In-house Experiments Research Articles

Effective diffusional limitation modeling of a heterogeneous reaction system for computational fluid dynamics application

Diffusional limitation of heterogeneous reaction catalysts is typically fitted or over-generalized without considering the catalyst’s physical parameters upon conducting computational fluid dynamics (CFD) analysis of an industrial-scale chemical reactor. Fitting and over-generalization are done to meet computational cost constraints. However, this reduces the reliability of local thermodynamic state analysis and application of calculated results. This study proposes an effective catalytic diffusion-limited model for reliable and cost-effective three-dimensional CFD simulations to overcome such difficulties. The proposed model utilizes simplified equations to compute the diffusional limitation by incorporating the physical parameters of the catalyst. The model is validated using Ni-based cylindrical catalyst pellets for steam–methane reforming and ammonia decomposition reactions, with in-house experiments supporting the fidelity of the model. For practical implementation, the model is applied to three-dimensional CFD simulations of a commercial-scale solid oxide fuel cell (SOFC) hotbox housing a large-scale reformer. Furthermore, a parametric study for inlet gas temperatures and catalyst size is carried out, which provides useful insights into how operating conditions and catalysts affect the transport phenomena and local thermodynamic state of the SOFC hotbox. Consequently, the practicality and versatility of the presented model are established through experiments and simulations.

A Decoding Method Using Riemannian Local Linear Feature Construction for a Lower-Limb Motor Imagery Brain–Computer Interface System

Recently, motor imagery brain–computer interfaces (BCIs) have been developed for use in motor function assistance and rehabilitation engineering. In particular, lower-limb motor imagery BCI systems are receiving increasing attention in the field of motor rehabilitation, because these systems could accurately and rapidly identify a patient’s lower-limb movement intention, which could improve the practicability of the motor rehabilitation. In this study, a novel lower-limb BCI system combining visual stimulation, auditory stimulation, functional electrical stimulation, and proprioceptive stimulation was designed to assist patients in lower-limb rehabilitation training. In addition, the Riemannian local linear feature construction (RLLFC) algorithm is proposed to improve the performance of decoding by using unsupervised basis learning and representation weight calculation in the motor imagery BCI system. Three in-house experiment were performed to demonstrate the effectiveness of the proposed system in comparison with other state-of-the-art methods. The experimental results indicate that the proposed system can learn low-dimensional features and correctly characterize the relationship between the testing trial and its k-nearest neighbors.

Experimental Study on the Coefficient of Internal Frictional Resistance in the Annular Gap during the Plunger Gas Lift Process

Plunger gas lift process technology is an economical solution to the problem of gas well liquid buildup. However, in-house simulation experiments revealed that the high-speed movement of the plunger may lead to fluid leakage and generate annular gap frictional resistance. To address this issue, a detailed experimental study was conducted to comparatively analyze five existing frictional-resistance models, which were found to have significant deviations. Therefore, we propose a new model of annular gap frictional resistance and validate it with experimental data, and the results show that the new model is more accurate and reliable. We also conducted a comparative analysis of production-site examples by using VB programming and found that when considering the annular gap frictional resistance, the upward travel time of the plunger was delayed, the difference between the upper and lower end face pressures was significant, and the difference in speed was 1.73 m/s. This indicates that the annular gap frictional resistance cannot be ignored and is crucial for optimizing plunger gas lift process technology and improving the drainage efficiency of gas wells.

An investigation comparing various numerical approaches for simulating the behaviour of condensing flows in steam nozzles and turbine cascades

The process of condensation takes place within the low-pressure stages of the steam turbine when undergoing rapid expansion. The dry supercooled steam turns into very tiny liquid droplets, and the resulting wet steam mixture causes additional losses and maintenance problems. Accurate simulation of steam condensing flows helps to obtain accurate information about the thermodynamics of such flows and use this information in the design of highly efficient steam turbine stages. In this study, by using the available numerical models in commercial CFD codes, seven cases are considered for simulating steam condensing flows. The important difference between these cases is the use of different condensation models and different equations of state to calculate the thermodynamic properties of wet steam. The objective of this research is to identify an appropriate model that can be used to simulate the flow of steam condensation effectively. In recent studies, it is still necessary for further improvement of the modelling methods because the numerical results did not fully match the experimental findings, underscoring the persisting uncertainties within this domain. The current study explores various condensation models and equations of state for calculating the thermodynamic properties of wet steam. This diversity of approaches is novel and allows for the comparison of different models to determine the most suitable one for effective flow simulation. The geometries of the IWSEP nozzle (International Wet Steam Experimental Project) and linear cascade of the last-stage rotor blades were considered. To identify the most appropriate model for the simulation of steam condensing flows, the numerical results are validated against in-house experiments. The results indicate that combining the Young droplet growth model with the Vukalovich and Young equations in cases 1 and 2 is not a suitable method for modelling steam condensing flows. However, cases 3 & 4, which are the combination of Gyarmathy and Fuchs-Sutugin droplet growth models with NIST real gas models, are the most appropriate ones. The findings of this research confirm the high sensitivity of the modelling of such flows, and emphasize that the necessary care should be taken in choosing the appropriate numerical model.

Thermal runaway fault prediction in air-cooled lithium-ion battery modules using machine learning through temperature sensors placement optimization

The rise of severe accidents caused due to thermal runaway (TR) and its propagation in lithium-ion battery (LiB) modules is one of the most challenging factors that decelerate the rapid expansion of the electric vehicle (EV) industry. Timely detection of the TR undergoing cells in the module is crucial as the heat generated during TR is adequate to trigger the TR of the surrounding cells. In this study, an accurate machine learning (ML) based faulty cell position prediction model is developed for the air-cooled cylindrical LiB modules with the cells in aligned, staggered, and cross arrangements. The CFD model used for data generation is validated with the in-house experiments on an aligned surrogate 32-cell module for multiple failure positions. Further, to predict the TR cell position in the battery module, the random forest classification (RFC) model is developed based on the temperature distribution data obtained from the optimized temperature sensors derived for the two types of initial temperature sensor distributions (single and multiple-planes) using a heat map approach. The model developed is tested for varying design and operating conditions, and the prediction results, along with the error metrics and the prediction timings, are compared. It is revealed that except for the cross-cell arrangement in the single-plane temperature sensors distribution scenario, the RFC model produces higher accuracy when tested on the optimized temperature sensor layouts for the multiple-plane sensor distribution. The results of this study can allow early failure detection in battery modules, resulting in increased safety and cost savings.

An inverse methodology to estimate the thermal properties and heat generation of a Li-ion battery

Accurate estimation of temperature-dependent orthotropic thermal properties and volumetric heat generation of a Li-ion battery is crucial for thermal modeling, thermal safety, and the design of thermal management systems for electric vehicles. Though various studies are available on estimating thermophysical properties and heat generation, simple and easily applicable methods are rare. Moreover, these studies failed to report temperature sensitivity and standard deviation of the estimates. In this study, the temperature-dependent orthotropic thermal conductivities (kr,kθ,kz), specific heat (cp), and volumetric heat generation (qv) of Panasonic NCR18650BD cylindrical battery are estimated using an inverse approach (Metropolis Hastings-Markov Chain Monte Carlo algorithm based Bayesian method) with the help of experimentally obtained surface temperatures measured at convenient locations on the battery. From the estimation, the average values of kr,kθ,kz and cv are observed to be 3.18 ± 0.19, 20.34 ± 1.26, 19.89 ± 1.29 (W/mK), and 3180 ± 202 (J/kgK), respectively. The average heat generation rates from the same battery obtained using the same methodology are 0.1 ± 0.005, 0.34 ± 0.012, and 1.51 ± 0.026 W for 0.5, 1, and 2C discharge rates, respectively. The estimated thermophysical properties and heat generation rates are in good agreement with the results obtained from both in-house experiments and literature. In addition to the estimation of thermophysical properties and heat generation, the proposed methodology opens vistas to predict the strength and location of hotspots in the battery domain, which helps in designing appropriate and effective thermal management systems for battery packs.

Part-scale thermal modelling of the transfusion step in the selective thermoplastic electrophotographic process

The working temperature of any 3D printer has a critical effect on process feasibility as well as the final quality of the product. In this respect, thermal analysis can provide a comprehensive understanding of operation parameters and optimization potential. This most certainly also is the case for the new layer-wise additive manufacturing system, selective thermoplastic electrophotographic process (STEP). In the present paper, we propose a 3D part-scale finite element thermal model for multi-materials which is developed in the commercial software Abaqus/CAE 2021. The reduced-order method, flash heating (FH), is adopted in the model to obtain good accuracy with acceptable simulation time. A specific analysis of the trade-offs between accuracy and CPU-time is carried out by varying the amount of lumping in the meta-layers in the FH method. Furthermore, we conduct an in-house experiment in which we use IR cameras for measuring temperatures during manufacturing, and the results are applied for model validation and calibration. We specifically compare measured and numerically predicted average surface temperatures when steady state is obtained after printing of each layer. Here we obtain a mean error up to 6% depending on the thickness of the meta-layers. Moreover, parametric studies show that pulse duration and heater intensity significantly influence both the surface and bulk temperature profiles, and this provides us with an increased understanding of the thermal behavior of the recently developed STEP process which in turn could make way for further process optimization.

Dynamic crashing behaviors of prismatic lithium-ion battery cells

With rapid rising use of lithium-ion batteries (LIBs) for electric vehicles (EV), the mechanical behaviors of LIBs have become more and more important to crash safety. This study aims to investigate dynamic crashing characteristics of prismatic LIB cells through compression tests and finite element (FE) modeling. First, the in-plane and out-of-plane constrained compressive tests of battery specimens without electrolyte are performed at various strain rates to characterize mechanical behaviors of LIB cells under the dynamic loading conditions. The experimental results indicated that the strain rate was of significant effect on the in-plane constrained compression tests but insignificant effect on the out-of-plane counterparts. The nominal stress–strain curves obtained from the dynamic in-plane loading conditions are generally higher than those from the quasi-static conditions. The nominal stress–strain curves obtained from the experimental tests were used to develop homogenized material models for the LIB cell components subject to dynamic crashing conditions. Second, the FE models for the prismatic LIB cell were created by incorporating the homogenized material model. The simulation results were compared with those obtained from in-house experiments including the in-plane and out-of-plane indentation tests on the LIB cell specimens, which exhibited fairly good agreement. Finally, based upon the validated FE models of LIB cell, the indentation of entire prismatic LIB was simulated to further characterize the mechanical behavior of the Lithium-ion batteries under dynamic crushing. The established FE models are anticipated to provide a useful approach for crash safety design in the LIB packs for different impact accidents of electric vehicles.

Lesion Dosimetry for [177Lu]Lu-PSMA-617 Radiopharmaceutical Therapy Combined with Stereotactic Body Radiotherapy in Patients with Oligometastatic Castration-Sensitive Prostate Cancer.

A single-institution prospective pilot clinical trial was performed to demonstrate the feasibility of combining [177Lu]Lu-PSMA-617 radiopharmaceutical therapy (RPT) with stereotactic body radiotherapy (SBRT) for the treatment of oligometastatic castration-sensitive prostate cancer. Methods: Six patients with 9 prostate-specific membrane antigen (PSMA)-positive oligometastases received 2 cycles of [177Lu]Lu-PSMA-617 RPT followed by SBRT. After the first intravenous infusion of [177Lu]Lu-PSMA-617 (7.46 ± 0.15 GBq), patients underwent SPECT/CT at 3.2 ± 0.5, 23.9 ± 0.4, and 87.4 ± 12.0 h. Voxel-based dosimetry was performed with calibration factors (11.7 counts per second/MBq) and recovery coefficients derived from in-house phantom experiments. Lesions were segmented on baseline PSMA PET/CT (50% SUVmax). After a second cycle of [177Lu]Lu-PSMA-617 (44 ± 3 d; 7.50 ± 0.10 GBq) and an interim PSMA PET/CT scan, SBRT (27 Gy in 3 fractions) was delivered to all PSMA-avid oligometastatic sites, followed by post-PSMA PET/CT. RPT and SBRT voxelwise dose maps were scaled (α/β = 3 Gy; repair half-time, 1.5 h) to calculate the biologically effective dose (BED). Results: All patients completed the combination therapy without complications. No grade 3+ toxicities were noted. The median of the lesion SUVmax as measured on PSMA PET was 16.8 (interquartile range [IQR], 11.6) (baseline), 6.2 (IQR, 2.7) (interim), and 2.9 (IQR, 1.4) (post). PET-derived lesion volumes were 0.4-1.7 cm3 The median lesion-absorbed dose (AD) from the first cycle of [177Lu]Lu-PSMA-617 RPT (ADRPT) was 27.7 Gy (range, 8.3-58.2 Gy; corresponding to 3.7 Gy/GBq, range, 1.1-7.7 Gy/GBq), whereas the median lesion AD from SBRT was 28.1 Gy (range, 26.7-28.8 Gy). Spearman rank correlation, ρ, was 0.90 between the baseline lesion PET SUVmax and SPECT SUVmax (P = 0.005), 0.74 (P = 0.046) between the baseline PET SUVmax and the lesion ADRPT, and -0.81 (P = 0.022) between the lesion ADRPT and the percent change in PET SUVmax (baseline to interim). The median for the lesion BED from RPT and SBRT was 159 Gy (range, 124-219 Gy). ρ between the BED from RPT and SBRT and the percent change in PET SUVmax (baseline to post) was -0.88 (P = 0.007). Two cycles of [177Lu]Lu-PSMA-617 RPT contributed approximately 40% to the maximum BED from RPT and SBRT. Conclusion: Lesional dosimetry in patients with oligometastatic castration-sensitive prostate cancer undergoing [177Lu]Lu-PSMA-617 RPT followed by SBRT is feasible. Combined RPT and SBRT may provide an efficient method to maximize the delivery of meaningful doses to oligometastatic disease while addressing potential microscopic disease reservoirs and limiting the dose exposure to normal tissues.

On the primary silicon precipitation during the eutectic solidification of Al–Si alloys

During the Al–Si eutectic alloy solidification, silicon distribution throughout the melt is not homogeneous which causes silicon segregation. A four phase Eulerian model of solidification is developed and invoked in Ansys Fluent simulation software to track the silicon segregation location and to understand the distribution of the segregating silicon in the Al–Si metal matrix. The recalescence phenomena are also observed during the solidification process. It is found that the silicon segregation is nearly 1.08 volume %, and the silicon is mostly found near the top surface of the simulation domain. It is also revealed through the numerical simulation that the columnar solid phase grains grow rapidly towards the center of the casting in the early stage of the solidification and the segregating silicon phase is generated near the columnar phase. However, the globular solid phases are also observed at the center of the simulation domain. The above numerical predictions are satisfactorily validated with the in-house experiment conducted using a specific alloy known as eutectic Al–Si (LM6).

Cementron: Machine learning the alite and belite phases in cement clinker from optical images

The performance of cement hydrate depends on constituent phases, viz., alite, belite, aluminate, and ferrites present in cement clinker. Traditionally, clinker phases are analyzed from the optical image using empirical image processing techniques. However, the non-uniformity of images, variations in geometry, size of phases, experimental approaches, and imaging methods make it challenging to identify individual phases. Here, we present a machine learning (ML) approach to automatically and accurately detect the major clinker microstructure phases, namely, alite and belite, from optical microscopy images. To this extent, we create an annotated dataset of cement clinker by manually labelling alite and belite phases in microscopy images. We then demonstrate the use of supervised ML methods to train models for identifying alite and belite regions by using a small fraction of annotations on a single image. Further, we finetune the image detection and segmentation Mask R-CNN model using Detectron-2 by considering all the annotated images of the cement microstructure to develop a model for detecting cement phases, namely, Cementron. We demonstrate that Cementron, trained on literature data, generalized well on completely new data obtained from in-house experiments, demonstrating its wider applicability.

Modeling of Nitrogen Removal from Natural Gas in Rotating Packed Bed Using Artificial Neural Networks.

Novel or unconventional technologies are critical to providing cost-competitive natural gas supplies to meet rising demands and provide more opportunities to develop low-quality gas fields with high contaminants, including high carbon dioxide (CO2) fields. High nitrogen concentrations that reduce the heating value of gaseous products are typically associated with high CO2 fields. Consequently, removing nitrogen is essential for meeting customers' requirements. The intensification approach with a rotating packed bed (RPB) demonstrated considerable potential to remove nitrogen from natural gas under cryogenic conditions. Moreover, the process significantly reduces the equipment size compared to the conventional distillation column, thus making it more economical. The prediction model developed in this study employed artificial neural networks (ANN) based on data from in-house experiments due to a lack of available data. The ANN model is preferred as it offers easy processing of large amounts of data, even for more complex processes, compared to developing the first principal mathematical model, which requires numerous assumptions and might be associated with lumped components in the kinetic model. Backpropagation algorithms for ANN Lavenberg-Marquardt (LM), scaled conjugate gradient (SCG), and Bayesian regularisation (BR) were also utilised. Resultantly, the LM produced the best model for predicting nitrogen removal from natural gas compared to other ANN models with a layer size of nine, with a 99.56% regression (R2) and 0.0128 mean standard error (MSE).

Multi-objective optimization of a metal hydride reactor coupled with phase change materials for fast hydrogen sorption time

Recently, the utilization of phase change materials (PCM) for the heat storage/recovery of the metal hydride's reaction heat has received increasing attention. However, the poor heat management process makes hydrogen sorption very slow during heat recycling. In this work, the H2 charging/discharging performance of a metal hydride tank (MHT) filled with LaNi5 and equipped with a paraffin-based (RT35) PCM finned jacket as a passive heat management medium is numerically investigated. Using a two-dimensional mathematical model validated with our in-house experiments, the effects of design parameters such as PCM thermophysical properties and the fin size on hydrogen charging/discharging times of the MHT are investigated systematically. The results showed that the PCM's melting point and apparent heat capacity have a conflicting impact on the hydrogen sorption times, i.e., the low melting point and high specific heat capacity reduce the H2 charging time. In contrast, the hydrogen discharging time follows the opposite trend. As a result, a multi-objective optimization was conducted to simultaneously minimize the H2 charging/discharging times using the thermal properties and size of the PCM. The optimum solutions selected from the Pareto front show that the PCM melting point should be around 42–43 °C for fixed hydrogen ab/desorption pressures of 10/1.5 bar. Moreover, the comparison between the MHT-PCM using the optimized PCM and reference PCM showed that the former reduced the hydrogen charging/discharging times by 48.6 % and 4 %, respectively. At the same time, the hydrogen storage efficiency of the optimal design is 100 % as compared to 96 % for the reference design. Besides, among the practical PCMs, inorganic PCMs (salt hydrates and eutectics) display favorable hydrogen charging time below 3000 s at the expense of hydrogen discharging time (above 7000 s) in our case study.

Experimental and Numerical Study on the Combined Jet Impingement and Film Cooling of an Aero-Engine Afterburner Section

The recent advancement of cooling methodologies for critical components such as turbine blades, combustor liners, and afterburner liners has led to the development of a combination of impingement and film cooling. The present study proposes an efficient cooling technique for a modern aero-engine afterburner liner based on the combination of jet impingement and film cooling. To achieve this, a numerical model is devised to model the film flow over a corrugated liner with several jets impinging over it. The numerical model is validated in a set of in-house experiments as well as against experimental data available in the literature. The experiment is performed for a limited temperature range (i.e., with a low-density ratio). However, the numerical simulations are carried out by varying the blowing ratio from 0.3 to 0.6. The density ratio during the simulations is kept at 3.5. The minimum distance between the impinging plate and the liner is kept at h/D = 1. A detailed analysis of the numerical results indicates a significant drop in the temperature distribution over the liner surface because of the employed cooling technique. The present study also reveals that, under similar operating conditions, the combined jet impingement and film cooling system has the ability to achieve the targeted cooling effect at a lower bleed air flow rate due to its higher effectiveness than that of the standard film cooling arrangement.

Validation of the CFD Tools against In-House Experiments for Predicting Condensing Steam Flows in Nozzles

The issues addressed in this work concern the condensing steam flows as a flow of a two-phase medium, i.e., consisting of a gaseous phase and a dispersed phase in the form of liquid droplets. The two-phase character and the necessity to treat steam as a real gas make the numerical modeling of the flow in the last steam turbine channels very difficult. There are many approaches known to solve this problem numerically, mainly based on the RANS method with the Eulerian approach. In this paper, the two Eulerian approaches were compared. In in-house CFD code, the flow governing equations were defined for a gas–liquid mixture, whereas in ANSYS CFX code, individual equations were defined for the gas and liquid phase (except momentum equations). In both codes, it was assumed that there was no velocity slip between phases. The main aim of this study was to show how the different numerical schemes and different governing equations can affect the modeling of wet steam flows and how difficult and sensitive this type of computation is. The numerical results of condensing steam flows were compared against in-house experimental data for nozzles determined at the Department of Power Engineering and Turbomachinery of the Silesian University of Technology. The presented experimental data can be used as a benchmark test for researchers to model wet steam flows. The geometries of two half nozzles and an International Wet Steam Experimental Project (IWSEP) nozzle were used for the comparisons. The static pressure measurements on the walls of the nozzles, the Schlieren technique, and the droplet size measurement were used to qualitatively identify the location of the condensation onset and its intensity. The CFD results obtained by means of both codes showed their good capabilities in terms of proper prediction of the condensation process; however, there were some visible differences in both codes in the flow field parameters. In ANSYS CFX, the condensation wave location in the half nozzles occurred much earlier compared to the experiments. However, the in-house code showed good agreement with the experiments in this region. In addition, the results of the in-house code for the mean droplet diameter in the IWSEP nozzle were closer to the experimental data.

Numerical analysis of unsteady flow features around a realistic Estate vehicle with hybrid RANS/LES methods

A comparative study is conducted to analyze the flow around a realistic Peugeot Estate car (308 SW) using RKE and SST RANS models, as well as DDES hybrid RANS/LES approach. The authors used previous work on 25° Ahmed bodies to establish the best grid and DDES parameters for external aerodynamics prediction. The aim of this work is to extend and transpose the numerical methodology to a realistic car. Numerical and physical characteristics related to the simulation of this complex flow are therefore carefully investigated. The numerical results are compared to in-house experiments carried out in S2A Stellantis Wind Tunnel. Two grids with 115 and 185 million elements, based on 20 prism layers cells near the wall, are used to compare the three turbulence models. Even with a smaller grid size in the wake and underbody, the predictions of drag and lift coefficients are almost identical. Moreover, refining the near-wall region from 20 to 30 prism layers has no effect on drag coefficient and flow prediction. Consequently, the grid with 20 prism layers and 115M cells is used as reference grid in this work. Then, the flow features between RKE, SST and SST DDES models are compared to two-dimensional (2D) and three-dimensional (3D) experimental results. RANS gives better agreement on aerodynamics forces. However, DDES reproduces accurately the specific three-dimensional topology of the flow around the car, in the wake and the underbody when RANS results are globally wrong.

A Compressible, Coupled Three-Dimensional/One-Dimensional Flow Solution Method for the Seamless Simulation of Centrifugal Pumps and Their Piping

Abstract A compressible finite volume Navier–Stokes flow solver is coupled to a method of characteristics for the seamless turbulent flow simulation of entire pump systems. For the pump, three-dimensional (3D) simulations including cavitating flow conditions are performed, and the piping is treated one-dimensional (1D) by a method of characteristics. Thus, classical boundary conditions at the suction and pressure pipe of the 3D computational domain of the pump are substituted by a two-way coupled 1D piping simulation method. Particular emphasis has been placed on the non-reflecting properties of the 3D–1D coupling interface. For validation, in-house experiments are performed on a low specific speed centrifugal pump in a closed-loop facility. For cavitating flow conditions, excitation on the pump's pressure side by rotor–stator interaction is enhanced over a broad frequency spectrum due to collapsing voids. The suction side piping is shielded by void regions within the blading from the excitation on the pump's pressure side, leading to an acoustic decoupling of the suction side. These experimental observations are reproduced by the new seamless simulation method. In particular, the measured pressure amplitudes are well reproduced for a broad frequency spectrum, at several piping positions, and for a variation of the flow rate and the cavitation intensity. Remaining deviations to experimental data are traced back to the omission of structural compliance and uncertainties regarding the pressure side piping modeling, which will be addressed in future studies.

Numerical and experimental investigation of thermosyphon-driven liquid desiccant loop performance for sustainable indoor humidity removal

Indoor humidity management is one of the most challenging aspects of the control of indoor environment and is energy intensive, especially in hot and humid climates. A promising approach is the removal of the water vapor from the room using hygroscopic materials, otherwise known as desiccants. Desiccant cycles are thermally driven but require a heat sink and electricity-driven mechanical parts for desiccant circulation. Accordingly, this work proposes a thermosyphon-driven membrane-based liquid desiccant loop for sustainable humidity pumping between two areas at different humidity conditions. Mathematical models were developed for the different system subcomponents, which were validated with published data and in-house experiments. The models were used in a parametric study to determine the influence of the design and operation parameters on the system performance under different air conditions surrounding its various sections.It was found that the system was able to pump the air moisture from high to low and low to high humidity areas. Four cases were considered with outdoor conditions raging from moderate and hot and humid to semi-arid climates. The latent load removal density per unit area of the exposed membrane varied between 4 W/m2 to 35 W/m2 over the entire range of considered cases at required heat inputs varying between 150 W and 570 W respectively. The heat input increased with the channel height and the air humidity surrounding both system sides. The added sensible load depended on the heat sink temperature and was more than 65 % lower than removed latent load in most considered cases.

Two-phase analytical modeling and intelligence parameter estimation of proton exchange membrane electrolyzer for hydrogen production

The proton exchange membrane electrolyzer (PEME) is a promising tool for hydrogen production, and internal two-phase transport significantly influences its performance. In this study, a two-phase analytical PEME model incorporating the liquid saturation jump effect was developed, and intelligent parameter estimation using a genetic algorithm was proposed to achieve high-efficiency model validation. In-house experiments and experimental results from numerous papers in the literature were employed to prove the effectiveness of the proposed intelligent parameter estimation. Moreover, the two-phase simulation results demonstrated that the PEME voltage increased significantly when the current density reached the limiting value, and the liquid saturation in the anode catalyst layer (ACL) dropped to nearly zero. Increasing ACL porosity, decreasing ACL permeability, and decreasing ACL thickness could increase the limiting current density within the investigated range. The simulated limiting current density could be > 5 A cm−2 through proper design of the ACL parameters. For high-pressure cathode operation, increasing the cathode pressure and membrane permeability generally benefits water management inside the PEME and therefore increases the limiting current density. This study provides critical support for the design of cells and operating conditions for future PEME studies.

On the possibility of doing reduced order, thermo-fluid modelling of laser powder bed fusion (L-PBF) – Assessment of the importance of recoil pressure and surface tension

Meso-scale, multi-physics simulations of metal additive manufacturing (MAM) processes have so far proven their capability as a reliable tool for predicting potential defect formations. Nevertheless, there is a large number of uncertainty contributions involved with the input process parameters as well as the implemented material properties in these models. As expected, both the process-related and material-related uncertainties affect the outcome of these multi-physics simulations to a large degree. The present work is therefore intended to quantify the impacts of some of the important material/process-related uncertainties involved with meso-scale multi-physics models, on the heat transfer conditions within melt pool. In this respect, a meso-scale multi-physics model of the laser powder bed fusion process of stainless steel 316-L is developed in the commercial Finite Volume Method (FVM) based software Flow-3D and then validated against in-house experiments prior to the main investigation. In the first part of the study, the impact of recoil pressure at different laser linear energy densities (LED) and different laser beam sizes on the melt pool morphology are investigated. It is found that there is a specific threshold of LED below which the melt pool shape is not affected by the recoil pressure and the melt pool fluid dynamics is mostly governed by the Marangoni effect. This threshold increases from 80 J/mm to 280 J/mm when the beam size is increased from 20 μm to 120 μm. Moreover, a parametric study using dimensionless numbers is carried out to understand the impact of different capillary forces on the melt pool shape and size. It is observed that for inverse Bond numbers below 4.105, the depth-to-width ratio of the melt pool is above 1 where the recoil pressure dominates the melt pool dynamics and a keyhole forms. In summary, this study specifies in essence the process window over which specific physics are unimportant so that a lower-fidelity meso-scale model could replace the higher-fidelity multi-physics models.