Engineering topics
Publications and source records attributed to Fu, Yucheng.
Cell performance optimization is important for improving the system efficiency of a redox flow battery. To gain better insights into key controlling factors Of system efficiency, this work first proposed a theoretical model for a unit cell by extending a two-dimensional analytic model to a full cell. The model is then used for cell performance optimization after validating it with experimental and numerical modeling data.
Here, we have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.
Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low-capacity degradation in 10 cm$^2$ cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier to commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm$^2$ DHP-based AORFB by combining a physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. Such combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.
Since 2004, there has been extensive effort towards the development of a uranium molybdenum monolithic fuel system to convert high performance research and test reactors. The RERTR-6 experiment was the first to attempt a monolithic fuel instead of a dispersed fuel form. The fabrication methods evolved overtime and provided the basis for current fabrication methods. The various steps in the process inevitably tailor the fuel alloy microstructure which is known to influence irradiation behavior. This document aims to present and discuss the fabrication evolution that transpired through a recounting of historical data from the various fabrication campaigns. By overlaying this data with basic science studies on the U-Mo that explored transformation kinetics, it is possible to estimate a measure of the impact heat treatments have on the final as-fabricated microstructure.
Aqueous organic redox flow batteries (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness and scalability. The rapid discovery of aqueous soluble organic (ASO) redox-active materials necessitates efficient machine learning surrogates for predicting battery performance. The physics-guided continual learning (PGCL) method proposed in this study can incrementally learn data from new ASO electrolytes while addressing catastrophic forgetting issues in conventional machine learning. Using a AORFB database with a thousand potential materials generated by a 780 $\text{cm}^2$ interdigitated cell model, PGCL incorporates AORFB physics to optimize the continual learning task formation and training strategies to retain previously learned battery material knowledge. Finally, the trained PGCL demonstrates its capability in assessing emerging ASO materials within the established parameter space when evaluated with the dihydroxyphenazine isomers.
The contact angle is a critical factor for determining the effective mass transfer area for carbon dioxide (CO 2 ) capture via the chemical absorption process in a packed column, and thus the overall capture efficiency of the packed column. Many widely used commercial packings involve microscale features (perforation, corrugation, etc.) that may also affect the wetting behavior. This study proposes a systematic method of using a modified Wilhelmy plate to measure the effective contact angle to characterize the solvent and featured packing interaction. In lieu of computational efforts relying on assumptions guided by semi-informed correlation, the proposed method directly measures the effective contact angle as a function of the solvent and packing thermophysical and hydrodynamic properties. The characterization of the effective contact angle is then integrated in the computational fluid dynamics modeling to reduce uncertainty in the prediction of effective mas transfer area. Experiments were conducted for stainless steel coupons using water and aqueous sodium hydroxide (NaOH) solvent for verification of the proposed experimental protocol. The surface tension of the aqueous NaOH solvent was altered using surfactant and antifoam. The effective contact angle increases with the increased value of the surface tension for flat stainless steel sheets. On the other hand, effective contact angles do not vary in Mellapak coupons for aqueous monoethanolamine (MEA) and NaOH solvent. In this case, surface textures and sheet design play a dominant role in the surface tension of solvents. Furthermore, CO 2 loading has a significant effect on the contact angle for Mellapak coupons. As expected, the contact angle decreases with the increasing temperature of CO 2 capture solvents (MEA, EEMPA). The effective contact angle measurement using the Wilhelmy plate method can provide more accurate and efficient solvents for characterizing the solvent-packing surface interactions. Subsequently, the present method enhances the accuracy of the prediction of the effective mass transfer area in carbon capture by solvent absorption.
In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a detailed representation of the electrochemical reactions, mass transport and hydrodynamics occurring inside the redox flow battery. To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift. To fix the shift, the PINN is enhanced by further constrains derived from the current collector boundary. Finally, we show that the enhanced PINN can be even further improved if a small number of labeled data is available.
Ductile-phase toughened tungsten (DPT W) composites have demonstrated a great potential for plasma-facing components in fusion power systems. These materials can retain the outstanding thermomechanical properties of W while significantly enhancing the fracture toughness. The DPT W composite in this study consists of W particles (88 wt.%) embedded in a ductile NiFeW (12 wt.%) solution matrix. Irradiation of the composite was performed with 90 keV He + ions to 1.0 × 10 17 He + /cm 2 at 973 K. The He cavities formed in both W and NiFeW phases are examined using convergent-beam scanning transmission electron microscopy (CB-STEM) at an atomic-level resolution. Full-range depth profiles of the He cavity diameters and number densities are analyzed. The results show that the average cavity diameter in NiFeW is ~2.7 times that in W, while the number density is an order of magnitude lower. A quantitative analysis of the He density and pressure in the cavities is also achieved using STEM electron energy loss spectroscopy (STEM-EELS) mapping. The He atoms in the cavities in NiFeW are found to be under-pressurized, while those in similar-sized cavities in W are not detectable. Further, the He energy-loss shift exhibits a linear relationship with the He density in cavities in NiFeW, with the slope comparable to that for martensitic steel.
CO2 Capture CFD (C3FD) dataset contains STAR-CCM+ simulations of a CO2-capturing solvent flowing across packing structures in a column. This dataset contains 50 2D and 50 3D simulations, each of which spans 500 timesteps. The 2D and 3D simulation meshes have 150,073 and 3,129,815 nodes, respectively. Each node contains information about its physical location, liquid volume fraction, momentum, and node type.
Abstract The lithium–sulfur (Li–S) battery is a promising technology for large‐scale energy storage and vehicle electrification due to its high theoretical energy density and low cost. Reducing the sulfur cathode porosity has been identified recently as a viable strategy for improving the cell practical energy density and minimizing pore‐filling electrolytes to extend cell life at lean electrolyte conditions. Direct use of a low‐porosity cathode for Li–S battery results in poor electrode wetting, nonuniform electrode reactions, and thus early cell failure. To understand and mitigate the barriers associated with the use of low‐porosity electrodes, multiscale modeling is performed to predict electrode wetting, electrolyte diffusion, and their impacts on sulfur reactions in Li–S cells by explicitly considering the electrode wettability impacts and electrode morphologies. The study elucidates the critical impact of low tortuosity and large channel pore design for promoting electrode wetting and species diffusion. It is suggested that the secondary particle size should be comparable with the electrode thickness to effectively promote electrolyte wettability and sulfur reactivity. This study provides new insights into the low‐porosity electrode material and designs and is expected to accelerate the development of practical high‐energy Li–S batteries.
In this work, a previously validated coupling of Kinetic Monte Carlo (KMC) Potts Model and finite element method (FEM) simulations was implemented to investigate the effects of microstructural features in as-cast and homogenized monolithic U-10Mo foils on the emergent microstructure after rolling and reheating. Parameters that could potentially affect recrystallization behavior of the rolled U-10Mo foils were considered: grain size distribution, uranium carbide (UC) size distribution, UC volume fraction, spatial distribution of UC, and rolling reduction magnitude. Grain structure and the magnitude of rolling reduction have the strongest influence on recrystallization kinetics and the fabricated grain size distribution. The UC distribution had only a weak effect on the recrystallization kinetics and final microstructures. While particle-stimulated nucleation (PSN) occurred in simulation more frequently as grain size increased, its incidence did not appear to considerably affect the recrystallization kinetics or grain size distribution.
Use of aqueous redox flow batteries with organic redox-active materials holds great promise for large-scale and sustainable energy storage. The development of low-cost, highly efficient aqueous redox flow batteries lies in a comprehensive understanding of the electrochemical behaviors of redox-active compounds. In this work, an alkaline redox battery with organic dihydroxyphenazine sulfonate (DHPS) anolyte and ferro-/ferricyanide (Fe(CN) 6 ) catholyte is investigated as a typical example of aqueous redox flow batteries using organic redox-active materials. The electrochemical kinetics of DHPS and Fe(CN) 6 are separately characterized using the symmetrical cell design. The resistance components are calculated directly from the experimental measurement. The key kinetic parameters are extracted and compared for DHPS and Fe(CN) 6 electrolytes. The extracted parameters are validated with symmetrical and full flow cell simulations at different operating conditions. Key parameters and internal loss are also compared with all-vanadium redox flow batteries, representing current state of the art. In addition, our extracted key parameters from a symmetrical flow cell are compared with the measured key parameters by cyclic voltammetry, a widely deployed electroanalytical technique. The cell performance prediction of DHPS anolyte on a 780 cm 2 interdigitated cell is made and found the power density is peaked at 475 mW cm -2 at our measurement condition.
A three-dimensional (3-D) pore-scale model has been developed to construct multiscale fibrous electrodes for redox flow batteries (RFB). New designs, such as biporous electrodes modify electrode structures by creating secondary pores on single carbon fiber to reduce internal battery resistance. Existing pore-scale models only resolve electrodes to the single carbon fiber scale and cannot incorporate recent multiscale electrode designs into numerical models. Our new model aims to bridge this gap and provide a tool to rapidly screen new electrode configurations. Two multiscale electrodes, laser-perforated and biporous electrodes, were investigated at varying operation conditions with the proposed 3-D pore-scale models. The laser-perforated electrode exhibits a reduced pressure drop, but follows the same permeability correlation compared to the corresponding pristine electrode. For the biporous electrode, the added specific surface area and faster reaction kinetics from the secondary pores are the most influential factors leading to improved battery efficiency. However, operating the biporous electrode in limiting current density conditions should be avoided due to the decreased mass transfer efficiency and a more significant voltage loss. Finally, we believe that our 3-D pore-scale model can accelerate the flow battery electrode design process and provide new insights into electrode geometry optimizations.
Single-particle-layer sulfur electrodes are designed to build low-tortuosity through-pores across both vertical and planar directions of an electrode, enabling operation of low-porosity cathodes under practical conditions.
We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.
Computational investigation of the countercurrent flows in an additively manufactured (AM) column packed with triply periodic minimal surface (TPMS) is challenging. Multiphase flow simulations using the volume of fluid (VOF) method can explain the column’s hydrodynamic characteristics, including interfacial area, liquid holdup, pressure drop, flow regime, etc. The interfacial area is a key factor dictating the mass transfer via the absorption process, and thereby the overall efficiency of the column. The impacts of a solvent's physical properties, liquid and gas loads, and contact angle on the hydrodynamics are extensively explored. The solvents include traditional solvent (monoethanolamine [MEA]) used in industry as well as a water-lean solvent (EEMPA) as prospective solvents for carbon capture. As expected, interfacial area and liquid holdup increase with increasing liquid loads (q L ). The EEMPA exhibits higher values for the interfacial area and liquid holdup compared to those for the MEA. The dry and wet pressure drops per unit length in the column at different gas loads (F G ) are calculated and further compared with the empirical correlations for traditional packings. At low gas loads, i.e., in the preloading regime, the predicted values of the wet and dry pressure drops match well with the corresponding values calculated from available correlations. The gas load has a marginal impact on the interfacial area at its lower value, but the higher value of F G (>2.18 $\sqrt{Pa}$) causes flooding in the column. The effect of varying contact angles on the interfacial area and liquid holdup in the preloading regime is also studied. Both interfacial area and liquid holdup decrease with the increase in the value of the contact angle. Altogether, the effect of the contact angle on interfacial area is more pronounced than other parameters influencing the interfacial area.
Effective mass-transfer area plays a key role for post-combustion carbon capture. The static area, an inactive part relative to mass transfer, needs to be differentiated in the interfacial area. In the present study, CFD method is employed to investigate static and dynamic interfacial areas in Mellapak 500.Y structured packing. Here, a wide range of physical properties and loading conditions are included to understand their effects on both areas. The result shows that the relationship between interfacial area and interfacial velocity at the local scale can be described by gamma distribution with good accuracy. The influences of various physical properties derived from simulation are compared with existing experiment-based correlations for both static and interfacial areas. The result shows the dominant influence of viscosity on the static area, and the most importance of the influence of contact angle on the interfacial area. This study also highlights the importance of viscosity, which is usually ignored.
Numerical simulation has become indispensable in advancing cost-effective process optimization and control of flow batteries. We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions in the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is to enforce the physics-based zero-dimensional (0D) VRFB model in a neural network to assure model generalization for various battery operation conditions. However, limited by the simplifications of the 0D model, the PCDNN cannot capture sharp voltage changes in the extreme SOC regions. To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself and call the resulting approach enhanced PCDNN (ePCDNN). By comparing with experimental data, we demonstrate that the ePCDNN approach can accurately capture the voltage response throughout the charge–discharge cycle, including the tail region of the voltage discharge curve. The loss function for training the ePCDNN is designed to be flexible by adjusting the weights of the physics-constrained DNN and the enhanced DNN. In conclusion, this allows the ePCDNN framework to be transferable to battery systems with variable physical model fidelity.