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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
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 OSTI2011

Automatic Parallelization of Numerical Python Applications using the Global Arrays Toolkit

Global Arrays is a software system from Pacific Northwest National Laboratory that enables an efficient, portable, and parallel shared-memory programming interface to manipulate distributed dense arrays. The NumPy module is the de facto standard for numerical calculation in the Python programming language, a language whose use is growing rapidly in the scientific and engineering communities. NumPy provides a powerful N-dimensional array class as well as other scientific computing capabilities. However, like the majority of the core Python modules, NumPy is inherently serial. Using a combination of Global Arrays and NumPy, we have reimplemented NumPy as a distributed drop-in replacement called Global Arrays in NumPy (GAiN). Serial NumPy applications can become parallel, scalable GAiN applications with only minor source code changes. Scalability studies of several different GAiN applications will be presented showing the utility of developing serial NumPy codes which can later run on more capable clusters or supercomputers.

99 GENERAL AND MISCELLANEOUS↗
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↗
DOE OSTI2022

NLIT 2022 Axonius Presentation

Axonius, a cybersecurity asset management tool used at INL, provides visibility into users, devices, software, and hardware in use at the lab. This visibility, provided by aggregating data on lab entities from many tools' perspectives, makes incident response, configuration management, and IT operations more capable.

42 ENGINEERING↗
DOE OSTI2019

A Python Package for Fuel Cycle and Waste Management Analysis - 19545

The future of nuclear power utilization in the United States may involve a transition from the once-through light water reactor fuel cycle to a so-called advanced fuel cycle. An advanced fuel cycle may offer improvements in natural resource utilization, reactor safety, and environmental impact. In addition, adoption of an advanced fuel cycle will result in changes in the amount, composition, and disposal of the nuclear wastes they produce. Characteristics intrinsic to the wastes themselves, such as mass and radioactivity at specific times, were used as waste management metrics in the Department of Energy 2014 Fuel Cycle Evaluation and Screening (FCES) study. Binning of these metrics informed fuel cycle according to relative potential benefit. However, waste properties alone cannot qualify management strategy. Therefore, comparing fuel cycles from the perspective of waste management can provide additional insights. To that end, an object-oriented Python package has been developed to perform fuel cycle waste management calculations. The package, named nwpy (short for Nuclear Waste analysis in Python), mimics the mass flow operations reported in the FCES study and interfaces with the ORIGEN-S code to assess metric data as reported in the FCES. In addition, this Python package determines the loading of waste streams into forms suitable for final disposal in a geological repository and uses such data for repository performance assessment. This work describes the Python package methodology, assumptions, and capabilities. As input data, the package requires information about the fuel cycle such as the mass of fuel discharged, the cooling time between reactor discharge and subsequent operations, and the separations process efficiencies and waste forms it produces. In addition, the isotopic composition of the reactor discharge stream, such as that available from the FCES database, is required. With that, it can perform prescribed mass flow and decay operations throughout the back end of the fuel cycle, the latter carried out by the ORIGEN-S code distributed with the SCALE6.2 software suite. The result is time-dependent data characterizing the waste on a per-package basis. Package loading for waste streams depends on the stream form (i.e. UNF vs. HLW, or glass HLW from aqueous reprocessing vs. ceramic HLW from electrochemical processing) and is based on literature review and engineering assumption. As an example of the application of the code, calculations for the areal footprint of direct-contact repositories for different fuel cycles is shown. Development is ongoing, and the package is expected to grow incorporating capabilities for waste management analyses such as waste transportation and radionuclide transport through the biosphere. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs

We report that community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Inventory Management Improvement Project

The Lawrence Livermore National Laboratory (LLNL) conducts research and development for the National Nuclear Security Agency (NNSA) and its affiliates. The Polymer AM team at LLNL conducts research and development in 3D printing, specifically direct-ink-write, in accordance with LLNL and NNSA missions. The team is composed of machine operators, chemists, testing engineers, and project engineers across multiple lab spaces with one shared storage area at LLNL. The operations use a variety of different consumables and hardware to conduct research applications for LLNL and NNSA. These items are critical to performing operation tasks; if an item is out of stock, operations associated with the respective item could be suspended for weeks. As such, the Polymer AM team manages an inventory of spare consumables and hardware to ensure these items are always available. However, the current management system, an excel sheet managed by the project engineer group, is not intuitive in providing inventory information despite the high labor utilization needed to maintain the system. As such, the team is looking to improve their inventory management that can send notifications regarding inventory needs, accessible to other team members, provide all relevant information to a specific item, and store historical information for budget and operation planning. The proposed solution is a Computer Maintenance Management System (CMMS): a web-based management software that stores inventory information and provides automated notifications. The system will store information on each item including quantity in stock, technical information, supplier information, costs, lead times, and expiration date. The system has a notifications function that can send notifications to the respective team member’s when an item needs to be counted, item inventory is low, or an item is approaching their expiration date. Information for each item can be tracked over time creating data to be used for budget planning and process improvement purposes. Due to its web-based source, the system can be accessed by the respective team member viewing technical information, quantity, and purchasing information.

42 ENGINEERING↗