Spawn: coupling Modelica Buildings Library and EnergyPlus to enable new energy system and control applications
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A spectral decomposition method has been implemented to identify and quantify isotopic source terms in high-resolution gamma-ray spectroscopy in static geometry and shielding scenarios. Monte Carlo simulations were used to build the response matrix of a shielded high-purity germanium detector monitoring an effluent stream with a Marinelli configuration. The decomposition technique was applied to a series of calibration spectra taken with the detector using a multi-nuclide standard. These results are compared with decay-corrected values from the calibration certificate. For most nuclei in the standard ( 241 Am, 109 Cd, 137 Cs, and 60 Co), the deviations from the certificate values were generally no more than 6% with a few outliers as high as 10%. Furthermore, for 57 Co, the radionuclide with the lowest activity, the deviations from the standard reached as high as 25%, driven by the meager statistics in the calibration spectra. In addition, a complete treatment of error propagation for the technique is presented.
The 347H stainless steel is a primary high-temperature material for many energy and power generation industries. Stress relief cracking (SRC) has been a particular concern in welding of this material. The residual stress induced by welding and its evolution during post-welding heat treatment (PWHT) and subsequent operating and service conditions is one of the primary factors contributing to SRC. The lifetime of welded structure components is also controlled by the precipitation kinetics that accompanies PWHT, stress relaxation process, and long-term aging and complex synergistic factors. Various theories have been proposed in the past to explain SRC. However, a widely accepted approach to predicting the entire damage evolution and the resulting performance reduction is still lacking. This study is to demonstrate a reliable solution for two critical issues that affect the predictions. First, the residual stress distribution obtained from both simulation and neutron diffraction is compared, which increases the accuracy of mechanical analysis simulation model and therefore builds a solid basis for the lifetime prediction model. Second, the generation, evolution, and annihilation of precipitates are monitored by the synchrotron diffraction experiment. Preliminary results demonstrate the critical importance of precipitation kinetics on the residual stress distribution/redistribution during heat treatments.
The software is a generic, price- and load-responsive control algorithm integrating heat pumps with thermal energy storage. The algorithm leverages simple models of the system and easily accessible data to schedule operation of heat pumps and thermal energy storage in ways that minimize the cost of operating the heating/cooling system. This tool is specifically designed to be easy to interact with, and something that industry partners are able to adopt. There are two current state of the art approaches. Industry tends to develop very simple algorithms, with predetermined schedules that are not capable of changing operation in response to changes in operating environment. For example, a control designed to avoid high-price electricity from 5-8 PM will not be able to adapt if the high-price period changes to 4-9 PM. Academia commonly develops algorithms called Model predictive control (MPC). MPC requires extensive data and highly trained staff to develop a specific type of simulation model of the building, connect the building to optimization algorithms, and leverage powerful computers. Industry, with limited time/finance budgets for any project, is resistant to adopting MPC due to the associated high complexity and cost.
The Portable Extensible Toolkit for Scientific Computation (PETSc) library provides scalable solvers for nonlinear time-dependent differential and algebraic equations and for numerical optimization via the Toolkit for Advanced Optimization (TAO). PETSc is used in dozens of scientific fields and is an important building block for many simulation codes. During the U.S. Department of Energy’s Exascale Computing Project, the PETSc team has made substantial efforts to enable efficient utilization of the massive fine-grain parallelism present within exascale compute nodes and to enable performance portability across exascale architectures. We recap some of the challenges that designers of numerical libraries face in such an endeavor, and then discuss the many developments we have made, which include the addition of new GPU backends, features supporting efficient on-device matrix assembly, better support for asynchronicity and GPU kernel concurrency, and new communication infrastructure. In conclusion, we evaluate the performance of these developments on some pre-exascale systems as well as the early exascale systems Frontier and Aurora, using compute kernel, communication layer, solver, and mini-application benchmark studies, and then close with a few observations drawn from our experiences on the tension between portable performance and other goals of numerical libraries.
Effective treatment of Parkinson’s disease (PD) is a continual challenge for healthcare providers, and providers can benefit from leveraging emerging technologies to supplement traditional clinic care. We develop a data-driven reinforcement learning (RL) framework to optimize PD medication regimens through wearable sensors. We leverage a data set of n = 26 PD patients who wore wrist-mounted movement trackers for two separate six-day periods. Using these data, we first build and validate a simulation model of how individual patients’ movement symptoms respond to medication administration. We then pair this simulation model with an on-policy RL algorithm that recommends optimal medication types, timing, and dosages during the day while incorporating human-in-the-loop considerations on medication administration. The results show that the RL-prescribed medication regimens outperform physicians’ medication regimens, despite physicians having access to the same data as the RL agent. To validate our results, we assess our wearable-based RL medication regimens using n = 399 PD patients from the Parkinson’s Progression Markers Initiative data set. We show that the wearable-based RL medication regimens would lead to significant symptom improvement for these patients, even more so than training RL policies directly from this data set. In doing so, we show that RL models from even small data sets of wearable data can offer novel, generalizable clinical insights and medication strategies, which may outperform those derived from larger data sets without wearable data.
This is a final technical report for the University of Maryland work in the SciDAC Center for Technology for Advanced Scientific Component Software (TASCS). The Maryland work focused on software tools for coupling parallel software components built using the Common Component Architecture (CCA) APIs. Those tools are based on the Maryland InterComm software framework that has been used in multiple computational science applications to build large-scale simulations of complex physical systems that employ multiple separately developed codes.
This report has been prepared as part of an effort to design and build a modeling and simulation (M&S) framework to assess the economic viability of a nuclear-renewable hybrid energy system (N-R HES). In order to facilitate dynamic M&S of such an integrated system, research groups in multiple national laboratories have been developing various subsystems as dynamic physics-based components using the Modelica programming language. In fiscal year (FY) 2015, Idaho National Laboratory (INL) performed a dynamic analysis of two region-specific N-R HES configurations, including the gas-to-liquid (natural gas to Fischer-Tropsch synthetic fuel) and brackish water reverse osmosis desalination plants as industrial processes. In FY 2016, INL has developed two additional subsystems in the Modelica framework: a high-temperature steam electrolysis (HTSE) plant and a gas turbine power plant (GTPP). HTSE has been proposed as a high priority industrial process to be integrated with a light water reactor (LWR) in an N-R HES. This integrated energy system would be capable of dynamically apportioning thermal and electrical energy (1) to provide responsive generation to the power grid and (2) to produce alternative industrial products (i.e., hydrogen and oxygen) without generating any greenhouse gases. A dynamic performance analysis of the LWR/HTSE integration case was carried out to evaluate the technical feasibility (load-following capability) and safety of such a system operating under highly variable conditions requiring flexible output. To support the dynamic analysis, the detailed dynamic model and control design of the HTSE process, which employs solid oxide electrolysis cells, have been developed to predict the process behavior over a large range of operating conditions. As first-generation N-R HES technology will be based on LWRs, which provide thermal energy at a relatively low temperature, complementary temperature-boosting technology was suggested for integration with the HTSE process that requires higher temperature input. Simulation results involving several case studies show that the suggested control scheme could maintain the controlled variables (including the steam utilization factor, cathode stream inlet composition, and temperatures of the process streams at various locations) within desired limits under various plant operating conditions. The results also indicate that the proposed HTSE plant could provide operational flexibility to participate in energy management at the utility scale by dynamically optimizing the use of excess plant capacity within an N-R HES. A natural-gas fired GTPP has been proposed as a secondary energy supply to be included in an N-R HES. This auxiliary generator could be used to cover rapid dynamics in grid demand that cannot be met by the remainder of the N-R HES. To evaluate the operability and controllability of the proposed process during transients between load (demand) levels, the dynamic model and control design were developed. Special attention was given to the design of feedback controllers to regulate the power frequency, and exhaust gas and turbine firing temperatures. Several case studies were performed to investigate the system responses to the major disturbance (power load demand) in such a control system. The simulation results show that the performance of the proposed control strategies was satisfactory under each test when the GTPP experienced high rapid variations in the load.
This document is intended to be a reference manual for the Appendix G Performance Rating Method (PRM) of ANSI/ASHRAE/IES Standard 90.1-2016 (Standard 90.1-2016). The PRM can be used to demonstrate compliance with the standard and to rate the energy efficiency of commercial and high-rise residential buildings with designs that exceed the requirements of Standard 90.1. Use of the PRM for demonstrating compliance with Standard 90.1 is a new feature of the 2016 edition. The procedures and processes described in this manual are designed to provide consistency and accuracy by filling in gaps and providing additional details needed by users of the PRM.
This report has been prepared as part of an effort to design and build a modeling and simulation (M&S) framework to assess the economic viability of a nuclear-renewable hybrid energy system (N-R HES). In order to facilitate dynamic M&S of such an integrated system, research groups in multiple national laboratories have been developing various subsystems as dynamic physics-based components using the Modelica programming language. In fiscal year 2015 (FY15), Idaho National Laboratory (INL) performed a dynamic analysis of two region-specific N-R HES configurations, including the gas-to-liquid (natural gas to Fischer-Tropsch synthetic fuel) and brackish water reverse osmosis desalination plants as industrial processes. In FY16, INL developed two additional subsystems in the Modelica framework: (1) a high-temperature steam electrolysis (HTSE) plant as a high priority industrial plant to be integrated with a light water reactor (LWR) within an N-R HES and (2) a gas turbine power plant as a secondary energy supply. In FY17, five new components (i.e., a feedwater pump, a multi-stage compression system, a sweep-gas turbine, flow control valves, and pressure control valves) have been incorporated into the HTSE system proposed in FY16, aiming to better realistically characterize all key components of concern. Special attention has been given to the controller settings based on process models (i.e., direct synthesis method), aiming to improve process dynamics and controllability. A dynamic performance analysis of the improved LWR/HTSE integration case was carried out to evaluate the technical feasibility (load-following capability) and safety of such a system operating under highly variable conditions requiring flexible output. The analysis (evaluated in terms of the step response) clearly shows that the FY17 model resulted in superior output responses with much smaller settling times and less oscillatory behavior in response to disturbances in the electric load than those observed with the FY16 model. Simulation results involving several case studies show that the suggested control scheme could maintain the controlled variables (including the steam utilization factor, cathode stream inlet composition, and temperatures and pressures of the process streams at various locations) within desired limits under various plant operating conditions. The results also indicate that the proposed HTSE plant could provide operational flexibility to participate in energy management at the utility scale by dynamically optimizing the use of excess plant capacity within an N-R HES.
This report has been prepared as part of an effort to design and build a Modeling and Simulation (M&S) framework to assess the economic viability of a Nuclear-Renewable Hybrid Energy System (N-R HES). In order to facilitate dynamic M&S of such an integrated system, research groups in multiple national laboratories and universities have been developing various subsystems as dynamic physics-based components using the Modelica programming language. In Fiscal Years (FYs) 2015, Idaho National Laboratory (INL) performed a dynamic analysis of two region-specific N-R HES configurations, including the gas-to-liquid (natural gas to Fischer-Tropsch synthetic fuel) and brackish water Reverse Osmosis (RO) desalination plants as industrial processes. In FYs 2016–2017, INL developed two additional subsystems in the Modelica framework: (1) a high-temperature steam electrolysis plant as a high priority industrial plant to be integrated with a light water reactor within an N-R HES and (2) a gas turbine power plant as a secondary energy supply. In FY 2018, the RO desalination system model developed in FY 2015 has been updated such that the model is compatible with the most recent version of the ThermoPower library. Special attention has been given to the controller settings based on process models, aiming to improve process dynamics and controllability. A dynamic performance analysis of the updated RO desalination plant was carried out to evaluate the technical feasibility (load-following capability) of such a system operating under highly variable conditions requiring flexible output. Simulation results involving several case studies show that the suggested control scheme could maintain the controlled variables (including the variable electrical load and RO feed pressure) within desired limits under various plant operating conditions. The results also indicate that the proposed RO plant could provide operational flexibility to participate in energy management at the utility scale by dynamically optimizing the use of excess plant capacity within an N-R HES. For a small-scale energy storage system, a sensible Thermal Energy Storage (TES) model has been developed in the Modelica Framework in FY 2018.
This report provides an assessment as to whether new buildings constructed to the commercial energy-efficiency provisions of the 2015 IECC would save energy and energy costs as compared to the 2012 IECC. PNNL also compared the energy performance of the 2015 IECC with the corresponding Standard 90.1-2013. The purpose of this analysis is to help states and local jurisdictions make informed decisions regarding model code adoption.
This report provides an assessment as to whether new buildings constructed to the commercial energy-efficiency provisions of the 2018 IECC would save energy and energy costs as compared to the 2015 IECC. The Commercial Energy Efficiency chapter in the 2018 IECC allows users to either follow the provisions in the IECC or use Standard 90.1-2016 as an alternative compliance path. PNNL also compared the energy performance of the 2018 IECC with the corresponding Standard 90.1-2016 to help states and local jurisdictions make informed decisions regarding model code adoption.
The Advanced Technology Development and Mitigation (ATDM) program at Sandia National Laboratories is a new effort to build next-generation simulation codes that will map well to upcoming exascale computing platforms. Rather than follow traditional single- program, multiple data (SPMD) programming techniques, ATDM is developing applications in an asynchronous many task (AMT) form that describes work as a graph of tasks that have data dependencies. The data management team is focused on developing a data warehouse for ATDM that will enable tasks to store and exchange data objects efficiently. This report summarizes the data management teams efforts during FY15, and documents: (1) an initial API and implementation for the data warehouses key/value store, (2) API requirements for use with ATDMs runtime, (3) initial requirements for storing ATDM-specific data, and (4) the current organization of software components that will be used by the data warehouse.
This report describes the design development process and recommendations of a Community-Scale Zero Net Energy Master Plan for a residential block in Oakland, the Oakland EcoBlock. The recommended master plan includes an integrated system of energy efficiency retrofits, a direct current solar/storage/electric vehicle microgrid, alternating/direct current houses, and water efficiency retrofits with rainwater capture. The recommended master plan is projected to be close to zero-net energy (95 percent) for homes, reduce carbon emissions by 65 percent at the block scale (including transportation), and reduce water use 60–70 percent. The integrated system of energy efficiency and a direct current solar/storage/electric vehicle (EV) charging microgrid is the first of its kind at the residential block scale. This breakthrough because the deep energy efficiency retrofit savings free up enough capacity in the solar supply and storage to enable residents to switch from natural gas to electricity for heating and domestic hot water and to provide EV charging for 33 percent of vehicle miles traveled. The savings in household transportation costs are a game changer because when combined with savings in the electric and water utility bills, the total cash flow savings are projected to fund the capital improvements of the proposed systems. This project’s benefits include lower and more predictable utility bills; greater resiliency because the system can operate during outages, and vastly improved indoor air quality by reducing natural gas consumption. The local energy storage reduces peak demand for the utility. California benefits from a model that exceeds the targets for reductions in energy consumption and carbon emissions and the reductions in water use address California’s severe water challenges. The Oakland EcoBlock is a transformative model for accelerating the rapid deployment of advanced energy communities.
The U.S. Department of Energy, National Nuclear Security Administration Nevada Field Office (NNSA/NFO) has tasked Desert Research Institute (DRI) with identifying and evaluating additional resources that contribute to the significance of the Apple-2 Historic District as part of a Section 110 effort. Apple-2 was a 1955 atmospheric test in which the Federal Civil Defense Administration (FCDA) conducted an extensive civil effects program named Operation Cue. The objective of Operation Cue was to understand the blast and thermal effects of the test by using a simulated community of residential buildings, industrial structures, utilities, and commercial equipment arranged at various distances from ground zero (GZ). Military operations conducted by the Department of Defense (DoD) as part of the Desert Rock VI exercise were also associated with the Apple-2 test. The Apple-2 Historic District was originally defined in 2000 by Johnson and Edwards as 15 highly visible contributing elements dispersed across the landscape. Since its original recording, DRI has conducted additional archival research and this report details the identification and evaluation results of testing-related material remains at a distance of 10,500 ft from the Apple-2 GZ. Between 2017 and 2020, DRI conducted fieldwork at 10,500 ft from the Apple-2 GZ, revisited previously recorded contributing elements, and identified and evaluated newly recorded resources associated with Operation Cue as well as DoD operations. In total, DRI revisited or newly identified 15 principal resources. Of these, 13 are located within the historic district and contribute to the district’s significance. A set of military trenches and the associated material remains are discontiguous from the district, but also contribute to the district’s significance.
This report is part of a series describing different ResStock (TM) measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Propane Fuel 95% AFUE and Fuel Oil Furnace 88% AFUE" measure upgrade methodology and briefly discusses key results.
This report is part of a series describing different ResStock measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Natural Gas Water Heater" measure upgrade methodology and briefly discusses key results.