Numerical Simulation Model Research Articles

Impact of time-based variability on planning reliability in construction projects: analysis of production management systems

PurposeThis research article proposes a quantitative method for analyzing planning reliability and risk evaluation in construction projects through probabilistic numerical simulation models. The aim is to assess the combined time-based variability of activities across the project and its impact on planning reliability in various production management systems.Design/methodology/approachThe study examines different methods for comparing the impact of production management strategies, specifically standardization and lean construction practices, on project reliability. The article introduces Monte Carlo, importance sampling and first-order-second moment methods as quantitative tools for decision-making and continuous improvement in construction projects.FindingsThe utilization of multiple numerical simulation models demonstrates how the variability in activity durations directly affects planning reliability, leading to uncertainty and increased risk in construction projects. The study highlights the significance of lean construction principles and industrialized processes, such as standardization of processes and construction methods, in reducing variability and improving the average duration of activities. Furthermore, it quantifies the differential impact of various production management systems on project performance and planning reliability.Research limitations/implicationsThis study is based on a limited number of case studies, which may affect the generalizability of the findings. The results are specific to the construction projects analyzed in Colombia. Expanding the research to include a broader range of projects will improve the applicability of the findings. Developing more comprehensive numerical models can also enhance understanding of planning reliability across different construction management systems.Originality/valueCurrently, there are limited quantitative methods available for project managers to evaluate the impact of industrialized construction practices and production management strategies on planning reliability as well as quantifying the associated risk level in project delays. Consequently, the adoption of innovative construction methods is often limited to a few pilot projects, as managers lack numerical evidence to support the implementation of new methodologies that require changes in traditional operational practices.

Study on Metro Operation Safety Based on a Specific Contact Model for Resilient Wheel

Abstract Resilient wheel (RW) is gradually promoted for use in China’s subway vehicles recent years, which has been widely used in tram in Europe and China. As measured, the RWs in service on the metro trains have experienced eccentric wear and even polygonization of high order after 30,000 km running. Given that most subway lines have small curve radii, this study assesses the operation safety of vehicle passing through curves with RWs having polygonal wear, as well as their effect on vibration reduction compared to conventional wheels. A numeric simulation model is developed to model the metro vehicle-track dynamic system installed with resilient wheel, where a conventional wheel-rail contact model is modified considering the composite structure of a RW. A RW-rail contact model considering relatively independent motion of the wheel hub and rim is developed, which is integrated into a metro vehicle-track dynamic system model equipped with the RWs. Upon comparing the curving performance of vehicles equipped with conventional wheels, the findings reveal that, in scenarios of surplus-superelevation, the wheel load reduction ratio (WLRR) of conventional wheels demonstrates superior performance. While curving in the case of deficient-superelevation, the WLRR of RWs is lower. Furthermore, the safety performance of the RWs surpasses that of conventional wheels, particularly as the severity of polygonal wear on the wheel increases. These conclusions can be used as a reference for maintenance strategy of the RW applied on metro trains, which is urgently to be built considering the potentially wide promotion of the RW to the subway system.

Laboratory-Scale Natural Gas Hydrate Extraction Numerical Simulation Under Phase Transition Effect

Phase transition in gas hydrate reservoirs has a significant effect on the fluid flow dynamic when performing test production, which should be carefully studied. This study systematically investigates the phase transition characteristics of natural gas hydrates during the depressurization extraction process through laboratory-scale numerical simulations. First, a laboratory-scale numerical simulation model is established with dimensions of 1 m × 1 m × 1 m. In the simulation, the nanoscale and microscale effect on phase transition is considered. Then, the analysis of how different sediment types and their properties affecting gas production dynamics is presented. The results show that hydrate dissociation and formation are significantly influenced by factors such as the pore scale, salinity, and water content. In particular, montmorillonite had the most significant effect, leading to a 525.25% increase in gas production, while the impact of silty soil was relatively smaller. The increase in salinity inhibited hydrate formation but promoted dissociation, resulting in a significant increase in gas production, especially when the salinity reached to 3.5%, where gas production increased by 590.21%. An increase in water content led to a significant decrease in production. Through monitoring temperature and pressure changes during the extraction process, the different physical fields are analyzed, providing important theoretical support and practical guidance for the efficient extraction of natural gas hydrates.

Machine learning emulators of dynamical systems for understanding ecosystem behaviour

Minimal models (MM) aim to capture the simplified behaviour of complex systems to facilitate system-level analyses that would be unfeasible with more sophisticated numerical models. However, the choices involved in minimal model development heavily rely on expert knowledge, a source of bias that can interfere with good modelling practices. In this paper, a new method is proposed in which a machine learning (ML) model is trained with transient data generated by a detailed physically-based numerical model, predicting the rate of change of the target state variables given their current value and additional drivers. The trained model is then used to mimic the analysis made with traditional minimal models. This approach (ML-MM) is deployed in a semiarid hillslope ecosystem characterising its soil and vegetation components. The ML-MM outputs share most of the general features with previous expert-based results but show a better ability of the hillslope to (1) recover its vegetation, (2) resist total disappearance of the soil and (3) reach substantially higher soil depths in steady state. Furthermore, a new intermediate stable equilibrium is found between the already known healthy and degraded ones, revealing a more complex pattern of ecosystem collapse that avoids a critical shift, as supported by numerical model simulations. The transient behaviour is also investigated, from which we conclude that the system can exhibit strong reactivity, that is, an initial deviation away from equilibrium after a perturbation. In conclusion, the present study demonstrates the potential of ML-MM to obtain new scientific insights on complex systems that might be missed by expert-based alternatives. Hence, minimal models may benefit greatly from incorporating detailed numerical models and data-driven simplification in their development process. Ultimately, this methodology could be applicable to many fields of study and even be expanded to observational data, enhancing our understanding of real-world complex system dynamics.

Enhancing oil recovery and carbon sequestration through alternating injection of methane-rich associated gas and CO2 in deep reservoirs

The direct emission of methane-rich associated gas from oil fields is a significant contributor to the greenhouse effect. Currently, alternative gas flooding predominantly employs CO2 and N2 as displacement phases, with limited research focusing on the alternating injection of methane-rich associated gas and CO2 tailored for oil field production. This study investigates the feasibility of enhancing oil recovery and mitigating the greenhouse effect through the alternating injection of methane-rich associated gas and CO2 into reservoirs using experimental and numerical simulation approaches. Initially, core-scale laboratory experiments were conducted on the alternating injection of methane-rich associated gas and CO2, followed by establishing a corresponding numerical simulation model. The results indicate that, compared to continuous CO2 injection, the alternating injection method results in a 2.48% reduction in oil recovery efficiency. However, this method demonstrates superior control over the mobility of oil, gas, and water. This is because there are two ways to enhance oil recovery efficiency: improve extraction efficiency and increase the swept area. CO2 has a high extraction efficiency but a relatively small swept area. Therefore, we aim to increase the swept area by using an alternating injection method of CO2 and associated gas, where mobility is a key factor in expanding the swept area. Specifically, the mobility ratio between oil and gas decreases from 9.65 to 8.24, and the ratio of oil mobility in the remobilized zone to water mobility in the unswept zone reduces from 0.770 to 0.441, suggesting significant potential for improving the sweep efficiency of heterogeneous reservoirs through alternating injection. Subsequently, numerical simulations were performed at the reservoir scale to investigate the alternating injections of methane-rich associated gas and CO2 with varying slug sizes. At the reservoir scale, compared to continuous CO2 injection, the alternating injection method increases the swept area of injected gas from 4.72 to 7.82 km2, consequently resulting in an additional production of 18 969 m3 of oil and sequestration of 9.0619 × 108 kg CO2 equivalents, representing increases of 15.6% and 460.4%, respectively. Finally, the slug ratio for alternating injection was optimized using the surrogate optimization method in MATLAB. After optimization, the cumulative oil production increased by 31.6% and the carbon sequestration capacity increased by 74.1%, compared to continuous CO2 flooding. This study offers valuable theoretical support for improving oil recovery through gas injection and facilitating carbon sequestration in reservoirs.

A unified simplified numerical model for large-scale regional seismic simulation of building structures

A unified simplified numerical model for large-scale regional seismic simulation of building structures

A coupled particle dynamics-lattice Boltzmann method model for particle-resolved direct numerical simulation of gas–liquid–solid flow with an irregular particle expressed by multi-sphere algorithm

In this paper, we develop a lattice Boltzmann (LB) model for simulating the gas–liquid–solid three-phase flow. Based on the gas–solid two-phase fluid model in the framework of the lattice Boltzmann method (LBM), a multiphase fluid–solid two-way coupling algorithm is proposed. In this model, the fluid–fluid interface is tracked using a phase-field method. Kinetic-theory-based boundary treatment, such as the interpolated bounce back, combined with the momentum exchange methods handle flow–particle interactions. Particle dynamics (PD) equation depicts the particle's movement and direction. Multi-sphere algorithm is inserted into the LBM frame to efficiently express the irregular particle borrowing from the idea of multi-sphere model in a discrete element method frame. Several typical benchmark cases are used to validate the present model, including the flow around the static and rotating cylinder, the wetting behavior of regular and irregular particles on the liquid–gas interface, the setting of a cylindrical particle, and regular and irregular particles sinking into and pulled out water. The numerical results show that the model agrees well with analytical solutions, experimental data, and published literature. The coupled PD-LBM model is validated and can accurately simulate any gas–liquid–solid three-phase flow system containing moving contact line phenomena.

Prediction of adjustable steam ejectors performance through Integration of numerical simulation and theoretical model

Prediction of adjustable steam ejectors performance through Integration of numerical simulation and theoretical model

Effect of fluid–shale interaction on hydraulic fracture effectiveness

The effectiveness of hydraulic fractures is one of the critical factors determining the production performance of shale oil wells. To investigate the influence of fluid–shale interaction during formation stimulation on the effectiveness of hydraulic fractures and shale oil production, first, a series of conductivity tests were performed on shale cores after soaking in different fluids, revealing the evolution rules of conductivity of propped fracture under different fluid-shale interaction mechanisms. Furthermore, by considering the fluid–shale interaction in the reservoir numerical simulation model, the impact of hydraulic fracture effectiveness evolution on shale oil well productivity and its differences in reservoirs with different permeability anisotropy were analyzed. The research results indicate that the softening of fracture surfaces caused by fluid-shale interaction causes fracture conductivity damage, and the longer the soaking time, the more severe the damage. Generally, the degree of damage on the fracture conductivity caused by water–shale interaction is higher than that caused by supercritical CO2 shale interaction under the same soaking time. When considering the damage of hydraulic fracture effectiveness caused by fluid–shale interaction, the production of shale oil wells is significantly reduced. For shale reservoirs with strong permeability anisotropy, the cumulative oil production loss caused by fluid–shale interaction is more severe than that with weak permeability anisotropy.

Development and application of a comprehensive numerical simulation model for microalgal pneumatic photobioreactors.

A novel and comprehensive multiphysics numerical simulation model for microalgal pneumatic photobioreactors was developed using the open-source Computational Fluid Dynamics (CFD) software OpenFOAM. This model integrates three distinct submodels: multiphase fluid dynamics, light transport, and microalgae growth. In particular, the light transport submodel accounts for light scattering caused by air bubbles. The model effectively addressed the challenges associated with the disparate time scales of the submodels, and its accuracy was validated by comparing with several experiments in different configurations. This study focuses on how an outdoor flat plate photobioreactor can maximize solar energy utilization. The results indicate that the daily productivity of the light-intensity adaptive reactor increased by 12.31% to 26.31% compared to those at constant biomass concentrations, demonstrating significant potential for employing this model in conjunction with automated systems to enhance productivity.

Modeling the water transport in water invasion channel with water invasion unit numerical simulation based on intelligent proxies

For gas reservoirs with edge water, water easily transport into the gas reservoir along the water invasion dominant channel, which results that the water production is higher than expected during the development of gas wells, and then affects the production of gas wells. We had derived a water invasion unit numerical simulation model of oil–water two-phase (WINS). It simulated the water invasion dynamic change in the hyperosmotic zone. But the Buckley–Leverett water flooding front propulsion equation is no longer applicable to the saturation tracking method in the gas reservoirs. The main reason is that the fluid transport between the gas–water two-phases is different from the traditional incompressible fluid transport. In this paper, a new water invasion unit numerical simulation method based on intelligent proxies and optimal control theory in complex gas reservoirs (WINS-G) is proposed. Compared with the WINS, the saturation tracking method of water invasion channel is improved. And it demonstrates a new insight into the water transport in water invasion channel. Through the discussion of model fitting efficiency, it is found that the number of invalid grids is reduced by WINS-G based on the idea of proxy model, and the work efficiency is improved by automatic history fitting on the basis that the model can meet the basic requirements of accuracy.

A hydromechanical EFG-based model for numerical simulation of land subsidence induced by groundwater extraction in anisotropic aquifers

A hydromechanical EFG-based model for numerical simulation of land subsidence induced by groundwater extraction in anisotropic aquifers

Dynamical Analysis and Numerical Simulation of a Stochastic Influenza Transmission Model with Human Mobility and Ornstein–Uhlenbeck Process

Dynamical Analysis and Numerical Simulation of a Stochastic Influenza Transmission Model with Human Mobility and Ornstein–Uhlenbeck Process

Liquid–Solid Coupled Internal Flow Field Analysis of Natural Gas Hydrate Spiral-Swirling Downhole In Situ Separator

This study aims to solve the problem of the recovery efficiency of natural gas hydrate being affected by a large amount of mud and sand in the solid fluidization mining of natural gas hydrate. Therefore, a new method for the separation of in situ sediment and natural gas hydrate in a spiral-swirling downhole is proposed, and the corresponding numerical simulation model is established to realize the analysis and verification of key flow field parameters such as flow velocity, pressure, sediment, and natural gas hydrate phase distribution. The results show that the flow velocity field of the mixed slurry presents an ‘M’-shaped symmetrical distribution, and the slurry near the wall of the separator can obtain a larger flow velocity, which is beneficial to the separation of mud–sand and natural gas hydrate. The static pressure field shows an axisymmetric distribution that decreases first and then increases, indicating that the pressure of the mixed slurry increases with the increase in the radial position, and the closer to the wall, the greater the static pressure of the mixed slurry. Near the wall of the separator, the volume fraction of the sediment phase reaches the maximum. In contrast, the volume fraction of the natural gas hydrate phase reaches the minimum, which confirms the separation effect of the sediment and the natural gas hydrate. The results show that the separation of sediments and natural gas hydrate can be realized, thereby improving the exploitation efficiency of natural gas hydrate. The designed spiral cyclone coupling separator provides a new solution to solving the problem of sand removal in natural gas hydrate exploitation.

Richards equation for the assessment of landslide hazards

In this study, we investigate numerical simulation models for water flow in variably saturated (unsaturated) soils. These models are crucial for addressing soil-related challenges and analyzing water-related risks, particularly in the context of water resource management, soil water-induced disasters, and the agricultural impacts of global environmental changes. The Richards equation is one of the most widely used models for simulating water flow in porous media, especially in unsaturated soils. However, as a highly nonlinear parabolic partial differential equation (PDE), it has limited analytic solutions, which often lack precision in practical scenarios. This necessitates the development of innovative and robust numerical methods for accurate simulations. We introduce a numerical procedure that linearizes the Richards equation using the Kirchhoff integral transformation, followed by discretization with various time-stepping schemes. This approach enables efficient and accurate modeling of water flow. To extend its application, we integrate the numerical solution with a hydrological infinite slope stability model to evaluate landslide hazards. Specifically, we calculate the factor of safety index based on an axisymmetric form of the Richards equation, which helps identify potential landslidenprone areas. Furthermore, our model provides a framework for predicting landslides by considering the interplay between water flow and the physical, geological, and topographical characteristics of a landscape. This integrated approach offers valuable insights for geohazard assessment and the mitigation of water-induced risks

Optimization design and vibration reduction performance analysis of vehicle suspension structure

To improve the vibration reduction performance of vehicle suspension, two kinds of suspension structures (Inerter spring damper, ISD) consisting of inerter, spring and damper are proposed based on electromechanical similarity theory. A single-wheel vehicle system model with I1 and I2 ISD suspension structures is established. Through numerical simulation of single-wheel vehicle system model, the vibration responses of ISD suspension structures I1 and I2 and traditional suspension structures in single-wheel vehicle system are analyzed, and the influence of inertia-capacitance coupling between main and auxiliary parts in ISD suspension structure on vibration reduction performance is discussed. The results show that compared with the traditional suspension structure, the vibration reduction performance of the I1 and I2 ISD suspension structures are improved to a great extent, and the I1 ISD suspension structure is better than the I2 ISD suspension structure. To comprehensively consider the vibration reduction performance of the ISD suspension structure, a compromise optimization method is adopted to determine the inertia-capacitance coupling value.

Effect of turning angles on the internal cavitation characteristics in a waterjet propeller

ABSTRACT The cavitation not only generates vibration noise but also reduces the efficiency of waterjet propellers. Therefore, studying the cavitation characteristics of waterjet propellers is crucial for improving performance. This paper analyses waterjet propellers’cavitation characteristics under different turning angles using the Zwart cavitation model for numerical simulation. Specifically, it investigates the effect of turning angles on the distribution of cavity within the waterjet propellers. The study reveals that cavitation mainly occurs at the back of the blades due to the influence of a low-pressure region, with more intense cavitation observed at the rim. Additionally, the distribution of cavities in each blade and runner exhibits noticeable variations under the influence of non-uniform inlet flow. When cavitation reaches a certain extent, the severity of cavitation on the rim side surpasses that on the hub side. Furthermore, the left turn induces more intense internal cavitation within the impeller compared to the right turn.

Understanding the low-temperature drying process of sludge with machine learning in a sewage-source heat pump drying system.

Heat pump drying technology based on sewage heat source is an eco-friendly sludge drying method. It can effectively reduce the pollution of natural water bodies by waste heat while reducing energy consumption. However, the drying characteristics of sludge in this case remain unclear. Here, we proposed and constructed a novel sewage-source heat pump sludge low-temperature drying system, and combine sludge drying theory with machine learning algorithms to model and analyze the drying process. The results revealed that the performance of the machine learning models was significantly better than that of the existing numerical simulation models. After Bayesian optimization, the XGBoost model showed superior prediction effect. In addition, the interpretable analysis of the model indicated that in the constant rate stage, the drying rate is mainly influenced by external air parameters, with temperature being the most critical factor. When temperature exceeds 50°C, the effect of relative humidity becomes significant. During the falling rate stage, the dominant factors begin to gradually shift from external air parameters to internal characteristics within the sludge itself, with dry basis moisture content becoming the new key factor. When air velocity exceeds 1.5m/s, the response of drying rate to air velocity is significantly influenced by changes in dry basis moisture content. The results of this study indicated that it is feasible to use machine learning models for predicting and explaining the low-temperature drying process of sludge. This provides valuable insights for the application of machine learning models in the development and management of drying strategies.

Coupled modelling and analysis of lubrication-wear evolution of textured piston/cylinder pair in high-pressure common rail pumps

Abstract Surface texturing is an important approach to enhance fluid lubrication performance and reduce friction and wear. Research on the surface texturing of the piston/cylinder pair considering various characteristics such as multi-physical fields and the evolution of friction and wear is still insufficient. The novelty of this work aims to establish a coupled numerical simulation model considering the coupling effect of multiple physical fields, wear of the cylinder bore and surface texture, and determine the effective surface texture parameters to improve the friction performance of the mechanical components of high-pressure common rail pump. The influence of surface texture on friction and wear characteristics and the evolution of lubrication-wear under different geometric cases were in-depth investigated, and the action mechanism of surface texture on lubrication and wear was revealed. Through numerical analysis, optimal parameters were identified that significantly reduce the friction coefficient and wear amount, offering practical insights for mitigating friction and wear in mechanical systems.

Numerical and experimental simulation of the hydrodynamic performance of hydrofoil for a virtual mooring buoy

ABSTRACT This study addresses the alteration of buoy dynamics through the attachment of rotatable hydrofoils, thereby enabling a virtual mooring capability. Buoys equipped with this configuration are referred to as Virtual Mooring Buoys (VMB). The aim of this study is to alter the dynamic properties of the buoy by installing a rotatable hydrofoil. The research focuses on the effects of both steady and unsteady dynamics of the rotatable hydrofoil. Therefore, in order to realise the virtual mooring function, CFD numerical simulation and scale model experiment are used to analyse the hydrodynamic performance of the buoy when it dives in the target waters at the maximum diving speed of 0.3 m/s. A hydrofoil with a high lift-to-drag ratio suitable for the VMB has ultimately been designed. The hydrofoil designed for the VMB in this study fulfils the functional requirements of buoy virtual mooring.