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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Spectral properties of three-dimensional Anderson model

The three-dimensional Anderson model represents a paradigmatic model to understand the Anderson localization transition. In this work we first review some key results obtained for this model in the past 50 years, and then study its properties from the perspective of modern numerical approaches. Our main focus is on the quantitative comparison between the level sensitivity statistics and the level statistics. While the former studies the sensitivity of Hamiltonian eigenlevels upon inserting a magnetic flux, the latter studies the properties of unperturbed eigenlevels. We define two versions of dimensionless conductance, the first corresponding to the width of the level curvature distribution relative to the mean level spacing, and the second corresponding to the ratio of the Heisenberg time and the Thouless time obtained from the spectral form factor. We show that both conductances look remarkably similar around the localization transition, in particular, they predict a nearly identical critical point consistent with other well-established measures of the transition. We then study some further properties of those quantities: for level curvatures, we discuss particular similarities and differences between the width of the level curvature distribution and the characteristic energy studied by Edwards and Thouless (1972) in their pioneering work, in which the hopping at one lattice edge is changed from periodic to antiperiodic boundary conditions. In the context of the spectral form factor, we show that at the critical point it enters a broad time-independent regime, in which its value is consistent with the level compressibility obtained from the level variance. Finally, we test the scaling solution of the average level spacing ratio in the crossover regime using the cost function minimization approach introduced recently in Šuntajs et al. (2020). The latter approach seeks for the optimal scaling solution in the vicinity of the crossing point, while at the same time it allows for the crossing point to drift due to finite-size corrections. We find that the extracted transition point and the scaling coefficient agree with those from the literature to high numerical accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Optimizing design and dispatch of a resilient renewable energy microgrid for a South African hospital

Lack of access to reliable energy is a major concern for countries in sub-Saharan Africa. The national grids are unable to consistently satisfy demand. Therefore, users turn to distributed generation systems in the form of back-up generators. However, such systems are usually designed based on a rule of thumb. We employ a mixed-integer linear programming model that considers several options such as renewable energy, combined heat and power, and storage technologies, in addition to those on-site, to provide optimal design and dispatch decisions that minimize total cost. We apply this model to a case study for a hospital in South Africa, considering its need for reliable electricity in light of multiple outages that might occur over the course of a year, as well as its high heating and cooling loads. Our results show that optimal design and dispatch decisions for the distributed generation system address reliability challenges, regardless of the time at which they occur. And, these solutions yield millions of dollars in savings, suggesting that technologies such as the absorption chiller may be overlooked in typical designs; its integration can reduce demand charges even in the absence of combined heat and power. We show that total cost is most sensitive to changes in site electrical demand, followed by capital cost, fuel cost, photovoltaic production, and monthly demand charges; changes in fuel cost primarily affect system sizes of combined heat and power and the absorption chiller, while photovoltaic system size is more sensitive to the changes in capital and fuel costs, photovoltaic resource availability, and hourly electrical demand. Finally, an outage simulator demonstrates the ability of our optimized system to sustain with no interruptions in power five-hour outages with probability 1.0 and ten-hour outages with probability 0.65, significant improvements over 0.5 and 0.0, respectively, under a business-as-usual case.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Impacts of PTL coating gaps on cell performance for PEM water electrolyzer

The anode porous transport layer (PTL) is a critical component of polymer electrolyte membrane (PEM) water electrolyzers, providing electrical conduction and water/oxygen transport to the anode catalyst layer. Platinum group metal (PGM) coatings are applied to titanium PTLs to prevent oxidation and preserve conductivity. To implement PEM water electrolysis on a large scale, it is essential to minimize the cost of mass-manufactured cell components while ensuring that defects in materials and components do not adversely affect the performance of the cell. In this study, we created gaps in the Pt coatings that are typically applied to PTLs to understand their effects on cell performance and inform on fabrication tolerances. The PTL coatings were fabricated by sputter deposition of Pt, intentionally leaving uncoated regions in either large patches or thin strips. For either geometry, the cell performance decreased with the increasing fraction of the uncoated regions. However, PTLs containing multiple thin uncoated strips caused a less severe performance effect than PTLs with continuous uncoated regions of the same relative total area. Through analysis of cell performance, impedance, and 3D charge transport modelling, here we demonstrate that both the anode catalyst layer and membrane play a role in distributing current to mitigate the impact of coating defects at a short length scale. These results allow to draw conclusions about the tolerance requirements with regards to PTL coating uniformity in terms of both the size and total area of defects.

08 HYDROGEN↗
DOE OSTI2022

Analysis of predicted mean vote-based model predictive control for residential HVAC systems

Model Predictive Control (MPC) is an advanced process control method that has attracted much attention in building heating, ventilation, and air conditioning (HVAC) systems. Here, this paper analyzes the optimal precooling performance in residential buildings using MPC with two different comfort indices, namely, temperature and predicted mean vote (PMV). It first formulates, for each comfort index, an optimization problem that accounts for different factors, such as weather, home thermal condition, prediction horizon, time-of use (TOU) utility rate, and rated cooling capacity. The problem is then solved, resulting in an MPC strategy that determines the HVAC on/off control signal and minimizes energy cost over a receding time horizon while maintaining thermal comfort. The energy performance difference between temperature-based and PMV-based MPC strategies is subsequently investigated, especially in light of the interior wall surface temperature and under different combinations of the factors. Extensive simulation results demonstrated that the proposed MPC strategies are adaptive and their performances depend primarily on weather, home thermal condition, and prediction horizon, while the impact of TOU utility rate and rated cooling capacity is relatively small. Because the PMV-based MPC strategy can take advantage of the lower interior wall surface temperature due to precooling, it resulted in 8–45% cost savings for the scenarios investigated and an average increase of 0.042–0.113 in the absolute value of the PMV index compared to the temperature-based MPC strategy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Multiscale modeling and nonlinear model predictive control for flue gas desulfurization

The primary source of sulfur dioxide (SO 2 ) emissions is flue gas from fossil fuels-based power plants. SO 2 emissions are known to not only cause health issues, but also have an adverse effect on the environment in various ways. Several Flue Gas Desulfurization (FGD) technologies have been incorporated in power plants. The most popular technology is Wet FGD, where a limestone slurry is used to absorb SO 2 from the flue gas. A detailed droplet scale model describing the instantaneous and finite rate chemistry is developed. The ill-posed Differential-Algebraic Equation (DAE) droplet model is reformulated to a well-posed index-1 DAE through index reduction. The droplet model is integrated with the bulk phase by incorporating gas-liquid mass transfer, and an oxidation reactor model to simulate the dynamic operation of the counter-current spray scrubber. As a result, the model has a well-conditioned Jacobian and overcomes the modeling challenges of previous works and enables numerical solution without requiring carefully selected initialization or specialized solution procedures. The model is successfully validated using power plant measurements, and nonlinear model predictive control (NMPC) studies are demonstrated to optimize recycle stream flowrates to minimize pumping costs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Relaxations of the steady optimal gas flow problem for a non-Ideal gas

Natural gas ranks second in U.S. primary energy consumption. Because most production sites are remote, gas must be transported through pipeline networks equipped with compressors, valves, and other components. For both economic efficiency and system reliability, it is desirable to operate these networks optimally. The governing physics across pipeline components entails nonlinear, non-convex equality and inequality constraints, and the most general steady-flow operations problem is a Mixed-Integer Nonlinear Program (MINLP).This work focuses on one such steady-flow problem-the Optimal Gas Flow (OGF) for a natural gas pipeline network-which minimizes production cost subject to the steady-flow physics. For day-to-day operations, the ability to quickly compute a globally optimal solution and a strong lower bound for varying demand profiles is crucial. A promising strategy is to build tight relaxations of the OGF’s nonlinear constraints. However, many nonlinearities arising from non-ideal equations of state either lack relaxations or have relaxations that do not scale to realistic network sizes. We address this gap by combining recent advances in polyhedral relaxations for univariate functions to construct tight, computationally efficient relaxations of the OGF with a non-ideal equation of state. These relaxations solve within seconds on a standard laptop. In conclusion, we demonstrate their quality through extensive numerical experiments on very large-scale test networks from the literature and find that the proposed approach proves optimality in 92% of tested instances.

03 NATURAL GAS↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2022

A spatial superstructure approach to the optimal design of modular processes and supply chains

Modularity is a design principle that aims to provide flexibility for spatio-temporal assembly/disassembly and reconfiguration of systems. This design principle can be applied to multiscale (hierarchical) manufacturing systems that connect units, processes, facilities, and entire supply chains. Designing modular systems is challenging because of the need to capture spatial interdependencies that arise between system components due to product exchange/transport between components and due to product transformation in such components. In this work, we propose an optimization framework to facilitate the design of modular manufacturing systems. Central to our approach is the concept of a spatial superstructure, which is a graph that captures all possible system configurations and interdependencies between components. The spatial superstructure is a generalization of the notion of a superstructure and of a p-graph used in process design, in that it encodes spatial (geographical) context of the system components. Here, we show that this generalization facilitates the simultaneous design and analysis of processes, facilities, and of supply chains. Our framework aims to select the system topology from the spatial superstructure that minimizes design cost and that maximizes design modularity. We show that this design problem can be cast as a mixed-integer, multi-objective optimization formulation. We demonstrate these capabilities using a case study arising in the design of a plastic waste upcycling supply chain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Optimizing shift selection in multilevel Monte Carlo for disconnected diagrams in lattice QCD

The calculation of disconnected diagram contributions to physical signals is a computationally expensive task in Lattice QCD. To extract the physical signal, the trace of the inverse Lattice Dirac operator, a large sparse matrix, must be stochastically estimated. Because the variance of the stochastic estimator is typically large, variance reduction techniques must be employed. Multilevel Monte Carlo (MLMC) methods reduce the variance of the trace estimator by utilizing a telescoping sequence of estimators. Frequency Splitting is one such method that uses a sequence of inverses of shifted operators to estimate the trace of the inverse lattice Dirac operator, however there is no a priori way to select the shifts that minimize the cost of the multilevel trace estimation. Here we present a sampling and interpolation scheme that is able to predict the variances associated with Frequency Splitting under displacements of the underlying space time lattice. The interpolation scheme is able to predict the variances to high accuracy and therefore chooses shifts that correspond to an approximate minimum of the cost for the trace estimation. We show that Frequency Splitting with the chosen shifts displays significant speedups over multigrid deflation, and that these shifts can be used for multiple configurations within the same ensemble with no penalty to performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Trends in best-in-class energy-efficient technologies for room air conditioners

Improving the efficiency of room air conditioners (RACs) could provide significant energy and associated emissions savings, particularly in emerging economies with hot climates where the cooling demand is expected to increase dramatically. To help accelerate efficiency improvements, this study identifies “best-in-class” high-efficiency RAC components and products. The findings show that manufacturers tend to minimize manufacturing costs by using RAC designs that are readily available or standardized to their production, and they share components across various models. High-efficiency RAC models use advanced compressor technologies optimized at a low frequency, large heat exchangers with thermodynamically effective materials and designs, highly efficient direct current fan motors, advanced metering devices, and smart sensors for temperature and humidity control. Recently RAC manufacturers have been improving seasonal efficiency – better reflecting part-load operation – especially via variable-speed (inverter) drives for compressor motors. Recent highest-efficiency RAC models use low global warming potential (GWP) refrigerants, having transitioned from conventional high-GWP refrigerants, in regions where RACs that use these low-GWP refrigerants are commercially available. Recently demonstrated innovative technologies show trends toward smart hybrid designs, evaporative cooling, and solid-state materials beyond the conventional vapor-compression technology. This information could help policymakers improve their RAC market-transformation programs to align with the most-efficient global technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

An evaluation of the demand response potential of integrated dynamic window and HVAC systems

Demand response (DR) increases the flexibility and reliability of the electricity grid as use of intermittent renewable energy sources increases. HVAC and envelope DR measures present the largest aggregate energy and peak demand savings potential of all commercial building end uses because their net demand savings occur during critical peak demand periods. Controllable envelope measures include switchable electrochromic windows, operable window attachments such as outdoor louvers, roller shades, and awnings, as well as other innovative facade technologies that can modulate both solar heat gain and daylight admission over a broad solar-optical range. This study evaluated the technical potential of DR-enabled dynamic windows to reduce critical peak demand for a prototypical medium office building situated in all 16 U.S. climates. Model predictive control (MPC) algorithms were designed to minimize electricity cost in daylit perimeter office zones through control of an electrochromic window with and without HVAC thermostat setpoint control. Conventional and time-of-use rates were used to shape the degree of DR. Median annual peak demand savings with window and thermostat control across all climate zones were 24.3 kW (4.4 W/m 2 ) per building or 15.9 W/m 2 for non-north perimeter zones. Resource adequacy at the whole building level was estimated to be 13.1 to 43.4 $/kW per year over the 30-year life of the installation. Co-benefits were increased energy efficiency, and reduced electricity cost and emissions. Visual and thermal comfort requirements were met at all times. Dynamic facades controlled by MPC have substantial technical potential for DR across all U.S. climates and warrant serious consideration for inclusion in DR portfolios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗
DOE OSTI2020

DICE-gas turbine compound reheat combined cycle

Coal-fired Direct Injection Carbon Engine – Gas Turbine (DICE-GT) Compound-Reheat Combined Cycle (CRCC), is a combined cycle power plant comprising a multiplicity of coal-fired reciprocating internal combustion engines (RICE), which is commonly referred to by the acronym DICE (Direct Injection Carbon Engine); a natural gas-fired gas turbine (turbine and combustor); a heat recovery steam generator (HRSG); a steam turbine generator; and an integrally geared and intercooled centrifugal air compressor. Finally, the concept uses coal-water slurry/fluid in the DICE and natural gas (or hydrogen) in the gas turbine for unmatched efficiency (well above 50% net LHV) and modularity (block sizes of 120, 240 and 360 MW) with easy adaptability to carbon capture and sequestration with minimal additional cost, complexity and performance hit.

42 ENGINEERING↗
DOE OSTI2023

Fast model-based scenario optimization in NSTX-U enabled by analytic gradient computation

Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. Here, this work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Designing a repository in domal salt: The influence of design variants in different modelling environments

To understand the long-term environmental impact of disposing radioactive waste of in a deep geological repository and to optimise its design, performance assessments are used. In this study, four teams (COVRA, GRS, Quintessa, and DOE) modified the previously developed generic repository of DECOVALEX task F2 to identify commonalities and differences between the teams for specific changes in repository design. The teams tested six design modifications: (1) Replacing concrete abutments with run-of-mine salt; (2) Replacing the salt seal with a concrete abutment and using run-of-mine salt instead for the two concrete abutments in each drift seal; (3) Halving the size of the infrastructure area; (4) Using run-of-mine salt instead of gravel for backfilling the infrastructure area; (5) Disposal of spent nuclear fuel without the POLLUX-10 containers (6); Lower initial saturation of the spent nuclear fuel and vitrified waste disposal drifts. Despite different modelling strategies used, models agreed that a smaller infrastructure area has a limited effect on radionuclide transport. Responses to the absence of the two concrete abutments in each seal, the use of single large concrete abutments (200 m each), or the use of run-of-mine salt in the infrastructure area differ between teams due to differing modelling assumptions. Based on these results, the estimated efficacy of containment depends strongly on the model assumptions of each team. More specifically, it appears to depend on the compaction model used and therefore on the backfill material used in different areas of the repository. However, the drift seal appears to be a critical design element in all models, effectively limiting radionuclide transport by hydrologically disconnecting sections of the repository. Additional beneficial design choices include the use of dry salt in disposal drifts to limit radionuclide transport and reducing the infrastructure area costs and minimizing host rock damage.

DECOVALEX↗
DOE OSTI2022

Process design of the piperazine advanced stripper for a 460 MW NGCC

The Texas Carbon Management Program is conducting a Front-End Engineering Design (FEED) for Piperazine with the Advanced Stripper™ (PZAS™) at a natural gas combined cycle power plant in west Texas. Here, this paper presents the design of the advanced stripper system with an emphasis on the selection of packing type, packing height, column diameter, and heat exchanger size, demonstrates the effect of ambient temperature swing on the regeneration energy requirement, and investigates two alternate process configurations that provide better energy performance than PZAS™. The design uses 2 stripper trains, and each train uses two cold cross exchangers, one hot cross exchanger, a stripper column, a CO 2 exchanger, and a condenser. Each column consists of two sections of random packing with RSR 2 packing at the top and RSR 3 packing at the bottom, with a total packed height of 7.5 m. The cold exchanger was designed with an NTU of 6, the hot exchanger with an NTU of 2, and the CO 2 exchanger with an NTU of 5. At 0.4 rich loading and 0.2 lean loading, this design resulted in a heat duty of 2.85 GJ/tonne CO 2 . The heat duty can vary up to 3.1 GJ/tonne CO 2 due to lower rich loading at higher summer temperature, but the absolute steam requirement is 8–15% lower in the summer. An alternate design, PZAS with hot rich bypass, is proposed, which can reduce the equivalent work of stripping by 1.5–3.5% compared to PZAS at low heat exchanger size, due to a reduction in stripper driving force. Using a hot rich bypass increases the vapor fraction of the total rich bypass stream from 0.3 to 1 mol% compared to using the warm rich bypass, which also contributes to a reduction in lost work in the column. PZAS with hot rich bypass outperforms PZAS in energy requirement below a packing height of 15 m, but fails to do so above 15 m of packing due to a column pinch that results in little change in LMTD of the column. PZAS with hot rich bypass has the potential to minimize capital cost in addition to reducing energy requirement as it requires less packing to strip CO 2 than PZAS and requires only one cross exchanger with 8 heat transfer units.

03 NATURAL GAS↗
DOE OSTI2020

Phase field modeling of discontinuous dynamic recrystallization in hot deformation of magnesium alloys

We have developed a quantitative phase field model of discontinuous dynamic recrystallization (PF-DDRX) for investigating grain structure evolution and its impact on mechanical response in magnesium alloy during hot deformation. A set of isothermal compression tests were conducted by Gleeble thermo-mechanical simulator and used to determine PF-DDRX model parameters such as critical stresses of recrystallization, activation energies of dislocation, grain boundaries and recrystallization grain nucleation. In the simulations, the experimentally measured average grain size and distribution were used to generate initial microstructures. The results show that the typical ‘necklace’ microstructures observed in DDRX forms at the earlier stage of recrystallization. The inhomogeneity of microstructures first increases rapidly and then decreases gradually and reaches a constant with the increase of dynamic recrystallization volume fraction. Both predicted grain structure and mechanical response are in good agreement with experimental results. This demonstrates that the model parameters determined by experimental data are reliable for the developed PF-DDRX model to predict the grain structure evolution and flow behavior of AZ80 magnesium alloy under hot deformation. This work presents a path to combine experiments and modeling to give quantitative prediction and minimize experimental cost.

magnesium alloy, Phase field method, dynamic recry↗