International Journal of Simulation, Optimization & Modelling
The International Journal of Simulation, Optimization & Modelling (IJSOM) is a peer-reviewed scholarly journal that focuses on advancing research in the fields of simulation techniques, optimization methods, and mathematical modeling. The journal serves as a platform for academics, researchers, and professionals to share innovative methodologies, applications, and case studies that address complex real-world problems across various industries. It emphasizes interdisciplinary approaches and the integration of advanced computational tools to enhance decision-making processes, system performance, and efficiency in engineering, science, and management domains.
Applications of Machine Learning in Solving Optimisation Problems: Trends, Methods, and Practical Use Cases
Optimisation problems are central to diverse domains such as engineering, logistics, finance, and healthcare, yet traditional methods often rely on handcrafted heuristics and rigid mathematical programming, limiting their scalability and adaptability. Recent advances in machine learning (ML) have introduced transformative approaches that can address these limitations by offering adaptive, data-driven strategies. The purpose of this study is to provide a comprehensive analysis of ML-based optimisation methods, focusing on emerging trends, methodological distribution, performance comparisons, application domains, and the impact of research. The methodology employed integrates a systematic review of recent literature with comparative evaluations illustrated through six figures, covering publication trends (2019–2025), performance metrics, method usage distribution, domain-specific applications, workflow performance, and a research impact matrix. Results show a significant rise in the use of reinforcement learning, deep learning, and hybrid methods, with performance improvements of up to 85% in accuracy, a 3.2-fold speed enhancement, and a 67% cost reduction compared to traditional approaches. Domain analysis reveals that engineering and logistics are leading areas of adoption, while healthcare, finance, and aerospace represent emerging yet impactful applications. The impact matrix further highlights that no single method dominates all domains, reinforcing the importance of hybrid and adaptive strategies. The novelty of this study lies in its integrative framework, which combines trend analysis, performance evaluation, and impact mapping, offering a holistic understanding of how ML is reshaping optimisation research and practice.
Copyright (c) 2025 International Journal of Simulation, Optimization & Modelling https://creativecommons.org/licenses/by/4.0
Investigation of Ship Motion and Liquid Sloshing Interaction under Wave Conditions
This paper investigates internal liquid sloshing within tanks and its impact on ship motion responses. The external ship response is determined using an impulse response function (IRF) method, while the internal flow within the tanks is modelled using the two-phase incompressible Reynolds-averaged Navier-Stokes (RANS) equations. A Volume of Fluid (VOF) technique is employed to capture violent flows, and the Finite Volume Method (FVM) is used for discretization. The viscous flow solver is developed within the open-source CFD framework, Open Field Operation and Manipulation (Open FOAM). A three-dimensional simplified LNG ship with two partially filled tanks in waves is analyzed. Numerical simulations are conducted to examine the coupled effects between ship motion and liquid sloshing, with validation performed accordingly. The results indicate that coupling effects are dominant under low filling conditions. The roll response amplitude operators (RAOs) exhibit a typical anti-rolling effect, where roll motion significantly decreases near ship motion resonance, and double peaks appear at sloshing motion resonant frequencies.
Copyright (c) 2025 International Journal of Simulation, Optimization & Modelling
Combustion Performance of Jatropha and UCO Biodiesel Blends Under Varying Equivalence Ratios
This study investigates the combustion characteristics of biodiesel blends derived from Jatropha Methyl Ester (JME) and Used Cooking Oil Methyl Ester (UCOME) under various equivalence ratios (Φ = 0.8, 1.0, 1.1, and 1.2). The biodiesels were produced through a two-step transesterification process, yielding 94.3% for JME and 92.0% for UCOME. Combustion experiments were conducted in a horizontal combustion chamber using a data logger and K-type thermocouples to record wall temperatures. Blends with conventional diesel fuel (CDF) were prepared at 5%, 15%, and 25% biodiesel concentrations. Results showed that wall temperature decreases as the percentage of biodiesel in the blend increases. At stoichiometric conditions (Φ = 1.0), the highest wall temperature was recorded for CDF at 847.03°C, while B25 JME and B25 UCOME recorded the lowest at 782.43°C and 791.09°C, respectively. The percentage reduction in maximum temperature relative to diesel was 7.6% for B25 JME and 6.5% for B25 UCOME. Increasing equivalence ratios led to a rise in wall temperatures across all blends. From Φ = 0.8 to 1.0, temperature increased by up to 10.6% for B15 JME, while from Φ = 1.0 to 1.2, B25 JME saw an increase of 6.6%. The findings confirm that biodiesel blends produce lower combustion temperatures due to reduced energy content and heating values. Nonetheless, all tested biodiesel blends successfully sustained combustion under varying air-fuel ratios, indicating their potential as alternative fuels for low-emission combustion systems.
Machine Learning Applications for Solving Complex Optimisation Problems Across Various Domains
Optimization of complex problems is fundamental across industries, yet traditional methods often struggle with high-dimensionality, nonlinearity, and dynamic constraints. This study aims to explore how machine learning (ML) techniques address these challenges by enhancing optimization across diverse sectors, including engineering, healthcare, energy systems, finance, and transportation. A systematic review and analysis were conducted by mapping ML methods to specific domain applications, assessing their distribution and impact. Results show that reinforcement learning dominates with a 25% share across applications, followed by supervised and deep learning techniques, each representing 16.7% of total usage. Specialized approaches such as convolutional neural networks, predictive modeling, anomaly detection, Bayesian optimization, and sensor fusion account collectively for 41.6%, reflecting the growing diversity of ML-driven solutions. The novelty of this work lies in its cross-domain integration, quantifying how ML methods not only replace but enhance traditional optimization approaches through adaptability, scalability, and real-time decision-making capabilities. Additionally, all examined domains exhibited three major ML application areas, indicating a uniform breadth of adoption. In conclusion, ML is redefining optimization practices, offering dynamic, intelligent, and domain-adaptive solutions. Future directions are suggested toward enhancing interpretability, robustness under uncertainty, and cross-domain generalization. This study was fully self-funded through contributions from all authors without external financial support.
Comparative Study of Performance and Combustion Characteristics of Diesel and Methanol in Engines
The growing interest in alternative fuels has prompted investigations into methanol as a potential substitute for conventional Diesel fuel in internal combustion engines. This study aims to compare the performance and combustion characteristics of Diesel and Methanol fuels across a range of engine speeds, with a particular focus on Brake Mean Effective Pressure (BMEP), Brake Torque, Brake Specific Fuel Consumption (BSFC), Heat Transfer Rate, and In-Cylinder Pressure. Experiments were conducted at engine speeds from 1500 to 5000 RPM, evaluating detailed thermodynamic parameters throughout the entire crank angle cycle. The results demonstrated that Diesel consistently produced higher BMEP values, peaking at 11.2 bar at 4500 RPM, compared to Methanol’s maximum of 10.2 bar. Diesel also achieved superior brake torque, recording 2100 N·m at 4000 RPM, while Methanol reached around 1800 N·m. The lowest BSFC was observed at 2500 RPM, with Diesel at 281 g/kW·h and Methanol at 282 g/kW·h, though both fuels exhibited increasing BSFC beyond this point, reaching approximately 297 g/kW·h at 5000 RPM. Diesel also attained a significantly higher peak in-cylinder pressure of 1900 bar compared to Methanol’s 1300 bar at 4000 RPM. A major novelty of this study lies in the comprehensive high-speed analysis and the integration of combustion, heat transfer, and mechanical performance indicators, which are less explored in previous works. Overall, Diesel demonstrated superior thermomechanical performance, while Methanol exhibited smoother combustion characteristics, offering potential benefits for engine durability.
Statistical Modelling Approaches for Analyzing Patterns and Predicting Behavior in Complex Systems
Understanding and predicting behaviours in complex systems is a critical challenge across various fields, including climate science, financial markets, biological systems, and epidemiology. This study evaluates classical and modern statistical modelling approaches to analyze patterns and forecast outcomes in such systems. Classical methods, such as regression analysis and time series modelling, show high interpretability (effectiveness scores of 9 and 8, respectively) but are limited in handling nonlinearity and uncertainty (scores as low as 2–4). In contrast, modern techniques like machine learning models and Bayesian networks demonstrate superior performance in managing complexity and uncertainty (scores of 7–9), though they introduce greater computational demands. Using examples from real-world applications, including General Circulation Models (GCMs) in climate science and stochastic SEIR models in epidemiology, the study highlights that higher model complexity (score 8 in climate modelling) does not always guarantee higher prediction accuracy (score 7). In contrast, moderate complexity in epidemiological models (score 6) achieves excellent predictive performance (score 9). Model selection, validation, and interpretability challenges are discussed, emphasizing the trade-offs between complexity and practical usability. This research provides new insights by systematically comparing the predictive effectiveness and challenges across different application domains. The findings suggest that hybrid and domain-informed modelling strategies offer the best potential for improving prediction and understanding in complex systems. In conclusion, effective statistical modelling requires a balanced approach, integrating domain expertise, model adaptability, and ongoing validation to maximize interpretability and predictive accuracy.
Investigation of Fuel Nozzle Performance on Direct Injection Diesel Engines Using Computational Simulation
Single-cylinder model simulation designed for four-stroke direct-injection diesel engines requires advanced analysis of the performance effect of the direct-injection diesel engine model, focusing on fuel nozzle multi-hole geometries. The computational model simulation development used the commercial computational fluid dynamics of GT-POWER 6.2 software, specially developed for internal combustion engine performance simulation. The research concentrated on the one-dimensional model and focused on the variation of fuel nozzle multi-hole geometries developed from all of the engine components' size measurements of the original selected diesel engine. All the measurement data is inputted to the window engines component menu to run input data in the model. Results of the diesel engine fuel nozzle multi holes geometries model simulation running are in GT-POST. The model performance is shown in the engine cylinder and engine crank-train on the software window output. The performance analysis effect of the model investigated the fuel in-cylinder engine, indicating the model's specific fuel consumption, torque, and power. The simulation showed that the seven-hole nozzle provided the best burning for fuel in-cylinder burn, and the five-hole nozzle provided the best for indicated power, indicated torque, and indicated specific fuel consumption at any different engine speed in the simulation.
Simulation-Based Approaches to Improve Teaching, Learning, and Training Outcomes in Education Systems
Simulation-based learning has become an innovative instructional approach to improve educational outcomes across all levels. Traditional teaching methods are often limited in fostering critical thinking, practical skills, and student engagement gaps, which simulations increasingly address. This study examines the theoretical foundation, applications, implementation challenges, and benefits of simulation-based approaches in education systems. A structured literature review was conducted, synthesizing findings from peer-reviewed sources, international case studies, and impact metrics. The results show that simulations significantly enhance student outcomes, with engagement and motivation improvements reaching up to 90%, critical thinking and decision-making effectiveness at 85%, and practical skill acquisition at 88%. These benefits were observed in diverse educational contexts, including K-12 science education, medical training in higher education, and vocational programs using VR technologies. However, implementation challenges persist. Cost and infrastructure were identified as the most severe barrier (91% severity), followed by lack of teacher training (85%) and curriculum misalignment (80%). This article contributes a current and comprehensive synthesis of how simulations align with experiential and constructivist learning theories and offers real-world strategies to scale their adoption. The novelty of this research lies in its integration of quantitative impact scores with qualitative program evaluations, providing a holistic view of simulation efficacy and feasibility. In conclusion, simulation-based education is a timely and scalable solution for modern pedagogy, offering measurable improvements in learner engagement, skills development, and educational resilience.
Validation and Verification Techniques for Ensuring Accuracy in Modeling and Simulation Systems
Modelling and simulation have become indispensable tools across various scientific and engineering domains, supporting design optimization, risk evaluation, and policy decision-making. However, the increasing reliance on simulation outcomes necessitates rigorous verification and validation (V&V) to ensure model credibility, especially in high-impact fields such as aerospace, biomedicine, climate modelling, and defence. This study aims to review, compare, and synthesize contemporary V&V techniques and evaluate their practical effectiveness through domain-specific applications. Using a structured comparative methodology, we analyze core V&V practices, including code review, formal verification, experimental validation, predictive validation, and uncertainty quantification. A usage intensity scale (1–10) was introduced to quantify technique adoption, revealing that experimental data comparison is the most frequently applied method (9/10), followed by code review and uncertainty quantification (both 8/10). Case studies show that aerospace and defence domains report the highest V&V impact, with confidence gain and decision support rated at 9/10. At the same time, biomedical and climate modelling demonstrate slightly lower scores due to biological variability and system complexity. The novelty of this study lies in presenting an integrated cross-domain V&V framework and in highlighting the growing need for adaptive, hybrid validation methods tailored for emerging AI-driven and data-intensive models. Despite evident benefits, enhanced confidence, reduced costs, and improved decision-making challenges remain, particularly in novel simulation environments with limited validation data. In conclusion, this article reinforces the critical role of V&V in ensuring simulation reliability and calls for innovation in V&V strategies to match the evolving complexity of modern simulation systems.
Emission and Fuel Properties Analysis of Jatropha and UCO Biodiesel Blends for Combustion Applications
This study investigates the production, characterization, and emission performance of biodiesel blends derived from Jatropha Methyl Ester (JME) and Used Cooking Oil Methyl Ester (UCOME). Biodiesel was produced through a two-step transesterification process, resulting in an average yield of 94.3% for JME and 92.0% for UCOME from 9.6 liters of crude oil. Production cost analysis revealed that crude Jatropha oil constituted 83% of the total cost per liter, while UCO contributed only 5.8%, indicating significant economic advantages for waste-based feedstock. Physical properties of the biodiesel blends were assessed and compared to EN 590 (diesel) and EN 14214 (biodiesel) standards. Results showed that increasing biodiesel content in blends raised viscosity, density, specific gravity, and surface tension, while reducing calorific value. Despite these variations, all blends remained within acceptable biodiesel standard limits, making them suitable for industrial burner applications. SO₂ emissions were evaluated using combustion tests across lean, stoichiometric, and rich conditions. Compared to conventional diesel fuel (CDF), B25 UCOME achieved the highest SO₂ reduction of 69% under lean, 58.1% under stoichiometric, and 90.9% under rich conditions. These reductions are attributed to the inherently lower sulphur content in biodiesel. The findings demonstrate the environmental and economic potential of biodiesel blends, particularly UCOME, as cleaner and cost-effective alternatives to fossil fuels in combustion systems.
Comparative Analysis of Engine Performance and Emissions Using Experimental and GT-POWER Simulation
Internal combustion engines' performance and emission characteristics are critical for improving efficiency and environmental sustainability. This study aims to validate the predictive accuracy of GT-POWER simulation software by comparing it with experimental data across multiple engine parameters, including brake efficiency, brake power, brake torque, specific CO and CO₂ emissions, and in-cylinder heat transfer. The engine was tested at various speeds ranging from 1000 to 6000 rpm, and simulation data were generated using GT-POWER under equivalent conditions. Experimental results showed that the maximum brake efficiency occurred at 2000 rpm, reaching 27.3%, while the GT-POWER simulation predicted 26.3%. The peak brake power was 87 kW (experiment) and 84 kW (simulation) at 1600 rpm. Brake torque peaked at 71 bar (experiment) and 66 bar (simulation) at 2000 rpm. Emission results indicated a decrease in brake-specific CO from 75 g/kW·h at 1000 rpm to 21 g/kW·h (experiment) and 16 g/kW·h (simulation) at 6000 rpm. Similarly, CO₂ emissions dropped from 60 g/kW·h to 15 g/kW·h (experiment) and 14 g/kW·h (simulation). In-cylinder heat transfer showed a maximum of 43 kW (experiment) and 38 kW (simulation), with the most significant discrepancies at higher engine speeds. The novelty of this study lies in its multi-variable validation approach and inclusion of thermal behaviour through in-cylinder heat transfer analysis, which is rarely addressed in simulation studies. The results confirm that GT-POWER accurately captures performance trends, particularly within the 1600–2000 rpm range, though refinements are needed for thermal modelling at high speeds.
One-Year Performance Analysis of a 3000 Wp Grid-Connected PV System in Tropical Climate
This study presents a comprehensive performance evaluation of a 3000 Wp grid-connected photovoltaic (PV) system over a one-year operational period. The objective is to analyze system behavior under real environmental conditions using detailed statistical and graphical methods. The methodology includes daily and monthly assessments of irradiance, power output, temperature, voltage, and system losses. A total of 15 figures were used to visualize the system’s behavior, including input/output relationships, performance ratio trends, and power and temperature distributions. Results show an average annual reference incident energy of 4.846 kWh/m²/day, with peak values in February (6.0 kWh/m²/day) and minimum values in June (4.0 kWh/m²/day). The produced useful energy averaged 4.06 kWh/kWp/day, with total system losses of 0.79 kWh/kWp/day comprised of 12.9% collection loss and 3.3% inverter/system loss. The Performance Ratio (PR) was consistently high at 0.838, reflecting efficient energy conversion across all seasons. Power output was most frequently distributed between 1500 W and 2500 W, peaking around 2000 W. Thermal and electrical stability were confirmed with module temperatures mostly ranging from 30°C to 50°C and voltage concentrated around 150 V. The novelty of this study lies in its integration of cumulative tail distributions, voltage/temperature histograms, and irradiance correlation analysis—methods not commonly used together in existing literature. These findings confirm the system’s reliability and offer valuable insight into operational optimization and design strategies. The study contributes a practical, data-driven reference for PV performance benchmarking under tropical climate conditions.
Machine Learning-Driven Optimisation of Aerodynamic Designs for High-Performance Vehicles
Aerodynamic design optimization is critical in improving the performance of high-performance vehicles, primarily electric and autonomous vehicles. However, traditional methods such as Computational Fluid Dynamics (CFD) simulations face challenges such as long computational time and high cost. This article discusses the implementation of machine learning to overcome these limitations, highlighting algorithms such as neural networks, Gaussian process regression, and reinforcement learning. The results show that machine learning can reduce the design iteration time by up to 80%, from 24-48 hours/design in CFD methods to only 10-30 minutes/design. The accuracy of the predictive model is also very high, with an average error margin of less than 5%. Case studies on Formula 1 vehicles and electric vehicles show a reduction in drag coefficient of up to 10%, which directly improves the cruising efficiency of electric cars by up to 15% and increases downforce by 12% for high-speed vehicle stability. In addition, generative algorithms such as GANs enable the exploration of innovative designs, while reinforcement learning can generate adaptive designs responsive to changing operating conditions. With this capability, machine learning not only accelerates the design cycle and lowers development costs but also drives innovation in the development of electric, autonomous, and uncrewed aircraft vehicles. In conclusion, machine learning technology is a superior solution for optimizing aerodynamic design to meet the demands of efficiency, performance, and sustainability of future cars.
Dynamic Modelling and Optimisation of Heat Exchange Networks for Enhanced Energy Efficiency in Industrial Processes
This study proposes a novel approach to improve energy efficiency in industrial heat exchange networks by integrating dynamic modeling and genetic algorithm-based optimization. The developed dynamic model depicts more realistic thermal dynamics in a heat exchanger system while optimization is performed to minimize energy consumption and heat loss. Simulation results show that the optimal design can reduce energy consumption by 23%-25% compared to traditional heat exchanger network designs. In addition, this approach also allows for a reduction in the number of heat exchange units required, which contributes to a decrease in investment and operational costs. This approach is applied to various industrial sectors such as chemical, oil and gas, and manufacturing, each showing significant energy-saving potential. Compared with previous studies, it shows that energy savings can be increased by 10%-15% higher than conventional optimization. In addition, this study also provides an efficiency improvement strategy for existing heat exchanger networks, with energy savings of up to 20%-25% through adjustment of operational configurations. These results demonstrate that the approach integrating dynamic modeling and optimization is not only efficient in the design of new networks but also effective in improving energy efficiency in systems that are already in operation. In conclusion, this research provides a practical and efficient solution to improve energy performance in industrial applications, with significant energy savings and reduced operational costs.
Analysis of NOx, HC, and CO Emission Prediction in Internal Combustion Engines by Statistical Regression and ANOVA Methods
This study investigates the impact of engine operating parameters on exhaust emissions, mainly nitrogen oxides (NOx), hydrocarbons (HC), and carbon monoxide (CO), using a statistical regression modelling approach. The analysis employs the Analysis of Variance (ANOVA) method to assess model accuracy and sensitivity. The results indicate that the developed regression models exhibit high predictive accuracy, with the CO model demonstrating the best performance. The NOx model achieved an R-squared value of 0.9827, explaining 98.27% of data variation, but showed the highest standard deviation (35.14) and PRESS value (1.549E+005), indicating more significant data variability. The HC model performed slightly better with an R-squared value of 0.9865, a standard deviation of 3.68, and the lowest coefficient of variation (C.V.%) at 1.68%, ensuring high stability. The CO model outperformed both, with the highest R-squared value (0.9910), the lowest standard deviation (0.11), and a moderate C.V.% of 3.70%, making it the most reliable. Additionally, the Adeq Precision values for NOx, HC, and CO models were 45.791, 51.764, and 66.569, respectively, confirming strong signal-to-noise ratios. The findings indicate that increasing engine speed and throttle opening significantly influences emissions. Higher speeds increase NOx emissions but reduce HC and CO emissions, while larger throttle openings increase CO levels. These results provide valuable insights into optimizing engine parameters for reducing emissions. The developed models can be practical tools for emission predictions and mitigation strategies, contributing to environmentally friendly combustion technologies.
Engine Performance Analysis Based on Speed and Throttle Through Simulation
Internal combustion engine (ICE) performance is greatly influenced by engine speed and throttle opening, which contribute to brake power, brake thermal efficiency (BTE), and brake-specific fuel consumption (BSFC). This study used simulation and statistical analysis methods, including ANOVA and response surface methodology (RSM), to evaluate the impact of these two parameters on engine performance. The simulation results showed that brake power increased with increasing engine speed and throttle opening, with an average value of 15.09 kW and an R-squared of 0.9886, indicating high model accuracy. Brake thermal efficiency also increased with an average value of 24.03% and an R-squared of 0.9868, indicating increased fuel energy utilization into mechanical energy. Meanwhile, specific fuel consumption decreased with increasing engine speed and throttle opening, with an average of 385.30 g/kWh and an R-squared of 0.9584. This study confirms that optimization of engine speed and throttle opening significantly improves thermal efficiency and reduces specific fuel consumption. These findings have significant implications for designing more efficient and environmentally friendly engines. In addition, the simulation approach allows for a more precise analysis of engine performance without expensive and time-consuming experimental testing.
Finite Element Modelling and Optimisation of Structural Components for Lightweight Automotive Design
Lightweight design in the automotive industry plays an important role in improving fuel efficiency and reducing greenhouse gas emissions. This study uses Finite Element Modeling (FEM) to analyze and optimize vehicle structural components to achieve weight reduction without sacrificing mechanical strength. Simulation results show that topology optimization can reduce component weight from 5.2 kg to 4.3 kg, equivalent to a reduction of 18%, while maintaining a safety factor above 1.5. In addition, the use of lightweight materials such as aluminium alloy (Al 7075-T6) and carbon fiber composites results in a weight reduction of up to 30%, while the maximum stress is reduced from 390 MPa to 380 MPa, and the maximum deformation is reduced from 1.82 mm to 1.75 mm. The validation of the FEM model shows a high level of accuracy, with the difference between simulation and experimental results being less than 5%, making this method reliable for structural performance prediction. However, challenges in the application of FEM include high computational costs and limitations in handling complex operational conditions. As a further development, Machine Learning (ML) based approaches can improve the efficiency of the optimization process, with previous studies showing that the combination of FEM and ML can reduce computational time by up to 40%. Thus, this research provides strategic insights in the development of lighter, more efficient and sustainable vehicles.
Integrated simulation and optimisation of traffic flow management systems in urban smart cities
Traffic congestion in urban areas is a significant challenge for smart city management. This study aims to integrate SUMO software-based simulation and the Ant Colony Optimization (ACO) algorithm to improve traffic management efficiency. The simulation model maps vehicle flow on a road network in an urban area with high congestion. At the same time, the ACO algorithm is used to optimize traffic light settings and vehicle routes dynamically. The data includes travel time, fuel consumption, carbon emissions, and road congestion. The results show that traffic optimization reduces the average travel time from 35 minutes to 25 minutes (a reduction of 28.57%) and fuel consumption from 0.12 litres/km to 0.09 litres/km (a saving of 25%). In addition, vehicle carbon emissions are reduced from 450 g CO₂/km to 360 g CO₂/km (a reduction of 20%). The even distribution of vehicle flow also reduces congestion levels on main roads by 30%. Although the results are promising, real-world implementation faces technical obstacles, such as the need for IoT infrastructure and non-technical barriers, such as public resistance to changes in traffic policies. This study proves that an integrated simulation and optimization approach can effectively overcome congestion in smart cities while supporting environmental sustainability and transportation efficiency. Further research is recommended to develop more adaptive algorithms and test implementations in regions with different traffic characteristics.
Optimisation of combustion parameters in turbocharged engines using computational fluid dynamics modelling
This study aims to optimize combustion parameters in a turbocharged engine using Computational Fluid Dynamics (CFD) simulation. The main parameters analyzed include fuel injection pressure, air-fuel ratio, and ignition timing. Simulations were performed using ANSYS Fluent software with a k-ε turbulence model and a non-premixed combustion model. The simulation results show that a fuel injection pressure of 1500 bar produces a maximum combustion efficiency of 85%, increasing from 78% in the initial configuration. This increase also impacts increasing engine power by 7.5%, from 80 kW to 86 kW. Although NOx emissions increased by 15.56% to 520 ppm, CO and HC emissions decreased significantly by 43.33% and 40%, respectively, indicating more perfect combustion. The maximum temperature distribution was recorded at 2200 K, while the maximum pressure in the combustion chamber reached 9 MPa, with an average error of the simulation results to the experimental data of less than 5%. This indicates the high accuracy of the model used. This study proves that CFD simulation is an effective tool for optimising combustion parameters, improving energy efficiency, and reducing exhaust emissions in turbocharged engines. This study makes a significant contribution to the development of more efficient and environmentally friendly engine technology. Further research is recommended to explore alternative fuels and more innovative combustion chamber designs.
Mathematical Modelling and Optimisation of Supply Chain Networks Under Uncertain Demand Scenarios
Demand uncertainty is a significant challenge in modern supply chain management, affecting operational efficiency, costs, and customer service levels. This study develops a mathematical model based on stochastic programming and robust optimization to optimize the supply chain network in the face of such uncertainty. The model considers decision variables, such as product allocation, facility location, and various operational constraints, including capacity and logistics costs. Multi-scenario simulations are applied to evaluate supply chain performance under various uncertain conditions. The results show that the applied mathematical model can significantly improve supply chain efficiency. The stochastic programming approach successfully reduces operational costs by 15%, stock-out rates by 25%, and storage costs by 10%. Meanwhile, robust optimization can reduce the risk of supply chain disruptions by 20% while maintaining optimal customer service levels. The scenario-based approach increases customer service levels by up to 95%, demonstrating the superiority of this strategy in responding to dynamic market changes. These findings confirm that mathematical optimization methods can improve supply chain resilience and efficiency, even under uncertain conditions. Although this model has challenges in its implementation, such as the need for accurate data and the complexity of calculations, integration with digital technologies such as big data analytics can be a solution in the future. This research contributes to supply chain management and offers new directions for more adaptive and effective decision-making strategies.
Multi-objective optimization of renewable energy systems using genetic algorithms: A case study
This study optimizes a renewable energy system using a genetic algorithm (GA) in a multi-objective context, focusing on cost minimization, efficiency maximization, and emission reduction. The optimized system consists of a combination of solar PV, wind turbine, and energy storage battery. Simulation results show that the optimal population size is 100 to 150 individuals, with a crossover probability of 0.8 to 0.9 and a mutation probability of 0.01 to 0.05. In the cost minimization scenario, the Pareto optimal solution has a total cost of $150,000, an energy efficiency of 85%, and an emission reduction of 10%. The emission reduction scenario results in a total cost of $170,000, an efficiency of 88%, and an emission reduction of 25%. The compromise scenario costs $160,000, an efficiency of 90%, and an emission reduction of 20%. The trade-off analysis shows that emission reduction often incurs higher costs but provides long-term benefits. This study shows that GA effectively finds optimal solutions in multi-objective problems despite data assumptions and computational time limitations. These findings provide a solid foundation for designing efficient, sustainable, and environmentally friendly renewable energy systems.
Optimising Engine Performance and Emission Characteristics Through Advanced Simulation Techniques
This article discusses the role of advanced simulation techniques, such as Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), and Machine Learning (ML), in optimizing engine performance and reducing emissions. CFD enables in-depth analysis of fluid flow, heat transfer, and chemical reactions inside the engine, while FEA ensures the structural reliability of engine components under extreme conditions. ML and AI technologies provide predictive capabilities, enabling real-time optimization and predictive maintenance. Integrating these techniques yields significant synergies, such as real-time fuel injection timing optimization and automated engine component design. The study results show that simulation accelerates engine development and offers a cost-effective solution compared to conventional methods. However, challenges such as high computational resource requirements, model validation, and the complexity of multiphysics interactions remain to be overcome. In the future, adopting real-time simulation, cloud-based platforms, and integration with the Internet of Things (IoT) can accelerate the design cycle by up to 50% and improve efficiency and predictive accuracy. However, data security and technology synchronization are concerns that must be addressed.