Simulation Engine Research Articles

Simple models for ice simulation for hydrotechnical engineering

The mechanical behaviour of ice is a complex phenomenon that is influenced by various factors, such as temperature, loading conditions, and structural geometry. To accurately predict the response of ice structures and estimate ice loads, appropriate models are required. In this article, we have reviewed several widely known material models for ice, including elastic, viscoelastic, plastic, damage, and fracture models. Elastic models are simple and easy to use, but they do not account for the time-dependent behaviour of ice. Viscoelastic models, on the other hand, can predict the evolution of damage and failure in ice structures but can be computationally ex-pensive. Plastic models can simulate the ductile behaviour of ice under high stress but do not account for damage and fracture. Damage models can simulate the evolution of damage and failure in ice structures but can also be computationally expensive. Fracture models can simulate the brittle behaviour of ice and predict crack propagation but require accurate input data. In practice, a combination of models is often used to account for different aspects of ice behaviour. With the advances in computer technology and simulation techniques, it is be-coming increasingly possible to simulate more complex ice structures and loading conditions. This could lead to the development of more accurate and efficient ice models that can be used for a wider range of applications, such as predicting the behaviour of ice structures in response to climate change. The effects of climate change on the behaviour of ice and the resulting impact on infrastructure are a growing concern. Therefore, the development of more accurate and efficient ice models is critical for the sustainable development of these regions.

Study on Working Fluids Testing and Multiphase Flow Model Optimization in Thermoacoustic Engines

This study investigates the impact of different working fluids on the performance of non-phasechange thermoacoustic engines and compares the application of two multiphase flow models in phasechange thermoacoustic engines. Simulation results show that, at the same heating temperature, air and nitrogen can achieve thermoacoustic oscillations, while helium cannot. This is due to helium’s higher sound speed, lower density, and higher thermal conductivity et al. In the simulation of phase-change thermoacoustic engines, the Mixture model demonstrates higher predictive accuracy compared to the VOF model, as the VOF model emphasizes the effects of phase interfaces, while the Mixture model is better suited for macro-scale multiphase flow analysis and relatively uniform phase-change processes. The findings provide a theoretical basis for the optimization design of phase-change thermoacoustic engines.

Butane Dehydrogenation: Thermodynamic Modeling and Performance Analysis of Selected Process Simulators

The critical role of process simulation in modern chemical engineering cannot be overstated, with its capacity to facilitate process scale-up, assess alternative designs, and comprehend plant efficiency. This research delves into the performance of three software programs, Cape-Open to Cape-Open (CC), DWSim, and Aspen HYSYS (AH), in modeling butane dehydrogenation. The focus is on their ability to accurately model thermodynamic properties and chemical reaction dynamics. Butane dehydrogenation was evaluated with specific thermodynamic parameters using a Gibbs reactor model with Gibbs minimization. The Soave Redlich-Kwong thermodynamic model was employed to investigate the impact of temperature of 700 °C and pressures of 0.1 MPa and 1.0 MPa on the yield and selectivity of butadiene and butene. The CC and AH simulation results closely agreed with the available experimental data. The consistency of freeware simulators with a commercial simulator was also assessed, with AH serving as the reference standard. It was revealed that CC demonstrates higher consistency with it than DWSim under both low- and high-pressure conditions. This study confirms that CC is a reliable process simulator suitable for use in resource-constrained settings where expensive commercial licenses are prohibitive.

Special Section on Recent Advances in Simulation in Science and Engineering

Special Section on Recent Advances in Simulation in Science and Engineering

Molecular dynamics studies of disulfide bonds for enhancing the stability of serine protease PB92

Employing innovative disulfide bond engineering and theoretical simulation, we introduced two bonds into PB92, enhancing thermal stability. The graphic illustrates these bonds, showcasing their crucial role in stabilizing the proteinase structure.

A Model Predictive Control Algorithm for Autonomous Mining Dump Trucks

Mines have a significant impact on increasing the gross production and per capita income of a country. However, the mining environment is hazardous, and increasing productivity and efficiency while ensuring safety is crucial. One way to achieve this goal and reduce human fatalities is to use autonomous dump trucks. A suitable lateral controller for a dump truck can ensure that the truck moves on the desired path, increasing safety and productivity. This paper focuses on controlling mining dump trucks. A model predictive control based on the lateral model of a dump truck is designed to achieve the control objective. Some of the parameters of the dynamical model are estimated using the least squares method. Finally, an approximate model of an actual dump truck, the Sarcheshmeh mine environment, and the proposed controller are implemented in the Unreal Engine simulation environment to evaluate the designed controller.

Effects of compression ratio on the performance and emission levels of a CI engine fueled with safflower oil methyl ester through an engine simulation approach

In recent years, the research community has shown significant interest in the potential of biodiesel as a renewable alternative to conventional fossil-based fuels. Nevertheless, the experimental investigation of the effects of diverse biodiesel formulations on internal combustion engines demands a significant investment of time and financial resources. Consequently, the numerical alternative methodologies are advocated as a viable substitute for practical experiments. Numerical simulations offer the opportunity for a meticulous examination of the characteristics of internal combustion engines under diverse operational conditions and various biodiesel blends, thereby optimizing efficiency and cost-effectiveness. This study focused on the simulation of performance and emission characteristics of a diesel engine running on safflower (Carthamus tinctorius L.) oil methyl ester (SOME) and traditional diesel fuel using AVL simulation software. Furthermore, the simulation results were compared with a laboratory study carried out under identical conditions. The simulated engine underwent testing across various compression ratios (CRs) (ranging from 12:1 to 18:1) and engine loads (from 25% to full load) while sustaining a consistent speed of 1500 rpm. The simulation findings revealed that the engine exhibited its highest BSFC as 0.495 kg/kWh with SOME fuel, at a CR of 12:1, modestly lower than the corresponding experimental observation of 0.520 kg/kWh. Concurrently, the lowest value of BSFC, recorded as 0.267 kg/kWh with diesel fuel and a CR of 18:1, demonstrated a marginal deviation from the experimental result of 0.281 kg/kWh. Additionally, SOME fuel usage was correlated with diminished CO and HC emissions. The experimental findings indicated the lowest value of CO and HC emissions, as 0.14% and 21.7 ppm, respectively, with SOME fuel at a CR of 18:1, marginally below the simulation-derived values of 0.13% and 20.8 ppm. Conversely, diesel fuel at a CR of 12:1 exhibited maximal CO and HC emissions, registering 0.38% and 199.5 ppm, respectively, in the experimental study. In comparison, the simulation values were slightly lower at 0.36% and 194.1 ppm. Moreover, the experimental investigation identified SOME fuel as yielding the highest CO2 emission, reaching a peak of 11.9% under a CR of 18:1, while the simulation showed a slightly lower value of 11.2%. In contrast, diesel fuel at a CR of 12:1 resulted in the lowest CO2 emission at 3.85% in the experiment, with the simulation reporting a slightly reduced value of 3.77%. Regarding NOx emissions, the experiment recorded the peak at 1687 ppm with SOME fuel and a CR of 18:1, slightly surpassing the simulation’s value of 1643 ppm. Conversely, the experimental data indicated the lowest NOx emission as 103 ppm with diesel fuel and a CR of 12:1, with the simulation suggesting a slightly lower value of 98.2 ppm under identical conditions. The simulation results demonstrated favorable concordance with experimental findings, notably strengthening with an increase in CR.

APPLICATION OF DIESEL-RK SIMULATION TOOL FOR REDUCING NOX EMISSION FROM BIODIESEL BLEND FUELS AT RETARDED INJECTION CONDITION

In renewable energy resources, although biodiesels have occupied the top place as an alternative for compression ignition engines, their high NO<sub>x</sub> discharge causes concern. Retarded fuel injection is a probable solution to reduce NO<sub>x</sub> emission. In the present work, tests are conducted with corn seed methyl ester (CSME) blend at standard and retarded fuel injection timings. Improvement in brake thermal efficiency and reduction in NO<sub>x</sub> emissions are observed due to the retardation of fuel injection timing. DIESEL-RK engine simulation software is used as a potential tool to simulate retardation conditions, and simulation results are compared with experimental values. It is seen that along with the performance and combustion parameters, the simulated NO<sub>x</sub> emission results are in good agreement with experimental values. Hence, the modeling used in the DIESEL-RK software captures the experimental observations well.

An exploratory study of social presence and cognitive engagement association in a collaborative virtual reality learning environment

As the utilization of virtual reality (VR) in engineering education increases, researchers are increasingly focused on enhancing teaching and learning within VR environments. Cognitive engagement among the participants is a fundamental outcome in any learning environment, where social interaction often plays a pivotal role. This study contributes to VR teaching and learning research by investigating the association between social presence indicators and modes of cognitive engagement in a desktop collaborative virtual reality (VR) learning environment based on the social presence theory and the cognitive engagement definition of the Interactive-Constructive-Active-Passive framework. The study participants logged into the VR platform to collaboratively solve simulated engineering statics problems. The transcripts of students' interaction videos were coded based on codebooks for both social presence and cognitive engagement. The study revealed that social presence indicators are significantly associated with the three modes of cognitive engagement (active, constructive, and interactive). Additionally, interactive open communication emerged as the primary social presence element utilized by students during cognitive engagement with peers in the learning environment. This study's findings have implications for research and practice in enhancing students' cognitive engagement through their social interactions within VR learning environment.

Simulation Discovery and Semi-Automatic Scenario Generation for Evaluation of Turbulence in Production Systems

Production systems have always faced changes and disruptions, which require dynamic decision-making to adjust existing plans to the unfolding reality. The interdependence of highly interconnected supply chain networks further adds to this volatility. Given this complexity, mainly caused by ambiguity and the systems’ dynamic, achieving transparency to make decisions in the context of production planning and control is challenging. Simulation models can help assess the outcome of different scenarios through experiments. However, building simulation models by hand requires extensive manual effort and expert knowledge of simulation tools. Although often partly automated, simulation experiments still require the exertion of simulation engineers to be conducted on a large scale. Moreover, the created models are often static and require additional resources to be updated in order to reflect changes in the physical system. To reduce this effort, the authors propose a concept combining the automatic discovery of simulation models from execution data with the semi-automatic generation of scenarios. This facilitates logistical risk analysis and prediction by evaluating the consequences of possible disruptive events. The concept aims to enable domain experts to use digital twins in large-scale virtual scenario evaluation, which is fundamental for increasing the agility of manufacturing systems by speeding up decision processes.

The Importance of Virtual Reality in the Conservation of Cultural Heritage: An Examination of the Penataran Temple Case Study in Blitar Regency, East Java

Introduction: Visualization of digital archives and applications play a crucial role in preserving cultural heritage. Utilizing efficient 3D scanning, modeling, and deep learning-based reconstruction techniques enables the revelation of hidden parts of Penataran Temple Sample in Blitar Regency, East Java. This research emphasizes the utilization of virtual reality (VR) systems as digital repositories for Penataran Temple Example, facilitating in-depth exploration of this cultural asset. The importance of applications such as guided tours, computer-aided design, and geographic information systems increases in the examination of cultural heritage, offering a deeper and more comprehensive understanding of the preserved cultural wealth. Methods: The methodology used in this research includes photogrammetry, accurate photogrammetric assessment, and the utilization of deep learning. Information obtained from these three avenues is combined to construct a three-dimensional Point cloud, serving as the foundation for the digital repository of Penataran Temple Sample. Virtual reality (VR) platforms enable individuals to observe the temple through both first-person and aerial perspectives, featuring customizable interfaces within the virtual reality setting. Results: The research activities are dedicated to creating a digital repository for Penataran Temple Sample located in Blitar Regency, East Java using 3D Point cloud data. Data acquisition of Point cloud for comprehensive coverage of temple structures and hidden reliefs is achieved through the application of advanced photogrammetric measurements and deep learning technology. A virtual reality (VR) system is developed to offer deep visualization capabilities, including first-person perspectives and aerial views, fostering a profound understanding of the cultural significance and architectural design of the temple. To enhance the realism of the VR experience and enable mapping functions, a 3D simulation engine is incorporated. The utilization of the first-person perspective allows detailed virtual exploration and collision detection, while aerial views contribute to a comprehensive understanding of the temple architecture. Discussion: The digital archive system of Penataran Temple Sample leverages Virtual Reality (VR) technology to integrate 3D Point cloud data from various sources. This system facilitates both first-person perspectives and aerial perspectives, allowing comprehensive examination and in-depth understanding of the temple architectural layout. Accurate 3D data generation is achieved through the utilization of Unmanned Aerial Vehicles (UAVs), photogrammetry technology, and deep learning techniques. Conclusion: The investigation focuses on the utilization of VR technology as a digital repository at Penataran Temple Sample, employing 3D Point cloud methodology. Its primary aim is to provide engaging visual encounters and comprehensive understanding of the architectural complex. Furthermore, this research will encompass detailed 3D measurements and the creation of high-precision VR renderings of the complete temple structures. Additionally, there are opportunities to design VR applications for remote utilization and to produce stereoscopic perspectives through holographic means. The anticipation is that VR will play a significant role in the preservation and interpretation of the heritage of Penataran Temple Sampled in Blitar Regency, East Java, while also demonstrating potential for diverse applications across various platforms and settings.

Machine Learning Tabulation Scheme for Fast Chemical Kinetics Computation

<div>This study proposes a machine learning tabulation (MLT) method that employs deep neural networks (DNNs) to predict ignition delay and knock propensity in spark ignition (SI) engines. The commonly used Arrhenius model and Livengood–Wu integral for fast knock prediction are not accurate enough to account for residual gas species and may require adjustments or modifications to account for specific engine characteristics. Detailed kinetics modeling is computationally expensive, so the MLT approach is introduced to solve these issues. The MLT method uses precalculated thermochemical states of the mixture that are clustered based on a combustion progress variable. Hundreds of DNNs are trained with the stochastic Levenberg–Marquardt (SLM) optimization algorithm, reducing training time and memory requirements for large-scale problems. MLT has high interpolation accuracy, eliminates the need for table storage, and reduces memory requirements by three orders of magnitude. The proposed MLT approach can operate across a wider range of conditions and handle a variety of fuels, including those with complex reaction mechanisms. MLT computational time is independent of the reaction mechanism’s size. It demonstrates a remarkable capability to reduce computation time by a factor of approximately 300 when dealing with complex reaction mechanisms comprising 621 species. MLT has the potential to significantly advance our understanding of complex combustion processes and aid in the design of more efficient and environmentally friendly combustion engines. In summary, the MLT approach has acceptable accuracy with less computation cost than detailed kinetics, making it ideal for fast model-based knock detection. This article presents a detailed description of the MLT method, including its workflow, challenges involved in data generation, pre-processing, data classification and regression, and integration into the engine cycle simulation. The results of the study are summarized, which includes validation against kinetics for ignition delay and engine simulation for knock angle prediction. The conclusions are presented along with future work.</div>

GPKPDviz: A flexible R shiny tool for pharmacokinetic/pharmacodynamic simulations using mrgsolve.

GPKPDviz is a Shiny application (app) dedicated to real-time simulation, visualization, and assessment of the pharmacokinetic/pharmacodynamic (PK/PD) models. Within the app, gPKPDviz is capable of generating virtual populations and complex dosing and sampling scenarios, which, together with the streamlined workflow, is designed to efficiently assess the impact of covariates and dosing regimens on PK/PD end points. The actual population data from clinical trials can be loaded into the app for simulation if desired. The app-generated dosing regimens include single or multiple dosing, and more complex regimens, such as loading doses or intermittent dosing. When necessary, the dosing regimens can be defined externally and loaded to the app for simulation. Using mrgsolve as the simulation engine, gPKPDviz is typically used for population simulation, however, with a slight modification of the mrgsolve model, gPKPDviz is capable of performing individual simulations with individual post hoc parameters, individual dosing logs, and individual sampling timepoints through an external dataset. A built-in text editor has a debugging feature for the mrgsolve model, providing the same error messages as model compilation in R. GPKPDviz has had stringent validation by comparing simulation results between the app and using mrgsolve in R. GPKPDviz is a member of the suite of Modeling and Simulation Shiny apps developed at Genentech to facilitate the typical modeling work in Clinical Pharmacology. For broader access to the Pharmacometric community, gPKPDviz has been published as an open-source application in GitHub under the terms of GNU General Public License.

Nudging based computational wind engineering simulation of the Atmospheric Boundary Layer

Modeling homogeneous Atmospheric Boundary Layers (ABLs) in Computational Wind Engineering (CWE) requires that all of the conservation equations are in equilibrium. A standard approach is to apply appropriate boundary conditions in order to maintain the inlet prescribed ABL profiles. Recently, the investigation has turned to the implementation of suitable source terms in the momentum equation. In the present work, the ABL is numerically simulated for neutral, stable and unstable atmosphere using nudging, a type of Data Assimilation. Simulations were performed with an in-house code that solves the Reynolds-averaged Navier-Stokes equations and the standard k-ε turbulence model in steady state. The prescribed inlet flow profiles correspond to the reference data, which is used to define the nudging source terms that are added to the equations. The source terms play a role resembling a body-force and a source-sink mechanism in the momentum and TKE equations, respectively. This novel approach is shown to be reliable in ensuring streamwise homogeneity, under both neutral and non-neutral conditions, for the equilibrium ABL.

A Simulation Framework of Unmanned Aerial Vehicles Route Planning Design and Validation for Landslide Monitoring

Unmanned aerial vehicles (UAVs) have emerged as a highly efficient means of monitoring landslide-prone regions, given the growing concern for urban safety and the increasing occurrence of landslides. Designing optimal UAV flight routes is crucial for effective landslide monitoring. However, in real-world scenarios, the testing and validating of flight path planning algorithms incur high cost and safety concerns, making overall flight operations challenging. Therefore, this paper proposes the use of the Unreal Engine simulation framework to design UAV flight path planning specifically for landslide monitoring. It aims to validate the authenticity of the simulated flight paths and the correctness of the algorithms. Under the proposed simulation framework, we then test a novel flight path planning algorithm. The simulation results demonstrate that the model reconstruction obtained using the novel flight path algorithm exhibits more detailed textures, with a 3D model simulation accuracy ranging from 10 to 14 cm. Among them, the RMSE value of the novel flight route algorithm falls within the range of 10 to 11 cm, exhibiting a 2 to 3 cm improvement in accuracy compared to the traditional flight path algorithm. Additionally, it effectively reduces the flight duration by 9.3% under the same flight path compared to conventional methods. The results confirm that the simulation framework developed in this paper meets the requirements for landslide damage monitoring and validates the feasibility and correctness of the UAV flight path planning algorithm.

Dynamic simulation for process hazard analysis: Affordances and limitations in the application to complex process systems

Improving process safety is a high priority in process and manufacturing industries to address unwanted accidents and process disruptions. Research into the applications of dynamic simulators for hazard analysis has been rapidly growing. However, there is scant discussion in literature regarding the benefits and limitations associated with the utilization of dynamic simulations for process hazard analysis. A fundamental systems conceptualization, underpinned by the P3 model of people, plant and procedures, provides a framework for the characteristics to be considered by subject matter expertise when identifying the affordances and limitations within a dynamic simulation used for process hazard analysis. This subject matter expertise includes people with operations, process engineering, process safety and process simulation experience. All dynamic simulations will have limitations and understanding the affordances and limitations is crucial for the correct use of a model and the interpretation of results. A case study is presented on the application of a systematic approach for interrogating the dynamic simulation from an industrial operator training simulator of an operating residue catalytic cracking complex with the aim of conducting failure mode and effects analysis by identifying the significant consequences and the associated independent protection layers. Issues with respect to how one could automate the analysis rather than use human observation to assess the outcomes are considered and how to present the outcomes to complement existing process hazard analysis techniques are discussed. Future research requirements and developments that may assist in practically and effectively applying the technique in industry are also discussed.

Proposal and validation of a numerical framework for 3D-CFD in-cylinder simulations of hydrogen spark-ignition internal combustion engines

In the light of the recent standards proposed by the European Union in terms of CO2 emissions for the internal combustion engines, the attention is increasingly focused on e-fuels, among which the renewable-produced hydrogen. The present paper proposes a numerical methodology for 3D-CFD in-cylinder simulations of hydrogen-fuelled internal combustion engines. The proposed framework includes in-house developed models for ignition, knock and heat transfer and it is based on the G-equation combustion model. The predictive capabilities of the methodology are validated against experimental data on a single-cylinder naturally-aspirated diesel engine converted for spark-ignited hydrogen operation. Specifically, H2 is direct injected thanks to two injectors installed on the cylinder head. Moreover, a spark plug is added and the compression ratio is lowered. The investigated conditions cover different revving speeds (from 1500 rpm to 3000 rpm) and equivalence ratios (0.4, 0.6 and 0.8). The satisfying agreement between numerical results and experimental counterparts paves the way to future studies (e.g. on emission modelling) and engine optimization.

Experimental and CFD analysis on the impact of hydrogen as fuel on the behavior of a passenger car gasoline direct injection engine

The influence of hydrogen induction on the behavior of a Multi cylinder automotive GDI (Gasoline Direct Injection) engine was studied. An advanced 3D engine simulation was done and validated with experimentally acquired 5% and 10% hydrogen mass coupled with gasoline direct injection. Comparing to the pure gasoline mode, introducing hydrogen showed considerable improvement in engine's BTE (brake thermal efficiency) at all power outputs. The maximum BTE was observed as 27.2%, 30% and 32.5 respectively with pure gasoline, 5% and 10% hydrogen mass shares at the engine's power output of 21 kW. At all power outputs, hydrogen admission was shown to produce a smaller amount hydrocarbon (HC) and carbon monoxide (CO) emissions than pure gasoline operation. At 21 kW engine power the HC was noted as 3360, 2400 and 2050 ppm respectively with Pure Gasoline, 5% and 10% hydrogen mass shares. CFD results on the HC and CO emissions indicated a strong agreement with the experimental results. NOx emission was noted as higher with hydrogen addition (80 and 100 ppm respectively with 5% and 10% hydrogen mass shares) as compared to 60 ppm with pure gasoline mode. High temperature of the gas supported by the CFD due to the rapid combustion of hydrogen was noted as the reason for the higher NOx emissions with hydrogen addition. CFD results on cylinder pressure and HRR matched very well with the experimental results. A small amount of hydrogen added to the gasoline as a fuel supplement might greatly improve BTE while lowering HC and CO emissions. The recommended optimum hydrogen mass share is 10% for the best performance of the above said engine without any modification in engine design.

A localized subdomain smoothing MMALE particle method for efficient modeling FSI problems

Fluid–structure-interaction (FSI) phenomena with multi-phase flow dynamics and structural damage commonly exist in engineering practice, which however bring great challenges to nowadays numerical FSI algorithms. A novel localized subdomain smoothing MMALE particle method (ls-ALEPM) is proposed in this paper for efficient and accurate simulations of large scale FSI problems. The MMALE method and the MPM are strongly coupled by immersing the MPM particles into the MMALE grid. In order to avoid the spurious strain induced by the mixed FSI velocity field, a decoupled stress updating scheme is proposed to update the stress of solid particles by introducing a virtual velocity field in the vicinity of FSI interface. And specifically, the highly accurate polyhedron intersection based method is employed for its remapping phase, which however is time-consuming. Thus, the localized subdomain smoothing method (LSSM) is put forward to accelerate the remapping phase which only involves the distorted regions of computational grid. The LSSM is composed of a distorted subdomain determination step and a combinated mesh smoothing step. Each iteration of the combinated mesh smoothing step consists of the modified GETMe and the weighted average method, and the transfinite interpolation method is adopted if the quality criteria is still not satisfied after prescribed maximum number of iterations. The validity and efficiency of ls-ALEPM is verified by several benchmark numerical examples and practical engineering simulations.

Study on the Effect of Parameter Sensitivity on Engine Optimization Results

The effects of six control parameters, intake valve opening timing (IVO), exhaust valve opening timing (EVO), compression ratio (CR), engine speed, intake temperature, and intake pressure on engine output power, indicated specific fuel consumption (ISFC), and nitrogen oxides (NOx) emissions, are analyzed through engine simulation. The six parameters were categorized into two groups based on the degree of influence: high influence (EVO, speed and intake pressure) and low influence (CR, IVO and intake temperature). The relationship between these two groups of parameters and power, ISFC and NOx emissions was explored. Optimization was carried out for each of the two groups of parameters, and the optimization of the high impact parameters resulted in a higher diversity and wider distribution of the solution set. On the other hand, the optimization of the low-impact parameters resulted in a more concentrated distribution of the solution set, while better reflecting the trade-off between the optimization objectives. For the optimal solutions for both sets of parameters, the high-impact parameters provided significant optimization performance compared to the standard operating conditions. Although power and ISFC were optimized, the optimal solution for the low-impact parameter performed poorly with a significant increase in NOx emissions. Therefore, the parameters should be evaluated for optimization using high impact parameters to improve engine performance.