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DOE OSTI2025

The impact of simplified window and exhaust fan assumptions on indoor air quality in multifamily buildings

In residential buildings, the indoor air quality can be significantly affected by ventilation measures initiated by occupants, including the operation of windows and in-unit exhaust fans in kitchens and bathrooms. Many simulations simplify these factors by disregarding window opening behaviors and using fixed schedules for exhaust fan operation across all residential units. To estimate the impact of these simplifications in the U.S. context, this study used coupled CONTAM and EnergyPlus models to simulate airflow and contaminant transport in multifamily buildings. The coupled models parametrically varied climate zone, building airtightness, and mechanical ventilation system types. The study conducted a sensitivity analysis on two key occupant behaviors: (1) operating kitchen and bathroom exhausts on different schedules in individual dwelling units, and (2) scheduling open windows on ground and top floors. The simplified assumptions (i.e. uniform in-unit exhaust fan operation and window operation) had a minimal impact on inter-unit air flow and contaminant transport simulations across a broad range of building air leakage and mechanical ventilation system types. These findings suggest that for buildings with tight construction it is reasonable for most modelling and simulation efforts to ignore the effects of non-uniform exhaust fan operation and window opening.

Occupant behavior↗

DOE OSTI2025

Electrifying Airport GSE: Monte Carlo Grid Impacts

Airports globally are shifting from ICE-powered to electric Ground Support Equipment (eGSE) to enhance efficiency, reduce operational costs, and improve operator health. Leveraging predictable routes, flat terrain, and low operational speeds, airports provide ideal conditions for electrification. This study evaluates freight GSE electrification at Dallas-Fort Worth International Airport (DFW), USA, using the Agile@ platform, which integrates three analytical methods: Freight Facility Model (FFM), Activity-Structure-Intensity-Fuel (ASIF), and Monte Carlo simulations. Results from 10,000 simulations indicate modest but critical increases in electricity demand and significant variability in GSE energy consumption. These insights emphasize the importance of data-driven scheduling, targeted maintenance, and strategic infrastructure planning. For high-uncertainty scenarios, airports are advised to deploy buffer energy storage systems (battery banks), implement demand-response charging strategies, schedule flexible workforce shifts, and prioritize proactive maintenance-particularly for equipment with higher operational uncertainty, such as tug tractors with trailers. Agile@ thus offers a robust, scalable, and data-driven framework to optimize long-term GSE planning and enhance reliability across diverse airport environments.

Bose, Ranjan [ORNL] (ORCID:0009000791026327)↗

DOE OSTI2025

Proposed Classifications of Remote Operations for Nuclear Reactors Based on Physical and Cybersecurity Considerations

The incorporation of remote operations into reactor operations is a topic of high interest among advanced and small modular reactor (A/SMR) vendors, with some considering it essential to the success of their business models. However, remote operations are a concept novel to the nuclear industry. While various technical aspects of remote operations have been explored, a significant gap remains in understanding the security implications of integrating remote operations into reactor designs, particularly concerning the security requirements for remote-operations facilities and infrastructure. This report aims to address this gap by first defining classes of remote operation based on the extent of remote access to reactor control systems and grounded in the existing regulatory framework with compatible terminology. Secondly, the report outlines the physical and cybersecurity requirements applicable to remote-operations facilities and infrastructure at each defined class. These requirements are based on existing licensing frameworks provided by 10 Code of Federal Regulations (CFR) Part 50 and 10 CFR Part 52, as well as the upcoming A/SMR licensing framework in the proposed Part 53. The assessment focuses specifically on security regulations, such as 10 CFR Part 73, which includes provisions for both cybersecurity (§ 73.54) and physical security (§ 73.55). This report proposes five classes of remote reactor operations. Class 1 involves remote monitoring only, with no control over reactor systems. Class 2 allows for the remote issuance of allowlisted commands to the reactor facility. Class 3 extends control to non-safety-significant, non-safety-related, or not important to safety systems and equipment. Class 4 permits remote control of safety-significant systems. Finally, Class 5 allows remote control of safety-related systems. It is important to note that these classes were defined purely with functionality in mind, without considering the practicality or feasibility of implementation for each class under current or upcoming regulatory guidance. The intention behind this approach is to enable an assessment of which security requirements apply to each class, allowing readers to evaluate the implementation possibilities for their specific use cases. Following the definition of remote-operation classes, the report assesses the specific physical and cybersecurity requirements applicable to the remote-operations facility and infrastructure within each defined class. This includes defining the types and locations of operators that are possible at each class of operation and, based on operator type and location, as well as functionality within each class, outlining the physical and cybersecurity requirements. By detailing the security requirements by class, the report provides readers with the information needed to determine the type of security program they may need to implement for their desired concept of operation. The next contribution of this report was to assess the practicality of implementing each proposed class of remote operations based upon the security requirement assessment. In short, three of the five proposed remote-operation classes were found to possibly have a practical path forward to implementation under the U.S. regulatory framework. Class 1 remote operations are currently in use in the U.S. while Class 2 and 3 remote operations may be logistically possible to implement under the U.S. regulatory framework. The final two Classes, 4 and 5, would likely be logistically difficult, if not infeasible to implement within the current U.S. physical- and cybersecurity regulatory framework. Given the results of the feasibility assessment, an example architecture is proposed for both Class 2, remote allowlisted commands, and Class 3, remote control of non-safety systems as well as security implication assessments of each architecture. These example implementations are not meant to be prescriptive in terms of how Class 2 or Class 3 remote operations should be deployed; instead, they are intended to be informative to stakeholders on how Class 2 or Class 3 could potentially be applied in order to inform their system design. An example architecture for Class 1 remote monitoring was not provided as Class 1 in already in use in U.S. nuclear operations. Example architectures for Class 4 and Class 5 were not provided due to their assessment of being likely infeasible to implement. The final contribution is an assessment of the physical- and cybersecurity implications of introducing autonomous operations into an A/SMR. What was found was that the security implications can be separated into two cases. Autonomous operations supported by SSCs located only at the reactor site, and autonomous operations supported by SSCs outside of the reactor site. For the first case, the introduction of autonomous systems will likely not change the facility’s requirement to comply with existing cyber and physical security regulation

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

REFSafE: A RAG-Enabled Framework for Predictive Risk Analysis and Automated Safety Report Generation in Mission-Critical Environments

Operational safety in mission-critical environments requires AI systems that are accurate, interpretable, and resistant to hallucination. We present an agentic Retrieval-Augmented Generation (RAG) framework, REFSafe, for grounded hazard analysis and automated safety report generation. The system integrates Large Language Models (LLMs) with structured operational data, historical incident repositories, policy documents, and external authoritative sources. Through iterative agentic reasoning, the framework retrieves, verifies, and synthesizes evidence prior to generation, enforcing citation-backed outputs with explicit source attribution (documents, links, and prior events) to ensure traceability and trust. To mitigate hallucinations and unsupported claims, all risk assessments and forecasts are constrained to retrieved evidence, with confidence signals derived from retrieval relevance and source consistency. A transparent pipeline enables subject matter experts (SMEs) to validate predictions, and provide structured feedback, forming a continuous performance calibration loop. Preliminary deployment demonstrates improved reliability in hazard detection and safety/vulnerability report generation. This work advances trustworthy, evidence-grounded AI for predictive safety intelligence in mission-critical operations.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗

DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗

DOE OSTI2025

Optimal strategies for optical quantum memories using long-lived noble-gas spins

Nuclear spins of noble gases exhibit exceptionally long coherence times and can potentially serve as a long-lived storage medium for quantum information. We analyze and compare the performance of two mechanisms for mapping the quantum state of light onto the collective spin state of noble gases. The first mechanism utilizes collisional exchange with the electronic spin state of metastable noble-gas atoms, while the second relies on spin-exchange collisions with ground-state alkali-metal atoms. We describe the operation of an optical quantum memory relying on these two mechanisms using a compact model and study strategies that optimize the memory storage efficiency. Through numerical simulations, we identify optimal sequences for storing optical signals with different signal bandwidths and electronic spin relaxation rates. This work highlights the qualitative difference between the two approaches for using noble gases as long-lived quantum memories at noncryogenic conditions and outlines the regimes in which they are expected to be efficient.

atomic ensemble↗

DOE OSTI2026

High-Fidelity Building Emulator

This dataset provides high-fidelity time series data for an emulated commercial office building sited in the Chicago, IL area during a Typical Meteorological Year (TMY). This dataset consists of air-side HVAC measurements and control inputs, and it includes normal operations as well as various implemented faults (with associated ground truth measurements) implemented on selected days. This data could be used to quantify and compare the impacts of different faults, and it could also be used as training or validation data for machine learning algorithms (e.g., reduced-order modelling, fault detection and diagnosis).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Automated tape-target centering based on template matching for construction measurement tasks using robotic total station

Robotic total stations have transformed surveying and construction mapping through precise, efficient, and automated measurements. These instruments integrate a theodolite, which measures horizontal and vertical angles, with an electronic distance measurement (EDM) unit to determine distances, allowing accurate 3D measurement of observable points. Traditionally, users manually aimed the total station at a target. Recent advancements in robotic motor control and integration of cameras now enable automatic rotation and aiming, typically requiring only a single operator to position the target. Automated aiming is commonly performed using retroreflector prisms, which reflect light back to the source with minimal scattering, enabling high-precision measurements over long distances. However, retroreflectors, especially those designed for 360 degree, can be expensive and impractical for certain applications. This paper presents an algorithm for automating the center detection of low-cost disposable tape targets using the total station's onboard camera and a template matching algorithm. The algorithm identifies the target's center pixel in the image, and the instrument is commanded to aim at that location. We evaluated the performance of this template-matching-based centering method under various distances, angles, lighting conditions, and field environments, comparing its results to both manual aiming and conventional automated aiming of tape targets. Manual aiming was used as the baseline operational reference in the absence of an independent ground-truth measurement. The proposed approach achieves target centering results comparable to the manual baseline. This method provides a cost-effective alternative for high-precision applications where retroreflector targets are constrained by budget or logistics.

Harrington, Joshua [ORNL]↗

DOE OSTI2024

A Preliminary Investigation on the Performance of a Dual-Source Heat Pump using both the Air and the Ground

Air source heat pumps (ASHPs) and ground source heat pumps (GSHPs) are the two most common types of electric-driven heat pumps in the marketplace to replace fossil fuel-based heating systems. However, the performance and efficiency of ASHPs depend on the ambient air conditions. Therefore, ASHPs usually are equipped with electric resistance heaters to provide supplemental heating when the ambient temperature is low, and the heating demand is high. The electric resistance heaters could result in high power draws when they are turned on. On the other hand, due to the relatively steady temperature of the ground, GSHPs are more energy efficient than ASHPs when providing space heating and cooling to the buildings. However, the adoption of GSHP is hindered by its high initial cost, mostly due to the cost of drilling boreholes for installing ground heat exchangers (GHE). To solve the above issues, our study investigates the performance of dual-source heat pumps (DSHPs) with respect to that of ASHPs and GSHPs. The DSHP will use ambient air when its temperature is favorable for the efficient operation of the heat pump. When the ambient temperature is too hot or too cold, the ground source will be utilized to retain the high-efficiency operation of the heat pump. Because the cumulative thermal load of the GHE is shared with the ambient air, the size of the GHE could be smaller than those of the conventional GSHPs. The study will model the DSHP and simulate its performance in providing heating and cooling for a typical single-family house in regions with hot, mild, or cold climates. In addition, the required GHE size of the DSHP system will be determined through annual simulations and compared with that of conventional GSHPs.

Anees, Fady↗

DOE OSTI2025

Biased degenerate ground-state sampling of small Ising models with converged quantum approximate optimization algorithm

The quantum alternating operator ansatz, a generalization of the quantum approximate optimization algorithm (QAOA), is a quantum algorithm used for approximately solving combinatorial optimization problems. QAOA typically uses the transverse field mixer as the driving Hamiltonian. One of the interesting properties of the transverse field driving Hamiltonian is that it results in nonuniform sampling of degenerate ground states of optimization problems. In this study, we numerically examine the fair sampling properties of the transverse field mixer QAOA, and Grover mixer QAOA (GM-QAOA), which provides theoretical guarantees of fair sampling of degenerate optimal solutions, up to a large enough p such that the mean expectation value converges to an optimal approximation ratio of 1. This comparison is performed with high-quality heuristically computed, but not necessarily optimal, QAOA angles, which give strictly monotonically improving solution quality as p increases. These angles are computed using the Julia based numerical simulation software JuliQAOA. Fair sampling of degenerate ground states is quantified using the Shannon entropy of the ground-state amplitudes distribution. The fair sampling properties are reported on several quantum signature Hamiltonians from previous quantum annealing fair sampling studies. Small random fully connected spin glasses are shown, which exhibit exponential suppression of some degenerate ground states with transverse field mixer QAOA. The transverse field mixer QAOA simulations show that some problem instances clearly saturate the Shannon entropy of 0 with a maximally biased distribution that occurs when the learning converges to an approximation ratio of 1 while other problem instances never deviate from a maximum Shannon entropy (uniform distribution) at any p step. Published by the American Physical Society 2025

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Kinematically enhanced interpolating operators for boosted hadrons

We propose to use interpolating operators for lattice quantum chromodynamics calculations of highly boosted pions and nucleons with kinematically enhanced ground-state overlap factors at large momentum. Because this kinematic enhancement applies to the signal but not the variance of the correlation function, these interpolating operators can achieve better signal-to-noise ratios at large momentum. We perform proof-of-principle calculations with boosted pions and nucleons using close-to-physical and larger quark masses to explore the utility of our proposal. Results for effective energies and matrix elements, as well as Lanczos ground-state energy estimators, are consistent with theoretical expectations for signal-to-noise improvement at large momenta.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Soil Desiccation Treatability Testing at BC Waste Disposal Cribs

During Hanford’s production period, low-level waste products generated from chemical processing of uranium fuel rods were discharged directly to the ground through a system of cribs and trenches located in the 200-BC-1 Operable Unit (OU). The site consists of 6 cribs and 20 trenches that received more than 117,000 m3 of radioactive liquid waste discharged to the soil. These unlined infiltration galleries held volumes of liquid waste while it seeped into the ground, with the understanding that the 100 m (330 ft) thick vadose zone in the area would effectively capture the effluent and prevent groundwater impacts. A conceptual model showing the operation of cribs and trenches is shown in Figure 1. Data show effluent from the 26 cribs and trenches containing about 410 curies of Technetium-99 (Tc-99) is primarily located between 30 m and 70 m (98 ft and 230 ft) depth (Corbin et al., 2005; Ward et al., 2004). Despite no evidence indicating that the contamination has reached the groundwater at BC cribs and trenches, the mobility of Tc-99 had been demonstrated in laboratory tests and was recognized as a threat to groundwater at the site. Using data from numerical models, laboratory analyses, field investigations, and information on historical discharges, the EPA and Ecology identified Tc-99 and U contamination of the vadose zone as a remediation priority. The U.S. DOE was notified by EPA and Ecology regarding risks associated with Tc-99 contamination in a letter requesting development of a strategy for improved methods to understand the nature and extent of vadose zone contamination, specifically Tc-99, and to develop remedial options for addressing such contamination. To develop the appropriate technology for characterizing, remediating, and monitoring the deep vadose zone Tc-99 contamination, the U.S. DOE worked with the EPA and Ecology to create a Treatability Test Plan under a Remedial Investigation/Feasibility Study (RI/FS) for the Hanford 200 Areas. Under this RI/FS, it was determined that a treatability test for soil desiccation should be carried out as it was identified as a promising in-situ treatment technology for mitigating risks posed by Tc-99 contamination to the groundwater table. The BC Cribs and Trenches site was identified as a representative site for Tc-99 and U contamination and selected for the soil desiccation treatability test. In this chapter, we summarize the overlying regulatory framework of RI/FS and treatability tests and illustrate how development and experimentation supported the evaluation of selected remedies. We briefly discuss the RI/FS for the 200 Areas of the Hanford Site and focus on the soil desiccation treatability testing performed at the BC cribs and trenches site under the Deep Vadose Zone Treatability Test Plan for the Hanford Central Plateau (DVZ-TT). The DVZ-TT is one component of the remedial investigation/feasibility study for the Hanford 200 Areas and represents the underlying regulatory framework that drives site operations towards records of decision and site closure.

Mangel, Adam R.↗

DOE OSTI2024

Modular Subsurface Sensors and Integrated Software for Advanced Subsurface Characterization and Monitoring using Unoccupied Vehicles

The advent and subsequent proliferation of autonomous airborne, waterborne, and groundbased vehicles (i.e., “drones”) promises to broadly transform the geosciences and associated industries, including fossil energy exploration and development, mineral resource exploration and development, water-resource management, and environmental remediation. For geophysical characterization and monitoring, the prospect of programming highly repeatable and low-cost drone missions for subsurface imaging will allow for deployments in hazardous and previously inaccessible areas. Coupled with autonomous workflows for data processing, management, and visualization, drone-based geophysical characterization and monitoring will enable unprecedented, real-time insight into diverse subsurface properties and processes of scientific and engineering importance. Toward this end, the objectives of this Lab Directed Research and Development (LDRD) project were to develop new (1) instrumentation for dronebased electromagnetic induction (EMI) geophysical imaging, including separated transmitter and receivers and associated electronics, (2) software for real-time data telemetry, processing, management, and visualization. Although EMI has been previously deployed using unoccupied aerial systems (UASs), these applications failed to capitalize on the game-changing capabilities of drone platforms. Whereas drone-based data acquisition allows for collection of rich, three-dimensional (3D) multi-offset/multi-angle configurations between transmitters and receivers, past efforts have relied on conventional instrumentation that was designed for ground-based data collection with the transmitter and a single receiver housed in the same unit; nor did these previous applications demonstrate real-time delivery of results to support rapid management decisions in the field. In this 1-year project, we (1) designed and constructed new lightweight independent transmitter and receiver antenna platforms that communicate with a laptop computer; (2) developed software to control data acquisition, manage/transfer data, and visualize data as its collected; and (3) demonstrated the operation of the new hardware and software systems in a ground-based field test. Our work entails major technological advances for EMI and established a foundation on which to build a new drone-based, real-time geophysical EMI imaging capability to support diverse challenges facing the nation.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Revealing the coupled oxygen and hypochlorite chemistry in saltwater batteries through operando pH and oxygen monitoring

Saltwater batteries (SWBs) that utilize Na⁺ ions from seawater have emerged as promising candidates for low-cost and sustainable grid-scale energy storage. To date, the cathode reaction mechanism of SWBs has been predominantly described by oxygen evolution and reduction reactions (OER/ORR). However, this assumption is valid only under idealized ocean-like conditions with constant pH and continuous oxygen replenishment. In practical systems, SWBs operate in finite volumes of saltwater, where saltwater composition dynamically evolves during cycling. Here, in this work, we systematically investigate the cathode reaction mechanisms of SWBs under finite saltwater conditions using galvanostatic cycling combined with electrochemical diagnostics and operando monitoring of dissolved oxygen and pH. Our results reveal that the cathode chemistry during SWB operation is considerably more complex than previously assumed. In addition to OER and ORR, hypochlorite formation and consumption reactions, along with pH-dependent switching of dominant reaction pathways, play critical roles. We further identify the sequence and relative contributions of these reactions throughout charge–discharge cycling. These findings provide a comprehensive and mechanistically grounded understanding of SWB cathode processes under relatively realistic cell design and operation condition. The insights presented here establish a new framework for interpreting SWB electrochemistry and offer directions for future strategies aimed at improving performance, stability, and practical viability.

Hypochlorite redox reaction↗

DOE OSTI2025

Scaling up the transcorrelated density matrix renormalization group

Explicitly correlated methods, such as the transcorrelated method which shifts a Jastrow or Gutzwiller correlator from the wave function to the Hamiltonian, are designed for high-accuracy calculations of electronic structures, but their application to larger systems has been hampered by the computational cost. We develop improved techniques for the transcorrelated density-matrix renormalization group (DMRG), in which the ground state of the transcorrelated Hamiltonian is represented as a matrix product state (MPS), and demonstrate large-scale calculations of the ground-state energy of the two-dimensional Fermi-Hubbard model. Our developments stem from three technical inventions: (i) constructing matrix product operators (MPOs) of transcorrelated Hamiltonians with low bond dimension and high sparsity, (ii) exploiting the entanglement structure of the ground states to increase the accuracy of the MPS representation, and (iii) optimizing the nonlinear parameter of the Gutzwiller correlator to mitigate the nonvariational nature of the transcorrelated method. Here, we examine systems of size up to 12×12 lattice sites, four times larger than previous transcorrelated DMRG studies, and demonstrate that transcorrelated DMRG yields significant improvements over standard nontranscorrelated DMRG for equivalent computational effort. Transcorrelated DMRG reduces the error of the ground-state energy by 2.4×–14×, with the smallest improvement seen for a small system at half filling and the largest improvement in a dilute closed-shell system.

Density matrix renormalization group↗

DOE OSTI2026

Heavy-Duty Nonroad Material Handler Electrification Part 1: Real-World Drive Cycle Development

Knowing a detailed operating cycle is critical for developing and testing equipment. Operating cycles can be separated by two clear distinctions: (1) regulatory or non-regulatory and (2) application at the engine-only or full machine level. The Environmental Protection Agency’s (EPA) Nonroad Transient Cycle (NRTC) may be a good representation of engine use in many types of equipment, but there is a gap in standardized and validated drive cycles specifically for nonroad material handlers. Lacking a standardized drive cycle makes it difficult to accurately benchmark machine performance and validate new powertrain technologies. The objective of this investigation is to illustrate the development of a custom drive cycle augmented with real-world customer use data that serves multiple purposes: (1) understand the range of operation and utilization that formulated inputs for electrified architecture analysis and (2) develop a repetitive and consistent maneuver to establish baseline energy consumption enabling equivalent comparison to future electrified prototype builds. This article presents a solution specifically for a 23-ton nonroad material handler in which material handling, machine transport, and extended idle were homologated to form representative short cycles defined by machine velocity and hydraulic cylinder position. The most intensive material handling short cycles had a load factor of 40% and an average fuel rate of 16 L/h. Combined with a visual aid, the short cycles exhibited low variability, having less than 5% root mean square (RMS) error in lift and reach position with respect to the average. The machine’s performance on these short cycles at the Advanced Power Systems Research Center (APSRC) was compared to results from two real-world customer locations operating the instrumented test machine in a cyclical manner, and for similar ground conditions were found to be comparable in fuel consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Multi-frequency signatures of space-leader evolution in negative cloud-to-ground lightning stepped leaders

In this study, we examined 364 space leaders in 18 negative natural cloud-to-ground lightning strokes whose stepped leaders created new channels to ground. All strokes were captured on ultra-high-speed video cameras operating at frame rates ranging from 400k to 783k frames per second. Additionally, broadband electromagnetic field measurements were available for a subset of these strokes. The median space leader inception-to-attachment-point length and retrograde propagation speed towards the pre-existing leader channel (PELC) were 8.2 m and 4.0 x 10 6 m/s, respectively. Space leader lengths were longer and retrograde propagation speeds faster for return strokes with higher peak currents. This is likely due to the relative proximity of space leader inception points to the PELC, which makes the electric field produced by the PELC line charge density one of the primary factors in determining space leader characteristics. Space leader characteristics were weakly related to their inception altitude. We observed bursts of very high frequency (VHF) emissions preceding, by around 0.5 – 1 μs, electric field leader-step pulses; visible-frequency-range luminosity pulses started during the step pulses. The median downward leader propagation speed for all 18 strokes was 4.3 x 10 5 m/s; leader propagation speeds were generally faster for return strokes with higher peak currents. Also, leaders appeared to accelerate (on their way to ground) at altitudes lower than about 200 and 1000 m above ground level for strokes in the 10 – 60 and 84 – 228 kA peak current ranges, respectively.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Numerically exact configuration interaction at quadrillion-determinant scale

The combinatorial growth of configuration interaction (CI) has long limited this formally exact quantum chemistry method to only the smallest molecules. Here, we report a numerically exact CI calculation exceeding one quadrillion (10 15 ) determinants, made possible by a lossless categorical compression strategy within the small-tensor-product distributed active space (STP-DAS) framework. This approach overcomes the traditional memory bottlenecks of CI by a numerically exact compression of the wavefunction representation and reformulating the most computationally demanding matrix–vector operations. Using this method, we performed a fully relativistic CI calculation of the ground state of HBrTe with over 10 15 complex-valued determinants in just 34.5 h on 1000 computing nodes—the largest CI calculation ever reported. We further achieved fast computation for systems with hundreds of billions of determinants on only a few compute nodes. Extensive benchmarks confirm that the method retains full numerical exactness while cutting memory and computational cost by orders of magnitude. Compared to previous state-of-the-art CI calculations, this work achieves a 1000 times increase in CI space, a 10 6 -fold increase in floating-point operations performed, and a 10 6 -fold improvement in computational speed.

Computational chemistry↗