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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A Comparison Study of Offshore Wind Support Structures with Monopiles and Jackets for U.S. Waters: Preprint

U.S. experience in offshore wind is limited, and high costs are expected unless innovations are introduced in one or multiple aspects of the project, from the installed technology to the balance of system (BOS). The substructure is the main single component responsible for the BOS capital expenditure (CapEx) and thus one that, if improved, could yield significant levelized cost of energy (LCOE) savings. For projects in U.S. waters, multimember, lattice structures (also known as jackets) can render required stiffness for transitional water depths at potentially lower costs than monopiles (MPs). In this study, we used a systems engineering approach to evaluate the LCOE of prototypical wind power plants at six locations along the eastern seaboard and the Gulf of Mexico for both types of support structures. Using a reference wind turbine and actual metocean conditions for the selected sites, we calculated loads for a parked and an operational situation, and we optimized the MP- and jacket-based support structures to minimize their overall mass. Using a suite of cost models, we then computed their associated LCOE. For all water depths, the MP-based configurations were heavier than their jacket counterparts, but the overall costs for the MPs were less than they were for jackets up to depths of slightly less than 30 m. When the associated manufacturing and installation costs were included, jackets resulted in lower LCOE for depths greater than 40 m. These results can be used by U.S. stakeholders to understand the potential for different technologies at different sites, but the methodology illustrated in this study can be further employed to analyze the effects of innovations and design choices throughout wind power plant systems.

17 WIND ENERGY↗
DOE OSTI2024

Impact of the Exciter and Governor Parameters on Forced Oscillations

In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability.

42 ENGINEERING↗
DOE OSTI2026

A Scalable Reduced‐Order Model for the Steady Navier–Stokes Equations

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, the component reduced order modeling method has been developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has only been demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier–Stokes flow, with application to general nonlinear physics in view. The large‐scale, global domain is decomposed into a combination of small‐scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected from each small‐scale unit component. Velocity bases are augmented with a pressure supremizer to satisfy the inf–sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: A third‐order tensor projection operator and the empirical quadrature procedure. The proposed method is demonstrated on the flow over arrays of five different unit objects, achieving a 23‐fold speedup with less than 4% relative error in domains up to 256 times larger than the unit components. Furthermore, a numerical experiment with the pressure supremizer strongly indicates the need for a supremizer for stable pressure prediction. A comparison between the tensorial approach and the empirical quadrature procedure revealed a slight advantage of the empirical quadrature procedure. The framework is compared with an alternating Schwarz‐based reduced‐order approach, demonstrating improved efficiency and robustness for the DG‐based global solver while retaining flexibility for sub‐scale iterative solvers. The method is further extended to a coupled advection–diffusion and Navier–Stokes system, illustrating its applicability to multi‐physics problems and its potential for more general, inter‐coupled nonlinear systems.

42 ENGINEERING↗
DOE OSTI2022

Security Enhancements for Distributed Energy Resource Systems Interconnected with Distribution Networks: Final Technical Report

The revised IEEE 1547 Standard defines new complex communication-adjustable voltage and frequency regulation and ride-through characteristics, while maintaining the general antiislanding requirement for unintentional islanding situations. Unintentional islanding is prohibited while intentional islanding is specifically allowed, thus effectively enabling microgrid operation mode. Further, IEEE 1547-2018 Standard introduces new requirements in terms of interoperability so that the DER plant/circuit segments may be seamlessly integrated with the utility networks but at the same time become vulnerable to a cyber-attack. Traditional cybersecurity measures including encryption, authentication and role-based access control may not be fully implementable to all communication protocols specified in the IEEE 1547 Standard. Therefore, in this project we have identified, researched, implemented and tested several cyberphysical approaches that rely mostly on the behavior of the DER circuit and may help with validating the incoming command and control action potentially coming through an insecure communications channel. Additionally, we have built semantic models and communications profiles for DER facilities and have implemented lightweight IEC 61850 based publisher-subscriber GOOSE messaging mechanism, with security extensions in terms of authentication and encryption. The project proposed information models for integration into UCA OpenFMB 2.0 profiles focusing on grid code compliance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Agentic AI vs ML-Based Autotuning: A Comparative Study for Loop Reordering Optimization

High Performance Computing (HPC) applications rely heavily on code optimizations to achieve good performance on modern CPU and GPU architectures. Traditional Machine Learning auto-tuning approaches have demonstrated success in exploring high-dimensional spaces, but they often require expensive compile-run evaluations and lack adaptability for large HPC applications. The recent advances in Large Language Models (LLMs) and Agentic AI systems raise intriguing questions about the potential of these approaches to address specific optimization methodologies. This work aims to answer an essential question for the HPC community: “How Agentic AI Systems Compare to Traditional ML Autotuning Techniques?” To address this question, we present a comparative analysis between a traditional ML-based optimization approach and an Agentic AI system, evaluating their respective capabilities and limitations for loop-level optimization. In addition, we introduced a new Agentic AI system named LoopGen-AI using three different Large Language Models: GPT-4.1, Claude 4.0, and Gemini 2.5. A key finding is that LoopGen-AI achieves competitive per-formance with only a few program runs, the reasoning logs from the agents revealed that their decisions rely heavily on the combination of semantic understanding of the target kernel with dynamic feedback from the environment, highlighting a promising new dimension in performance tuning. In contrast, ML-based autotuners focus on statistical exploration, and require orders of magnitude more runs to reach peak performance. Additionally, our analysis shows that prompt engineering, particularly using Persona + Context Manager patterns, significantly impacts the effectiveness of Agentic AI. Our results indicate that while Agentic AI systems are not yet a complete replacement for ML-based autotuners, it can effectively complement traditional methods.

Rosas, Miguel Romero↗
DOE OSTI2017

Engineering glucose metabolism of Escherichia coli under nitrogen starvation

A major aspect of microbial metabolic engineering is the development of chassis hosts that have favorable global metabolic phenotypes, and can be further engineered to produce a variety of compounds. In this work, we focus on the problem of decoupling growth and production in the model bacterium Escherichia coli, and in particular on the maintenance of active metabolism during nitrogen-limited stationary phase. We find that by overexpressing the enzyme PtsI, a component of the glucose uptake system that is inhibited by α-ketoglutarate during nitrogen limitation, we are able to achieve a fourfold increase in metabolic rates. Alternative systems were also tested: chimeric PtsI proteins hypothesized to be insensitive to α-ketoglutarate did not improve metabolic rates under the conditions tested, whereas systems based on the galactose permease GalP suffered from energy stress and extreme sensitivity to expression level. Overexpression of PtsI is likely to be a useful arrow in the metabolic engineer’s quiver as productivity of engineered pathways becomes limited by central metabolic rates during stationary phase production processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Identifying Suitable Degradation Parameters for Individual-Based Prognostics

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. Traditionally, individual-based prognostic methods use a measure of degradation to make lifetime estimates. Degradation measures may include sensed measurements, such as temperature or vibration level, or inferred measurements, such as model residuals or physics-based model predictions. Often, it is beneficial to combine several measures of degradation into a single parameter. Parameter features such as trendability, monotonicity, and prognosability can be used to compare candidate prognostic parameters to determine which is most useful for individual-based prognosis. By quantifying these features for a given parameter, the metrics can be used with any traditional optimization technique to identify an appropriate parameter. This parameter may be used with a parametric extrapolation model to make prognostic estimates for an individual unit. The proposed methods are illustrated with an application to simulated turbofan engine data.

Diagnostics↗
DOE OSTI2016

Bacterial microcompartments as metabolic modules for plant synthetic biology

Summary Bacterial microcompartments ( BMC s) are megadalton‐sized protein assemblies that enclose segments of metabolic pathways within cells. They increase the catalytic efficiency of the encapsulated enzymes while sequestering volatile or toxic intermediates from the bulk cytosol. The first BMC s discovered were the carboxysomes of cyanobacteria. Carboxysomes compartmentalize the enzyme ribulose‐1,5‐bisphosphate carboxylase/oxygenase (RuBis CO ) with carbonic anhydrase. They enhance the carboxylase activity of RuBis CO by increasing the local concentration of CO 2 in the vicinity of the enzyme's active site. As a metabolic module for carbon fixation, carboxysomes could be transferred to eukaryotic organisms (e.g. plants) to increase photosynthetic efficiency. Within the scope of synthetic biology, carboxysomes and other BMC s hold even greater potential when considered a source of building blocks for the development of nanoreactors or three‐dimensional scaffolds to increase the efficiency of either native or heterologously expressed enzymes. The carboxysome serves as an ideal model system for testing approaches to engineering BMC s because their expression in cyanobacteria provides a sensitive screen for form (appearance of polyhedral bodies) and function (ability to grow on air). We recount recent progress in the re‐engineering of the carboxysome shell and core to offer a conceptual framework for the development of BMC ‐based architectures for applications in plant synthetic biology.

Gonzalez‐Esquer, C. Raul↗
DOE OSTI2025

Identifying microbial functional guilds performing cryptic organotrophic and lithotrophic redox cycles in anaerobic granular biofilms

Granular biofilms used in anaerobic digester systems contain diverse microbial populations that interact to hydrolyze organic matter and produce methane within controlled environments. Prior research investigated the feasibility of utilizing granular biofilms obtained from an anaerobic digester to remove nitrate without the addition of exogenous electron donors. These granules possessed a unique structure of alternating light and dark iron sulfide and pyrite rich layers that potentially served as both an electron source and sink, linking carbon, nitrogen, sulfur, and iron cycles. To characterize the functional roles of diverse microbial populations enriched within these layered biofilms, we analyzed metagenomes obtained from three different granules. Comparisons between the functional gene content of forty metagenome assembled genomes (MAGs) identified phylogenetically cohesive functional guilds. Each of these functional MAG clusters was assigned to specific steps in anaerobic digestion (hydrolysis, acidogenesis, acetogenesis, and methanogenesis) and anaerobic respiration (denitrification and sulfate reduction). Comparisons with metagenomes derived from a variety of natural and engineered ecosystems confirmed that the enriched denitrifying bacteria were similar to populations typically found in wetlands and biological nitrogen removal systems. Analysis of read alignments to individual genes within the forty MAGs identified conserved genomic features that were representative of the functions that distinguished functional guilds. Overall, this research illustrates the utility of functional based classification of microorganisms for characterizing ecosystem functions and highlights the potential application of engineered ecosystems to serve as experimental models for complex natural ecosystems.

Ecosystem engineering↗
DOE OSTI2012

Decision Insight into Stakeholder Conflict for ERN

Participatory modeling has become an important tool in facilitating resource decision making and dispute resolution. Approaches to modeling that are commonly used in this context often do not adequately account for important human factors. Current techniques provide insights into how certain human activities and variables affect resource outcomes; however, they do not directly simulate the complex variables that shape how, why, and under what conditions different human agents behave in ways that affect resources and human interactions related to them. Current approaches also do not adequately reveal how the effects of individual decisions scale up to have systemic level effects in complex resource systems. This lack of integration prevents the development of more robust models to support decision making and dispute resolution processes. Development of integrated tools is further hampered by the fact that collection of primary data for decision-making modeling is costly and time consuming. This project seeks to develop a new approach to resource modeling that incorporates both technical and behavioral modeling techniques into a single decision-making architecture. The modeling platform is enhanced by use of traditional and advanced processes and tools for expedited data capture. Specific objectives of the project are: (1) Develop a proof of concept for a new technical approach to resource modeling that combines the computational techniques of system dynamics and agent based modeling, (2) Develop an iterative, participatory modeling process supported with traditional and advance data capture techniques that may be utilized to facilitate decision making, dispute resolution, and collaborative learning processes, and (3) Examine potential applications of this technology and process. The development of this decision support architecture included both the engineering of the technology and the development of a participatory method to build and apply the technology. Stakeholder interaction with the model and associated data capture was facilitated through two very different modes of engagement, one a standard interface involving radio buttons, slider bars, graphs and plots, while the other utilized an immersive serious gaming interface. The decision support architecture developed through this project was piloted in the Middle Rio Grande Basin to examine how these tools might be utilized to promote enhanced understanding and decision-making in the context of complex water resource management issues. Potential applications of this architecture and its capacity to lead to enhanced understanding and decision-making was assessed through qualitative interviews with study participants who represented key stakeholders in the basin.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Survival and Passage of Juvenile Chinook Salmon and Steelhead Passing through Bonneville Dam, 2011

Pacific Northwest National Laboratory (PNNL) and subcontractors conducted an acoustic-telemetry study of juvenile salmonid fish passage and survival at Bonneville Dam in 2011. The study was conducted to assess the readiness of the monitoring system for official compliance studies under the 2008 Biological Opinion and Fish Accords and to assess performance measures including route-specific fish passage proportions, travel times, and survival based upon a virtual/paired-release model. The study relied on releases of live Juvenile Salmon Acoustic Telemetry System tagged smolts in the Columbia River and used acoustic telemetry to evaluate the approach, passage, and survival of passing juvenile salmon using a virtual release, paired reference release survival model. This study supports the U.S. Army Corps of Engineers’ continual effort to improve conditions for juvenile anadromous fish passing through Columbia River dams.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Analysis of Aurora's Performance Simulation Engine for Three Systems

Aurora Solar Inc. is building a cloud-based optimization platform to automate the design, engineering, and permit generation process of solar photovoltaic (PV) installations. They requested that the National Renewable Energy Laboratory (NREL) validate the performance of the PV system performance simulation engine of Aurora Solar’s solar design platform, Aurora. In previous work, NREL performed a validation of multiple other PV modeling tools 1, so this study builds upon that work by examining all of the same fixed-tilt systems with available module datasheets that NREL selected and used in the aforementioned study. Aurora Solar set up these three operating PV systems in their modeling platform using NREL-provided system specifications and concurrent weather data. NREL then verified the setup of these systems, ran the simulations, and compared the Aurora-predicted performance data to measured performance data for those three systems, as well as to performance data predicted by other PV modeling tools.

14 SOLAR ENERGY↗
DOE OSTI2018

2019 Site Sustainability Plan (Narrative)

Clean energy and sustainability, long at the core of the United States (U.S.) Department of Energy (DOE) mission and championed at Sandia National Laboratories (SNL), are reinforced in Executive Order (EO) 13834, Efficient Federal Operations. Though no longer in effect as of May 2018, EO 13693, Planning for Federal Sustainability in the Next Decade, is referenced throughout this document. DOE's Strategic Sustainability Performance Plan (SSPP) embodies the Department's sustainability commitment and establishes the foundation for DOE to meet the objectives of the EO. The SNL Site Sustainability Plan (SSP) is prepared annually to support DOE's Strategic Sustainability Performance Plan (SSPP) and the National Nuclear Security Administration's (NNSA) sustainability goals and broader sustainability program. Accordingly, the content of this SSP covers the SNL contributions toward meeting the DOE sustainability goals, including the DOE requirement to comply with EO 13834. This SSP fulfills the contractual requirement for National Technology & Engineering Solutions of Sandia, LLC (NTESS), the management and operating (M&O) contractor for SNL, to deliver an annual sustainability plan to the NNSA and Sandia Field Office (SFO). The SNL Environmental Management System (EMS) implements sustainable practices for enhancing environmental, energy, and transportation management performance. SNL's EMS is a quality-based system modeled on the Plan-Do-Check-Act structure for continuous improvement. The EMS is a part of SNL's Integrated Safety Management System and functions within the overall Laboratory Operating System. The SNL EMS is International Organization for Standardization (ISO) 14001 certified and incorporates all relevant federal, state, local, and DOE requirements. The EMS is coordinated and implemented through 20 environmental programs, five of which are included within the scope of this SSP: Air Quality Compliance (AQC); Materials; Sustainability & Pollution Prevention; Energy and Water; Fleet Services; and, Waste Management.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

White Paper: Scalable Digital Twin Capabilities for Aging and Surveillance of Engineered Systems

This white paper presents a multi-year initiative to develop practical, secure, and scalable digital twin capabilities for engineered systems in aging and surveillance contexts—an approach pioneered at the National Nuclear Security Administration (NNSA) Lawrence Livermore National Laboratory (LLNL) that maps directly onto the needs and ambitions of the Navy for ship- and fleet-level digital twins. LLNL’s work in building part- and process-level digital twins for advanced manufacturing, with a vision to scale up to entire factory floors and, ultimately, enterprise-wide digital twins, offers an adaptable pathway for the Navy as it seeks to modernize lifecycle management, readiness, and predictive maintenance across ships and fleets. For our application, we integrate physics-based modeling with automated data ingestion, processing, and AI-driven calibration, creating hybrid models that are both interpretable and data responsive. We modernized legacy workflows, established centralized data infrastructure, automated experimental pipelines, and demonstrated end-to-end coupling of accelerated aging data with finite element simulations via optimization and surrogate modeling. The result is a generalizable framework that supports part-level digital twins today and lays the groundwork for future system-level twins suitable for Navy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A cell-free system for production of 2,3-butanediol is robust to growth-toxic compounds

The need for sustainable, low-cost production of bioenergy and commodity chemicals is increasing. Unfortunately, the engineering potential of whole-cell catalysts to address this need can be hampered by cellular toxicity. When such bottlenecks limit the commercial feasibility of whole-cell fermentation, cell-free, or in vitro, based approaches may offer an alternative. Here, we assess the impact of three classes of growth toxic compounds on crude extract-based, cell-free chemical conversions. As a model system, we test a metabolic pathway for conversion of glucose to 2,3-butanediol (2,3-BDO) in lysates of Escherichia coli . First, we characterized 2,3-BDO production with different classes of antibiotics and found, as expected, that the system is uninhibited by compounds that prevent cell growth by means of cell wall replication and DNA, RNA, and protein synthesis. Second, we considered the impact of polar solvent addition (e.g., methanol, n-butanol). We observed that volumetric productivities (g/L/h) were slowed with increasing hydrophobicity of added alcohols. Finally, we investigated the effects of using pretreated biomass hydrolysate as a feed stock, observing a 25% reduction in 2,3-BDO production as a result of coumaroyl and feruloyl amides. Overall, we find the cell-free system to be robust to working concentrations of antibiotics and other compounds that are toxic to cell growth, but do not denature or inhibit relevant enzymes.

09 BIOMASS FUELS↗
DOE OSTI2026

T-FSM: A Scalable Distributed Task-Based System for Frequent Subgraph Pattern Mining from a Big Graph

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel and distributed systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, poor scale-out capability, and possibly inaccurate results. In this article, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. The execution engine of T-FSM supports both intra-machine parallelism and inter-machine parallelism. For intra-machine parallelism, T-FSM adopts a novel task-based execution model to ensure high multithreading concurrency, bounded memory consumption, and effective load balancing. For inter-machine parallelism, T-FSM ensures good scale-out performance with a lightweight pattern rebalancing approach that reduces workload skewness of pattern evaluations among machines. To avoid recomputing the contexts for migrated patterns, we design a novel context cache table to support concurrent and asynchronous requesting and caching of remote context data, which can timely evict and garbage collect used pattern contexts that are no longer needed to keep memory consumption bounded. Extensive experiments show that T-FSM is orders of magnitude faster than existing state-of-the-art parallel systems (more than 10×, 51×, 131×, 55× speedup over ScaleMine, DistGraph, Pangolin and Peregrine, respectively) and distributed systems (more than 42× and 88× over ScaleMine and DistGraph, respectively) for frequent subgraph pattern mining, and it scales out satisfactorily to 512 CPU cores on the Polaris supercomputer at Argonne National Laboratory.

97 MATHEMATICS AND COMPUTING↗