Numerical Simulation Techniques Research Articles

Numerical simulation of CO2 enhanced condensate gas recovery: A case study in pen 5 gas reservoir

AbstractCO2 flooding is an effective technology for suppling condensate gas reservoir pressure and mitigating global warming. In this study, numerical simulation studies have been conducted to enhance gas recovery in Pen5 condensate gas reservoir using CO2 flooding. Pen5 reservoir has lasted 20 years of primary depletion, which affected by water invasion and retrograde condensation. In numerical simulation study, the GEM module is applied to implement history‐matching of production data, and good agreement has been achieved. Based on comprehensive geological understanding, production dynamics, and numerical simulation techniques, areas with minimal impact from water invasion and retrograde condensation contamination have been delineated. The comparison between continued depletion and the implementation of CO2 injection as a replacement technology in favorable areas has demonstrated the feasibility of CO2 flooding development in these regions. The analysis of the advantages and disadvantages of using miscible flooding and immiscible flooding was conducted, and the optimal development well pattern was evaluated. In the end, the plan of CO2 flooding of Pen5 reservoir has been designed and the gas production rate in the next 10 years has been predicted. The research findings of this study have reference significance for the practical application of CO2 flooding in developing condensate gas reservoirs.

Performance and regression analysis of air-cooled thermoelectric cooling system using response surface methodology and numerical simulation

In this study, the response surface methodology (RSM) combined with numerical simulation (NS) techniques is used to perform regression model of an air-cooled thermoelectric cooler (TEC) system. To obtain the interaction models between the thermal resistance (Rh) of the external hot-side heat sink and the internal structure parameters of the TEC (leg height, h; leg width, w; numbers of P/N leg pairs, N), regarding the key performance of the TEC. According to the NS and RMS results Imax, Qcmax, and Qh decline with the rise of Rh under different N, h, and w. The decline rate is rise with the elevation of N, the increase of the w, and the decrease of the h. COP and Tcmin decrease with an increase in h, while increase with an increase in w, N, and Rh. The key factors Rh, w, h and N in the air-cooled TEC system are studied using RSM. The second-order response models for the response variables (COP, Tcmin, Qcmax,Imax and dTmax) with respect to the influencing factors (Rh, w, h and N) are established using the Box-Behnkn Design (BBD) method in RSM.

Numerical simulation of ice loading characteristics of ships in a wave-current environment in a broken ice area

A numerical simulation technique with CFD-DEM coupling is used to simulate a full-scale KCS model sailing in the pancake ice region. Based on the actual polar information, the Stokes fifth-order wave model is selected. The pancake ice particle of two scales is established based on the DEM, and the corresponding broken-ice region is generated. The contact and collision problems of ice-ice and ice-wall (ship) were analyzed by solving the Hertz-Mindlin model, and the monitored data were processed and summarized. The results show that the ice load on the ship is a repetitive “loading-unloading” process, and the peak ice load curve is characterized by segmented and concentrated fluctuations. In terms of ice parameters, the pancake diameter has a great degree of influence on the crushed ice loads, and the size of the crushed ice loads increases with the diameter of the crushed ice particles.

Numerical simulation of welding residual stresses in 6082-T6 thin aluminum alloy

Thin plate aluminum alloys of the 6082 series are widely used in welded car bodies because of their excellent characteristics such as light weight and high strength. For welded joints, residual stresses significantly affect fatigue strength properties, especially in thin plate welding. In this study, the residual stresses arising from inert gas welding of thin sheet aluminum alloy metals were investigated using numerical simulation techniques. The thermo-elastic–plastic method was used to calculate simulated weld temperature and stress fields. Temperature and stress variations during the welding process were discussed to investigate the causes of residual stresses. The changes in stress during the experimental welding process were tracked, and the results were in agreement with the simulations. The impact of process parameters on welding stress was analyzed through simulation and experimentation. The results showed that residual stress decreases as speed increases and increases as power increases.

Complex Deposit Slope Excavation Deformation Mechanism and Seismic Reinforcement Effect Evaluation

Background To further investigate the excavation deformation mechanism and remedial strategies for slope reinforcement in the southwest mountain area, the Baihetan-Jiangsu (Zhejiang) UHV transmission project deposit slope deformation reinforcement was used as a case study. Methods Deep displacement monitoring, on-site testing, and FLAC3D numerical simulation techniques were employed. The assessment of the deposit slope excavation deformation mechanism and the effectiveness of seismic reinforcement for different design options and pile parameters are conducted while analyzing the seismic reinforcement mechanism. Results and Discussions The results showed that (1) The deposit slope composed of “multi-genetic type soil” in the converter station is prone to deformation, where the dominant instability mode is “traction creep and tension failure mode.” (2) Both circular and rectangular anti-slide piles significantly reduce the amount of slope body deformation; employing circular anti-slide piles for addressing this type of deposit slope provides enhanced anti-slide retention and economic benefits. (3) Under the influence of an earthquake, the shear force and bending moment of the anti-slide pile first increase and then decrease with the increase of seismic intensity. Conclusion The distribution law of shear force and bending moment in a pile should be considered comprehensively in slope support design.

Comparison of Single-Phase Mathematical Models for Solid-State Packed Beds for Thermal Energy Storage

This article presents an analytical solution for the evaluation of the thermal performance of packed bed sensible heat storage. The numerical model developed was tested for four different solid storage mediums. The thermal energy equation is solved numerically by deploying the finite difference method. The presented analytical solution is based on a novel mathematical approach. The numerical model was validated using the computer simulation package Comsol Multiphysics v5.3. Our numerical model results are in good agreement with the published experimental data, with an overall difference of ~10%. Hence, the numerical model is an efficient way of evaluating the thermal performance of packed bed thermal energy storage systems compared to other numerical strategies or computer simulation techniques. This proves that the novel analytical model has shown to be a reliable and broadly accurate approach to acquire the thermal performance of sensible heat storage.

Numerical simulation of the lubrication characteristics of bionic surface textures on mechanical sealing rings

Inspired by the shark scale surface in natural world, five different bionic surface textures were designed. Through numerical simulation, the influence mechanism of the geometrical parameters on the lubrication characteristics of the bionic surface textures on the mechanical sealing rings under refrigerant conditions was investigated. The results revealed that the lubrication characteristics of the bionic surface textures is jointly influenced by the texture shape, arrangement, vortex phenomenon, and hydrodynamic pressure effect. The lubrication characteristics of all five bionic surfaces designed in this study was superior to those of the smooth surface. The effects of density and texture depth on the lubrication characteristics of the surface textures exhibits criticality. The results provide valuable insights into the effective design of bionic surface textures on mechanical seal rings, especially in improving their lubrication characteristics under refrigerant conditions. The effectiveness of numerical simulation techniques in evaluating and optimizing the performance of bionic texture design in various micro- and nanofabrication processes is further validated.

Comparison Different Techniques of Optimum Location for Infill Well Drilling

Increasing oil production from a reservoir can be achieved by decreasing the distance between the injector and the producer through a process known as infill drilling, which involves a pattern water flood. The main objective of this study is to provide a comprehensive overview of the optimal infill well location and the research and applications available to enhance the oil recovery factor, leading to increased economic profits. one effective empirical approach used in this study is based on decline curve analysis, which analyzes the production history of the well to determine the final economic recovery. Additionally, a numerical method that combines numerical simulation and optimization techniques has been proven to be successful in determining optimal infill drilling locations. The research results show that the volumetric computation of oil in place is a useful method for estimating the number of infill wells needed, but it does not consider heterogeneity and continuity. On the other hand, the numerical simulation and optimization techniques can quantify the remaining mobile oil post-infill drilling and establish optimal pattern configurations for maximum recovery at their centers.

Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model

PM2.5 constitutes a complex and diverse mixture that significantly impacts the environment, human health, and climate change. However, existing observation and numerical simulation techniques have limitations, such as a lack of data, high acquisition costs, and multiple uncertainties. These limitations hinder the acquisition of comprehensive information on PM2.5 chemical composition and effectively implement refined air pollution protection and control strategies. In this study, we developed an optimal deep learning model to acquire hourly mass concentrations of key PM2.5 chemical components without complex chemical analysis. The model was trained using a randomly partitioned multivariate dataset arranged in chronological order, including atmospheric state indicators, which previous studies did not consider. Our results showed that the correlation coefficients of key chemical components were no less than 0.96, and the root mean square errors ranged from 0.20 to 2.11 µg/m3 for the entire process (training and testing combined). The model accurately captured the temporal characteristics of key chemical components, outperforming typical machine-learning models, previous studies, and global reanalysis datasets (such as Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA)). We also quantified the feature importance using the random forest model, which showed that PM2.5, PM1, visibility, and temperature were the most influential variables for key chemical components. In conclusion, this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data, improved air pollution monitoring and source identification. This approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.

A mechanically consistent unified formulation for fluid-porous-structure-contact interaction

Fluid–structure interaction with contact poses profound mathematical and numerical challenges, particularly when considering realistic contact scenarios and the influence of surface roughness. Computationally, contact introduces challenges in altering the fluid domain topology and preserving stress balance. This work introduces a new mathematical framework for a unified continuum description of fluid-porous-structure-contact interaction (FPSCI), leveraging the Navier–Stokes–Brinkman (NSB) equations to incorporate porous effects within the surface asperities in the contact region. Our approach maintains mechanical consistency during contact, circumventing issues associated with contact models and complex interface coupling conditions, allowing for the modeling of tangential creeping flows due to surface roughness. The unified continuum and variational multiscale formulation ensure robustness by enabling stable and unified integration of fluid, porous, and solid sub-problems. Computational efficiency and ease of implementation – key advantages of our approach – are demonstrated by solving two benchmark problems of a falling ball and an idealized heart valve. This research has broad implications for fields reliant on accurate fluid–structure interactions and promising advancements in modeling and numerical simulation techniques.

Physics informed deep learning for flow and force predictions in dense ellipsoidal particle suspensions

Solid-fluid multiphase systems are ubiquitous in many chemical, pharmaceutical, and energy based applications. These flows are challenging to study experimentally, thus several numerical simulation techniques have been developed. In this paper we use Particle Resolved Simulations (PRS) combined with Deep Learning (DL) to predict drag forces on each particle in suspensions of ellipsoidal particles for use in lower fidelity Euler-Lagrange and Euler-Euler simulations. Suspensions with solid fraction of 0.1, 0.2 and 0.3 are investigated at Reynolds numbers of 10, 50, 100 and 200. A UNet architecture is used to predict the velocity and pressure fields and a CNN is used to predict the drag forces. It is shown that drag force predictions using the predicted velocity and pressure fields give more accurate results than predictions that only use geometric information.

Simulation of viscoelastic free-surface flows with the Particle Finite Element Method

AbstractViscoelastic fluids are central in numerous applications from polymer manufacturing to the pharmaceutical industry and biological research. However, since analytical solutions are generally not available or too complex, it is common practice to study free-surface viscoelastic flows through numerical simulation techniques. This work proposes the use of the so-called particle finite element method (PFEM), a Lagrangian approach combining standard FEM techniques with a remeshing strategy. The PFEM is able to efficiently handle mesh distortion and to accurately track the free-surface evolution. Therefore, it is exploited in this work to deal with large displacements problems in the context of nonlinear viscoelasticity. An implementation of the Oldroyd-B constitutive model in the PFEM framework is here presented including details regarding how to deal with the transfer of the internal variables during remeshing events. Additionally, an innovative approach to impose unilateral Dirichlet boundary conditions ensuring optimal mass conservation is presented. The implementation is verified with two free-surface highly viscous benchmark flows: the impacting drop and the jet buckling problems. The results show perfect agreement with those obtained with other numerical techniques. The proposed framework opens the way for using PFEM in various applications, ranging from polymer extrusion to more sophisticated scenarios involving viscoelastic and viscoelasto-plastic constitutive laws.

Non-destructive Prediction of Quality Parameters in Alphonso Mangoes Using Near-infrared Spectroscopy: A Comprehensive Study on Physicochemical Characteristics and Ripening Dynamics

This study focuses on the application of Near-Infrared Spectroscopy (NIRS) to predict key quality parameters in Alphonso mangoes, namely Total Soluble Solids (TSS), Titratable Acidity (TA), pulp firmness, and carotenoid content. Alphonso mangoes, known as the "king of fruits," are a significant tropical fruit, with India being the leading global producer. Quality control is crucial for fruit export, and the mango supply chain involves various procedures. Physiological transformations occur in mangoes from production to consumption, affecting their internal quality. The challenges in assessing internal quality factors such as soluble solids, dry matter, firmness, starch, and sugar content hinder the exportation of mangoes. The ripening process in mangoes involves metabolic changes leading to modifications in physical, mechanical, and chemical attributes. Non-destructive identification of internal defects in Alphonso mangoes is an underexplored field, with limited research on internal quality factors. The study aims to enhance understanding of light penetration into fruit tissues using numerical simulation techniques. NIR spectroscopy's significance in post-harvest technology is highlighted, and the study develops prediction models for quality parameters using Partial Least Squares (PLS) regression. The calibration models are optimized with spectral pre-processing techniques, and the Savitzky–Golay first derivative method yields favorable results. The results indicate changes in TSS, carotenoid content, firmness, and acidity during storage, providing insights into mango ripening dynamics. The Unscrambler software is employed for multivariate analysis, emphasizing the importance of the 600–1000 nm wavelength range. The study contributes to the development of portable instruments for rapid and accurate quality prediction of Alphonso mangoes in the supply chain.

Advanced numerical simulation techniques in MHD fluid flow analysis using distributed fractional order derivatives and Cattaneo heat flux model

AbstractThis study investigates the flow and heat transfer characteristics of a second‐grade fluid over a flat surface with variable heat flux. Utilizing a mathematical model based on distributed order fractional derivatives, we offer a more precise representation of non‐Newtonian fluid behavior. The associated highly nonlinear equations are tackled numerically through an innovative amalgamation of the finite difference scheme and the technique. Our investigation thoroughly evaluates the influence of several critical parameters on the fluid's motion and thermal characteristics. Notably, the magnetic parameter significantly inhibits the fluid's velocity, illustrating the impact of magnetic forces. Additionally, the power law parameter leads to a decrease in both velocity and temperature profiles, highlighting its influence on fluid dynamics. Furthermore, changes in the Reynold number and the second‐grade fluid parameter are observed to cause a substantial increase in skin friction, by approximately 40% to 50%. These simulation results provide valuable insights into the complex interaction between fluid flow and heat transfer characteristics, enhancing our understanding of such systems in industrial applications.

Hybrid uncertainty propagation based on multi-fidelity surrogate model

There always exist multiple uncertainties including random uncertainty, interval uncertainty, and fuzzy uncertainty in engineering structures. In the presence of hybrid uncertainties, the hybrid uncertainty propagation analysis can be a challenging problem, which suffers from the computational burden of double-loop procedure when numerical simulation techniques are employed. In this work, a novel method for efficient hybrid uncertainty propagation analysis with the three types of uncertainties is proposed. Generally, multi-fidelity surrogate models, such as Co-Kriging, can greatly improve the computational efficiency by leveraging information from a low-fidelity model to build a high-fidelity approximate model. However, the traditional multi-fidelity surrogate model methods always calculate the hybrid uncertainty propagation result by combining with several numerical simulation techniques. This process can introduce post-processing errors unless unlimited number of samples are used, which is impossible in engineering application. In order to address this issue, the analytical solutions of the output mean and output variance are derived based on the Co-Kriging, and the resulting mean and variance are both random variables. Moreover, a new adaptive framework is established to strengthen the estimation accuracy of the hybrid uncertainty propagation result, by combining the augmented expected improvement function and the derived mean random variable. Several applications are introduced to demonstrate the effectiveness of the proposed method for solving hybrid uncertainty propagation problems.

A Deep Learning Method for Dynamic Process Modeling of Real Landslides Based on Fourier Neural Operator

AbstractThe conventional numerical solvers for partial differential equations encounter a formidable challenge, as their computational efficiency and accuracy are heavily contingent on grid size. Recently, machine learning (ML) has exhibited substantial promise in addressing partial differential equations. Nevertheless, substantial hurdles persist in practical applications. In this work, we endeavor to establish a deep learning framework founded on the Fourier neural operator (FNO) for resolving the intricacies of simulating real landslide dynamic processes. Our findings demonstrate that the current FNO approach adeptly replicates landslide dynamic processes and boasts exceptional computational efficiency. Additionally, it is noteworthy that this data‐driven ML methodology can seamlessly incorporate data from other experimental sources or numerical simulation techniques. Consequently, this work underscores the significant potential of utilizing ML methodologies to supplant conventional numerical simulation methods.

Subcooled forced convection boiling flow measured using high-resolution techniques at the COSMOS-L facility and accompanying CFD simulation employing an interface-tracking scheme

This paper describes state-of-the-art experimental measurement techniques and computational fluid dynamics methods applied to subcooled forced convection boiling flow at 2-3 bar, both featuring high spatial- and time-resolution measurements. The objectives are: (i) to provide the measured boiling flow data, e.g. average and RMS profiles of axial and radial velocities and bubble size distributions, and (ii) to evaluate the capabilities of state-of-the-art numerical simulation techniques to model the observed phenomena, by comparison between measured data and numerical prediction. In the experiment, the bubble velocities in the axial and radial directions were measured using laser Doppler anemometry, and the bubble size was estimated by a shadowgraphy method. The simulation results, incorporating an interface capturing technique, showed good agreement with measurement in terms of the wall temperature and axial velocity, but underestimation of the radial velocity, and overestimation of bubble sizes. The paper shows that the discrepancies between measurement and simulation may be traced to: (i) the uncertainties in the nucleation site density model; (ii) too coarse a grid being adopted, which is not able to resolve the thermal boundary layer around the bubbles; and (iii) spurious numerical bubble coalescence, a feature not seen in the experiment. The mechanism of bubble lift-off from the hot surface, without any sliding motion, as observed in the experiment, is discussed in detail, based on the simulation results, and the condensation on the bubble cap as the bubble is ‘sucked’ into the bulk flow is also examined.

Study on the Impact of Cutting Platform Vibration on Stalk Cutting Quality in Industrial Hemp

In response to the unclear impact of header vibration on cutting performance (stalk cutting quality and cutting energy consumption) during field operations of industrial hemp harvesters, this study utilized a vibration recorder to collect information on header vibration during the operation of industrial hemp harvesters. Through data processing, the dominant range for the resonant frequency and amplitude of the cutting platform is primarily concentrated between 5–45 Hz and 0–35 mm, respectively. Using numerical simulation techniques, a quadratic orthogonal rotation combination experiment was conducted with vibration frequency and amplitude as experimental factors and stalk cutting quality and cutting energy consumption as indicators. Regression equations were established to reveal the relationships between indicators and factors, elucidating the influence of each factor and its interactions on the indicators. Specifically, for the crack length indicator, the amplitude has a highly significant influence on the model, and there is a significant interaction effect between vibration frequency and amplitude on the model. As for the cutting energy consumption indicator, frequency and the interaction between frequency and amplitude significantly affect the model, while amplitude has an extremely significant impact on the model. Through comprehensive fuzzy evaluation, the optimal vibration parameter combination satisfying comprehensive cutting performance indicators was determined as a vibration frequency of 37.86 Hz and an amplitude of 5.34 mm. Furthermore, the reliability of the model has been further validated. This research can provide a reference for improving the field performance of industrial hemp harvesters.

Study on cavitation performance improvement of high-power centrifugal pump impeller

High power centrifugal pump is frequently used in large project for water resources allocation. Cavitation phenomenon causes noise and vibration and affects the centrifugal pump unit stability. The impeller model was used an initial impeller to be developed, and the impeller geometry, the blade profile, blade inlet edge were optimized by design method based on numerical simulation technique to improve cavitation performances. The 3D model with the initial and optimized impeller were simulated to obtain cavitation characteristic. The results of optimization impeller are compared and verified by model test. The numerical simulation results show that the pressure distribution is distributed on the blade surface is uniform and the location of lowest pressure is reasonable. The test results show that the optimized design significantly increases the ratio of plant cavitation coefficient σp to incipient cavitation coefficient σi and critical cavitation coefficient σ1 of the impeller under normal operation conditions, the safety margin of cavitation is increased. It was confirmed from numerical simulation and model test results that the impeller blade optimization is a reasonable and effective method for cavitation performance improvement. The study results provides design reference for the cavitation performance improvement and safe operation of high power centrifugal pump.

Numerical analysis on dynamic response and damage threshold characterization of deep rock mass under blasting excavation

The excessive destruction of surrounding rock in deep tunnel will change the original environmental state and destroy the natural ecological balance. Research on the dynamic response characteristics and damage thresholds of rock masses in deep environments plays a crucial role in determining the excavation range of blasted rock and establishing safety construction scheme. This study employs numerical simulation techniques to investigate the dynamic response characteristics of surrounding rock under different ground stress conditions. By introducing the dynamic ultimate tensile strength criterion, critical fracture stress threshold, and maximum damage radius of rock under coupled dynamic-static loading conditions are determined. The research shows that under uniaxial ground stress condition, increasing ground stress inhibits damage to the surrounding rock and the extension of cracks in the excavation area, while imposing restrictions on the attenuation rate of explosive stress. Under bidirectional equal ground stress condition, an increase in lateral pressure coefficient inhibits the development of damage zones along the excavation contour, yet enhances the extension of cracks in the maximum principal stress direction. Moreover, when lateral pressure coefficient becomes excessively large, the attenuation rate of explosive stress significantly increases. Based on the threshold values of peak particle velocity (PPV), the functional relationship is established to predict safety criteria for deep blasting excavation.