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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Hemiptera phylogenomic resources: Tree‐based orthology prediction and conserved exon identification

Abstract High‐throughput sequencing of transcriptomes and targeted genomic regions are advancing our knowledge of The Tree of Life. Building phylogenies with regions of the genome requires 1‐to‐1 orthologue resources of genes and noncoding loci. One organismal group that has received little attention in this area is the Hemiptera, the fifth largest insect order represented by ~103,590 named species. Here, we present a set of 3,872 Hemiptera 1‐to‐1 orthogroups based on tree‐based orthology inference of eight Hemiptera species with publicly available genome sequences. We also estimate a set of 406 orthologous exons with similar mRNA splice sites that can be used for Sanger sequencing and develop enrichment probes for targeted genome sequencing for phylogenomic inference. We show this novel set of orthologues is informative at the protein, coding sequence and exon molecular levels and provides robust branch support in both gene tree–species tree methods and concatenated sequence phylogenies. In addition, we demonstrate the utility of these loci to resolve relationships in whiteflies, Bemisia tabaci , a large species complex with few phylogenomic resources. Last, we compare our Hemiptera phylogeny with previously published phylogenies and other orthologue databases, while providing suggestions on further improvement to this phylogenomic resource.

Owen, Christopher L.↗
DOE OSTI2021

DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]

DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.

Gasper, Paul↗
DOE OSTI2022

Advanced Thin Film Core Technology: CIGS Final Technical Report (FTR)

Cu(In,Ga)Se 2 (CIGS) thin-film photovoltaics are a high-efficiency and reliable technology. This project completed research in two important areas and was designed to work collaboratively with industrial partners. Task 1: Alkali Science focused on alkali post-deposition treatments (PDT). PDTs have been instrumental in the dramatic voltage improvements that have moved CIGS device efficiencies from 20% to 23.35% [1]. Based on a survey of CIGS companies at the beginning of the project, the single biggest breakthrough for the CIGS community would be a mechanistic understanding of the role of alkalis in the material system. Significant accomplishments of Task 1: Alkali Science: 1) KF post-deposition treatments were shown to improve lifetime, open-circuit voltage (VOC), and efficiency of industrial partner samples, even when done as a later step, separate from the original CIGS deposition. 2) KF boosted efficiency when incorporated at the end of the third stage of NREL CIGS growth. 3) XPS characterization of CIGS surfaces with and without PDTs led to a proposed mechanism whereby K drives structural transformation at 350 degrees C that is locked in at room temperature even after K is rinsed away. 4) Published recipes for KF and RbF PDTs. Literature to date did not provide enough detail to quickly reproduce experimental results. 5) Identified most important parameters (RbF cell temperature and lamp setpoint temperature) and set boundaries for successful RbF PDTs. The purpose of Task 2: Cell-level Reliability was to overcome the largest challenges to investor confidence and long product lifetime in CIGS-based photovoltaic products: metastability, shading-induced hot spots, and potential-induced degradation (PID). Key findings were made in each of these areas by studying CIGS reliability at the cell level: 1) Published NREL's cell-level reliability testing procedures along with challenges that were encountered while developing them. These were also distributed to the community through an MRS conference presentation. 2) Decreased metastability by adding a CdS hole-injection layer between the CIGS and Zn(O,S) in the device stack. It also improved device performance. Materials other than CdS can be used for the same purpose. 3) Reduced front-glass PID by replacing soda-lime glass with low-Na borosilicate glass. 4) Found that PID depends on leakage current and light/electrical bias. This will help labs avoid test-specific degradation. 5) Discovered that CIGS can suffer from two different types of PID with different mechanisms. Front is slower and leads to shunting ZnO. Back is faster and degrades the p-n junction. 6) Holding cells at open circuit slows PID compared to short circuit. This affects testing protocols for glass/glass modules.

14 SOLAR ENERGY↗
DOE OSTI2024

50 MW Segmented Ultralight Morphing Rotors for Wind Energy

A multi-institutional team designed a 50 Megawatt (MW) rated wind turbine featuring downwind aeroelastic morphing to reduce blade loads and allow an ultralight segmented rotor (mass reduction of about 25% compared to a conventional upwind rotor). The team used a control co-design approach with state-of-the art simulations including for the rotor and tower design using non-linear fluid-structure interactions and control algorithms, and the team also designed, built, and field-tested an aeroelastically-scaled downwind rotor to demonstrate this novel technology and validate the design tool fidelity. In a follow-on phase, these results were used along with an updated Levelized Cost Of Energy (LCOE) methodology to co-design a more detailed set of 25 MW rated turbine designs (including individual pitch control) based on minimum LCOE with highly flexible blades for an Atlantic Ocean offshore fixed-bottom design targeted towards market technology evaluation. A set of upwind and downwind designs at 25 MW rated scales were found to provide the best LCOE, with strong improvements over all previous offshore reference turbines (NREL 5MW, DTU 10 MW and IEA 15 MW). The optimized design would represent the world’s largest offshore turbine design, which combines several state-of-the-art structural, aerodynamic, and control technologies into a new and optimized system concept.

17 WIND ENERGY↗
DOE OSTI2024

Intelligent Partitioning based Fully Parallel AC Security-Constrained Optimal Power Flow

Today’s power grid is becoming more diverse and integrated with high-level distributed energy resources and smart control technologies that is creating a new set of grid management challenges in terms of large-scale, nonlinear, and non-convex problem modeling, complex and time-consuming computation, as well as difficult uncertainty handling. This project focused on solving a challenging multi-period security-constrained generation scheduling problem, which is of great importance for maximizing the social welfare of real-time dispatch, day-ahead market, as well as weekly planning of power systems. Our developed software explored parallel optimization algorithms for complex and realistic power system models, and develop fast, efficient, and robust grid optimization solutions on the high-performance computing platform that will enable increased grid economics, flexibility, resilience, as well as energy security in the United States.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Data Driven Commercial Building Energy Code Compliance and Technology Inventory for New York City

Building Performance Standards (BPS) are gaining national traction. A BPS will require new processes in the design, construction, and operation of buildings that take the occupants into account and enable predictive analysis to ensure compliance with current and future GHG emissions caps. In New York City, most buildings over 25,000 square feet will be regulated by a BPS starting in 2024, regardless of whether it is new construction permitted under current energy codes or an existing building. This research is one of the first to begin the evaluation of a long-term series of building policies in the context of an open data ecosystem, in cooperation with city agencies. Existing building policies enacted in NYC have ranged from building energy benchmarking and labeling to energy audits to the regulation of GHG emission in buildings. Through the development of a dataset related to building technologies and energy consumption, this project can help to evaluate if meaningful conclusions can be drawn for the data that has been largely self-reported in compliance with city regulations. This project will also provide lessons learned from a deep dive into these types of datasets to provide best practices for municipalities or states seeking to embark on policies like those enacted in NYC. In addition, a Building Automation System (BAS) Stretch Standard of Care (SSOC) for owners, designers, and building operators will enable the measurement and predictive analysis of energy consumption and GHG emissions at the plant, system, or component level, in anticipation of regulated GHG limits on buildings based on energy use. The SSOC is expected to be suitable for use on a national level. The primary feature of an SSOC is a standardized format for a set of BAS points that can be used to control and to gather data from individual plants, systems, or components that are related to building energy consumption. This project examined how measurements compare to prescriptive or simulation-based energy code targets, finding little correlation between predictive 8760-hour energy modeling and actual energy consumption for a small sample (n=27) of buildings constructed after 2015. Other analysis found that, while large multifamily housing (MFH) buildings showed a general trend similar to predicted reductions in energy use from the implementation of model commercial energy codes, this trend was not evident in the office, K-12 school, and hotel use groups in NYC. No upward or downward trends in energy consumption were found when buildings were grouped by size. Energy audit data were analyzed and it appears that there is bias by audit company on measures recommended to clients. Further research should be performed to cross-analyze this with other attributes, such as building size, vintage, and number of stories. Analysis found that for 281 buildings that were permitted and completed after 2015 and had submitted benchmarking data in 2022, between 81% and 96% (by use group) were found to be in compliance with the 2024 to 2029 NYC BPS emission caps, and between 55% and 89% were in compliance with the 2030-2034 caps. This work is beneficial to the public in helping policymakers and building stakeholders better understand the wide-ranging implications of a BPS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

A Scalable, Distribution Network-Aware, Customer Privacy-Preserving Framework for Operation of Virtual Power Plants

This poster presents a hierarchical control framework for a virtual power plant that leverages behind-the-meter resources for grid services while maintaining customer privacy during setpoint disaggregation. Unlike many existing approaches, the virtual power plant model uses a hierarchical control strategy and an iterative approach to determine the optimal set point dis-aggregation without direct load control while maintaining system-level power flow and voltage constraints. The proposed approach is numerically validated on a synthetic distribution feeder in San Francisco, demonstrating the ability of the framework to provide privacy-preserving virtual power plant services.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

High-Fidelity Modeling of Fuel-To-Coolant Thermomechanical Transport Behaviors Under Transient Conditions

This report summarizes the work completed under NEUP project number 21-24006. The objectives of this project are to advance the high-fidelity modeling capabilities and important phenomena that is important for high-burnup UO 2 and accident tolerant fuels (ATF) during transient conditions. Accurate modeling of the time-dependent phenomena that impact material performance must be used to determine the figures of merit and safety margin. Phenomena such as fuel fragmentation, cladding oxidation, pellet-clad interaction, clad ballooning, and clad rupture are examples that pose challenges to modeling during these transients. This project focused on the development of high-fidelity tightly coupled multiphysics models that can capture the time-dependent material response and associated thermal hydraulic conditions during these events. These models can then be validated against existing separate effects tests and in-pile integral experiments and will be used to model Transient Reactor Test facility (TREAT) loss-of-coolant accidents (LOCA) experiments. To achieve the project objective, we used a combination of NEAMS and NRC codes to model various LOCA test sets for the separate effects and in-pile integral experiments. BlueCRAB tool set, which can accurately predict material response at a sub-fuel pin level, as well as modeling the entire reactor system response to these events. Fuel performance was modeled using BISON (various versions) and FAST (version 1.2.1). BISON and FAST can model on a sub-fuel pin level the fuel performance under transient conditions. Both have simplified thermal hydraulic models that are capable of providing basic coolant boundary conditions. To better capture the thermomechanical interaction between the fuel, clad, and coolant, more sophisticated thermal hydraulic models are necessary.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A Comprehensive Characterization of Galaxy-cool CGM Connections at z < 0.4 with DESI Year 1 Data

We investigate the relationships between the cool circumgalactic medium (CGM), traced by Ca II absorption lines, and galaxy properties at z < 0.4 using ∼900,000 galaxy–quasar pairs within 200 kpc from the Year 1 data of the Dark Energy Spectroscopic Instrument (DESI). This large data set enables us to obtain composite spectra with sensitivity reaching to the mÅ level and to explore the Ca II absorption as a function of stellar mass, star formation rate (SFR), redshift, and galaxy types, including active galactic nuclei (AGNs). Our results show a positive correlation between the absorption strength and stellar mass of star-forming galaxies with $\langle$$W$$^{Ca II}_{0}$$\rangle$ α $M$$^{0.5}_{*}$ over 3 orders of magnitude in stellar mass from ∼10 8 to 10 11 M ⊙ , while such a mass dependence is weaker for quiescent galaxies. At a fixed mass, Ca II absorption is stronger around star-forming galaxies than quiescent ones especially within impact parameters <30 kpc. Among star-forming galaxies, the Ca II absorption further correlates with SFR, following ∝SFR 0.3 . However, in contrast to the results at higher redshifts, stronger absorption is not preferentially observed along the minor axis of star-forming galaxies, indicating a possible redshift evolution of CGM dynamics resulting from galactic feedback. Moreover, no significant difference between the properties of the cool gas around AGNs and galaxies is detected. Finally, we measure the absorption profiles with respect to the virial radius of dark matter halos and show that the total Ca II mass in the CGM is comparable to the Ca mass in the ISM of galaxies.

Circumgalactic medium↗
DOE OSTI2025

Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest

Wind-farm control strategies aim to increase the efficiency, and therefore lower the levelized cost of energy, of wind farms. This is done by using turbine settings such as the yaw angle, blade pitch angles, or generator torque to manipulate the wake that negatively affects downstream turbines in the farm. Two inherently different wind-farm control methods have been identified in the literature: wake steering (WS) and active wake mixing (AWM). As one of two companion papers focused on understanding practical aspects of these two wind-farm control strategies using large-eddy simulation (LES), we below analyze the wake quantities of interest for a single wind turbine performing WS and AWM, while the companion article (Frederik et al., 2025) focuses on turbine quantities of interest including power and structural loads for the same computational setup and also includes two-turbine arrays with full and partial wake overlap. The simulations, which are based in the LES solver AMR-Wind, are tailored to have inflow conditions representative of measurements from a site off the East Coast of the US, including with strong veer and low turbulence. The turbine, which is modeled in OpenFAST and coupled to the LES, is the IEA 15 MW, an open-source offshore design. After presenting an overview of the wake recovery for the different wake-control cases, the analysis probes the fluid-dynamic causes for the different performance of the arrays reported in the companion article by examining control volumes around the wakes and the budget of the mean-flow kinetic energy (MKE) within these volumes. In the high veer environment considered, the MKE recovery is dominated by mean convection, and this is shown to especially benefit the WS strategy when a neighboring turbine is directly downstream: there is ≈65 % more available power for a downstream turbine than in the baseline case, and this power is gained primarily through mean convection on the left-tip and top-tip faces of the control volume. However, the case with imperfect knowledge of the exact wind direction favors the pulse-type AWM strategy, largely because of ≈9 % increased turbulent entrainment from aloft versus the baseline that could be related to an apparent resistance to skewing in the pulsed wake. The general reduced effectiveness of helix-type and other individual-pitch-based AWM strategies for inflow with high veer and low turbulence as reported in the companion paper is due, in part, to low magnitudes of phase-averaged turbulent entrainment. Two main findings of this study are thus that veer has a significant impact on the effectiveness of different wake-control strategies and that pulse-type AWM may be a useful strategy when the objective is power maximization in realistic, offshore flow environments with imperfect knowledge of the exact wake overlap position on the downstream turbine.

17 WIND ENERGY↗
DOE OSTI2025

Comparison of wind farm control strategies under realistic offshore wind conditions: turbine quantities of interest

Abstract. Wind farm flow control is a strategy to increase the efficiency and therefore lower the levelized cost of energy of a wind farm. This is done using turbine settings such as the yaw angle, blade pitch angles, or generator torque to manipulate the flow behind the turbine, affecting downstream turbines in the farm. Two inherently different wind farm flow control methods have been identified in the literature: wake steering and wake mixing. This paper focuses on comparing the turbine quantities of interest between these methods for a simple two-turbine wind farm setup, while a companion article (Brown et al., 2025) focuses on the wake quantities of interest for a single wind turbine setup. Both papers use the same set of wind farm simulations based on high-fidelity large-eddy simulations (LESs) coupled with OpenFAST turbine models. First, precursor simulations are executed in order to match wind conditions measured with lidars in an offshore wind farm off the east coast of the USA. These measurements show general wind conditions that exhibit substantially higher vertical wind shear and veer than any of the LES studies performed with wind farm flow control strategies currently available in the literature. The precursors are used to evaluate the effectiveness of the control methods. In the LES, the wind veer leads to highly skewed wakes, which have considerable influence on the power uplift of wind farm flow control strategies. In addition to a baseline controller, four different control strategies, each of which uses either pitch or yaw control, are performed on the upstream turbine of a simple two-turbine wind farm. Assuming that the wind direction is known and constant over time, the simulations show that wake steering is generally the superior wind farm flow control strategy, considering both wind farm power production and turbine damage equivalent loads when substantial wind veer is present. This result is consistent over different wind speeds and wind directions. On the other hand, for similar wind conditions with lower veer, wake mixing was found to yield the highest power production, although at the expense of generally higher loads. This leads us to conclude that the effect of wind veer, which has so far not usually been considered, can not be neglected when determining the optimal wind farm flow control strategy.

17 WIND ENERGY↗
DOE OSTI2023

Scaling Wind Power Innovation Assessment for Rapid Energy Transition with Artificial Intelligence

Planning for energy system decarbonization requires new insights into the potential of renewable technologies, deployed at unprecedented scale, to meet urgent sustainability goals. However, limited scalability of current wind energy research tools restricts characterization of innovation impacts to isolated reference sites, challenging investment and decision making under rapid growth. We demonstrate the transformative potential of artificial intelligence (AI) to inform future technology advancement and energy systems design by leveraging a state-of-the-art surrogate model to conduct a series of fleet-wide wind plant layout optimizations for greater than 6,800 projected U.S. onshore buildout locations. We show how innovative wake steering technology can address an array of barriers to large-scale deployment and integration of wind power. Specifically, wake steering reduces required plant area by an average of 18% and could preserve upwards of 13,000 km2 for future greenfield deployment, potentially easing siting challenges associated with wind energy infrastructure. Further, by enabling reduced turbine spacing and increased energy production, flexible operations of wake steering improve levelized cost of energy, particularly for large plants and in land-constrained settings. Finally, optimizations that consider dynamic energy prices can deliver increased power production and revenue capture during high-value (often low-wind) periods, further bolstering plant economics. Our computationally efficient approach offers a pathway to accelerate nationwide geographic evaluation of innovative technologies.

graph neural networks↗
DOE OSTI2023

A Novel Multi-Agent Deep Reinforcement Learning-enabled Distributed Power Allocation Scheme for mmWave Cellular Networks

We consider the power allocation problem over shared spectrum for millimeter-Wave (mmWave) cellular downlink. Existing approaches usually find sub-optimal solutions by solving a non-convex optimization which leads to scalability issues due to centralized control. Therefore, distributed and adaptive approaches are desirable. Recently, model-free Deep Reinforcement Learning (DRL) has achieved success in such wireless resource management tasks. By modeling the radio environment as a Markov Decision Process (MDP) with the base stations (BSs) being the agents, power allocation can be automated at the agent level with comparable throughput performance to conventional centralized schemes. The multi-agent setting presents new challenges as the radio environment is impacted by the joint actions of the agents and is no longer stationary from any individual agent’s perspective. Existing literature bypasses this non-stationarity violation by ignoring it which may cause performance degradation. To tackle this issue, we propose a distributed continuous power allocation scheme based on a modified version of multi-agent Deep Deterministic Policy Gradient (MADDPG) that is tailored for the distributed multiple-agent setting. The proposed scheme employs a centralized-training distributed-execution framework where Q-functions are trained over subsets of BSs while each BS determines its transmit power based only on its own local observation. It admits constant per-BS communication and computation complexity and is thus scalable to large networks. Numerical evaluation shows that the proposed scheme adapts well to a wide range of interference conditions and can achieve comparable or better performance than several state-of-the-art non-learning approaches.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Overview of Virtual Test Bed (VTB)

The Virtual Test Bed (VTB) was established to support the deployment of advanced reactors by facilitating the use of state-of-the-art modeling and simulation capabilities. These tools can solve previously prohibitive challenge problems with greater levels of fidelity and in a multiphysics framework. A VTB repository was recently set up to host models for a wide variety of reactor types and using a range of Nuclear Energy Advanced Modeling and Simulation-developed software. The repository currently includes models for High-Temperature Gas-Cooled Reactors, Sodium Fast Reactors, Fluoride-salt-cooled High-temperature Reactors, Molten Salt Reactors, and Heat-Pipe Microreactors. This article provides an overview of the capabilities currently in the VTB repository.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Power Analysis of an ePump Applied to the Linear Functions of an Agricultural Planter

Like many other industries, the agricultural industry has recently ex-perienced pressure to reduce vehicle emissions while improving productivity. Electric actuation is perceived as a viable solution to replace or augment hydrau-lic and mechanical actuation. However, electrification presents challenges with regards to linear functions, where hydraulic actuators have advantages in terms of compactness, tolerance to contamination and resistance to shocks. A combined electro-hydraulic actuation architecture can leverage the benefits of both electric and hydraulic actuation, while reducing the drawbacks of both approaches. This work investigates the potential of a centralized electric driven pump (ePump) system powering the pressure-controlled linear functions of an agricul-tural planter, with the goal of improving the operating point of the main supply pump. In this application, the rotary functions are hydraulically actuated, alt-hough such a solution could be applied also to electric rotary actuation. This is accomplished by setting the ePump to boost the pressure supplied by the tractor to the level required by the linear functions. An accumulator is used to stabilize the flow requirements of the linear functions, and control the pressure supplied to the actuators. Two control schemes are proposed for the regulation of the ac-cumulator pressure, one favoring an efficient operating point for the ePump, the other favoring stable steady state operation. A simulation model of the baseline system and proposed system is developed and validated using experimental data from a full-scale machine. Using the vali-dated simulation, both control architectures are then evaluated for improvement in system power consumption and dynamic requirements on the ePump to assess their effectiveness. Both systems demonstrate significant improvement in power consumption over the baseline system, with the best solution improving effi-ciency by 64%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Application of a Triple Bubbler Inside Molten Salt for Pyroprocessing Safeguards Purposes

The application of a triple bubbler system within molten salt environments, specifically a eutectic mixture of LiCl-KCl, is explored for nuclear safeguards purposes, focusing on nuclear material accountancy (NMA) and holdup accountancy in pyroprocessing techniques. Traditional holdup accountancy methods, which involve periodic sampling and lengthy lead times, are being supplemented by the development of actinide sensors using electrochemistry. However, these methods alone are insufficient to meet regulatory requirements set by agencies like NRC and IAEA. The triple bubbler device measures salt level, density, and surface tension simultaneously using three dip tubes of varying diameters and heights. This study investigates the accuracy and robustness of the triple bubbler in molten salt conditions at temperatures ranging from 450-550°C. The updated bubbler design, utilizing tantalum metal and larger tube geometry, demonstrated minimal plugging and maintained measurement accuracy within acceptable uncertainties: 0.611% for depth, 1.34% for density, and approximately 40% for surface tension. Results indicate that the triple bubbler system is effective for real-time, accurate measurements in pyroprocessing environments, making it a promising tool for ensuring compliance with nuclear safeguards and supporting nuclear non-proliferation efforts.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques

Here, the US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.

42 ENGINEERING↗
DOE OSTI2025

The Workforce Readiness Index: A Local and Regional Assessment Tool for Energy Sector Preparedness

Building up a workforce that is properly trained and adequately sized is essential to ensuring that the deployment of energy generation sources in the United States meets future energy demand. Workforce development to support supply chain or large infrastructure investments typically occurs at a local level. However, there is a critical gap in understanding where and to what extent regional workforces are equipped to meet the needs of an energy industry. The Workforce Readiness Index ("the index") was developed to assess and compare energy sector workforce readiness across the United States at the county level to provide granular information to various stakeholders such as industry members, state decision makers, and training program developers. Workforce readiness is defined as the ability to recruit from the general labor market, transition workers with comparable skill sets, and train a workforce using scalable or existing training programs near a specific location. Therefore, the index evaluates readiness levels based on the likelihood that a county possesses the workforce development infrastructure needed to support the occupations required by an energy-related industry or sector. Furthermore, the index offers a standardized yet flexible approach that captures regional variations and highlights local strengths and challenges.

17 WIND ENERGY↗