Results for “technical modeling” — Page 99

DOE OSTI2015

Unforced, Forced and Resonance-Forced Waves in a Spherical Atmosphere

This technical report discusses a longstanding issue of atmospheric tides in weather-prediction and general circulation models (GCMs). Tidal signatures consistent with observations have appeared in the surface pressure output of GCMs since their inception (Hardy 1968, Hunt and Manabe 1968). Such models, however, are sufficiently complicated that the possibility of “getting the right answer for the wrong reasons” arises. Lindzen et al. (1968, hereafter LBK) showed that wave reflection at the upper boundary of a GCM can artificially enhance the tides. Covey et al. (2011, 2014) found that tidal output from a number of modern GCMs is surprisingly independent of their forcing. This finding is consistent with earlier suggestions that a compensating effect occurs in some models: lowering the model top reduces the forcing (solar heating of the ozone layer) but also enhances spurious wave reflection (Zwiers and Hamilton 1986, Hamilton et al. 2008).

58 GEOSCIENCES↗

DOE OSTI2015

Collaborative Project: Understanding the Chemical Processes tat Affect Growth rates of Freshly Nucleated Particles

This final technical report describes our research activities that have, as the ultimate goal, the development of a model that explains growth rates of freshly nucleated particles. The research activities, which combine field observations with laboratory experiments, explore the relationship between concentrations of gas-phase species that contribute to growth and the rates at which those species are taken up. We also describe measurements of the chemical composition of freshly nucleated particles in a variety of locales, as well as properties (especially hygroscopicity) that influence their effects on climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Advanced Hydrogen Turbine Development

Under the sponsorship of the U.S. Department of Energy (DOE) National Energy Technology Laboratories, Siemens has completed the Advanced Hydrogen Turbine Development Program to develop an advanced gas turbine for incorporation into future coal-based Integrated Gasification Combined Cycle (IGCC) plants. All the scheduled DOE Milestones were completed and significant technical progress was made in the development of new technologies and concepts. Advanced computer simulations and modeling, as well as subscale, full scale laboratory, rig and engine testing were utilized to evaluate and select concepts for further development. Program Requirements of: A 3 to 5 percentage point improvement in overall plant combined cycle efficiency when compared to the reference baseline plant; 20 to 30 percent reduction in overall plant capital cost when compared to the reference baseline plant; and NOx emissions of 2 PPM out of the stack. were all met. The program was completed on schedule and within the allotted budget

42 ENGINEERING↗

DOE OSTI2015

Component-Level Prognostics Health Management Framework for Passive Components - Advanced Reactor Technology Milestone: M2AT-15PN2301043

This report describes research results to date in support of the integration and demonstration of diagnostics technologies for prototypical advanced reactor passive components (to establish condition indices for monitoring) with model-based prognostics methods. Achieving this objective will necessitate addressing several of the research gaps and technical needs described in previous technical reports in this series.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Quantum Testbeds Stakeholder Workshop (QTSW) Report meeting purpose and agenda.

Quantum computing (QC) is a promising early-stage technology with the potential to provide scientific computing capabilities far beyond what is possible with even an Exascale computer in specific problems of relevance to the Office of Science. These include (but are not limited to) materials modeling, molecular dynamics, and quantum chromodynamics. However, commercial QC systems are not yet available and the technical maturity of current QC hardware, software, algorithms, and systems integration is woefully incomplete. Thus, there is a significant opportunity for DOE to define the technology building blocks, and solve the system integration issues to enable a revolutionary tool. Once realized, QC will have world changing impact on economic competitiveness, the scientific enterprise, and citizen well-being. Prior to this workshop, DOE / Office of Advanced Scientific Computing Research (ASCR) hosted a workshop in 2015 to explore QC scientific applications. The goal of that workshop was to assess the viability of QC technologies to meet the computational requirements in support of DOE’s science and energy mission and to identify the potential impact of these technologies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

GeoVision Analysis Supporting Task Force Report: Electric Sector Potential to Penetration

This report describes research and analysis performed in support of the U.S. Department of Energy Geothermal Technologies Office for its Geothermal Vision Study. A summary of the study is captured in DOE’s report, GeoVision: Harnessing the Heat Beneath Our Feet and included ground-breaking, detailed research on geothermal technologies. The study projects and quantifies the future electric and nonelectric deployment potentials of these geothermal technologies within a range of scenarios in addition to their impacts on U.S. jobs, the economy, and environment. Coordinated by the U.S. Department of Energy Geothermal Technologies Office, the Geothermal Vision Study development relied on collecting, modeling, and analyzing robust data sets through seven national laboratory partners that were organized into eight technical task force groups. These task forces and their respective principal leading national laboratory are listed in Table F-1. The table also provides a guide to the final research documents produced by each GeoVision task force. In most cases, these were prepared as laboratory reports, and they are referenced accordingly. Consult these external reports for detailed discussions of the topics contained within, which form the basis of the GeoVision analysis.

15 GEOTHERMAL ENERGY↗

DOE OSTI2019

GeoVision Analysis Supporting Task Force Report: Impacts-The Employment Opportunities, Water Impacts, Emission Reductions, and Air Quality Improvements of Achieving High Penetrations of Geothermal Power in the United States

This report describes research and analysis performed in support of the U.S. Department of Energy Geothermal Technologies Office for its Geothermal Vision Study. A summary of the study is captured in DOE’s report, GeoVision: Harnessing the Heat Beneath Our Feet and included ground-breaking, detailed research on geothermal technologies. The study projects and quantifies the future electric and nonelectric deployment potentials of these geothermal technologies within a range of scenarios in addition to their impacts on U.S. jobs, the economy, and environment. Coordinated by the U.S. Department of Energy Geothermal Technologies Office, the Geothermal Vision Study development relied on collecting, modeling, and analyzing robust data sets through seven national laboratory partners that were organized into eight technical task force groups. These task forces and their respective principal leading national laboratory are listed in Table F-1. The table also provides a guide to the final research documents produced by each GeoVision task force. In most cases, these were prepared as laboratory reports, and they are referenced accordingly. Consult these external reports for detailed discussions of the topics contained within, which form the basis of the GeoVision analysis.

15 GEOTHERMAL ENERGY↗

DOE OSTI2019

Science Case for a Wide Field-of-View Very-High-Energy Gamma-ray Observatory in the Southern Hemisphere

This white paper outlines the wide science program that would be enabled by the construction of a next-generation gamma-ray observatory using ground-level particle detectors in the Southern Hemisphere: the Southern Gamma-ray Survey Observatory (SGSO). This science case rests on four main pillars: unveiling galactic and extragalactic particle accelerators, monitoring the transient sky at very high energies, probing particle physics beyond the Standard Model, and the characterization of the cosmic ray flux. Several currently ongoing efforts to advance the technical performance of such an observatory will briefly be discussed in Section 8. However, the focus of this document will be on the broad spectrum of astrophysical questions SGSO will address, rather than the details of the technical implementation of a detector. To assess the potential to specific astrophysical applications, a “straw man” detector design is used. This design provides an increase in effective area, number of channels, and a lower energy threshold compared to existing observatories such as the HAWC gamma-ray observatory. Intrinsic figures of merit that quantify the potential performance of SGSO (like angular resolution and background rates) are based on the demonstrated performance of HAWC. Details on this design and its performance are given in Section 3. This approach is chosen as a balance to obtain both an ambitious and feasible performance, suitable to a wide range of applications, while keeping the assumptions about the technical implementation to a minimum. It is foreseen that some applications could benefit from enhanced performance over the straw man design in certain energy ranges; the feasibility and need of reaching enhanced performance will be discussed case by case. The outcome of the studies presented in this document will give an overview of the scientific potential of a next-generation gamma-ray observatory in the southern hemisphere and provide guidance to the finalization of the design of such an observatory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Semi-supervised Bayesian Low-shot Learning

Deep neural networks (NNs) typically outperform traditional machine learning (ML) approaches for complicated, non-linear tasks. It is expected that deep learning (DL) should offer superior performance for the important non-proliferation task of predicting explosive device configuration based upon observed optical signature, a task which human experts struggle with. However, supervised machine learning is difficult to apply in this mission space because most recorded signatures are not associated with the corresponding device description, or “truth labels.” This is challenging for NNs, which traditionally require many samples for strong performance. Semi-supervised learning (SSL), low-shot learning (LSL), and uncertainty quantification (UQ) for NNs are emerging approaches that could bridge the mission gaps of few labels and rare samples of importance. NN explainability techniques are important in gaining insight into the inferential feature importance of such a complex model. In this work, SSL, LSL, and UQ are merged into a single framework, a significant technical hurdle not previously demonstrated. Exponential Average Adversarial Training (EAAT) and Pairwise Neural Networks (PNNs) are chosen as the SSL and LSL methods of choice. Permutation feature importance (PFI) for functional data is used to provide explainability via the Variable importance Explainable Elastic Shape Analysis (VEESA) pipeline. A variety of uncertainty quantification approaches are explored: Bayesian Neural Networks (BNNs), ensemble methods, concrete dropout, and evidential deep learning. Two final approaches, one utilizing ensemble methods and one utilizing evidential learning, are constructed and compared using a well-quantified synthetic 2D dataset along with the DIRSIG Megascene.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Valuing Residential Energy Efficiency: Analysis for a Prototypical Southeastern Utility [Slides]

The increasing amount of variable renewable energy resources and shifts towards more end-use and vehicle electrification suggests profound changes to power system planning and operation. Specifically, renewable energy is expected to shift net peak demand from late afternoon to early evening and end-use electrification may significantly increase winter peak demand. Residential energy efficiency is likely to align well with these shifts as it tends to produce savings in the early evening (e.g., from lighting measures) and coincident with heating loads (e.g., from envelope and space conditioning measures). Despite the opportunity to decrease system costs and emissions, residential energy efficiency is often limited by static valuation methods and its economic potential is considerably less than its technical potential. Using hourly residential energy efficiency characterizations, utility program cost data, and a capacity expansion model, we estimate the benefits of residential energy efficiency for a prototypical, summer-peaking utility in the Southeastern region. We first establish the cost-effective residential energy efficiency portfolio through “competition” with supply-side resources in a forward-looking capacity expansion model. Importantly, we then evaluate several scenarios intended to drive an increasing amount of cost-effective residential energy efficiency through measure cost reductions, increased customer adoption, policy goals (e.g., carbon price), and delivery of an integrated package of measures. The results quantify total system cost and emissions reductions, fossil-fuel plant retirements, and peak demand reductions. Results suggest the design and prioritization of policies and programs to access the untapped amount of cost-effective residential energy efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Energy Resilient Recovery in Puerto Rico: Hurricane Maria Recovery Support

This report covers microgrids as a special application of distributed energy resources (DERs) in Puerto Rico. This is one of a series of reports describing the U.S. Department of Energy (DOE) multi-laboratory efforts undertaken. To ensure the sustainable, long-term recovery of Puerto Rico's electric power grid from hurricanes Maria and Irma and to build capacity to manage future potential natural disasters in the most secure and resilient way, DOE convened experts from multiple national laboratories to develop a comprehensive set of data, models, analytic tools, and studies, considering inputs from a wide variety of stakeholder groups, to support technically sound recommendations for Puerto Rico's energy investment decisions. In Phase 1 of the multi-laboratory effort to support Puerto Rico's recovery, the National Renewable Energy Laboratory (NREL) provided the utility company Puerto Rico Electric Power Authority (PREPA) recommendations for a new framework of interconnection standards to accelerate the integration of utility-scale, transmission-connected, renewable electrical generation and energy storage that ensure cross-technology compatibility and enable high deployment levels without compromising grid reliability, safety, or security (https://www.nrel.gov/docs/fy19osti/73848.pdf). In Phase 2, NREL published a report focused on the interconnection of distributed energy resources (DERs) to the electric distribution system in Puerto Rico (https://www.nrel.gov/docs/fy21osti/77127.pdf). This report familiarized the reader with Puerto Rico's distribution infrastructure and operational practices and procedures that are relevant to DER interconnection. The report also provided considerations for streamlining the interconnection process given the expected increase in deployments resulting from Puerto Rico's renewable portfolio standard goal of 100% renewables by 2050. Accordingly, the report identifies considerations and concerns associated with the increase in intermittent generation, strategies for DER interconnection best practices, and the potential use of the latest technological solutions identified in the latest revision of the Institute of Electrical and Electronics Engineers 1547-2018 interconnection standard. Additionally, the report identified ways to improve the physical resiliency of installed DERs. This task describes how distributed energy systems may participate in and contribute to improving the resiliency of Puerto Rico's energy infrastructure in the future. This report presents an analysis of the Puerto Rico grid to illustrate the possible different microgrid and minigrid scenarios. The capability to form sustained microgrids and minigrids has the potential to provide a high level of resiliency during disturbances on the grid. DERs, on the other hand, have the capability to aid in the microgrid operation by providing grid support as well as grid-forming functionality. Other tasks in Phase 3 include voltage regulation in distribution networks using DERs and transmission-and-distribution co-simulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)

This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 1

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024). Three distinct rounds of FSP experiments were performed by the experimental team, producing replicate samples utilizing across different nominal processing conditions (Condition IDs) listed in Table 1. The starting material on which FSP was applied was commercially available unprocessed stainless-steel type 316L material. Chosen processing conditions were very diverse, and some were intentionally chosen to produce defects. Several samples experienced tool breakage during experimentation, so a full set of three replicates was not produced for every nominal processing condition.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 2

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 3

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 4

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗

DOE OSTI2026

Analysis of Persistent Soiling Losses: Cooperative Research and Development (Final Report)

The Contractor will analyze data from sensors and equipment provided by Participant in order to provide the basis for understanding of the processes and rates for the formation of 'persistent' soiling (soiling not removed by rain). The resulting, data, technical report, and publications will provide the basis for Participant to improve it's predictive soiling model.

14 SOLAR ENERGY↗

NASA NTRS1976

The requirements for batteries for electric vehicles

Analysis of automobile use patterns shows that the battery requirements for an urban car can be met by mid-term battery technology. The far-term technology potentially offers greater range but does not proportionately increase the usefulness of the vehicle. This suggests that emphasis should be shifted toward more modest energy density goals, if such a shift would ease technical problems and allow the use of lower cost materials and construction methods. A technology diffusion model indicates that the impact of the mid-term batteries by the year 2000 would be greater than that of the far-term batteries because of their earlier introduction and nearly equal market potential. From the standpoint of maximizing both the cumulative impact and the benefits derived in the year 2000, however, a strategy of early introduction of near-term and mid-term cars followed by the far-term vehicle would produce the optimum results.

Schwartz, H. J.↗