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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Capturing Travel Mode Adoption in Designing On-Demand Multimodal Transit Systems

This paper studies how to integrate rider mode preferences into the design of on-demand multimodal transit systems (ODMTSs). It is motivated by a common worry in transit agencies that an ODMTS may be poorly designed if the latent demand, that is, new riders adopting the system, is not captured. This paper proposes a bilevel optimization model to address this challenge, in which the leader problem determines the ODMTS design, and the follower problems identify the most cost efficient and convenient route for riders under the chosen design. The leader model contains a choice model for every potential rider that determines whether the rider adopts the ODMTS given her proposed route. To solve the bilevel optimization model, the paper proposes an exact decomposition method that includes Benders optimal cuts and no-good cuts to ensure the consistency of the rider choices in the leader and follower problems. Moreover, to improve computational efficiency, the paper proposes upper and lower bounds on trip durations for the follower problems, valid inequalities that strengthen the no-good cuts, and approaches to reduce the problem size with problem-specific preprocessing techniques. The proposed method is validated using an extensive computational study on a real data set from the Ann Arbor Area Transportation Authority, the transit agency for the broader Ann Arbor and Ypsilanti region in Michigan. The study considers the impact of a number of factors, including the price of on-demand shuttles, the number of hubs, and access to transit systems criteria. The designed ODMTSs feature high adoption rates and significantly shorter trip durations compared with the existing transit system and highlight the benefits of ensuring access for low-income riders. Finally, the computational study demonstrates the efficiency of the decomposition method for the case study and the benefits of computational enhancements that improve the baseline method by several orders of magnitude. Funding: This research was partly supported by National Science Foundation [Leap HI Proposal NSF-1854684] and the Department of Energy [Research Award 7F-30154].

Operations Research & Management Science↗
DOE OSTI2026

Model Data Archive Associated with Manuscript "Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon"

This data package supports the publication “Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon” by Li et al. (2026). The package contains processed model inputs, configuration files, restart files, simulation outputs, scripts, and visualization products used to evaluate post-fire dissolved organic carbon (DOC) dynamics in the Naches River Watershed, Washington, USA, following the 2021 Schneider Springs Fire. The modeling workflow couples ELM-BGC, the biogeochemistry-enabled Energy Exascale Earth System Model Land Model; ATS, the Advanced Terrestrial Simulator for integrated surface-subsurface hydrology; and PFLOTRAN, a reactive transport model for multicomponent aqueous geochemistry. Together, these models simulate how wildfire-induced changes in vegetation, litter, coarse woody debris, and soil organic matter influence DOC production, transport, and reaction from burned hillslopes to stream networks. The archive includes preprocessed meteorological, geospatial, hydrologic, and biogeochemical forcing data; ELM-BGC-derived DOC source terms; ATS mesh files; PFLOTRAN reactive-transport inputs; model configuration files; spin-up and transient restart files; watershed-scale diagnostic outputs; stream concentration time series; and figures or visualization files used to inspect and reproduce key results. File types include Hierarchical Data Format 5 (HDF5) files for gridded forcing and model-coupling data, model input and configuration files for ELM-BGC, ATS, and PFLOTRAN, restart and simulation-output files generated by the modeling workflow, tabular or time-series diagnostic outputs, scripts for post-processing and figure generation, and image or visualization products associated with the manuscript. Use of the package depends on the intended task. Re-running the simulations requires the relevant modeling software, including ELM-BGC, ATS, and PFLOTRAN as ATS's geochemical engine. Inspecting outputs and reproducing figures requires Python with scientific plotting libraries such as Matplotlib, and three-dimensional model outputs may be viewed with ParaView. Geographic information system files or maps may be inspected with ArcGIS Pro or comparable GIS software. The data package is intended to enable traceability, reuse, and partial reproduction of the coupled land-to-watershed hydro-biogeochemical modeling workflow used to test how wildfire disturbance affects terrestrial carbon pools and downstream DOC dynamics.

ATS↗
DOE OSTI2020

Joint optimal scheduling for electric vehicle battery swapping-charging system based on wind farms

Insufficiencies in charging facilities limit the broad application of electric vehicles (EVs). In addition, EV can hardly represent a green option if its electricity primarily depends on fossil energy. Considering these two problems, this paper studies a battery swapping-charging system based on wind farms (hereinafter referred to as W-BSCS). In a W-BSCS, the wind farms not only supply electricity to the power grid but also cooperate with a centralized charge station (CCS), which can centrally charge EV batteries and then distribute them to multiple battery swapping stations (BSSs). The operational framework of the W-BSCS is analyzed, and some preprocessing technologies are developed to reduce complexity in modeling. Then, a joint optimal scheduling model involving a wind power generation plan, battery swapping demand, battery charging and discharging, and a vehicle routing problem (VRP) is established. Then a heuristic method based on the exhaustive search and the Genetic Algorithm is employed to solve the formulated NP-hard problem. Numerical results verify the effectiveness of the joint optimal scheduling model, and they also show that the W-BSCS has great potential to promote EVs and wind power.

17 WIND ENERGY↗
DOE OSTI2021

Biomass supply chain equipment for renewable fuels production: A review

The production of renewable fuels is a critical component of global strategies to reduce greenhouse gas (GHG) emissions. Moreover, the collection of raw materials for its production can provide added benefits such as reduction of wildfire risk, additional income for farmers, and decreased disposal costs. Although there is substantial literature on design and modeling of supply chains, the authors were unable to find a single reference with the information needed for the selection and cost estimation of each type of equipment involved in the supply chain. Therefore, the goal of this research is to gather information necessary for the construction and utilization of models that might drive the identification of a feasible supply chain to produce renewable fuels at a commercial scale. The primary objectives are to 1) understand the supply chain of critical feedstocks for renewable fuels production; 2) identify the equipment commercially available for collection and adequation of feedstock; and 3) consolidate information regarding equipment cost, energy consumption, and efficiency, as well as feedstock storage and transportation systems. This paper provides a compilation for five feedstock types studied for sustainable aviation fuel production: 1) agricultural residues and grasses, 2) forest residues, 3) urban wood waste, 4) oilseeds, 5) fats, oils & greases. All the technologies involved from the field to the gate of the preprocessing or conversion unit were reviewed. The information on fats, oils & greases supply chains and equipment purposely designed for forest thinning and pruning was very limited.

: Feedstock, Collection and Adequation, Renewable ↗
DOE OSTI2023

Workflows Community Summit 2022: A Roadmap Revolution

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from the execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing (often referred to as a computing continuum) and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large-scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC, enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enable the publication of workflows and their associated products according to the FAIR principles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout

Qubit readout is a critical operation in quantum computing systems, which maps the analog response of qubits into discrete classical states. Deep neural networks (DNNs) have recently emerged as a promising solution to improve readout accuracy . Prior hardware implementations of DNN-based readout are resource-intensive and suffer from high inference latency, limiting their practical use in low-latency decoding and quantum error correction (QEC) loops. This paper proposes LUNA, a fast and efficient superconducting qubit readout accelerator that combines low-cost integrator-based preprocessing with Look-Up Table (LUT) based neural networks for classification. The architecture uses simple integrators for dimensionality reduction with minimal hardware overhead, and employs LogicNets (DNNs synthesized into LUT logic) to drastically reduce resource usage while enabling ultra-low-latency inference. We integrate this with a differential evolution based exploration and optimization framework to identify high-quality design points. Our results show up to a 10.95x reduction in area and 30% lower latency with little to no loss in fidelity compared to the state-of-the-art. LUNA enables scalable, low-footprint, and high-speed qubit readout, supporting the development of larger and more reliable quantum computing systems.

Farooq, M. A. [Arizona State U., Tempe]↗
DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

System Modeling and Robust Stability Region Analysis for Multi-inverters Based on VSG

As the number of inverter-interfaced distributed generators (IIDGs) increases, the decrease in the inertia of the microgrid affects the stability of microgrid voltage and current. The virtual synchronous generator (VSG) control method simulates the output characteristics of the synchronous machine to improve the inertia and damping of the microgrid. However, low-frequency oscillations can occur in systems with multi-VSGs connected in parallel, primarily when operating in island mode. Therefore, this article develops the small-signal state-space model for multi-inverters based on VSG and analyses the robust stability region (RSR) of the control parameters. Firstly, an extensible small-signal state-space model is established and simplified in the dq axis, which is a necessary preprocessing for the subsequent stability analysis. Secondly, the guardian map method based on bialternate product operation is constructed, and the RSR is established through translation mapping and rotation mapping. Therefore, the solution of RSR is transformed into the problem of identifying the Hurwitz matrix. Finally, the simulations and experiments verify that the calculated region can ensure stable operation during startup and meet the requirement to suppress low-frequency oscillations. In conclusion, the method of solving RSR proposed in this paper provides a basis for selecting controller parameters in practical engineering.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimal Control of Biomass Feedstock Processing System Under Uncertainty in Biomass Quality

Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. Note to Practitioners—This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput.

09 BIOMASS FUELS↗
DOE OSTI2023

Retina-inspired narrowband perovskite sensor array for panchromatic imaging

The retina is the essential part of the human visual system that receives light, converts it to neural signal, and transmits to brain for visual recognition. The red, green, and blue (R/G/B) cone retina cells are natural narrowband photodetectors (PDs) sensitive to R/G/B lights. Connecting with these cone cells, a multilayer neuro-network in the retina provides neuromorphic preprocessing before transmitting to brain. Inspired by this sophistication, we develop the narrowband (NB) imaging sensor combining R/G/B perovskite NB sensor array (mimicking the R/G/B photoreceptors) with a neuromorphic algorithm (mimicking the intermediate neural network) for high-fidelity panchromatic imaging. Compared to commercial sensors, we use perovskite “intrinsic” NB PD to exempt the complex optical filter array. In addition, we use an asymmetric device configuration to collect photocurrent without external bias, enabling a power-free photodetection feature. These results display a promising design for efficient and intelligent panchromatic imaging.

42 ENGINEERING↗
DOE OSTI2022

An Open-Source Frequency-Domain Model for Floating Wind Turbine Design Optimization

A new frequency-domain dynamics model has been developed that uses open-source components to efficiently represent a complete floating wind turbine system. The model, called RAFT (Response Amplitudes of Floating Turbines), incorporates quasi-static mooring reactions, strip-theory and potential-flow hydrodynamics, blade-element-momentum aerodynamics, and linear turbine control. The formulation is compatible with a wide variety of support structure configurations and no manual or time-domain preprocessing steps are required, making RAFT very practical in design and optimization workflows. The model is applied to three reference floating wind turbine designs and its predictions are compared with results from time-domain OpenFAST simulations. There is good agreement in mean offsets as well the statistics and spectra of the dynamic response, verifying RAFT’s general suitability for floating wind analysis. Follow-on work will include verification of potential-flow and turbine-control features and application to optimization problems.

17 WIND ENERGY↗
DOE OSTI2023

Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning

Nuclear reactors and related systems are becoming increasingly complex due to advancing technologies in next-generation power reactors. This increased complexity necessitates enhanced automation and data management capabilities. To successfully realize autonomous systems, methods must be developed to handle vast volumes of data and effectively distinguish anomalous data from noise and expected data. While impressive models utilizing digital twins and similar approaches are under development, here we propose a simplified model for analyzing fundamental methods and techniques. Initially, we created a general dataset by using initial data from PCTRAN in order to represent ideal steady-state conditions. We then inserted anomalies based on prevalent sensor anomaly types (e.g., point anomalies, linear drift, and downward deviations), along with unusual anomalies such as exponential drift and upward deviations. To detect anomalies, we developed a program that employs data partitioning and linear regression to preprocess and filter the anomalous data. A K-Means machine learning (ML) method was then applied to separate and count the data within the anomalous partition. The results from all datasets—apart from exponential growth—demonstrated positive outcomes, with each returning multiple instances of greaterthan-95% accuracy. We conducted further investigations using Idaho National Laboratory’s RAVEN software to perform a sensitivity analysis on the input variables (R 2 Tolerance, Slope Tolerance, and Window Size) and found that the output variables (Accuracy and Time) were most sensitive to the Window Size. Despite the promising results published, further development is required to effectively apply these methods to nuclear systems. Nevertheless, the strengths of this approach are evident and hold promise for future applications in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Addressing GPU memory limitations for Graph Neural Networks in High-Energy Physics applications

Introduction Reconstructing low-level particle tracks in neutrino physics can address some of the most fundamental questions about the universe. However, processing petabytes of raw data using deep learning techniques poses a challenging problem in the field of High Energy Physics (HEP). In the Exa.TrkX Project, an illustrative HEP application, preprocessed simulation data is fed into a state-of-art Graph Neural Network (GNN) model, accelerated by GPUs. However, limited GPU memory often leads to Out-of-Memory (OOM) exceptions during training, due to the large size of models and datasets. This problem is exacerbated when deploying models on High-Performance Computing (HPC) systems designed for large-scale applications. Methods We observe a high workload imbalance issue during GNN model training caused by the irregular sizes of input graph samples in HEP datasets, contributing to OOM exceptions. We aim to scale GNNs on HPC systems, by prioritizing workload balance in graph inputs while maintaining model accuracy. Our paper introduces diverse balancing strategies aimed at decreasing the maximum GPU memory footprint and avoiding the OOM exception, across various datasets. Results Our experiments showcase memory reduction of up to 32.14% compared to the baseline. We also demonstrate the proposed strategies can avoid OOM in application. Additionally, we create a distributed multi-GPU implementation using these samplers to demonstrate the scalability of these techniques on the HEP dataset. Discussion By assessing the performance of these strategies as data loading samplers across multiple datasets, we can gauge their effectiveness in both single-GPU and distributed environments. Our experiments, conducted on datasets of varying sizes and across multiple GPUs, broaden the applicability of our work to various GNN applications that handle input datasets with irregular graph sizes.

Lee, Claire Songhyun↗
DOE OSTI2023

Particle Scale Impacts on Deconstruction Energy of Pine Residues

The goal of this Case Study was to quantify the impacts of variable moisture and ash on hammer mill throughput and energy consumption and on generation of fines that are not able to be fed to conversion, as compared to a status quo Base Case system. Also considered was convertible carbon content (minimum carbon specification) and maximum ash content and the delivered feedstock cost impacts of not being able to feed residue not meeting both specifications to the conversion reactor. Laboratory data on the impacts of input particle size and moisture content on the exit particle size were received from FCIC Subtask 5.2: Preprocessing, High Temperature Conversion Preprocessing from their single particle impact population balance modeling study (Tiasha Bhattacharjee, INL). Additional throughput and energy consumption data were obtained from FCIC Subtask 5.2 (Jordan Klinger, INL) for the same grinder with a 6 mm screen in place. These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Because the ash contents in the separated fines had not been analyzed in the laboratory at the time of the model runs, we chose to assume that the ash distributed proportionally with total mass into the overs and unders in the disk screen following grinding.

energy consumption↗
DOE OSTI2026

REDI – Readiness Engine for Data Integration

The Readiness Engine for Data Integration (REDI) is an open-source framework for automating, standardizing, and assessing the process of preparing scientific data for AI training. REDI implements a five-stage pipeline (ingest, preprocess, transform, structure, output) with per-stage provenance instrumentation via Flowcept, domain-aware transformation logic (PII anonymization, regridding, graph encoding, and more), and built-in readiness assessment and validation modes. REDI has been evaluated across climate, proteomics, materials science, and nuclear fusion datasets, demonstrating near-ideal parallel scaling to 100 nodes on OLCF's Frontier system. REDI is deployable as an agent-callable skill in coding environments such as Claude Code and OpenAI Codex, and is complemented by SetGo for FAIR compliance and catalog publication.

Brewer, Wesley [Oak Ridge National Laboratory (ORN↗
DOE OSTI2024

Techno-economic and life-cycle analysis of strategies for improving operability and biomass quality in catalytic fast pyrolysis of forest residues

Many of the challenges faced by the first commercial biorefineries were associated with feedstock handling, quality, and cost. Strategies are needed to enable further expansion of biorefineries and meet the growing demand for bio-based fuels and products. Here, we examine 2 key feedstock challenges and mitigation strategies in the context of a catalytic fast pyrolysis (CFP) biorefinery: (1) the operability of the feed system, which may be improved by modifying the minimum particle size fed to the reactor, and (2) the quality of the biomass, which may be improved by employing air classification to remove undesirable material and increase fuel yields. We conduct techno-economic analysis (TEA) and life-cycle analysis for these strategies, employing a discrete event simulation model for biomass preprocessing combined with a series of correlations developed from literature data and a rigorous CFP conversion model. Our results highlight the importance of balancing increased cost and material losses from preprocessing against improved operability and fuel yields. Economics and sustainability were optimized when operating at the lowest minimum particle size, emphasizing the importance of minimizing material losses while maintaining the operability of the process. Economically, additional costs and material losses from air classification could be acceptable due to improved biomass conversion, and an optimum air classification speed was identified; however, the fuel GHG emissions were minimized when air classification was not used. Valorizing material removed during preprocessing as a coproduct could improve economics and sustainability, decreasing the burden of material losses.

09 - BIOMASS FUELS↗
DOE OSTI2021

A Preprocessing Tool for Enhanced Ion Mobility–Mass Spectrometry-Based Omics Workflows

The ability to improve the data quality of ion mobility–mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Further, other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Optimization Framework to Assess the Demand Response Capacity of a Water Distribution System

As large electricity consumers, water distribution system (WDS) pumping stations have the potential to become meaningful participants in demand response (DR) programs. The authors propose an optimization framework for assessing the DR capacity of a WDS and identifying the optimal bidding strategy for maximizing WDS revenue in the DR spot market. The proposed mixed integer linear programming (MILP) model overcomes computational constraints of previous DR optimization models by adopting a preprocessing procedure to minimize the number of binary variables and implementing a convex relaxation technique to linearize the hydraulic equations. The proposed MILP model also explicitly accounts for varying levels of risk tolerance of WDS operators by varying the recovery period over which pumping returns to business-as-usual operation. The optimization framework is implemented on a skeletonized 48-node WDS model that includes 7 pumps, 6 tanks, and 39 pipes. Using a simulated DR event and water consumption profile, the authors derive the optimal DR supply curves (i.e., compensation price versus load curtailment quantity) and revenue potential of the WDS under six scenarios for DR participation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗