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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.

Santos Souza, Renan↗
DOE OSTI2022

Panorama 360 (Final Report)

This final technical report from the lead institution, USC grant #DE-SC0012636, serves as the final technical report for collaborative institution UNC-CH grant #DE-SC0012390. The goal was to develop a repository and associated capabilities for data collection, ingestion, and analysis for a broad class of DOE applications that span experimental and simulation science workflows. In particular, this work focuses on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: (1) A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); (2) A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; (3) A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and (4) Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Direct Visualization of Charge Migration in Bilayer Tantalum Oxide Films by Multimodal Imaging

Inspired by biological neuromorphic computing, artificial neural networks based on crossbar arrays of bilayer tantalum oxide memristors have shown to be promising alternatives to conventional complementary metal-oxide-semiconductor (CMOS) architectures. In order to understand the driving mechanism in these oxide systems, tantalum oxide films are resistively switched by conductive atomic force microscopy (C-AFM), and subsequently imaged by kelvin probe force microscopy (KPFM) and spatially resolved time-of-flight secondary ion mass spectrometry (ToF-SIMS). These workflows enable induction and analysis of the resistive switching mechanism as well as control over the resistively switched region of the film. In this work it is shown that the resistive switching mechanism is driven by both current and electric field effects. Reversible oxygen motion is enabled by applying low (<1 V) electric fields, while high electric fields generate irreversible breakdown of the material (>1 V). Fully understanding oxygen motion and electrical effects in bilayer oxide memristor systems is a fundamental step toward the adoption of memristors as a neuromorphic computing technology.

36 MATERIALS SCIENCE↗
DOE OSTI2021

SANS quantification of bound water in water-soluble polymers across multiple concentration regimes

Contrast-variation small-angle neutron scattering (CV-SANS) is a widely used technique for quantifying hydration water in soft matter systems, but it is predominantly applied in the dilute regime or for systems with a well-defined structure factor. Here, CV-SANS was used to quantify the number of hydration water molecules associating with three water-soluble polymers with different critical solution temperatures and types of water–solute interactions in dilute, semidilute, and concentrated solution through the exploration of novel methods of data fitting and analysis. Multiple SANS fitting workflows with varying levels of model assumptions were evaluated and compared to give insight into SANS model selection. These fitting pathways ranged from general, model-free algorithms to more standard form and structure factor fitting. In addition, Monte Carlo bootstrapping was evaluated as a method to estimate parameter uncertainty through simulation of technical replicates. The most robust fitting workflow for dilute solutions was found to be form factor fitting without CV-SANS ( i.e. polymer in 100% D 2 O). For semidilute and concentrated solutions, while the model-free approach can be mathematically defined for CV-SANS data, the addition of a structure factor imposes physical constraints on the optimization problem, suggesting that the optimal fitting pathway should include appropriate form and structure factor models. The measured hydration numbers were consistent with the number of tightly bound water molecules associated with each monomer unit, and the concentration dependence of the hydration number was largely governed by the chemistry-specific interactions between water and polymer. Polymers with weaker water–polymer interactions ( i.e. those with fewer hydration water molecules) were found to have more bound water at higher concentrations than those with stronger water–polymer interactions due to the increase in the number of forced water–polymer contacts in the concentrated system. This SANS-based method to count hydration water molecules can be applied to polymers in any concentration regime, which will lead to improved understanding of water–polymer interactions and their impact on materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Laser Driven Hydrothermal Processing (LDHP) for Rapid Dissolution of Glassy Nuclear Debris

Discovered in 2009 on the SLAB laser system at LLNL, Laser-Driven Hydrothermal Processing (LDHP) both spalls and transiently dissolves a wetted surface (e.g., rock, concrete, paint) when that surface is struck by a laser beam pulse under specific conditions. Previous projects have explored the application of LDHP to expediting sample dissolution for post-detonation nuclear forensics. Early results were very promising, indicating that this method could decrease material dissolution time by a factor of 10 or more. However, the large and complicated lasers previously used are impractical for this application. Under the R^3 venture we aimed to benchmark the LDHP capabilities of turn-key commercial off-the-shelf (COTS) lasers, and to establish how this process could be integrated into an analytical workflow for nuclear forensic analysis. We demonstrated that geological rock standards could be quickly processed into fine powder by LDHP followed by complete acid digestion in 15 minutes, without elemental fractionation that would skew analytical results. We determined that the COTS laser can be used for LDHP that could potentially reduce dissolution time for solid nuclear debris samples in post detonation nuclear forensics. One downside to this approach, however, is that the laser interactions are different with every material, so extensive method validation would be required for all likely material compositions. The LDHP processing rates of several materials were tested and determined to be favorable in the beginning with significant slowdown due to particulate scattering. This could be mitigated either by stopping and centrifuging the sample at specific intervals, or through design of a continuous solvent flow through mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2025

GADRAS-DRF for Safeguards (FY 2025 Mid-Year/Annual Report)

Task 1 – Extract GADRAS-DRF capabilities for a workflow intuitive to safeguards analysis. This task is almost complete. Significant improvements have been made to the functionality of customizing peak fits for use in the isotopics application, as well as peak-based FSA model analysis. The remaining tasks revolve around bug fixes, testing the application, and adding a density scroll bar to isotopics for real time analysis updates. The density scroll bar values will be reflected in summary tables and the self-shielding form accessed within isotopics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

TEAL Output Visualization

Tool for Economic Analysis (TEAL) is an open-source economics calculation package written and maintained by Idaho National Laboratory (INL). As a plugin of the Risk Analysis Virtual ENvironment (RAVEN), TEAL provides economics analysis models to RAVEN workflow users. In addition, TEAL is used in other RAVEN plugins such as LOGOS and the Holistic Energy Resource Optimization Network (HERON) to complement other analyses with economic calculations. This report documents the efforts in developing the capability of TEAL output visualization for users to better understand and investigate TEAL simulation results. In the past, outputs (e.g., various cash flows) were generated and printed to screen. According to the need and purpose for visualization, users can select various bar charts and donut charts to visualize the cash flows including inflows, outflows, and net cash flows in each year or in a selected time range, in addition to observing data stored in comma-separated value files (CSVs). The visualization capability is flexible and user-friendly by introducing several user-defined parameters, for instance, the start and the end project year of interest and the type of colors to be assigned to the cash flows.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Electrical and Thermal Load Impacts of Three District Heating and Cooling Designs for an Existing Community in Washington, DC

District energy systems that provide building heating and cooling are a promising option to provide low-cost, energy efficient heating and cooling solutions for communities. As some buildings move to electrified heating designs, there may be significant increases in electric grid demand, particularly in the winter. District energy systems may be able to help reduce these high electrical demands on the electric grid, while also providing decreased overall energy consumption with an increased flexibility in electrical energy usage. To evaluate and compare district options for district-based heating and cooling, an existing neighborhood of Washington, DC consisting of 35 existing buildings was selected as a case study. This study models and compares a fourth generation (4G) district heating and cooling systems that provide hot and chilled water from a central plant directly to each prosumer and a fifth generation (5G) district heating and cooling systems that provide near-ambient water via geothermal boreholes to interface with an energy transfer station with a heat pump at each prosumer. These district systems are compared against a baseline of the non-connected buildings with their self-contained and current HVAC systems. The neighborhood was selected as a good candidate for a district system for its diversity of loads (mix of different commercial and property types) and the availability of building characteristics. URBANopt District Energy Systems (DES) was used to model the buildings and create initial Modelica models for the district systems. Finally, the DES models were tuned and simulated in Dymola, and the outputs were post-processed for verification and to calculate the electrical and thermal grid impact metrics. This is the first documentation of this workflow and its complete analysis. The three designed systems are compared via their grid metrics including: total energy consumption, electrical demand peak loads, daily peak-to-valley ratios, and system ramping. A detailed discussion is provided about how each system impacts and interacts with the electric grid.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Evaluating FRI3D for Cost Savings in Fire Hazard Analysis at DOE Sites

A fire hazard analysis, required for many U.S. Department of Energy (DOE) facilities, is a complex, cumbersome, and costly process. Fire hazard analyses may be viewed as a checkbox, but ideally and in spirit with the DOE-STD-1066, the fire hazard analysis (FHA) should be a part of the workflow and used to help in modifications, maintenance, and improving operational safety. With current FHA development processes, it is both time and cost prohibitive for true integration. A tool called Fire Risk Investigation in 3D or FRI3D was developed under the DOE Light Water Reactor Sustainability program to simplify and automate many aspects of a fire probabilistic risk analysis for existing nuclear power plants. The FRI3D tool automates fire scenarios by combining approved fire simulation codes, U.S. Nuclear Regulatory Commission fire calculations methods, 3D modeling and visualization, and probabilistic risk analysis models into a single workflow supported with a user interface. FRI3D was initially designed for used in combination with a PRA, this case study, evaluated using FRI3D for a plant modification, determined the benefits that detailed fire modeling can have for U.S. Department of Energy facilities with or without a PRA model. It also looked at what tasks from DOE requirements could be reduced using the tool and what is needed to integrate fire hazard analysis into site workflow.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advancements in Multiphysics Microdepletion Analysis of an eVinci TM -like Microreactor Leveraging OpenMC-CRAB Workflow

Nuclear microreactors (MRs) are a class of nuclear reactor technology, characterized by reduced dimensions, modular design, and reduced power output in contrast to conventional Light Water Reactors (LWRs). MRs are proposed for supplying electricity and eventual process heat to remote locations, such as military installations and disaster-affected areas. Current research work sponsored by the US Department of Energy Microreactor Program (MRP) is devoted to the development of novel modeling and simulation tools to better support MR vendors and regulatory bodies. Notably, the NRC is projected to utilize the CRAB multiphysics software driver for executing both design and beyond-design-basis accident analyses. Furthermore, the NRC has been utilizing the MELCOR code to calculate mechanistic source terms during accidents. Since MELCOR relies on isotopic inventory and reactor temperature/power profiles under accident conditions, which theoretically can be derived from CRAB, the goal is to establish a comprehensive CRAB-MELCOR computational framework. Past work was focused on testing and demonstrating CRAB's capability to generate results that can be used to inform mechanistic source term calculations in MELCOR. In particular, a computational workflow leveraging OpenMC-generated microscopic cross sections and CRAB was first applied to perform multiphysics microscopic depletion calculation followed by an accident scenario for a stylized microreactor problem. In fiscal year 2024, the research work has been focused on applying the OpenMC-CRAB workflow, which was first tested in fiscal year 2023, to a realistic 3D heat-pipe cooled MR problem representative of the eVinci TM design. The latter computational problem was developed with inputs from WEC to conserve selected neutronic and thermal characteristics of the eVinci TM design without releasing proprietary data. The results of this simulation, encompassing isotopic inventory, power density distribution, and kinetic parameters, will inform both MELCOR and the WEC-developed FATE code for mechanistic source terms calculations. The results from the two codes will then be compared for code verification purposes. This report contains the design characteristics of the realist heat pipe cooled microreactor developed as a use-case for the verification exercise, and the current results for the multiphysics microscopic depletion performed with the OpenMC-CRAB workflow. The results include eigenvalue as a function of time, power distribution at EOL, in addition to nuclides inventory's time evolution and spatial distribution. Finally, we report improvements to the workflow efficiency achieved through a collaboration with the NEAMS programs. Through this collaborative effort, we were able to strongly decrease the computational time for the multiphysics microdepletion calculation (i.e., from 17.4 hours to 5.7 hours on 280 processors) in addition to simplifying the interface to generate isotopics spatial distribution utilizable by FATE and MELCOR. Future work, including the improvement of the current microscopic cross-sections' library and the simulation of an accident scenario at EOL, is also discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Shutdown dose rate analysis with the Shift Monte Carlo radiation transport code and modular verification workflow

Calculation of the shutdown dose rate is crucial for safe fusion reactor operations. The Rigorous-two-step (R2S) method is a method that requires connected neutron transport, activation, and gamma transport. Shift has integrated variance reduction with a deterministic solver Denovo, supports multiple geometry formats, and is scalable. These features make it an attractive transport solver choice for an R2S workflow. An R2S workflow for the Shift Monte Carlo code is developed and compared to the existing Oak Ridge National Laboratory Shutdown Dose Rate Code Suite (ORCS) workflow. Also, a Python framework for integrating two R2S workflows is developed to mix and match each step in the R2S workflow for improved collaboration and verification experience. In this study, results show that the Shift-Denovo R2S workflow and the ORCS workflow calculate the shutdown dose rate of the ITER Shutdown Dose Rate benchmark problem with an average relative error of 2.285%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

A simulation framework for evaluating electronic order workflows in integrated health records

Electronic health record (EHR) systems are critical to modern healthcare delivery, yet the dynamic workflows that govern electronic order processing remain underexplored. Inefficiencies in these digital pathways can cause delays in care, repetitive workloads, and even patient harm. This study presents a discrete-event simulation framework used to reconstruct and evaluate EHR-based order workflows in a large integrated healthcare system. Using real-world data extracted from the Veterans Health Administration’s Corporate Data Warehouse, the authors mapped order events to standardized state transitions and modeled their progression across different facilities of varying complexity levels. After being calibrated with empirical distributions of transition times and validated against observed time-in-system metrics, the simulation demonstrates close alignment with historical performance. Scenario analyses reveal that resource capacity constraints significantly amplify the impact of electronic order surges, which are reflected in the disproportionate growth in backlogs and processing delays. Adjustments in transition probabilities further increased recirculation and extended workflow paths. Network-based analysis identified Reserved, InProgress, and Completed as structurally critical states that function as hubs within the process network but the transitions in-between also act as major bottlenecks. These results showcased the effectiveness of simulation-based approaches in monitoring EHR order processing performance and evaluating consequences of workflow changes on healthcare network resources planning. The proposed simulation framework provides a scalable data-driven tool to support operational decision-making and improve the efficiency of electronic order management in complex healthcare environments.

Engineering↗
DOE OSTI2021

Integration of Open-Source URBANopt and Dragonfly Energy Modeling Capabilities into Practitioner Workflows for District-Scale Planning and Design

High-performance districts and communities offer opportunities for reducing energy use, emissions, and costs, and can be instrumental in helping cities achieve their climate goals. The design of such communities requires identification of opportunities early on and their re-evaluation throughout the planning process. There is a need for energy modeling tools that connect 3D Computer-Aided Design (CAD) platforms to simulation engines, enabling detailed energy analysis of districts within the workflows and tools used by practitioners. This paper introduces the Dragonfly and URBANoptTM combined toolset that supports the creation of urban models from a range of geometry formats typically used by designers and planners, and provides an integrated pathway to simulate district-scale energy systems. The toolset is piloted by a global architecture and master planning firm to evaluate several key urban-scale technical questions for the design of a district in Chicago. The findings indicate that, while energy savings can be achieved through traditional architectural studies and enhancements to individual building efficiency, the modeling toolset helps identify additional savings and insights that can be achieved when considering district-scale energy systems. Finally, this study demonstrates how the Dragonfly/URBANopt toolset can integrate with master planning workflows, thereby enabling an iterative performance-based design process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Wilkins: HPC in situ workflows made easy

In situ approaches can accelerate the pace of scientific discoveries by allowing scientists to perform data analysis at simulation time. Current in situ workflow systems, however, face challenges in handling the growing complexity and diverse computational requirements of scientific tasks. In this work, we present Wilkins, an in situ workflow system that is designed for ease-of-use while providing scalable and efficient execution of workflow tasks. Wilkins provides a flexible workflow description interface, employs a high-performance data transport layer based on HDF5, and supports tasks with disparate data rates by providing a flow control mechanism. Wilkins seamlessly couples scientific tasks that already use HDF5, without requiring task code modifications. We demonstrate the above features using both synthetic benchmarks and two science use cases in materials science and cosmology.

HPC↗
DOE OSTI2026

NeuDiff Agent: a governed AI workflow for single-crystal neutron crystallography

Large-scale facilities increasingly face analysis and reporting latency as a limiting step in scientific throughput, particularly for structural studies that require iterative reduction, integration, refinement and validation. To improve the time to result and analysis efficiency, NeuDiff Agent is introduced as a governed, tool-using AI workflow for TOPAZ at the Spallation Neutron Source. NeuDiff Agent takes instrument data through reduction, integration, refinement and validation to a validated crystal structure and a publication-ready CIF. NeuDiff Agent coordinates established crystallographic tools under explicit governance by restricting actions to allowlisted tools, enforcing fail-closed verification gates at key workflow boundaries, and capturing complete provenance for inspection, auditing and controlled replay. The present benchmark is limited to structural crystallography for periodic structures; magnetic structure analysis and incommensurate or superspace refinement are outside the scope of the current workflow. Performance is assessed using a fixed prompt protocol and repeated end-to-end runs with two large language model backends, with user and machine time partitioned and intervention burden and recovery behaviors quantified under gating. In a reference-case benchmark, NeuDiff Agent reduces wall time from 435 min (manual) to 86.5 ± 4.7 to 94.4 ± 3.5 min (4.6–5.0× faster) while producing a validated CIF with no checkCIF level A or B alerts. These results establish a practical route to deploy agentic AI in facility crystallography while preserving traceability and publication-facing validation requirements.

Xiao, Zhongcan [ORNL] (ORCID:0000000220761961)↗
DOE OSTI2021

Sensitivity Analysis of MFiX-PIC Parameters Using Nodeworks, PSUADE, and DAKOTA

The study presented in this report was aimed to demonstrate UQ analysis performed not only with Nodeworks, but also two other well-established UQ software tools from the U.S. DOE’s National Laboratories (PSUADE from Lawrence Livermore National Laboratory and DAKOTA from Sandia National Laboratory). It is important to emphasize that the motivation of this study was not to determine the best UQ software, but to verify if the global sensitivity analyses from the end-to-end workflow in Nodeworks are consistent with the results of other two UQ software. The components of Nodeworks from Python’s ecosystem have been tested as standalone libraries. However, an assessment study for the complete workflow targeting a specific UQ analysis has not been performed for Nodeworks. Hence, this study is expected to serve as an equivalent of solution verification for Nodeworks using other established UQ tools as reference solution. For this purpose, three distinct flow configurations (i.e., settling bed, bubbling fluidized, and circulating fluidized bed) have been used as representative multiphase flow problems of interest. The results of the systematic simulation campaigns performed in an earlier study using the particle-in-cell (PIC) approach in the Multiphase Flow with Interphase eXchanges (MFIX) suite of solvers (i.e., MFiX-PIC) was utilized. The same set of tabulated results was provided as input to the different UQ software for global sensitivity analysis. Results for the three cases indicate that based on the Sobol’ Sensitivity Indices method the order of importance ranking determined by Nodeworks for the Sobol’ Total Sensitivity Indices is consistent with PSUADE and DAKOTA in each case for the five model parameters considered. The input files for Nodeworks for the three cases are also shared through NETL’s Gitlab repository for the reader interested in reproducibility and further analysis (See Section 1.2).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

FORCE Update 2024

The Framework for Optimization of Resources and Economics (FORCE) tool suite is the U.S. Department of Energy’s Nuclear Integrated Energy Systems (IES) Program flagship tool suite for technoeconomic IES analysis of IES. This tool suite is useful for analysis designed to evaluate and improve the technoeconomics of energy production systems, particularly for systems including nuclear technology. In this report, we document the development activity for the FORCE tool suite to extend its capabilities as performed during fiscal year 2024. In addition to reliability and accessibility, capability is one of the three standards guiding the development of the FORCE tool suite and the software codes that are its constituent parts. Extending the capabilities of the FORCE tool suite allows analysis both within the IES program as well as industry, university, and laboratory partners to perform analysis with more accuracy, insight, and impactful narrative. Four areas of capability development were the focus of activity this year: economic parameter uncertainty quantification, multiresolution analysis, components-to-optimization workflow automation, and statespace construction workflows for real-time optimal control. In economic parameter uncertainty quantification, the ability of HERON to capture risk due to scenarios (weather and energy demand uncertainty) was expanded to also include uncertainties in financial parameters such as capital cost or operation and maintenance costs. By including these sources of uncertainty, which are sometimes very large compared with scenario uncertainty, HERON is better able to capture the risk posed by investment in various IES technology. Because of this, analysts can also consider the reduction in risks that can be realized by choice of some technologies. In multiresolution analysis, development activity extended on work completed previously. In fiscal year 2023, methods for decomposing time series signals, such as demand, solar and wind availability, and price profiles, were analyzed and down-selected to those most effective at splitting signals into different resolutions. These resolutions allow considering the influence of different energy demand and supply behaviors across different time scales. For example, energy demand might be divided into seasonal, weekly, and hourly profiles. In fiscal year 2024, this preliminary work was extended and implemented within the Risk Analysis Virtual Environment (RAVEN) risk and uncertainty analysis platform, which is used throughout the FORCE framework. This development of the “multi-resolution time series analysis” (MR-TSA) module in RAVEN allows training synthetic history generators on complex time series. These synthetic history generators can then be used in HERON for generating scenarios that represent possible market and weather scenarios that can be analyzed on different time scales. We envision completing this work in the future, implementing multiresolution dispatch optimization strategies that can make the most beneficial use of these stratified time histories. In components-to-optimization workflow development, workflows for translating user inputs of components into algorithms for algebraic optimization were selected and implemented. Similar algorithms within the Holistic Energy Resource Optimization Network (HERON) were separated from the main code base of HERON and gathered with the components-to-optimization workflows in the new Dispatch Optimization Variable Engine (DOVE) software library. This modularization allows FORCE users to analyze dispatch optimization and energy system duty cycles independently of HERON, which previously was a burdensome task. Additionally, these dispatch optimization algorithms, set up in an independent library, can now be used across all software applications within FORCE, especially including the real-time optimal control software Optimization of Real-time Capacity Allocation (ORCA). Allowing FORCE software to share dispatch optimization algorithms within a single library allows for improved software maintenance and reliability. In statespace characterization workflow development, alternative workflows for optimizing dispatch with additional technical accuracy was the focus, particularly to improve the real-time optimization decision making in ORCA. Using algorithms and workflows initially developed for the Feasible Actuator Range Modifier (FARM), workflows for determining the statespace representation of IES were identified and demonstrated. The resulting dispatch optimization required a more robust optimization algorithm than that originally used in HERON (and moved to DOVE), which required adding an alternate workflow to DOVE that can more accurately match the behavior of physical systems using a partial differential equation representation. In conclusion, capability developments in the FORCE tool suite in fiscal year 2024 have improved the ability of the FORCE tool suite to perform

29 ENERGY PLANNING, POLICY, AND ECONOMY↗