Analysis of Performance and Efficiency of Conservation Voltage Optimization Considering Load Model Uncertainty
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The optimal configuration choice between positive triangularity (PT) and negative triangularity (NT) tokamaks for fusion power plants hinges on navigating different operational constraints rather than achieving specific plasma performance metrics. This study presents a systematic comparison using constrained multi-objective optimization with the integrated FUsion Synthesis Engine (FUSE) framework. Over 200 000 integrated design evaluations were performed exploring the trade-offs between capital cost minimization and operational reliability (maximizing q 95 ) while satisfying engineering constraints including 250 ± 50 MW net electric power, tritium breeding ratio > 1.1, power exhaust limits and an hour flattop time. Both configurations achieve similar cost-performance Pareto fronts through contrasting design philosophies. PT, while demonstrating resilience to pedestal degradation (compensating for up to 40% reduction), are constrained to larger machines (R 0 > 6.5 m) by the narrow operational window between L–H threshold requirements and the research-established power exhaust limit (P sol /R < 15 MW m –1 ). This forces optimization through comparatively reduced magnetic field (∼8 T). NT configurations exploit their freedom from these constraints to access compact, high-field designs (R 0 ~ 5.5 m, B 0 >12 T), creating natural synergy with advancing HTS technology. Sensitivity analyses reveal that PT’s economic viability depends critically on uncertainties in L–H threshold scaling and power handling limits. Notably, a 50% variation in either could eliminate viable designs or enable access to the compact design space. These results suggest configuration selection should be risk-informed: PT offers the lowest-cost path when operational constraints can be confidently predicted, while NT is robust to large variations in constraints and physics uncertainties.
Heating, ventilation, and air conditioning (HVAC) systems account for the highest share of home energy consumption in the United States. Optimized HVAC control can provide thermal improved comfort to the occupants, improve energy efficiency, reduce energy cost, and support grid services. In this paper, we discuss a multi-agent and cloud-based software framework that has been deployed in occupied residential neighborhood. This system enables automatic data collection, learning, optimization, and dispatches signals to neighborhood devices. HVAC optimization is based on model predictive control (MPC). Since the operational performance of MPC depends on model forecasting accuracy, it is crucial to evaluate the model continuously and modify or retrain it as necessary. In this research, we developed an automated workflow to evaluate the performance of temperature and power forecasts based on measured data in the real world. This will provide researchers with a deeper understanding of the model and how it can be improved.
In situ processing produces reduced size persistent representations of a simulations state while the simulation is running. The need for in situ visualization and data analysis is often described in terms of supercomputer size and performance in relation to available storage size.
High performance LINPACK (HPL) remains the primary benchmark for evaluating supercomputing performance. It includes many parts with substantial internal complexity, and its performance is affected by a large number of parameters that interact in ways that are difficult to predict on large-scale heterogeneous supercomputer systems. We present a comprehensive performance analysis of HPL on Frontier, the world’s first exascale supercomputer, which achieved HPL performance of 1.35 exaflops. Through empirical parameter tuning, detailed modeling, and comparative evaluation, we uncover critical performance insights, share lessons learned, and outline best practices for effective parameter tuning on exascale systems. We introduce and evaluate two novel PDFACT strategies: a dedicated-thread (DT) variant and a GPU-based variant (GPUPDFACT) implementation using HIP cooperative groups, demonstrating that GPU-based factorization outperforms conventional CPU-based PDFACT on Frontier’s architecture. Our findings establish key performance factors for HPL on exascale systems and offer valuable guidance for future high-performance computing and benchmarking efforts.
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The petrochemical and refining sectors are challenged with reliably delivering safer and cleaner energy to US consumers, while meeting an ever-growing global demand. The petrochemical and refining infrastructure in the USA is aging, and corrosion and damage mechanisms are constant threats to mechanical integrity, safety, and profitability. However, governments and industry stakeholders are reluctant to replace or upgrade the existing infrastructure due to the immense cost. To better understand and mitigate the risks of aging energy infrastructure, strong technical analysis capabilities, combined with optimized monitoring and decision making, is critical. Today, this is accomplished via complex simulations and data analysis. To this end, advanced High-Performance-Computing (HPC) software will be leveraged and integrated in easy-to-use industrial trade tools, to lower the barrier for new users, increase the ease of access for experienced users, and allow for smarter decisions to be made in the midstream and downstream energy sectors.
Abstract not provided.
Summer 2023 Intern Poster Session
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Westinghouse is pursuing an advanced Nuclear Power Plant design based on Lead Fast Reactor (LFR) technology for global commercialization. To achieve an optimal combination of key attributes, such as safety, sustainability, and economic competitiveness, Westinghouse and ANL partnered in developing and applying a formalized core design optimization strategy. An LFR analysis workflow was developed to automate a suite of reactor physics, fuels performance, safety, and economics simulations on a selected LFR concept. The workflow streamlines analysis of a wide range of LFR designs with different dimensions and fuel types to assess their viability and economic performance, significantly reducing human processing time and risks of processing errors. The LFR optimization exercise was defined, resulting in selection of the design constraints (geometric, neutronics, thermo-mechanical, safety, thermal-hydraulics, and economics) and performance metrics researched (minimization of both the fuels LCOE and the first core inventory cost). A total of 14 varied design parameters were considered, including assembly dimensions, coolant temperature, and enrichment distribution throughout the core. The LFR analysis workflow was connected to DAKOTA for sensitivity and optimization analyses. Due to the extremely large size of the potential LFR optimization solution space relative to the computing time required to characterize one LFR solution, a multi-stage optimization approach was proposed to breakdown the problem into several stages with more reasonable sizes. This optimization approach enabled finding various viable core solutions with different cost tradeoffs that were considered by Westinghouse and justify selection of a smaller core with multi-batch 2-year cycle length.
Pump and Treat systems are being utilized to remediate contaminated groundwater in the Hanford 100 Areas adjacent to the Columbia River in Eastern Washington. Design of the systems was supported by a three-dimensional (3D) fate and transport model. This model provided sophisticated simulation capabilities but requires many hours to calculate results for each simulation considered. Many simulations are required to optimize system performance, so a two-dimensional (2D) model was created to reduce run time. The 2D model was developed as a equivalent-property version of the 3D model that derives boundary conditions and aquifer properties from the 3D model. It produces predictions that are very close to the 3D model predictions, allowing it to be used for comparative remedy analyses. Any potential system modifications identified by using the 2D version are verified for use by running the 3D model to confirm performance. The 2D model was incorporated into a comprehensive analysis system (the Pumping Optimization Model, POM) to simplify analysis of multiple simulations. It allows rapid turnaround by utilizing a graphical user interface that: 1 allows operators to create hypothetical scenarios for system operation, 2 feeds the input to the 2D fate and transport model, and 3 displays the scenario results to evaluate performance improvement. All of the above is accomplished within the user interface. Complex analyses can be completed within a few hours and multiple simulations can be compared side-by-side. The POM utilizes standard office computing equipment and established groundwater modeling software. (authors)
Pump and Treat systems are being utilized to remediate contaminated groundwater in the Hanford 100 Areas adjacent to the Columbia River in Eastern Washington. Design of the systems was supported by a three-dimensional (3D) fate and transport model. This model provided sophisticated simulation capabilities but requires many hours to calculate results for each simulation considered. Many simulations are required to optimize system performance, so a two-dimensional (2D) model was created to reduce run time. The 2D model was developed as a equivalent-property version of the 3D model that derives boundary conditions and aquifer properties from the 3D model. It produces predictions that are very close to the 3D model predictions, allowing it to be used for comparative remedy analyses. Any potential system modifications identified by using the 2D version are verified for use by running the 3D model to confirm performance. The 2D model was incorporated into a comprehensive analysis system (the Pumping Optimization Model, POM) to simplify analysis of multiple simulations. It allows rapid turnaround by utilizing a graphical user interface that: 1 allows operators to create hypothetical scenarios for system operation, 2 feeds the input to the 2D fate and transport model, and 3 displays the scenario results to evaluate performance improvement. All of the above is accomplished within the user interface. Complex analyses can be completed within a few hours and multiple simulations can be compared side-by-side. The POM utilizes standard office computing equipment and established groundwater modeling software.
The U.S. Agency for International Development (USAID), through their partnership with the U.S. Department of the Interior (DOI), requested the support of Oak Ridge National Laboratory (ORNL) to provide specialized technical assistance as part of the Smart Infrastructure for the Mekong (SIM) Program in Thailand. Introduced in July 2013 by U.S. Secretary of State John Kerry, SIM is a U.S. Government Inter-Agency program that provides Lower Mekong partner countries with targeted, demand-driven technical and scientific assistance to support environmentally sound, climate conscious and socially equitable infrastructure, clean energy development, and water resources optimization. The U.S. Government is committed to supporting sustainable economic development within the region by providing tools, best practices, technical assistance, and lessons learned for the benefit of partner countries. In response to a request from the Electricity Generating Authority of Thailand (EGAT), a SIM project was developed with two main activities: 1) to promote hydropower sustainability and efficiency through technical assessment training at two existing hydropower assets in Thailand, and 2) the design and implementation of one national and two or three regional science and policy workshops, to be co-hosted with EGAT, to build common understanding of and commitment to environmental and social safeguards for Mekong Basin hydropower projects. The U.S. Department of Energy (DOE) is leading the technical assessment (Activity 1), and has contracted ORNL to provide expert technical assistance focused on increasing efficiency at existing projects, with the goal of increasing renewable energy generation at little to no capital cost. ORNL is the leading national laboratory in hydropower analysis, with a nationally recognized and highly qualified team of scientists addressing small to large-scale systems (basin-, regional-, and national-scale) energy generation optimization analysis for DOE. The mission of the ORNL Water Power Program is to develop technologies, decision-support tools, and methods of analysis that enable holistic management of water-dependent energy infrastructure and natural resources in support of the DOE Energy Efficiency and Renewable Energy Office (DOE-EERE), Federal hydropower agencies, Federal Energy Regulatory Commission (FERC), Nuclear Regulatory Commission (NRC), energy producers, and other entities. In support of SIM, ORNL completed technical assessments of two hydropower plants owned and operated by the Electricity Generating Authority of Thailand (EGAT): Vajiralongkorn (VRK), with an installed capacity of 300 MW, and Rajjaprabha (RPB), with an installed capacity of 240MW. Technical assessment is defined as the assessment of hydropower operation and performance, and the identification of potential opportunities for performance improvement through plant optimization. At each plant, the assessment included an initial analysis of hydropower operating and performance metrics, provided by dam owners. After this analysis, ORNL engaged with the plant management team in a skills exchange, where best practices, operational methods, and technical challenges were discussed. The technical assessment process was outlined to plant management followed by a presentation of preliminary results and analysis based on 50 days of operational data. EGAT has agreed to provide a full year of operational data so a complete and detailed assessment that captures seasonal variability can be completed. The results of these assessments and discussions will be used to develop a set of best practices, training, and procedure recommendations to improve the efficiency of the two assessed plants.
Infrared scattering-type near-field optical microscopy, IR s-SNOM, and its broadband variant, nano-FTIR, are pioneering, flagship techniques for their ability to provide molecular identification and material optical property information at a spatial resolution well below the far-field diffraction limit, typically less than 25 nm. While s-SNOM and nano-FTIR instrumentation and data analysis have been discussed previously, there is a lack of information regarding experimental parameters for the practitioner, especially in the context of previously developed frameworks. Like conventional FTIR spectroscopy, the critical component of a nano-FTIR instrument is an interferometer. However, unlike FTIR spectroscopy, the resulting interference patterns or interferograms are typically asymmetric. Here, we unambiguously describe the origins of asymmetric interferograms recorded with nano-FTIR instruments, give a detailed analysis of potential artifacts, and recommend optimal instrument settings as well as data analysis parameters.
X-ray focus optimization and characterization based on coherent scattering and quantitative speckle size measurements was demonstrated at the Linac Coherent Light Source. Its performance as a single-pulse free-electron laser beam diagnostic was tested for two typical focusing configurations. The results derived from the speckle size/shape analysis show the effectiveness of this technique in finding the focus' location, size and shape. In addition, its single-pulse compatibility enables users to capture pulse-to-pulse fluctuations in focus properties compared with other techniques that require scanning and averaging.
The integration of renewable energy sources into power grids necessitates solutions for grid support and stability during fluctuations in electricity generation and demand. Gravity energy storage systems (GESS) are emerging as a promising technology for managing the balance between energy supply and demand. However, their capacity to optimize energy flow and offer voltage and frequency regulation amid imbalances in generation and demand is less reported. This paper investigates the control of GESS for optimizing energy flow during voltage and frequency regulation. The study evaluates the regulation capabilities of GESS with different motor inertias during a Texas grid event: one with a high-speed, low-inertia motor and another with a low-speed, high-inertia motor. Results indicate that both GESS scenarios provide fast frequency response by converting potential energy into kinetic energy and vice versa, with a response time of 1.5 s from the frequency variation. This aligns with grid requirements for primary frequency response from traditional synchronous generators and motors with large inertia. Furthermore, a GESS based on a high-inertia motor may be able to operate over a broader range of frequency variations, whereas a low-inertia system may be limited by thermal constraints of the motor.
The integration of renewable energy sources into power grids necessitates solutions for grid support and stability during fluctuations in electricity generation and demand. Gravity energy storage systems (GESS) are emerging as a promising technology for managing the balance between the energy supply and demand. However, their capacity to optimize energy flow and offer voltage and frequency regulation amid generation-demand imbalances is less reported. This paper investigates the control of GESS for optimizing energy flow during voltage and frequency regulation. The study evaluates the regulation capabilities of GESS with different motor inertias during a Texas grid event: one with a high-speed, low-inertia motor and another with a low-speed, high-inertia motor. Results indicate that both GESS systems provide fast frequency response by converting potential energy into kinetic energy and vice versa, with a response time of 1.5 seconds from the frequency variation. This aligns with grid requirements for primary frequency response from traditional synchronous generators and motors with large inertia. Furthermore, a GESS with a high-inertia motor may be able operate over a broader range of frequency variations whereas a low-inertia system may be limited by thermal constraints of the motor.