Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cost Data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Subsidence and eustasy at the continental margin of eastern North America

Biostratigraphic data from the COST B-2 well off New York and four deep commercial wells off Nova Scotia have been used to remove the effect of sediment loading at the Atlantic-type continental margin off the East Coast of North America. The resulting subsidence contains terms due to both 'tectonic' and 'eustatic' effects. By assuming the tectonic subsidence is thermal in origin these effects can be separated. The 'eustatic' effects have been isolated by least squares fitting an exponential curve to the subsidence data. The resulting sea-level curve shows a maximum rise in sea level during the Late Cretaceous era which probably does not exceed 150 m. The tectonic subsidence has been interpreted in terms of a simple thermal model for the cooling lithosphere. Based on this model the thermal thickness of the lithosphere and the total amount of crustal thinning are estimated. These estimates which are consistent with surface ship gravity and GEOS-3 altimeter measurements are used to define the structural elements which control the tectonic evolution of the margin.

Watts, A. B.↗
DOE OSTI2012

Analysis of Energy, Environmental and Life Cycle Cost Reduction Potential of Ground Source Heat Pump (GSHP) in Hot and Humid Climate

It has been widely recognized that the energy saving benefits of GSHP systems are best realized in the northern and central regions where heating needs are dominant or both heating and cooling loads are comparable. For hot and humid climate such as in the states of FL, LA, TX, southern AL, MS, GA, NC and SC, buildings have much larger cooling needs than heating needs. The Hybrid GSHP (HGSHP) systems therefore have been developed and installed in some locations of those states, which use additional heat sinks (such as cooling tower, domestic water heating systems) to reject excess heat. Despite the development of HGSHP the comprehensive analysis of their benefits and barriers for wide application has been limited and often yields non-conclusive results. In general, GSHP/HGSHP systems often have higher initial costs than conventional systems making short-term economics unattractive. Addressing these technical and financial barriers call for additional evaluation of innovative utility programs, incentives and delivery approaches. From scientific and technical point of view, the potential for wide applications of GSHP especially HGSHP in hot and humid climate is significant, especially towards building zero energy homes where the combined energy efficient GSHP and abundant solar energy production in hot climate can be an optimal solution. To address these challenges, this report presents gathering and analyzing data on the costs and benefits of GSHP/HGSHP systems utilized in southern states using a representative sample of building applications. The report outlines the detailed analysis to conclude that the application of GSHP in Florida (and hot and humid climate in general) shows a good potential.

14 SOLAR ENERGY↗
DOE OSTI2016

Scenario analysis for techno-economic model development of U.S. offshore wind support structures

Challenging bathymetry and soil conditions of future US offshore wind power plants might promote the use of multimember, fixed-bottom structures (or 'jackets') in place of monopiles. Support structures affect costs associated with the balance of system and operation and maintenance. Understanding the link between these costs and the main environmental design drivers is crucial in the quest for a lower levelized cost of energy, and it is the main rationale for this work. Actual cost and engineering data are still scarce; hence, we evaluated a simplified engineering approach to tie key site and turbine parameters (e.g. water depth, wave height, tower-head mass, hub height and generator rating) to the overall support weight. A jacket-and-tower sizing tool, part of the National Renewable Energy Laboratory's system engineering software suite, was utilized to achieve mass-optimized support structures for 81 different configurations. This tool set provides preliminary sizing of all jacket components. Results showed reasonable agreement with the available industry data, and that the jacket mass is mainly driven by water depth, but hub height and tower-head mass become more influential at greater turbine ratings. A larger sensitivity of the structural mass to wave height and target eigenfrequency was observed for the deepest water conditions (>40 m). Thus, techno-economic analyses using this model should be based on accurate estimates of actual metocean conditions and turbine parameters especially for deep waters. Finally, the relationships derived from this study will inform National Renewable Energy Laboratory's offshore balance of system cost model, and they will be used to evaluate the impact of changes in technology on offshore wind lower levelized cost of energy.

17 WIND ENERGY↗
DOE OSTI2012

Case study of visualizing global user download patterns using Google Earth and NASA World Wind

Geo-visualization is significantly changing the way we view spatial data and discover information. On the one hand, a large number of spatial data are generated every day. On the other hand, these data are not well utilized due to the lack of free and easily used data-visualization tools. This becomes even worse when most of the spatial data remains in the form of plain text such as log files. This paper describes a way of visualizing massive plain-text spatial data at no cost by utilizing Google Earth and NASAWorld Wind. We illustrate our methods by visualizing over 170,000 global download requests for satellite images maintained by the Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey (USGS). Our visualization results identify the most popular satellite images around the world and discover the global user download patterns. The benefits of this research are: 1. assisting in improving the satellite image downloading services provided by USGS, and 2. providing a proxy for analyzing the hot spot areas of research. Most importantly, our methods demonstrate an easy way to geovisualize massive textual spatial data, which is highly applicable to mining spatially referenced data and information on a wide variety of research domains (e.g., hydrology, agriculture, atmospheric science, natural hazard, and global climate change).

Zong, Ziliang↗
DOE OSTI2021

Data-driven building energy modeling with feature selection and active learning for data predictive control

Three gaps impede the development of cost-effective and accurate data-driven building energy modeling/models (DBEM) for energy forecasting and predictive control strategies. Gap 1: data bias is common in building operation data, but this topic is hardly studied in DBEM; Gap 2: high data dimensionality is common in DBEM, but a systematic and scalable methodology is lacking to solve the problem; Gap 3: the interactions between data bias and high dimensionality have not been systematically studied for DBEM and predictive control in buildings. In this work, to address the three gaps mentioned above, we develop a framework that integrates active learning and feature selection for DBEM used for whole building data predictive control (or DPC, which is a branch of model predictive control). The framework provides a systematic methodology and automatic workflow that starts with raw data from building automation systems to the establishment of data-driven energy models for DPC controllers. The developed strategies and framework are evaluated in a virtual testbed based on EnergyPlus and BCVTB. Improved performance and reduced computational complexity are observed from the DBEM built with the developed framework, as well as the DPC controller based on that DBEM, indicating the effectiveness of the developed framework.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Hazard-Based Model of Activity Generation Using Vehicle Trajectory Data

Travel behavior modeling suffers from lack of longitudinal data due to the high cost of procurement and difficulty with ensuring data quality. In the recent years ubiquitous data intercepted from social media, cell detail record, GPS traces are increasingly being explored to analyze human mobility. However, these data lack key demographic information which limits their use for developing causal models of traveler behavior. The primary contribution of the paper lies in using vehicle trajectory data from Ann Arbor along with multiple other data sources for developing a hazard-based activity generation model. The different category of data used in the analysis include (i) vehicle trajectory data (VTD), (ii) American Census Survey (ACS), (iii) land use data, (iv) household travel survey (HTS) data, and (v) smart location data from Environmental Protection Agency (EPA). Home and work are identified as the two most frequently visited locations from the VTD and validated against land use categories of the region. Next a random utility-based model is developed using HTS data to identify other activity purposes as a function of the polynomials of distance from home, activity duration and departure time. The developed model is then applied to identify the non-home activity purposes in the VTD. Demographic and accessibility information are appended to the VTD from ACS and smart location databases respectively. The fmal database is then used to develop a hazard-based activity generation model. (C) 2020 The Authors. Published by Elsevier B.V.

activity generation↗
DOE OSTI2013

Exploring power behaviors and trade-offs of in-situ data analytics, In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

As scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an in-situ data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance trade-offs on current as well as emerging systems.

Computer Science↗
DOE OSTI2020

Simple and efficient algorithms for training machine learning potentials to force data

Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of available training data, which can be expensive to obtain. A quantum simulation often provides all atomic forces, in addition to the total energy of the system. These forces provide much more information than the energy alone. It may appear that training a model to this large quantity of force data would introduce significant computational costs. Actually, training to all available force data should only be a few times more expensive than training to energies alone. Here, we present a new algorithm for efficient force training, and benchmark its accuracy by training to forces from real-world datasets for organic chemistry and bulk aluminum.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Integration of Solid Oxide Fuel Cell Systems Into Artificial Intelligence Data Centers

This report presents the results of a techno-economic analysis (TEA) that evaluates the economic benefits of integrating solid oxide fuel cell (SOFC) systems with artificial intelligence (AI) data centers. The analysis was completed in two phases: a scoping-level analysis was performed to identify impactful integration opportunities, followed by a more detailed TEA. Results show that, due to their modularity, SOFC can meet the 99.999% availability requirement of data centers with minimal additional costs. Heat integration via absorption chillers decreases data center electricity consumption at the tradeoff of increased water consumption. Higher SOFC exhaust temperatures are important for achieving larger electricity savings. Finally, power electronics integration with SOFC direct current electricity can reduce electricity consumption by 9 percent and reduce water consumption by 6.4 percent.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2007

Characterization of Forested Landscapes From Remotely Sensed Data Using Fractals and Spatial Autocorrelation

The characterization of forested areas is frequently required in resource management practice. Passive remotely sensed data, which are much more accessible and cost effective than are active data, have rarely, if ever, been used to characterize forest structure directly, but rather they usually focus on the estimation of indirect measurement of biomass or canopy coverage. In this study, some spatial analysis techniques are presented that might be employed with Landsat TM data to analyze forest structure characteristics. A case study is presented wherein fractal dimensions, along with a simple spatial autocorrelation technique (Moran s I), were related to stand density parameters of the Oakmulgee National Forest located in the southeastern United States (Alabama). The results of the case study presented herein have shown that as the percentage of smaller diameter trees becomes greater, and particularly if it exceeds 50%, then the canopy image obtained from Landsat TM data becomes sufficiently homogeneous so that the spatial indices reach their lower limits and thus are no longer determinative. It also appears, at least for the Oakmulgee forest, that the relationships between the spatial indices and forest class percentages within the boundaries can reasonably be considered linear. The linear relationship is much more pronounced in the sawtimber and saplings cases than in samples dominated by medium sized trees (poletimber). In addition, it also appears that, at least for the Oakmulgee forest, the relationships between the spatial indices and forest species groups (Hardwood and Softwood) percentages can reasonably be considered linear. The linear relationship is more pronounced in the forest species groups cases than in the forest classes cases. These results appear to indicate that both fractal dimensions and spatial autocorrelation indices hold promise as means of estimating forest stand characteristics from remotely sensed images. However, additional work is needed to confirm that the boundaries identified for Oakmulgee forest and the linear nature of the relationship between image complexity indices and forest characteristics are generally evident in other forests. In addition, the effects of other parameters such ,as topographic relief and image distortion due to sun angle and cloud cover, for example, need to be examined.

Al-Hamdan, Mohammad Z.↗
NASA NTRS2016

A Survey of Cost Estimating Methodologies for Distributed Spacecraft Missions

Satellite constellations and Distributed Spacecraft Mission (DSM) architectures offer unique benefits to Earth observation scientists and unique challenges to cost estimators. The Cost and Risk (CR) module of the Tradespace Analysis Tool for Constellations (TAT-C) being developed by NASA Goddard seeks to address some of these challenges by providing a new approach to cost modeling, which aggregates existing Cost Estimating Relationships (CER) from respected sources, cost estimating best practices, and data from existing and proposed satellite designs. Cost estimation through this tool is approached from two perspectives: parametric cost estimating relationships and analogous cost estimation techniques. The dual approach utilized within the TAT-C CR module is intended to address prevailing concerns regarding early design stage cost estimates, and offer increased transparency and fidelity by offering two preliminary perspectives on mission cost. This work outlines the existing cost model, details assumptions built into the model, and explains what measures have been taken to address the particular challenges of constellation cost estimating. The risk estimation portion of the TAT-C CR module is still in development and will be presented in future work. The cost estimate produced by the CR module is not intended to be an exact mission valuation, but rather a comparative tool to assist in the exploration of the constellation design tradespace. Previous work has noted that estimating the cost of satellite constellations is difficult given that no comprehensive model for constellation cost estimation has yet been developed, and as such, quantitative assessment of multiple spacecraft missions has many remaining areas of uncertainty. By incorporating well-established CERs with preliminary approaches to approaching these uncertainties, the CR module offers more complete approach to constellation costing than has previously been available to mission architects or Earth scientists seeking to leverage the capabilities of multiple spacecraft working in support of a common goal.

Foreman, Veronica L.↗
DOE OSTI2014

EXAMINING THE ROLE AND RESEARCH CHALLENGES OF SOCIAL MEDIA AS A TOOL FOR NONPROLIFERATION AND ARMS CONTROL TREATY VERIFICATION

Traditional arms control treaty verification activities typically involve a combination of technical measurements via physical and chemical sensors, state declarations, political agreements, and on-site inspections involving international subject matter experts. However, the ubiquity of the internet, and the electronic sharing of data that it enables, has made available a wealth of open source information with the potential to benefit verification efforts. Open source information is already being used by organizations such as the International Atomic Energy Agency to support the verification of state-declared information, prepare inspectors for in-field activities, and to maintain situational awareness . The recent explosion in social media use has opened new doors to exploring the attitudes, moods, and activities around a given topic. Social media platforms, such as Twitter, Facebook, and YouTube, offer an opportunity for individuals, as well as institutions, to participate in a global conversation at minimal cost. Social media data can also provide a more data-rich environment, with text data being augmented with images, videos, and location data. The research described in this paper investigates the utility of applying social media signatures as potential arms control and nonproliferation treaty verification tools and technologies, as determined through a series of case studies. The treaty relevant events that these case studies touch upon include detection of undeclared facilities or activities, determination of unknown events recorded by the International Monitoring System (IMS), and the global media response to the occurrence of an Indian missile launch. The case studies examine how social media can be used to fill an information gap and provide additional confidence to a verification activity. The case studies represent, either directly or through a proxy, instances where social media information may be available that could potentially augment the evaluation of an event. The goal of this paper is to instigate a discussion within the verification community as to where and how social media can be effectively utilized to complement and enhance traditional treaty verification efforts. In addition, this paper seeks to identify areas of future research and development necessary to adapt social media analytic tools and techniques, and to form the seed for social media analytics to aid and inform arms control and nonproliferation policymakers and analysts. While social media analysis (as well as open source analysis as a whole) will not ever be able to replace traditional arms control verification measures, they do supply unique signatures that can augment existing analysis.

nonproliferation, arms control, social media↗
DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

Varela, Sebastian↗
DOE OSTI2023

Front-End Engineering and Design: Project Tundra Carbon Capture System (Final Report)

As the next phase of Project Tundra, Minnkota Power Cooperative (MPC) completed a front-end engineering and design (FEED) study to install a post-combustion CO 2 capture system (CCS) at the MPC operated (Square Butte Electric Cooperative-owned) Milton R. Young Station (MRYS) Unit 2 (MRY2), a 477-MW power plant fueled by North Dakota lignite. The project team completed the FEED with Fluor’s Econamine FG Plus℠ (EFG+) technology and is taking the next steps leading up to start of construction. Team members included FEED technical lead Fluor Enterprises (Fluor); owner’s engineer and balance of plant (BOP) engineer Burns & McDonnell (BMcD); leading carbon capture consultants (David Greeson Consulting, Hunt International Energy Services); environmental consultants (AECOM, Agora Environmental), other BOP engineering consultants (Golder Associates, Nels); cost-share funding agency, the North Dakota Industrial Commission (NDIC); and the Energy & Environmental Research Center (EERC). Project Tundra’s goal is to implement carbon capture, utilization, and storage (CCUS) in North Dakota, while preserving the use of lignite. Future options could lead to revitalizing legacy oil fields and creating a new CO 2 enhanced oil recovery (EOR) industry. The topic of this report is the FEED study for the carbon dioxide capture portion of Project Tundra. Building on the findings of a pre-FEED study for MRY2, the key deliverables contained in this FEED study are: a) design, costing, and performance data needed to commence project financing activities; b) engineering and material balances required to file for all project permits; and c) a final project schedule. Based on the results of the previous pre-FEED study, MPC and its team evaluated two options to provide the large amount of steam needed to operate the CCS: 1) installation of new natural gas package boilers and a new pipeline to supply natural gas to the facility and 2) extraction of steam from the existing MRYS steam turbine generator. The FEED study was performed using the package boilers option, as it was deemed to have the lowest technical risk, overall cost, and cost uncertainty. A key feature of the design, however, was the mixing the natural gas boilers’ flue gas with the MRY2 flue gas. This increased the size of the CCS system. This design also enabled the tie-in of Unit 1 flue gas for times when Unit 2 is offline. The CCS was designed to capture a combined 12,978 short tons per day (STPD) of CO 2 from the flue gases produced by the existing MRY Unit 2 along with flue gas produced by the new boiler package within the CCS. The 12,978 STPD CO 2 capture is achieved by normally processing 100% of the total available MRY Unit 2 flue gas (considering full load operation) and 100% of the boiler package flue gas, then removing 90% of the CO 2 available from the processed flue gas streams.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2001

Assessment of Stirling Technology Has Provided Critical Data Leading Toward Flight Readiness of the Stirling Converter

The NASA Glenn Research Center is supporting the development of a Stirling converter with the Department of Energy (DOE, Germantown, Maryland) for an advanced Stirling Radioisotope Power System (SRPS) to provide spacecraft onboard electric power for NASA space science missions. A key technology assessment completed by Glenn and DOE has led to the SRPS being identified as a high-efficiency power source for such deep space missions as the Europa Orbiter and the Solar Probe. In addition, the Stirling system is now being considered for unmanned Mars rovers, especially where mission profiles may exclude the use of photovoltaic power systems, such as exploration at high Martian latitudes or for missions of long duration. The SRPS efficiency of over 20 percent will reduce the required amount of radioisotope by more than a factor of 3 in comparison to current radioisotope thermoelectric generators. This significantly reduces radioisotope cost, radiological inventory, and system cost, and it provides efficient use of scarce radioisotope resources. In support of this technology assessment, Glenn conducted a series of independent evaluations and tests to determine the technology readiness of a 55-We Stirling converter developed by Stirling Technology Company (Kennewick, Washington) and DOE. Key areas evaluated by Glenn included: 1) Radiation tolerance of materials; 2) Random vibration testing of the Stirling converter in Glenn's Structural Dynamics Lab to simulate operation in the launch environment; 3) Electromagnetic interference and compatibility (EMI/EMC) of the converter operating in Glenn's EMI lab; Independent failure modes, effects, and criticality analysis, and life and reliability 4. Independent failure modes, effects, and criticality analysis, and life and reliability assessment; and 5) SRPS cost estimate. The data from these evaluations were presented to NASA Headquarters and the Jet Propulsion Laboratory mission office by a joint industry/Government team consisting of DOE, Glenn, and Lockheed Martin Astronautics. This team concluded that there are no technical reasons that would rule out using the Stirling converter for deep space missions. As a direct result of the successful testing at Glenn, the DOE/Stirling Technology Company 55-We Stirling converter has been baselined for the SRPS. Glenn is now continuing an in-house project to assist in developing the Stirling converter for readiness for space qualification and mission implementation. As part of this effort, the Stirling converter will be further characterized under launch environment random vibration testing, methods to reduce converter EMI will be developed, and an independent performance verification will be completed. Converter life assessment and permanent magnet aging characterization tasks are also underway. Substitute organic materials for the linear alternator and piston bearing coatings for use in a high-radiation environment have been identified and have now been incorporated in Stirling converters built by Stirling Technology Company for Glenn. Electromagnetic and thermal finite element analyses for the alternator are also being conducted.

Thieme, Lanny G.↗
DOE OSTI2024

Optical versus radiographic imaging and tomography: introduction to the ROADS feature issue

Optical imaging is an ancient branch of imaging dating back to thousands of years. Radiographic imaging and tomography (RadIT), including the first use of X-rays by Wilhelm Röntgen, and then, $γ$ -rays, energetic charged particles, neutrons, etc. are about 130 years young. The synergies between optical and radiographic imaging can be cast in the framework of these building blocks: Physics, Sources, Detectors, Methods, and Data Science, as described in Appl. Opt. 61, RDS1 (2022). Optical imaging has expanded to include three-dimensional (3D) tomography (including holography), due in to part the invention of optical (including infrared) lasers. RadIT are intrinsically 3D because of the penetrating power of ionizing radiation. Both optical imaging and tomography (OIT) and RadIT are evolving into even higher dimensional regimes, such as time-resolved tomography (4D) and temporarily and spectroscopically resolved tomography (4D + ). Further advances in OIT and RadIT will continue to be driven by desires for higher information yield, higher resolutions, and higher probability models with reduced uncertainties. Synergies in quantum physics, laser-driven sources, low-cost detectors, data-driven methods, automated processing of data, and artificially intelligent data acquisition protocols will be beneficial to both branches of imaging in many applications. These topics, along with an overview of the Radiography, Applied Optics, and Data Science virtual feature issue, are discussed here.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗