers for semiconductor solar fuel electrodes. Here we demonstrate the use of sputter-deposited TiO 2 layers as hole selective contacts for WO 3 /CuWO 4 type 2 heterojunction water oxidation photoanodes for the first time. TiO 2 protection layers with varying thicknesses (2 to 128 nm) were deposited using the RF magnetron sputtering technique. The resulting TiO 2 films are amorphous based on Raman spectroscopy and powder XRD. Photoelectrochemical scans and Vibrating Kelvin probe photovoltage spectroscopy show that 2-8 nm TiO 2 layers nearly double the photocurrent to 0.97 mA cm -2 under AM 1.5 illumination (19% AQE at 350 nm), increase the surface photovoltage signal by 25%, and increase the WO 3 /CuWO 4 bandgap. These effects can be attributed to the selectivity of TiO 2 for photoholes. Additionally, SPV data suggest that TiO 2 overlayers suppress copper-based surface recombination defects. Reduced photocurrent and the photovoltage are seen in thicker TiO 2 films (16 to 128 nm) as a result of an increasing hole transfer resistance and because of light shading effects according to photoaction spectra. The TiO 2 films also improve the stability of the WO 3 /CuWO 4 photoelectrodes, allowing nearly constant O 2 evolution over 3 hours after an initial 20-35% loss. Overall, this work establishes RF magnetron sputtering as a useful method to install amorphous TiO 2 passivation layers for improved WO 3 /CuWO 4 solar fuel photoelectrodes. Furthermore, we show how the combination of PEC with SPV measurements provides insight into the function of the TiO 2 coatings.

CuWO4↗

Automated tabletop exfoliation and identification of monolayer graphene flakes

Over the past two decades, graphene has been intensively studied because of its remarkable mechanical, optical, and electronic properties. Initial studies were enabled by manual “Scotch Tape” exfoliation; nearly two decades later, this method is still widely used to obtain chemically pristine flakes of graphene and other 2D van der Waals materials. Unfortunately, the yield of large, pristine flakes with uniform thickness is inconsistent. Thus, significant time and effort are required to exfoliate and locate flakes suitable for fabricating multilayer van der Waals heterostructures. Here, we describe a relatively affordable tabletop device (the “eXfoliator”) that can reproducibly control key parameters and largely automate the exfoliation process. In a typical exfoliation run, the eXfoliator produces 3 or more large (≥ 400 μm 2 ) high-quality graphene monolayer flakes, allowing new users to produce such flakes at a rate comparable to manual exfoliation by an experienced user. Furthermore, we use an automated mapping system and a computer vision algorithm to locate candidate flakes. Our results provide a starting point for future research efforts to identify more precisely which parameters matter for the success of exfoliation and to optimize them.

47 OTHER INSTRUMENTATION↗

Using 2.5D super-resolution to improve flaw detection in metal additive manufacturing parts

Industrial X-ray computed tomography (XCT) enables non-destructive inspection of additively manufactured (AM) parts, but high-resolution scanning requires long acquisition times and significant computational resources, limiting throughput in production environments. Super-resolution techniques can recover high-resolution information from low-resolution scans, but existing methods face a trade-off between 2D approaches that ignore inter-slice information and 3D methods that are computationally prohibitive for practical deployment. To address this trade-off, we propose a 2.5D deep learning-based super-resolution approach that uses seven neighbouring low-resolution slices to super-resolve the centre slice. This work evaluates the method on real XCT scans of steel AM parts, comparing reconstruction quality and flaw detection performance of 2D, 2.5D, and 3D ESRGAN-based super-resolution methods. Results demonstrate that 2.5D super-resolution significantly improves detection of small, process-induced flaws (e.g. porosity) compared to 2D methods, while avoiding the prohibitive computational burden of full 3D approaches. These findings provide initial evidence of 2.5D super-resolution as a practical, deployable solution for improving flaw detection in high-throughput industrial XCT inspection.

X-ray CT↗

Investigation of the distribution of laser-induced damage precursors in etched grating sidewalls fabricated using inductively coupled plasma reactive-ion etching

Inductively coupled plasma reactive-ion etching for grating fabrication is explored with emphasis on the type and distribution of process-induced defects that can act as precursors to laser-induced damage initiation. To provide flexibility in the ability to perform a variety of material characterization methods, grating-like features with large pitch were fabricated in silica monolayers. The results indicate that the etch process introduces surface fluorine and subsurface carbon-containing contamination, which reduces the laser-induced damage threshold. The local relative concentrations of the laser-damage precursors introduced during the etch step of the manufacturing process were investigated using angle-resolved damage testing, taking advantage of the shift of the location of the peak electric-field intensity incident on the grating sidewall. The results suggest an increased level of etch-related defects near the base of the sidewall, with directly fluorinated silicon identified as the primary defect.

Dent, R. [Univ. of Rochester, NY (United States). ↗

SDA: a symbolic differential algebra package in C++

Truncated Power Series Algebra (TPSA), or Differential Algebra (DA), is a well-established tool in accelerator physics, commonly used for generating high-order maps of dynamic systems, as well as in symplectic tracking, normal form analysis, verified integration, optimization, and fast multipole methods. This package is the first to perform symbolic DA computations, enabling traceability of initial condition contributions and runtime reduction for repeated DA calculations, potentially expanding DA’s applications.

97 MATHEMATICS AND COMPUTING↗

Recyclable Design for Retaining High Solar Absorptivity of the Media in CSP

Efficient thermal energy storage is pivotal to lowering the levelized cost of electricity (LCOE) for Concentrating Solar Power (CSP) plants. In solid-particle systems, however, prolonged high-temperature service degrades particle solar absorptivity, eroding overall efficiency. This project demonstrates a hydrogen-assisted recovery process that reliably restores absorptivity to >90 %, offering a practical route to sustain long-term CSP performance. Bench-scale investigations mapped the reduction kinetics of optically faded particles across hydrogen concentrations, temperatures, and residence times. Coupling mass-spectrometric monitoring with machine-learning optimization minimized energy demand while maximizing absorptivity gain. The resulting process window—moderate hydrogen partial pressures, 15–30 min dwell times, and temperatures well below initial calcination levels—cuts energy consumption well below that of incumbent re-blackening methods. A prototype recovery reactor processed multiple 2 kg batches with repeatable outcomes, confirming scalability and operational robustness. Integrated techno-economic analysis indicates material and operating cost reductions exceeding 15 % relative to conventional particle replacement or chemical re-coating, translating directly into lower LCOE for next-generation CSP facilities. By uniting fundamental reaction-kinetics insight with pragmatic engineering, this work advances the solid-particle pathway, delivering a cost-effective, field-deployable solution to one of CSP’s key durability challenges and strengthening the commercial outlook for high-temperature renewable power.

14 SOLAR ENERGY↗

sPHENIX heavy flavor jet tagging studies in p+p at $\sqrt{s_{NN}}=200~GeV$

Heavy-flavor jets, which are initiated from heavy quarks, are ideal probes for studying flavor dependent parton energy loss. We report on the performance of jet flavor tagging using two Neural Network Machine Learning (ML) models: the Long Short-Term Memory (LSTM) model and an Attention-based Neural Network, in simulations of 200 GeV p + p collisions. The tagging performance of bottom quark initiated jets with both ML models surpasses that of the traditional cut-based method. Technical details, including sample and kinematic variable selections, the machine learning training and testing setup with parameter tuning, and outcome comparisons, will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Identifying Challenges in Safeguards for Metallic Fuel Fabrication Facilities

As new advanced reactors gain popularity, there is an increasing interest in metallic fuel fabrication for fast reactors. While metallic fuels themselves are not a new idea, as many of the first reactors employed metallic fuels, new designs, compositions, and fabrication methods are appearing throughout the nuclear community. As the interest grows and facilities are constructed, both domestic and international safeguards will need to be heavily involved to support safeguards-by-design (SBD) measures from the start. This work compiles a review of historical and modern fuel types and fabrication methods, fabrication processes, safeguards gaps, and potential safeguards solutions. Metallic nuclear fuel types have been around for many decades and were included in some of the first reactors including the Experimental Breeder Reactor (EBR)-I and -II, the Fermi 1 reactor, the Integral Fast Reactor (IFR), and the Dounreay Fast Reactor (DFR). These reactors used various compositions including pure uranium (U) metal, U-zirconium (Zr) alloys, plutonium (Pu)-aluminum (Al) alloys, U-fissium (Fs) alloys, U-Pu-Zr alloys, and U-molybdenum (Mo) alloys [1, 2, 3, 4, 5]. These small alloying additions are included to improve the material properties of the pure U metal. The alpha-phase U (stable below 661C) suffers elongation in one direction causing grain boundary cracking and increasing creep rate due to irradiation growth, thermal cycling, and preferential crystal orientation. It is ideal to utilize the gamma-phase U (typically stable above 769C) by adding small amounts of alloying elements such as Zr or Mo to stabilize this phase down to room temperature [3]. Additionally, some research has been focused on U with transuranic (TRU) elements present, typically coming from the used fuel recycling process. Including these elements in fast reactor fuel can aid in the reduction of nuclear waste by burning minor long-lived actinides. However, the additions of TRU elements can cause concerns to arise when trying to fabrication or safeguard metallic fuels. A typical metallic fuel element is shown in Figure 1. Sodium is added into the cladding to create a thermal bond between the fuel slug and cladding wall. The fuel slug is then inserted and the end plug is welded on to the top of the fuel element. A gas plenum is left to create a headspace for gaseous fission products to escape rather than continue to build in the fuel itself [1, 5]. Other fuel element geometries exist as well, such as the Lightbridge twisted cruciform geometry shown in Figure 2 [6]. This design allows for better cooling performance and provides room for fuel rod swelling without impacting the fuel rod diameter. There are many different fabrication methods for metallic fuels, which is one of the many benefits of these fuel types. Many of these fabrication methods are relatively easy and cost-efficient. The most popular fabrication method is injection casting, sometimes called vacuum induction melting (VIM), shown in Figure 3 [4, 8, 9, 7, 10]. This method was largely used for EBR-II fuel fabrication. The injection casting system is contained inside of a vessel consisting of a Y2O3-coated graphite crucible surrounded by an induction coil with ZrO2-coated quartz molds suspended above the crucible. The fuel feedstock is placed inside of the graphite crucible and melted using the induction furnace. The induction furnace utilizes a dual frequency with the high frequency melting the feedstock and the low frequency causing stirring of the melted feedstock to form a homogeneous mixture. The mixture is heated to approximately 1600C in an argon environment. The vessel is evacuated and then the quartz molds are lowered into the graphite crucible containing the molten metal and the vessel is repressurized to inject the metal fuel upwards into the molds. The molds are removed and then shattered to release the fuel slugs. This fabrication method was used to fabricate 39,000 metallic fuel pins for EBR-II. While injection casting has been the most common metallic fuel fabrication method throughout the decades, many other methods have been explored including low-pressure gravity casting, microwave casting, continuous casting, centrifugal casting, coextrusion, and many others [11, 12, 8, 13, 14, 15]. Some of these methods aim to mitigate challenges that arise with americium (Am) volatilization during the casting process for TRU-containing fuel feedstocks, an issue with injection casting. Coextrusion is one of the methods explored at the Idaho National Laboratory (INL) and has been utilized for the initial fabrication tests of Lightbridge's unique fuels, as well as other metallic fuels with cladding coextruded. In this process, large billets are formed and machined and then inserted into a molten salt bath for approximately 30 minutes. The billets are then loaded into the extrusion press and extruded. This process can be seen in Figure 4 [15].

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Improvement of Drop‐Hammer Impact Testing for Safety Assessment of High Explosives Using 10‐mg Samples

Here, in this study, we established an improved method for drop-hammer impact testing of small quantities of high explosives (10 mg). We performed about seven hundred impact tests under various experimental conditions (e.g., sandpaper vs bare anvil, different sample masses, drop-weights, and striker diameters) to determine an optimal set of conditions and reaction detection methods (e.g., gas analysis, video, and sound recordings) that give the most statistically reliable results with 10 mg samples. We used both Frequentist and Bayesian statistical approaches to compare estimates of the drop height (DH50) that initiates a reaction 50% of the time, and to quantify the associated uncertainty. Gas analysis proved to be the most reliable reaction detection method, showing unambiguous rises in HE decomposition products (e.g., CO 2 ) even when the other indicators (e.g., sound, video) were inconclusive. The impact tests performed with a bare anvil showed much better reproducibility than those conducted with sandpaper, reducing the largest uncertainty observed in the data sets by a factor of 1.7. The DH 50 values obtained from three different sample masses (10, 20, and 35 mg) fell within the uncertainties of the measurements. We demonstrated the improved procedure (i.e., 10-mg samples, gas analysis, bare anvil, and Bayesian approach) on a variety of PETN samples having different surface areas and thermal histories.

PETN↗

An AI-driven framework for evaluating local and state authorities’ permitting processes

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.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗