Modeling thermal transients of bulk particle lifting systems with CFD simulations.
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Abstract not provided.
We investigate the chemical freeze-out in heavy-ion collisions (HICs) and the impact of the hadronic spectrum on thermal model analyses [1, 2]. Detailed knowledge of the hadronic spectrum is still an open question, which has phenomenological consequences on the study of HICs. By varying the number of resonances included in Hadron Resonance Gas (HRG) Model calculations, we can shed light on which particles may be produced. Furthermore, we study the influence of the number of states on the so-called two flavor freezeout scenario, in which strange and light particles can freeze-out separately. We consider results for the chemical freeze-out parameters obtained from thermal model fits and from calculating net-particle fluctuations. We will show the effect of using one global temperature to fit all particles and alternatively, allowing particles with and without strange quarks to freeze-out separately.
Industrial process heating (IPH) relies primarily on thermal energy generated by fossil fuel combustion to produce, treat, and alter manufactured goods. Thermal energy storage (TES) helps reduce the carbon footprint of IPH systems by facilitating the utilization of renewable and waste heat sources. A promising new TES technology uses elemental sulfur as the heat-storage medium. The design of sulfur TES systems can be evaluated with the aid of computational fluid dynamics (CFD). However, the computational cost of such CFD efforts is prohibitive to comprehensive optimizations over design parameters. To reduce this computational cost, machine learning (ML) models can be developed to act as surrogates for CFD. In this paper, we describe the process of building and evaluating surrogate ML models for facilitating optimization of sulfur TES systems for IPH. To enforce the thermodynamic relationship between the two modeled quantities, we develop a hybrid model for sulfur temperature using both direct predictions of temperature and calculations of temperature from predictions of the heat transfer coefficient. The hybrid model enforces this constraint at the expense of the slightly reduced accuracy compared to two disjoint models. The overall high accuracy observed in our model evaluation demonstrates the usefulness of such surrogate modeling for studying TES systems. This work contributes to the field of TES surrogate modeling by offering a novel accurate hybrid approach to predicting simultaneously the heat transfer coefficient and temperature of the heat storage medium.
The report presents the 2023 Net Zero World Chile program results from activities of the the four program workstreams: 1) energy-system wide modeling LEAP modeling and analysis results including accelerated Net Zero scenarios with aggressive energy efficiency improvements, fuel switching, and electrification across demand sector; 2) district energy chapter provides thermal district energy systems modeling results of the Recoleta-Independencia pilot project and comparative analysis of district energy systems' potential versus competing technologies in Chile; 3) just transition action steps for the creation of a sister city relationship between Tocopilla and a US counterpart city; and 4) power decarbonization capacity building activities focused on the selection and adoption of grid-enhancing technologies.
Here, this paper proposes and validates a dual-season thermal energy storage-integrated heat pump (TES-HP) system that shifts building electric load in both cooling and heating modes using a single phase-change TES. The design uses the TES as a heat sink for the condenser in summer and a heat source for the evaporator in winter, thereby reducing on-peak electric demand while ensuring compatible with existing air-distribution systems. A 14-kW TES-HP prototype with six operating modes and novel refrigerant charge management strategies was experimentally tested under varied conditions. Subsequently, data-driven polynomial performance curves were developed and validated against experiments, then coupled with a phase change material (PCM) model and a calibrated building model within a rule-based controller. Single-day case studies demonstrated effective on-peak demand reduction while maintaining thermal comfort. During cooling, hourly power was lowered by up to 1.5 kW; in heating, average hourly power decreased from over 3.5 kW to 1.8 kW, compared to an HP-only system, achieving 64.5 % load shifting, and the need for resistance heating was eliminated. Seasonal simulations showed typical on-peak electricity savings of 25–35 % in cooling and 40–65 % in heating, with the largest benefits on the hottest and coldest days. Extended response-surface analysis and nationwide mapping quantified load shifting as functions of ambient and TES temperatures, revealing a seasonal trade-off in TES phase-change temperature. These results demonstrate the TES-HP system as a practical and scalable solution for grid-interactive buildings that reduces on-peak demand and electricity use while maintaining thermal comfort and enhancing grid flexibility and reliability.
Here we examine the modeling, simulation, and optimization of district heating systems, which are widely used for thermal transport using steam or hot water as a carrier. We propose a generalizable framework to specify network models and scenario parameters, and develop an optimization method for evaluating system states including pressures, fluid flowrates, and temperatures throughout the network. The network modeling includes pipes, thermal plants, pumps, and passive or controllable loads as system components. We propose basic models for thermodynamic fluid transport and enforce the balance of physical quantities in steady-state flow over co-located outgoing and return networks. We formulate an optimization problem with steam and hot water as the outgoing and return carriers, as in legacy twentieth century systems. The physical laws and engineering limitations are specified for each component type, and the thermal network flow optimization problem is formulated and solved for a realistic test network under several scenarios.
NLR shall develop a model of an electric thermal energy storage (ETES) system in the System Advisor Model (SAM) framework that uses a salt and particle slurry developed by PowderMet as the thermal energy storage medium. The ETES shall consist of a recuperated sCO 2 Brayton cycle using 2-tank indirect thermal energy storage and an electric heater for heat input. The salt slurry uses chloride salt and operates at a maximum temperature of 500-720°C. The ETES uses dry air cooling. The salt slurry will be modeled as a sensible heat storage medium and as a latent heat storage medium. NLR will use the model to do parametric analysis of the factors affecting the performance (ex., round trip efficiency) of the ETES system. NLR will work with PowderMet to estimate the costs of system components and total system costs, with the levelized cost of electricity (LCOE) as the primary cost metric.
The Closed-Loop Geothermal Working Group and the National Renewable Energy Laboratory (NREL) have studied the performance of disc-shaped and linear thermal enhancements (conductive material introduced around a wellbore) in closed-loop geothermal systems. COMSOL models were developed to investigate the increase in heat production in systems with thermal enhancements compared to those without, across a range of scenarios. This dataset includes two example COMSOL models, a summary of the simulation results, and a link to an accompanying paper. Studied are idealized versions of two thermal enhancement designs: (1) existing or newly created fractures surrounding the wellbore, filled with highly conductive material, and (2) a 'fishbone structure,' where short wellbores are drilled radially outward from the main wellbore and filled with highly conductive material.
Pumped-thermal energy storage (PTES) systems consume and produce electrical energy using thermal storage media as an intermediate stage. PTES lends itself to long-duration energy storage to facilitate high penetration of intermittent electricity generation. This study presents a model-based comparison of two thermal storage types within a PTES system: a conventional, single-phase, stratified water-glycol sensible storage system (SGS), and an ideal isothermal, two-phase heat exchanger that freezes a water reservoir (isothermal heat exchanger (IHEX)). The SGS thermal storage capacity is based on the liquid’s sensible heat change with temperature, whereas the capacity of the IHEX is based on the latent heat of isothermally freezing and melting water. The idealized IHEX modeled here undergoes steady-state melting and freezing (in contrast to transient rates, as observed with ice-on-coil storage). A computational model of a complete PTES system is presented and used to evaluate the PTES system-level performance with each type of cold storage. Compared to SGS-based PTES, under nominal operating conditions, the IHEX-based PTES increased electrical round-trip efficiency from 61% to 82% and increased energy density from 1.13 to 8.09 kWh/m 3 In conclusion, the performance of the PTES configured with IHEX storage was also analyzed under varying operating parameters.
We benchmark performance of long-short term memory (LSTM) network machine learning model and autoregressive integrated moving average (ARIMA) statistical model in temporal forecasting of distributed temperature sensing (DTS). Data in this study consists of fluid temperature transient measured with two co-located Rayleigh scattering fiber optic sensors (FOS) in a forced convection mixing zone of a thermal tee. We treat each gauge of a FOS as an independent temperature sensor. We first study prediction of DTS time series using Vanilla LSTM and ARIMA models trained on prior history of the same FOS that is used for testing. The results yield maximum absolute percentage error (MaxAPE) and root mean squared percentage error (RMSPE) of 1.58% and 0.06% for ARIMA, and 3.14% and 0.44% for LSTM, respectively. Next, we investigate zero-shot forecasting (ZSF) with LSTM and ARIMA trained on history of the co-located FOS only, which is advantageous when limited training data is available. The ZSF MaxAPE and RMSPE values for ARIMA are comparable to those of the Vanilla use case, while the error values for LSTM increase. We show that in ZSF, performance of LSTM network can be improved by training on most correlated gauges between the two FOS, which are identified by calculating the Pearson correlation coefficient. The improved ZSF MaxAPE and RMSPE for LSTM are 4.4% and 0.33%, respectively. Performance of ZSF LSTM can be further enhanced through transfer learning (TL), where LSTM is re-trained on a subset of the FOS that is the target of forecasting. We show that LSTM pre-trained on correlated dataset and re-trained on 30% of testing target dataset achieves MaxAPE and RMSPE values of 2.32% and 0.28%, respectively.
Background: Environmental-assisted fatigue (EAF) is a major issue for the long-term survival of nuclear power plant fleets in the U.S. and worldwide. Multi-material welded regions (e.g., nozzles) and other high-stress regions of reactor coolant system (RCS) components are prone to EAF-related damage. Objective: The discussed work describes a system-level finite element (FE) model of RCS components of a pressurized water reactor (PWR). This is with the goal of predicting the stress hotspots, strain residuals, strain amplitudes and the resulting fatigue lives. Methods: Herein, the FE model was developed considering system-level loading conditions (under connected system thermal-mechanical boundary conditions). Thermal-mechanical stress analysis was performed considering thermal stratification and a design-basis reactor loading cycle. Based on the FE model results, the strain residuals, strain amplitudes and resulting fatigue lives of RCS components were predicted. Results: The results show that some of the RCS components can have significantly different strain amplitudes, residual strain, and fatigue lives, despite having similar geometry and material. Higher residual strain can lead to accelerated cyclic hardening of material and the associated effect of EAF. The simulated component-level strain profile (under realistic multi-axial-multi-physics loading cycle) can guide the selection of appropriate test inputs for conducting laboratory-scale EAF tests, which is a focus of future works. Conclusions: Despite similar geometry and material the RCS component can have significantly different strain profiles and resulting fatigue lives. The discussed approach can help to identify and prioritize the RCS components for conducting expensive nondestructive evaluation (NDE) inspections.
A dynamic thermodynamic model is constructed in Modelica for the thermal battery portion of a heat pipe integrated thermal battery and is demonstrated using a shakedown test. The model is also leveraged to inform aspects of an experiment configuration. The theory section discusses modeling assumptions of the thermal battery system, establishes how melting and fusing is calculated, and how internal convection is modeled within the thermal battery. The shakedown test uses three charge-standby-discharge-standby cycles in which the charging cycles are 10 hours in length and the discharge cycles are 8 hours. System behavior including heat loss, temperature distributions and divergences, and internal convection behavior are presented. The model is then leveraged to show how experiment design can be informed through testing the model for insulation thickness impact on heat loss, round trip efficiency calculation, and heat tracing control.
Variable renewable energy availability has increased the volatility in energy prices in most markets. Nuclear power plants, with a large ratio of capital to variable costs, have historically operated as base load energy suppliers but the need for more flexible operation is increasing. We simulate the techno-economic performance of a 950 MWt nuclear power plant, based on the Westinghouse lead-cooled fast reactor, coupled with molten salt thermal storage as a method for flexible energy dispatch. We use the System Advisor Model to model the nuclear reactor thermal power input and power cycle operating modes. We combine this robust engineering model with a mixed-integer linear program model for optimized dispatch scheduling. We then simulate the coupled nuclear and thermal storage system under different market scenarios with varying price volatility. We find that the coupled plant outperforms the base plant under markets where energy price peaks fluctuate by a factor of two or more about the mean price. We show that a calculated power purchase agreement price for the plant improves by up to 10% when operating under California energy market conditions. Sensitivity analysis on the thermal storage cost shows that the optimal design remains unchanged even when doubling costs.
Renewable thermal energy systems (RTES) harness renewable energy sources to provide services for space heating and cooling, district heating, domestic hot water, and industrial process heat (IPH). The use of low-pressure steam generated by the combustion of fossil fuels is common today to provide process heat for industrial facilities. Solar IPH (SIPH) technologies could economically replace the steam or heat needs at many industrial sites by providing high-temperature pressurized hot water, a heat transfer fluid (HTF) such as synthetic-oil, or direct steam (Kurup and Turchi, 2015). RTES could be hybridized with technology options or combined with the existing heat supply (e.g. fuels), to give options for targeted IPH application and the reduction of fuel consumption. This work has tested hybrid system modelling approaches. Initial results show when a natural gas (NG) burner that feeds an IPH application of 300°C, has both air and NG streams pre-heated with a solar field/RTES exit temperature of 180°C (via an HTF), a 13% NG offset is possible. NG offsets reach up to 26%, when the RTES exit temperatures are at 300°C for a given annual capacity factor of 24%. This can be even higher with addition of thermal energy storage (TES).
Abstract not provided.
This report provides an overview on the verification and validation (V&V) of the Thermal Energy Distribution System (TEDS) model developed in the Modelica process modeling ecosystem using experimental data. Model development has led to the creation of a dynamic process model of the experimental TEDS facility housed within the Energy Systems Laboratory (ESL) at Idaho National Laboratory (INL). The model was then used during the preconstruction phase of the experimental effort to inform experimental design (e.g., insulation requirements, bypass line placement, expected performance of components) and to test innovative control schemes prior to the initial operation. The TEDS model developed in Modelica includes the primary components of the TEDS experimental unit: a 200kW Chromalox heater; a single-tank packed-bed thermal energy storage system filled with 0.125-inch alumina (Al2O3) beads; an ethylene-glycol-to-Therminol-66 heat exchanger; system piping; five control valves; and all associated temperature, pressure, and volumetric flow sensors. Using the Institute of Electrical and Electronics Engineers (IEEE) V&V methodologies, considered the gold standard in the engineering field, the model was verified using a combination of static analysis, spatial convergence, and regression tests. Then using dynamic time warping (DTW) initial runs to validate and tune the TEDS model versus the experiment were conducted. This tuning method was accomplished using the INL Risk Analysis Virtual ENvironment (RAVEN) software package. Tuning is required to account for physical phenomena that are less understood within the empirical heat transfer correlations. Through the commencement of this work, a systems-level model of TEDS with associated control systems, sensors, piping diameters, and component capabilities has been created. This model was utilized in the pre-experimental phase to inform system design, insulation thicknesses, and potential control schemes to operate the system effectively and safely. Then, initial experimental startup and operational data were used to demonstrate the validation and tuning methodology. This process demonstrates the classical two-step approach of a model informing experimental design followed by the experiment validation and tuning the model.
This report describes the continuation of the Computational Fluid Dynamics (CFD) model of the Supo cooling system described in the reports, Supo Thermal Model Development, II, and III. In the previous reports, the solution natural convection of Supo was simulated using a 2D, axisymmetric model that altered the actual geometry. A 3D geometry of Supo will more accurately estimate the natural convection heat transfer coefficient (HTC) of the system since Supo contains several non-symmetric internal components that can only be modeled in 3D. This, in turn, will improve the natural convection HTC results for comparison purposes with past operational data and previous 2D CFD results. Unfortunately, the complexities of performing multiphase 3D CFD simulations has proven too large of a task for our current capabilities. Instead, this report will focus on the 3D model development for the 2.39 kW/L (31.3 kW) case and limitations and challenges associated with the 3D simulations.