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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multiparameter optical fiber sensing for energy infrastructure through nanoscale light–matter interactions: From hardware to software, science to commercial opportunities

Monitoring of energy infrastructure through robust yet economical sensing platforms is becoming an area of increased importance, with ubiquitous applications including the electrical grid, natural gas and oil transportation pipelines, H2 infrastructure (storage and transportation), carbon storage, power generation, and subsurface environments. Plasmonic and functional nanomaterial enabled fiber optic sensors show excellent promise for a wide range of sensing applications due to their versatility to be engineered for specific analytes of interest while retaining inherent advantages of the optical fiber sensor platform. Through the design of novel sensing layers, the optical transduction mechanism and wavelength dependence can also be tailored for ease of integration with low-cost interrogation systems enabling an inexpensive yet highly functional optical fiber sensing platform. In addition, recent advances in artificial intelligence and machine learning theoretical methods have been leveraged to simultaneously extract multiple parameters through multi-wavelength interrogation such that unique wavelengths can also serve as unique sensing elements, analogous to electronic nose sensor technologies. The concept of an optical fiber based “photonic nose” via multiple interrogation wavelengths and/or sensor nodes offers a compelling platform technology to realize multiparameter speciation of chemical analytes within complex gas mixtures. In this Perspective, we further generalize the notion of multiparameter sensing through the novel “photonic nervous system” concept based upon low-cost, functionalized optical fiber sensor probes monitoring a variety of distinct analyte classes (physical, chemical, electromagnetic, etc.) simultaneously to provide broad situational awareness via integrated sensors.

Su, Yang-Duan (ORCID:0000000214820902)↗
DOE OSTI2021

The Kokkos EcoSystem: Comprehensive Performance Portability For High Performance Computing

State of the art Engineering and Science codes have grown in complexity dramatically over the last two decades. As a consequence application teams have adopted more sophisticated development strategies, leveraging third party libraries, deploying comprehensive testing and using advanced debugging and profiling tools. In todays environment of diverse hardware platforms, these applications also desire performance portability - avoiding the need to duplicate work for various platforms - which makes it necessary that these tools and libraries also work across the various systems. The Kokkos EcoSystem provides that portable software stack. Based on the Kokkos Core Programming Model, the EcoSystem provides math libraries, interoperability capabilities with Python and Fortran, and Tools for analysing, debugging, and optimizing applications. In this paper we will provide an overview of the components, discuss some specific use cases, and highlight how co-designing these components enables a more developer friendly experience.

42 ENGINEERING↗
DOE OSTI2019

Thermal Analysis of Rare Earth Additions to HY100

This paper discusses the results of a series of experiments on rare earth additions to HY100. Previous work has found little or no refinement to occur with rare earth additions to this alloy. Thermal analysis has been used to provide a better understanding of this and assist in the development of a suitable grain refiner. Heats of HY100 were melted in an induction furnace and tapped into a small hand ladle. Thermal analysis cups were poured from the ladle, and the resulting cooling data recorded. Heats with no additions and 0.3 wt.% rare earth silicide were poured. Commercial software analyzed the cooling curves to determine the liquidus and solidus temperatures. A manual analysis was employed for identifying the peritectic and other reactions. Examination of the cooling curves detected the liquidus, solidus and even peritectic reactions. It was observed that the solidification changed with the rare earth silicide or engineered grain refiner additions. Additional phase reactions appeared before the solidus were not present in the baseline heat. Phase identification was done with thermodynamic calculations. The solidus, peritectic and liquidus were found. Rare earth inclusions were observed in the microstructure of the treated steel. Interestingly, macrostructure examination indicated no reduction in overall grain size. Thermodynamic predictions also indicated that the inclusions formed late during solidification. It was postulated that the additions caused a change in solidification but those occurred late enough during freezing that the resulting microstructure was not affected.

36 MATERIALS SCIENCE↗
DOE OSTI2026

SIRENOpt.jl

SAND2026-22945O SIRENOpt.jl is a Julia software package for prototype hybrid power, storage, and platform dynamics. It integrates solar, wind, wave, hydrokinetic, diesel, generator, converter, battery, hydrogen, desalination, mooring, and floating-platform model interfaces in an automatic-differentiation-friendly simulation framework. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Michelen Strofer, Carlos [Sandia National Lab. (SN↗
DOE OSTI2023

Orchestration of materials science workflows for heterogeneous resources at large scale

In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Report on High-Fidelity Dynamic Modeling of a Coal-Fired Steam Power Plant

As a result of the growth of renewables including solar and wind energy with fluctuating production, fossil fuel power plants are being required to cycle between high and low power production. This cycling is both at a greater frequency and over a wider range than in the past. In many cases, power plants are not designed for this type of cycling operation but can nonetheless endure these challenging operation requirements under the right conditions. In this project, optimal solution for enhanced flexible operations are being investigated using model based estimation and control techniques. To support the development of model-based estimator and model-based controls at GE Global Research, GE Steam Power configured a dynamic model using a reference steam plant design including the boiler, turbine, and water/steam conditioning systems as well as the controls needed for plant cycling with stability and reliability. The dynamic model was built using the APROS® software from VTT, and then calibrated to multiple load conditions from full load (100%TMCR) to partial loads (75%TMCR, 50%TMCR and 25%TMCR) based on internally developed steady state heat balance models at the unit level. These internal heat balance models are based on first principles and extensive engineering experiences from GE Steam Power as an OEM and a services provider. This topical report presents the structure of the unit level dynamic model, the tuning process, and representative simulation results from typical load cycling simulations using the dynamic model.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Validation and Verification for INL Modelica-based TEDS models Via Experimental Results

This report provides an overview on the verification and validation (V&V) of the Thermal Energy Distribution System (TEDS) model developed in the Modelica process modeling ecosystem using experimental data. Model development has led to the creation of a dynamic process model of the experimental TEDS facility housed within the Energy Systems Laboratory (ESL) at Idaho National Laboratory (INL). The model was then used during the preconstruction phase of the experimental effort to inform experimental design (e.g., insulation requirements, bypass line placement, expected performance of components) and to test innovative control schemes prior to the initial operation. The TEDS model developed in Modelica includes the primary components of the TEDS experimental unit: a 200kW Chromalox heater; a single-tank packed-bed thermal energy storage system filled with 0.125-inch alumina (Al2O3) beads; an ethylene-glycol-to-Therminol-66 heat exchanger; system piping; five control valves; and all associated temperature, pressure, and volumetric flow sensors. Using the Institute of Electrical and Electronics Engineers (IEEE) V&V methodologies, considered the gold standard in the engineering field, the model was verified using a combination of static analysis, spatial convergence, and regression tests. Then using dynamic time warping (DTW) initial runs to validate and tune the TEDS model versus the experiment were conducted. This tuning method was accomplished using the INL Risk Analysis Virtual ENvironment (RAVEN) software package. Tuning is required to account for physical phenomena that are less understood within the empirical heat transfer correlations. Through the commencement of this work, a systems-level model of TEDS with associated control systems, sensors, piping diameters, and component capabilities has been created. This model was utilized in the pre-experimental phase to inform system design, insulation thicknesses, and potential control schemes to operate the system effectively and safely. Then, initial experimental startup and operational data were used to demonstrate the validation and tuning methodology. This process demonstrates the classical two-step approach of a model informing experimental design followed by the experiment validation and tuning the model.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Tools for Design and Scale-Up of Solar Thermochemical Reactors: Cooperative Research and Development Final Report, CRADA Number CRD-13-00530

NREL will be collaborating with the Participant on a United States - Australia Solar Energy Collaboration (USASEC) Project Number 1-USO034 "Tools for design and scale-up of solar thermochemical reactors." The grant funds for the Participant's 3.5 year project number 1-US034 commencing on 1 February 2013 have been awarded to the Participant by the Australian Renewable Energy Agency and NREL will be collaborating with the Participant during the final 28 months of this project. This project seeks to provide basic knowledge required to design solar thermochemical reactors able to perform the required energy conversions. In several proposed and demonstrated reactors, concentrated sunlight directly irradiates small solid particles suspended in fluid, enabling very high heat transfer rates to the particles which are the sites of chemical reaction. The reactors, therefore, involve the complex and couple dynamics of turbulent, chemically reacting, particle-laden flows and their interaction with concentrating solar radiation. A strong understanding of these coupled interactions will be crucial important in predicting and optimizing the performance of prototype reactors, but this understanding does not yet exist, since they have never been studied in any fundamental way. The project has a assembled an internationally leading team from The University of New South Wales (UNSW) and the University of Adelaide in Australia and the NREL in the United States to address this key gap in available know-how. The project will use U.S. Dept. of Energy (DOE) supercomputers, among the most powerful available worldwide, with cutting-edge software tools to perform first-principles simulations of the relevant interactions. These studies will be combined with detailed laser-based measurements in Australia to provide the first comprehensive databases concerning the governing phenomena in directly irradiated solar-thermochemical reactors. The outcomes will be the basic scientific knowledge, engineering knowhow and modeling tools necessary to design new reactor concepts and then scale up from the laboratory bench to practical size systems.

14 SOLAR ENERGY↗
DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗
DOE OSTI2022

Real-Time Hardware-in-The-Loop Implementation of Protection and Self-Healing of Microgrids

The design of a reliable protection scheme for microgrids often requires communication between protective devices and microgrid controllers. The authors have developed such a communication assisted scalable protection scheme with a self healing feature to protect a microgrid with 100% Inverter Based Resources (IBRs). The communication assisted scheme must be validated in real time with cyber physical co-simulation for successful demonstration. In this regard, the paper presents a co-simulation platform between a simulated power system model using RTDS and physical protective devices. The primary protection of the scheme is programmed in SEL 421-7 relay and backup protection is programmed in MATLAB on a generic computer acting as a microgrid controller. The IEC 61850 models are used to communicate between SEL-421-7 relay and RTDS, whereas TCP/IP communication connects the microgrid controller to RTDS. The paper’s focus is to demonstrate the co- simulation platform with communication links established using both protocols and validate the proposed scheme in real-time on the IEEE 123 node distribution feeder. The paper explains the configuration of the IEC 61850 and TCP/IP communications as the interface requires proper hardware and software setup. Furthermore, the real time performance indicates the Hardware In the Loop (HIL) framework as a competent testing environment for the developed protection scheme for microgrids.

42 ENGINEERING↗
DOE OSTI2018

Kinetic Learning vo.1

New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. This software implements a method to effectively predict pathway dynamics in an automated fashion using a combination of machine learning and abundant multiomics data (proteomics and metabolomics). The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.

Martin, HectorGarcia↗
DOE OSTI2023

Energy Storage Sizing, Placement and Valuation for Distribution Networks

SAND2023-05682O The Energy Storage Sizing, Placement, and Valuation for Distribution software allows a user to simulate sizing, placement, and operation of one or more storage assets on a distribution grid. Among its features are measuring differences to determine the best combinations, and options for configuring and comparing results. This new simulation capability leverages the Open Distribution System Simulator and the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Tamrakar, Ujjwol↗
DOE OSTI2015

Multi-component testing using HZ-PAN and AgZ-PAN Sorbents for OSPREY Model validation

In efforts to further develop the capability of the Off-gas SeParation and RecoverY (OSPREY) model, multi-component tests were completed using both HZ-PAN and AgZ-PAN sorbents. The primary purpose of this effort was to obtain multi-component xenon and krypton capacities for comparison to future OSPREY predicted multi-component capacities using previously acquired Langmuir equilibrium parameters determined from single component isotherms. Experimental capacities were determined for each sorbent using two feed gas compositions of 1000 ppmv xenon and 150 ppmv krypton in either a helium or air balance. Test temperatures were consistently held at 220 K and the gas flowrate was 50 sccm. Capacities were calculated from breakthrough curves using TableCurve® 2D software by Jandel Scientific. The HZ-PAN sorbent was tested in the custom designed cryostat while the AgZ-PAN was tested in a newly installed cooling apparatus. Previous modeling validation efforts indicated the OSPREY model can be used to effectively predict single component xenon and krypton capacities for both engineered form sorbents. Results indicated good agreement with the experimental and predicted capacity values for both krypton and xenon on the sorbents. Overall, the model predicted slightly elevated capacities for both gases which can be partially attributed to the estimation of the parameters and the uncertainty associated with the experimental measurements. Currently, OSPREY is configured such that one species adsorbs and one does not (i.e. krypton in helium). Modification of OSPREY code is currently being performed to incorporate multiple adsorbing species and non-ideal interactions of gas phase species with the sorbent and adsorbed phases. Once these modifications are complete, the sorbent capacities determined in the present work will be used to validate OSPREY multicomponent adsorption predictions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Turbine Aeration Design Software for Mitigating Adverse Environmental Impacts Resulting From Conventional Hydropower Turbines

Conventional hydropower turbine aeration test-bed for computational routines and software tools for improving environmental mitigation technologies for conventional hydropower systems. In achieving this goal, we have partnered with Alstom, a global leader in energy technology development and United States power generation, with additional funding from the Initiative for Renewable Energy and the Environment (IREE) and the College of Science and Engineering (CSE) at the UMN

13 HYDRO ENERGY↗
DOE OSTI2021

Monitoring Radiochemical Processing Streams for the 238 Pu Supply Program with Process Pulse II

Oak Ridge National Laboratory (ORNL) is developing advanced spectroscopic and real-time monitoring capabilities to improve the timeliness of analytical measurements and process decisions for the 238Pu Supply Program. Reducing the time, resources, and costs associated with each production campaign is critical because overlapping campaigns will be required to meet the production goals of the National Aeronautics and Space Administration. Real-time, in situ analytical measurements in the heavily shielded hot cells at the Radiochemical Engineering Development Center (REDC) will allow for rapid process information feedback and operational benefits that help the 238Pu supply program scale-up production efforts. Noteworthy steps were taken during Campaign 5 to establish the ability to monitor processing streams in real time with spectrophotometry and a commercially available online monitoring software called The Unscrambler X Process Pulse II (PP) multivariate statistical process monitoring system by Camo Analytics (version 5.60). PP automates univariate-type calculations within the software itself and executes multivariate models built using The Unscrambler X (version 10.4 or newer). The Unscrambler is a commercially available data analysis software made by the same company. PP is composed of easy-to-use-tools for all personnel, including data scientists and technicians. The software can be used to plot analyte concentration profiles, spectral data, and other process variables in real time. All process data are represented in a single view with interactive charts useful for viewing how a process evolves over time.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Small arms suppression project (LLNL final report)

US Special Operations Command (USSOCOM) was seeking a technological leap in small firearms weapon suppressor technology, because anticipated enemy capabilities are requiring the operators to have smaller detection cross sections to ensure the safe execution of missions. Suppressors have been developed almost exclusively through trial-and-error methods since the time of the original design by Hiram Maxim over one hundred years ago. Consequently USSOCOM deemed it prudent to perform a physicsbased study of weapon suppression to understand performance limits and possibly identify breakthrough technologies. Lawrence Livermore National Laboratory’s (LLNL’s) high performance production level computational tool called ALE3D (Arbitrary Lagrangian-Eulerian 3D and 2D) has unique physics models and numerical algorithms for modeling suppressor dynamics. The flexible and extendable code framework supports fully integrated hydrodynamics, heat transfer, solid and fluid dynamics, and chemistry that can be applied to simulating propellant-driven motion of a bullet down a gun barrel, the transfer of heat from the burning propellant to the barrel and suppressor, the chemistry of muzzle flash, and the shock/acoustic/optical signatures in the near-field. LLNL’s originally anticipated role was to augment ALE3D for this task, by developing the software and analysis methodologies specific to the simulation of blast and muzzle flash phenomena. It was believed that insights provided by our ALE3D simulations in tandem with a coordinated experimental component by our other team members from Oak Ridge National Laboratory (ORNL) and the U. S. Army Armament, Research, Development and Engineering Center (ARDEC), would have excellent prospects of yielding useful suppressor design improvements that could be transitioned to industry and utilized by US Special Operations Command. The three year effort has come to fruition with the development of revolutionary suppressor designs that far outperform any previous or current design by anyone outside this multi-lab team.

42 ENGINEERING↗
DOE OSTI2025

PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

24 POWER TRANSMISSION AND DISTRIBUTION↗