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DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Plan It Together: Optimizing Across Generation, Transmission, Distribution, and Distributed Energy Resources

Integrated planning holds the promise of unlocking lower total system cost solutions by connecting previously siloed planning processes. This article summarizes the state of bulk and local grid planning today and details multiple new analytical approaches that can enable more holistic planning methods to develop comprehensive solutions to generation, transmission, distribution, and distributed energy resource needs. These include both iterative approaches as well as cooptimization techniques. In addition to the benefits of these methods, the technical and institutional challenges and associated solutions are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Enabling DER visibility using a distributed dissemination network

The electrical grid is currently undergoing a series of rapid transformational changes that have resulted in the introduction of new actors and operational schemes that have fragmented the data and control planes. To help address the issue, this paper describes the implementation of a sensor-oriented, distributed data dissemination network that seeks to eliminate data silos. The implementation is based on the DGSS architecture previously described in [1]. The developed product seeks to facilitate the seamless integration of multi-operator, multi-origin, multi-domain sensor data by using a distributed systems approach. The proposed solution decouples the sensor’s data streams from the application-specific infrastructure and migrates them into a software-defined databus that can be configured to suit the end application’s demands. To further validate DGSS capabilities, a DER oriented use case has been developed.

Sensor Dissemination Networks, Enhanced DER visibi↗

DOE OSTI2022

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Beyond denitrification: The role of microbial diversity in controlling nitrous oxide reduction and soil nitrous oxide emissions

Many biotic and abiotic processes contribute to nitrous oxide (N 2 O) production in the biosphere, but N 2 O consumption in the environment has heretofore been attributed primarily to canonical denitrifying microorganisms. The nosZ genes encoding the N 2 O reductase enzyme, NosZ, responsible for N 2 O reduction to dinitrogen are now known to include two distinct groups: the well-studied Clade I which denitrifiers typically possess, and the novel Clade II possessed by diverse groups of microorganisms, most of which are non-denitrifiers. Clade II N 2 O reducers could play an important, previously unrecognized role in controlling N 2 O emissions for several reasons, including: (1) the consumption of N 2 O produced by processes other than denitrification, (2) hypothesized non-respiratory functions of NosZ as an electron sink or for N 2 O detoxification, (3) possible differing enzyme kinetics of Clade II NosZ compared to Clade I NosZ, and (4) greater nosZ gene abundance for Clade II compared to Clade I in soils of many ecosystems. Despite the potential ecological significance of Clade II NosZ, a census of 800 peer-reviewed original research articles discussing nosZ and published from 2013 to 2019 showed that the percentage of articles evaluating or mentioning Clade II nosZ increased from 5% in 2013 to only 22% in 2019. The census revealed that the slowly spreading awareness of Clade II nosZ may result in part from disciplinary silos, with the percentage of nosZ articles mentioning Clade II nosZ ranging from 0% in Agriculture and Agronomy journals to 32% in Multidisciplinary Sciences journals. In addition, inconsistent nomenclature for Clade I nosZ and Clade II nosZ, with 17 different terminologies used in the literature, may have created confusion about the two distinct groups of N 2 O reducers. Furthermore, we provide recommendations to accelerate advances in understanding the role of the diversity of N 2 O reducers in regulating soil N 2 O emissions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Fluctuant storage temperature increased the heterogeneous distributions of pH and fermentation products in large round bale silage

Abstract In practical of silage production, variations in the fermentation parameters of silage might be attributable to changes in weather data, especially for ambient temperature. The objective of this study was to investigate how the changed temperature of outside weather condition affects distributions of pH and fermentation products in large round bale silage. The bale silages (1.22 m × 1.25 m ø; density, about 168.9 kg dry matter [DM]/m 3 ) were produced by BR6090 Combi from New Holland Agriculture, wrapped with six layers of stretch film, stored at ambient temperature (2–20°C) and constant temperature (10°C; 15°C; 25°C) for 3, 9, 15, 45 and 90 days. Compared with other silages, silage stored at 25°C had higher ( p < 0.05) lactic acid content, lower ( p < 0.05) pH, butyric acid content and ammonia‐nitrogen (N) ratio of total N. Fluctuant storage temperature limited lactic acid fermentation process with delayed pH decline and continuous butyric production and crude protein (CP) degradation. The differences of pH, lactic acid, propionic acid and ammonia‐N were considerably less evident in the centre of silages stored at different temperatures, and they increased towards the boundaries. The data shows that fluctuant storage temperature had undesirable effect on the fermentation quality of silage, especially for the external part of silo; relatively high and stable storage temperature for initial stage of ensiling process was necessary, but did not promote a positive response to the homogeneous distributions of pH and fermentation products in large round bale silage.

Li, Ping↗

DOE OSTI2021

Preliminary Component Design and Cost Estimation of a Novel Electric-Thermal Energy Storage System Using Solid Particles

Energy storage will become indispensable to complement the uncertainty of intermittent renewable resources and to firm the electricity supply as renewable power generation becomes the mainstream new-built energy source and fossil fuel power plants are phased out to meet carbon-neutral utility targets. Current energy storage methods based on pumped storage hydropower or batteries have many limitations. Thermal energy storage (TES) has unique advantages in scale and siting flexibility to provide grid-scale storage capacity. A particle-based TES system is projected to have promising cost and performance characteristics to meet the future growing energy storage needs. This paper introduces the system and components required for particle TES to become technically and economically competitive. The system integrates electric particle heaters, particle TES within insulated concrete silos, and an efficient air-Brayton combined-cycle power system to provide power for storage durations up to several days via low-cost, high-performance storage cycles. Design specifications and cost estimation of major components in a commercial-scale system are presented in this paper. A techno-economic analysis based on preliminary component designs and performance indicates that particle TES integrated with an air-Brayton combined-cycle power system has a path to achieve the targeted levelized cost of storage of 5 ¢/kWh-cycle at a round-trip efficiency of 50% when taking low-cost energy-specific components and leveraging basic assets from existing thermal power plants. The cost model provides insights for further development and economic potentials for long-duration energy storage.

14 SOLAR ENERGY↗

DOE OSTI2023

Granular Flow in Novel Octet Shape–Based Lattice Frame Material

Moving packed-bed heat exchangers in concentrated solar power (CSP) plants involves heat transfer between heated falling particles and supercritical carbon dioxide. The overall effective thermal conductivity of the moving packed bed and particle-side channel contact resistances are still the bottlenecks in achieving the desirable thermal transport levels. To this end, a novel moving packed bed heat exchanger consisting of an Octet lattice packed between the walls of the particle-side channel is proposed in this study. Granular flow analysis in Octet lattice moving packed bed heat exchanger (OLHX) was conducted through experiments and discrete element method (DEM)-based numerical simulations. The experimental images clearly demonstrated stagnation regions upstream of lattice fibers, void regions downstream of the fiber junctions, and wavy-type unobstructed flow on the lateral sides of the fibers. DEM simulations were successful in capturing all these critical flow phenomena. Larger flow velocities were observed on the lateral sides of the fibers in the simulations. Also, when the particles in the silo were emptied, the final images showed an accumulation of particles on the inter-fiber as well as fiber–channel wall junctions. Moreover, the fiber connections resulted in some regions devoid of particle contact on the channel endwall, which means that these regions would suffer from poor thermal exchange. Finally, the overall mass flowrate increased with increasing porosity for a fixed particle diameter.

14 SOLAR ENERGY↗

DOE OSTI2022

C3F: Collaborative Container-based Model Coupling Framework

Solving complex real-world grand challenge problems requires in-depth collaboration of researchers from multiple disciplines. Such collaboration often involves harnessing multiscale and multi-dimensional data and combining models from different fields to simulate systems. However, the progress on this front has been limited mainly due to significant gaps in domain knowledge and tools that are typically employed in silos of the domains. Researchers from different fields face considerable barriers to understanding and reusing each other’s data/models in order to collaborate effectively. For example, in solving the global sustainability problems, researchers from hydrology, climate science, agriculture, and economics need to run their respective models to study different components of the global and local food, energy and water systems while, at the same time, need to interact with other researchers and integrate the results of one model with another. Developing this kind of model coupling workflow calls for (1) a large amount of data being processed and exchanged across domains and organizations, (2) identifying and processing the output of one model to make it ready for integration into another model, (3) controlling the workflow dynamically so that it runs until a certain convergence condition or other criteria is met, and (4) close collaboration among the modelers to explore, tune, and test the configuration and data transformation needed to link the models. We have developed C3F, a flexible collaborative model coupling framework to help researchers accelerate their model integration and linking efforts by leveraging advanced cyberinfrastructure such as high-performance computing and virtual containers. In this paper, we describe our experience and lessons learned in developing this cyberinfrastructure solution to support the linking of Water Balance Model (WBM) and SIMPLE-G agricultural economic model in an NSF funded INFEWS project and a DOE-funded Program on Coupled Human and Earth Systems (PCHES) to study the implications of groundwater scarcity for food-energy-water systems. The C3F model coupling framework can be extended to facilitate other model linkages as well.

containerization↗

DOE OSTI2025

FAIR Ecosystems for Science at Scale

High Performance Computing (HPC) centers provide resources to users who require greater scale to “get science done”. They deploy infrastructure with singular hardware architectures, cutting-edge software environments, and stricter security measures as compared with users’ own resources. As a result, users often create and configure digital artifacts in ways that are specialized for the unique infrastructure at a given HPC center. Each user of that center will face similar challenges as they develop specialized solutions to take full advantages of the center’s resources, potentially resulting in significant duplication of effort. Much duplicated effort could be avoided, however, if users of these centers found it easier to discover others’ solutions and artifacts as well as share their own. The FAIR principles address this problem by presenting guidelines focused around metadata practices to be implemented by vaguely defined “communities”; in practice, these tend to gather by domain (e.g. bioinformatics, geosciences, agriculture). Domain-based communities can unfortunately end up functioning as silos that tend both to inhibit sharing of solutions and best practices as well as to encourage fragile and unsustainable improvised solutions in the absence of best-practice guidance. We propose that these communities pursuing “science at scale” be nurtured both individually and collectively by HPC centers so that users can take advantage of shared challenges across disciplines and potentially across HPC centers. We describe an architecture based on the EOSC-Life FAIR Workflows Collaboratory, specialized for use with and inside HPC centers such as the Oak Ridge Leadership Computing Facility (OLCF), and we speculate on user incentives to encourage adoption. We note that a focus on FAIR workflow components rather than FAIR workflows is more likely to benefit the users of HPC centers.

Wilkinson, Sean [ORNL] (ORCID:0000000214437479)↗

DOE OSTI2025

CAFE AU LAIT: Compute-Aware Federated Augmented Low-Rank AI Training

Federated finetuning is crucial for unlocking the knowledge embedded in pretrained Large Language Models (LLMs) when data are geographically distributed across clients. Unlike finetuning with data from a single institution, federated finetuning allows collaboration across multiple institutions, enabling the utilization of diverse and decentralized datasets while preserving data privacy. Given the high computing costs of LLM training and the emphasis on energy efficiency in Federated Learning (FL), Low-Rank Adaptation (LoRA) has emerged as a widely adopted algorithm due to its significantly reduced number of trainable parameters. However, this assumes that all data silos have the necessary computing resources to compute local updates of LLMs. Nevertheless, in practice, the computing resources across clients are highly heterogeneous: while some may have access to hundreds of GPUs, others might have limited or no GPU access. Recently, federated finetuning using synthetic data has been proposed, allowing clients to participate in a collaborative training run without training LLMs locally. However, our experimental results reveal a performance gap between models trained using synthetic data and those trained using local updates. Motivated by the observed heterogeneity in computing resources and the performance gap, we propose a novel two-stage algorithm that leverages the storage and computing capabilities of a strong server. In the first stage, under the coordination of the strong server, clients with limited computing resources collaborate to generate synthetic data, which is transferred to and stored on the strong server. In the second stage, the strong server uses this synthetic data on behalf of the resource-constrained clients to perform federated LoRA finetuning alongside clients with sufficient computing resources. This approach ensures that all clients can participate in the finetuning process. Experimental results demonstrate that incorporating local updates from even a small fraction of clients improves performance compared to using synthetic data for all clients. Furthermore, we incorporate the Gaussian mechanism in both stages to guarantee client-level differential privacy.

Wang, Jiayi [ORNL]↗

DOE OSTI2020

Deep Lynx: Digital Engineering Integration Hub

The construction of megaprojects has consistently demonstrated challenges for project managers in regard to meeting cost, schedule, and performance requirements. Megaproject construction challenges are common place within megaprojects with many active projects in the United States failing to meet cost and schedule efforts by significant margins. Currently, engineering teams operate in siloed tools and disparate teams where connections across design, procurement, and construction systems are translated manually or over brittle point-to-point integrations. The manual nature of data exchange increases the risk of silent errors in the reactor design, with each silent error cascading across the design. These cascading errors lead to uncontrollable risk during construction, resulting in significant delays and cost overruns. Deep Lynx allows for an integrated platform during design and operations of mega projects.

Darrington, JohnW↗

DOE OSTI2022

BDEM (Discrete-element-simulator for high-solids granular flows) [SWR-22-72]

BDEM is a discrete element method based simulation tool developed specifically for modeling high-solids granular flows that include polydispersity, heat-transfer, moving boundaries and chemistry. Our solver provides facilities for simulating spherical/non-spherical particles with modified contact and friction models in complex dynamic geometries defined using level-sets or triangulated files. The solver is developed on top of NREL's open-source performance portable library, AMReX, providing parallel execution capabilities on current and upcoming high-performance-computing (HPC) architectures. Simulations at the scale of several millions to billion particles have been performed using this software on large scale computing resources. This software can be applied to non-reacting solids dominant flows in silos, hoppers and screw conveyors as well as in high temperature reacting systems such as screw kilns and auger reactors.

Sitaraman, Hariswaran↗

DOE OSTI2026

HPC ODA Commons [SWR-26-003]

HPC ODA Commons is a community-driven platform for standardizing HPC operational data analytics. HPC sites generate enormous volumes of operational data - scheduler logs, accounting records, monitoring streams - but turning that data into actionable insight is needlessly hard. Each site builds bespoke parsers, schemas, and evaluation pipelines. Results can't be compared across institutions. Promising analytics ideas stay siloed because there's no shared language for describing the data, the experiments, or the outcomes. HPC ODA Commons fixes this by establishing community-governed contracts - versioned schemas, canonical artifacts, and benchmark recipes - that make ODA workflows discoverable, reproducible, and comparable. It pairs these standards with a practical, CLI-first toolkit that lets operators and researchers go from raw logs to standardized results without sending data off-cluster.

Menear, Kevin [National Laboratory of the Rockies ↗

DOE OSTI2023

Co-Harvest and Anaerobic Co-Storage of Corn Grain and Stover as Biomass Feedstocks

This research investigated the utility of co-harvesting and anaerobic co-storage of corn grain and stover to positively influence their physical and chemical characteristics as a biomass feedstock. Corn grain and stover were harvested in 2019 and 2020 with a self-propelled forage harvester. Stover yield, moisture content, and composition were altered by the harvest date, stubble height, and header configuration. Harvest date had the utility of varying the stover moisture content (p < 0.001) from 42.3% to 53.5% (w.b.) and 43.1% to 53.9% (w.b.) for the 2019 and 2020 harvest years, respectively. Stubble height was also utilized to vary stover moisture content. A negative linear relationship was established between stubble height and stover moisture content for the early (R2 = 0.76) and late harvest (R2 = 0.91) dates for both years. Stover yield also showed a negative linear relationship (R2 = 0.76) with stubble height over both years. Regardless of the stubble height, the row-crop header collected more stover (p < 0.001) than the ear-snapper header. In 2020, harvested stover ranged from 5.0 to 10.5 Mg ha-1, with ha-1 representing 41% to 85% of the total available stover. In both years, stover ash content was less than 64 g kg-1. Material stored in pilot-scale silos (19 L) was well conserved during anaerobic storage, with average DM losses of 4.8% and 3.4% in 2019 and 2020, respectively. Grain moisture content averaged 23.6% (w.b.) at harvest, and 31.0% (w.b.) after storage as moisture migrated from the moist stover to the drier grain. Harvesting whole-plant corn with a forage harvester had the unwanted effect of reducing the particle size of the grain fraction, which would complicate downstream utilization. However, reducing the harvester cutterhead speed increased the fraction of intact kernels from 47% to 85% by mass. The studied system was a viable alternative to conventional corn grain and stover systems for producing feedstocks for biochemical conversion.

09 BIOMASS FUELS↗

DOE OSTI2020

Improving five-year survival prediction via multitask learning across HPV-related cancers

Oncology is a highly siloed field of research in which sub-disciplinary specialization has limited the amount of information shared between researchers of distinct cancer types. This can be attributed to legitimate differences in the physiology and carcinogenesis of cancers affecting distinct anatomical sites. However, underlying processes that are shared across seemingly disparate cancers probably affect prognosis. The objective of the current study is to investigate whether multitask learning improves 5-year survival cancer patient survival prediction by leveraging information across anatomically distinct HPV related cancers. Furthermore, data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program database. The study cohort consisted of 29,768 primary cancer cases diagnosed in the United States between 2004 and 2015. Ten different cancer diagnoses were selected, all with a known association with HPV risk. In the analysis, the cancer diagnoses were categorized into three distinct topography groups of varying specificity. The most specific topography grouping consisted of 10 original cancer diagnoses differentiated by the first two digits of the ICD-O-3 topography code. The second topography grouping consisted of cancer diagnoses categorized into six distinct organ groups. Finally, the third topography grouping consisted of just two groups, head-neck cancers and ano-genital cancers. The tasks were to predict 5-year survival for patients within the different topography groups using 14 predictive features which were selected among descriptive variables available in the SEER database. The information from the predictive features was shared between tasks in three different ways, resulting in three distinct predictive models: 1) Information was not shared between patients assigned to different tasks (single task learning); 2) Information was shared between all patients, regardless of task (pooled model); 3) Only relevant information was shared between patients grouped to different tasks (multitask learning). Prediction performance was evaluated with Brier scores. All three models were evaluated against one another on each of the three distinct topography-defined tasks. The results showed that multitask classifiers achieved relative improvement for the majority of the scenarios studied compared to single task learning and pooled baseline methods. In this study, we have demonstrated that sharing information among anatomically distinct cancer types can lead to improved predictive survival models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022

This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Reductions Without Regret: Historical Perspectives

This is the first of three papers (in addition to an introductory summary) aimed at providing a framework for evaluating future reductions or modifications of the U.S. nuclear force, first by considering previous instances in which nuclear-force capabilities were eliminated; second by looking forward into at least the foreseeable future at the features of global and regional deterrence (recognizing that new weapon systems currently projected will have expected lifetimes stretching beyond our ability to predict the future); and third by providing examples of past or possible undesirable outcomes in the shaping of the future nuclear force, as well as some closing thoughts for the future. This paper examines the circumstances and consequences of the elimination of The INF-range Pershing II ballistic missile and Gryphon Ground-Launched Cruise Missile (GLCM), deployed by NATO under a dual-track strategy to counter Soviet intermediate-range missiles while pursuing negotiations to limit or eliminate all of these missiles. The Short-Range Attack Missile (SRAM), which was actually a family of missiles including SRAM A, SRAM B (never deployed), and SRAM II and SRAM T, these last two cancelled during an over-budget/behind-schedule development phase as part of the Presidential Nuclear Initiatives of 1991 and 1992. The nuclear-armed version of the Tomahawk Land-Attack Cruise Missile (TLAM/N), first limited to shore-based storage by the PNIs, and finally eliminated in deliberations surrounding the 2010 Nuclear Posture Review Report. The Missile-X (MX), or Peacekeeper, a heavy MIRVed ICBM, deployed in fixed silos, rather than in an originally proposed mobile mode. Peacekeeper was likely intended as a bargaining chip to facilitate elimination of Russian heavy missiles. The plan failed when START II did not enter into force, and the missiles were eliminated at the end of their intended service life. The Small ICBM (SICBM), or Midgetman, a road-mobile, single-warhead missile for which per-unit costs were climbing when it was eliminated under the PNIs. Although there were liabilities associated with each of these systems, there were also unique capabilities; this paper lays out the pros and cons for each. Further, we articulate the capabilities that were eliminated with these systems.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗