Browse Topic: Optimization

Items (7,027)
With the rapid expansion of the electric vehicle (EV) market, the frequency of grid-connected charging has concentrated primarily during peak hours, notably from 7:00 a.m. to 10:00 a.m. and 6:00 p.m. to 10:00 p.m., resulting in substantial demand surges during both morning and evening periods. Such uncoordinated charging patterns pose potential challenges to the stability and economic efficiency of power systems. As vehicle-to-grid (V2G) technology advances, facilitating bidirectional energy exchange between EVs and smart grids, the need for optimized control of EV charging and discharging behaviors has become critical to achieving effective peak shaving and valley filling in the grid. This paper proposes a microgrid energy scheduling optimization algorithm based on existing smart grid and EV charging control technologies. The method establishes a multi-objective optimization model with EVs’ 24-h charging and discharging power as decision variables and microgrid load rate, load
Fan, LongyuChen, YuxinZhang, Dacai
Optimizing the parameters of asymmetric textures (AT) designed on the surface of sliding frictional pairs (SFP) can make each texture more reasonably distributed. Thereby, the oil film thickness can be more stable; and the lubrication and load ability of SFP can be improved. To clarify this issue, based on the SFP’s lubricating model added by AT using the rectangular structure, parameters of AT including the angle between the horizontal axe and bottom surface (φij), the angle between the lateral axe and bottom surface (γij), and texture’s depth (hij) are optimized. The study results show that the parameters of φij, γij, and hij of AT optimized can create the p (hydrodynamic pressure of liquid) better than the symmetric textures. Significantly, the pmax and load ability of the liquid in the SFP using optimal AT have been greatly increased compared to the liquid in the SFP using the symmetric textures. Accordingly, the results are an important reference for the design and distribution of
Wang, CuifangZhang, Lu
Continuous rubber track systems for heavy applications are typically designed using multiple iterations of full-scale physical prototypes. This costly and time-consuming approach limits the possibility of exploring the design space and understanding how the design space of that kind of system is governed. A multibody dynamic simulation has recently been developed, but its complexity (due to the number of model’s inputs) makes it difficult to understand and too expensive to be used with multi-objective optimization algorithms (approximately 3 h on a desktop computer). This article aims to propose a first design space exploration of continuous rubber track systems via multi-objective optimization methods. Using an existing expensive multibody dynamic model as original function, surrogate models (artificial neural networks) have been trained to predict the simulation responses. These artificial neural networks are then used to explore the design space efficiently by using optimization
Faivre, AntoineRancourt, DavidPlante, Jean-Sébastien
The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and
Ercisli, Safak
Due to the increasing precision requirements for stainless steel castings in the current industrial field, we take stainless steel as the object, use numerical simulation to analyze the manufacturing process of castings, and explore the mechanism of related defects and preventive measures. The results indicate that in the process optimization of small castings, the maximum shrinkage and porosity of the conventional scheme, the optimization scheme with the addition of cold iron and insulation riser, and the optimization scheme with the improved pouring system combined with the optimal parameters are 1.83%, 1.64%, and 1.42%, respectively. The optimal pouring temperature, pouring speed, and shell preheating temperature of medium- and large-sized castings are: 1620°C, 1.5 kg/s, and 1100°C, respectively. According to the aforementioned findings, the study raises the standard of precision production for stainless steel, and fuel the growth of the precision casting sector.
Huang, JieZhang, Hongshan
The existing variable speed limit (VSL) control strategies rely on variable message signs, leading to slow response times and sensitivity to driver compliance. These methods struggle to adapt to environments where both connected automated vehicles (CAVs) and manual vehicles coexist. This article proposes a VSL control strategy using the deep deterministic policy gradient (DDPG) algorithm to optimize travel time, reduce collision risks, and minimize energy consumption. The algorithm leverages real-time traffic data and prior speed limits to generate new control actions. A reward function is designed within a DDPG-based actor-critic framework to determine optimal speed limits. The proposed strategy was tested in two scenarios and compared against no-control, rule-based control, and DDQN-based control methods. The simulation results indicate that the proposed control strategy outperforms existing approaches in terms of improving TTS (total time spent), enhancing the throughput efficiency
Ding, XibinZhang, ZhaoleiLiu, ZhizhenTang, Feng
A cutting-edge EV powertrain NVH laboratory has been established at Dana Incorporated’s world headquarters in Ohio, significantly enhancing its capabilities in EV powertrain NVH development. This state-of-the-art, industry-leading facility is specifically designed to address diverse NVH requirements for EV powertrain development and validation processes. This capability substantially reduces development time for new drivetrain systems. Key features of the laboratory include a hemi-anechoic chamber, two AC asynchronous load motors, an acoustically isolated high-speed input motor, and two battery emulators capable of accommodating both low and high-voltage requirements. The NVH laboratory enables engineers to evaluate system performance and correlate results with digital twin models. This capability supports the optimization of NVH characteristics at both the system and component levels, as well as the refinement of CAE models for enhanced design precision. This paper details the design
Cheng, Ming-TeZugo, Chris
In active noise control, the control region size (same meaning as zone of control) decreases as the frequency increases, so that even a small moving of the passenger's head causes the ear position to go out of the control region. To increase the size of the control region, many speakers and microphones are generally required, but it is difficult to apply it in a vehicle cabin due to space and cost constraints. In this study, we propose moving zone of quiet active noise control technique. A 2D image-based head tracking system captured by a camera to generate the passenger's 0head coordinates in real time with deep learning algorithm. In the controller, the control position is moved to the ear position using a multi-point virtual microphone algorithm according to the generated ear position. After that, the multi-point adaptive filter training system applies the optimal control filter to the current position and maintains the control performance. Through this study, it is possible to
Oh, ChiSungKang, JonggyuKim, Joong-Kwan
In the development of engine mounting systems for passenger cars, accurately capturing dynamic loads during real-world driving conditions is crucial for optimizing performance, durability, and NVH (Noise, Vibration, and Harshness) characteristics. This paper introduces an innovative approach that integrates load cell and strain gauge technologies for Road Load Data (RLD) acquisition, specifically designed for engine mounting applications. By combining load cells and strain gauges, this method offers a comprehensive solution for measuring both direct forces and the resulting strains on engine mounts, providing a more detailed understanding of the load profiles. Load cells capture the overall forces exerted on the engine mounts, while strategically placed strain gauges measure local deformations and stress distributions within the mounts. This dual-method approach enables precise correlation of force and strain data, enhancing the accuracy of load calculations under various driving
Hazra, SandipKhan, Arkadip AmitavaMohare, Gourishkumar
Noise reduction at the source level is key to achieve the overall vehicle level interior targets. This paper presents a novel approach that integrates directivity analysis with simulation techniques to optimize acoustic encapsulation design for automotive sound sources to achieve the targeted radiation levels. The foundation for this methodology is to measure the angular distribution of sound pressure levels around the noise source so called Directivity, at every frequency of interest and determine the most effective acoustic encapsulation to achieve the targeted sound radiation. Accurate measurement of directivity in physical testing with fine angular resolutions can be complex and expensive, this study utilizes numerical simulation techniques using FEA to mitigate the challenges in mid frequency range. The scope of the study is focused on mid frequency sound pressure levels between 500-2500 Hz, which are determined to be significant contributors to overall DU noise. The first step is
Kaluvakota, SrikanthGhaisas, NikhilPilz, Fernando
To optimize vehicle chassis handling stability and ride safety, a layered joint control algorithm based on phase plane stability domain is proposed to promote chassis performance under complicated driving conditions. First, combining two degrees-of-freedom vehicle dynamics model considering tire nonlinearity with phase plane theory, a yaw rate and side slip angle phase plane stability domain boundary is drew in real time. Then based on the real-time stability domain and hierarchical control theory, an integrated control system with active front steering (AFS) and direct yaw moment control (DYC) is designed, and the stability of the controller is validated by Lyapunov theory. Finally, the lateral stability of the vehicle is validated by Simulink and CarSim simulations, real car data, and driving simulators under moose test and pylon course slalom test. The experimental results confirm that the algorithm can enhance the maneuverability and ride safety for intelligent vehicles.
Liao, YinshengZhang, ZhijieSu, AilinZhao, BinggenWang, Zhenfeng
Gear whine has emerged as a significant challenge for electric vehicles (EVs) in the absence of engine masking noise. The demand from customers for premium EVs with high speed and high torque density introduces additional NVH risks. Conventional gear design strategies to reduce the pitch-line velocity and increase contact ratio may impact EV torque capacitor or its efficiency. Furthermore, microgeometry optimization has limited design space to reduce gear noise over a wide range of torque loads. This paper presents a comprehensive investigation into the optimization of transfer gear blanks in a single-speed two-stage FDW electric drive unit (EDU) with the objective of reducing both mass and noise. A detailed multi-body dynamics (MBD) model is constructed for the entire EDU system using a finite-element-based time-domain solver. This investigation focuses on the analysis and optimization of asymmetric gear blank design features with three-slot patterns. A design-of-experiment (DOE
He, SongBahk, CheonjaeLi, BoDu, IsaacPatruni, Pavan KumarBaladhandapani, Dhanasekar
Tires have a significant impact on vehicle road noise. The noise in 80~160Hz is easily felt when driving on rough roads and has a great relationship with the tire structural design. How to improve the problem through tire simulation has become an important issue. Therefore, this paper puts forward the concept of virtual tire tuning to optimize the noise. An appropriate tire model is crucial for road noise performance, and the CDtire (Comfort and Durability Tire) model was used in the article. After conducting experimental validation to get an accurate tire model, adjust the parameters and structure of the tire model to generate alternative model scenarios. The transfer function of the tire center was analyzed and set as the evaluation condition for tire NVH (Noise, vibration, and harshness) performance. This enabled a comparison among various model scenarios to identify the best-performing tire scenario in focused frequency whose transfer function needed to be lowest. Manufacture the
Zhang, BenYu Sr, JingChen, QimiaoLiu, XianchenGu, Perry
Battery Electric Vehicles (BEVs) are extremely sensitive in terms of NVH requirements. While the engine is being replaced with an almost silent electric motor, the transmission noise appears persistent and demands more silent transmission. This has raised demand for improvement in design as well as manufacturing quality. Various innovations are being made to drive an improvement in the NVH. The following paper will discuss the improvement in NVH achieved through a design optimization of the housing using modal analysis. Firstly, the NVH results were co-related with the modal analysis and the cause for the dominant peak in amplitude of the NVH graph associated with the housing modes were mapped. A simple Excel based correlation matrix is used to map the list of all Eigenfrequencies of housing and its corresponding gear tooth frequency. Further optimization is done in housing design to defer the modal frequencies and another NVH test was run. It was proven that housing design
Pingale, Abhijeet SatishDeshpande, Prasannakumar
For electric vehicles, it is critical to develop drive units that produce a minimal amount of noise while meeting efficiency needs for a given application. Modern computational resources and accumulated experience allow for engineers to evaluate gear noise early in the development process and influence the design of the drive unit. This paper documents a high-fidelity virtual engineering approach to evaluate gear noise in a concept parallel axis drive unit and provide learnings to influence the design of external structures to improve NVH performance. By using the latest simulation tools to calculate and visualize the noise and vibration characteristics of the drive unit, designers and developers can implement design changes in optimization iterations to reduce noise and vibration. Gear harmonic response is firstly analyzed through a system model which considers structural deflection and misalignment, then a FE housing model is incorporated which is used for noise radiation evaluation
Lima, LuizShi, ZhenghongXu, HaiReynolds, CraigMiller, John
This study presents a novel methodology for optimizing the acoustic performance of rotating machinery by combining scattered 3D sound intensity data with numerical simulations. The method is demonstrated on the rear axle of a truck. Using Scan&Paint 3D, sound intensity data is rapidly acquired over a large spatial area with the assistance of a 3D sound intensity probe and infrared stereo camera. The experimental data is then integrated into far-field radiation simulations, enabling detailed analysis of the acoustic behavior and accurate predictions of far-field sound radiation. This hybrid approach offers a significant advantage for assessing complex acoustic sources, allowing for quick and reliable evaluation of noise mitigation solutions.
Fernandez Comesana, DanielVael, GeorgesRobin, XavierOrselli, JosephSchmal, Jared
The multifaceted, fast-paced evolution in the automotive industry includes noise and vibration (NVH) behavior of products for regulatory requirements and ever-increasing customer preferences and expectations for comfort. There is pressing need for automotive engineers to explore new and advanced technologies to achieve a ‘First Time Right’ product development approach for NVH design and deliver high-quality products in shorter timeframes. Artificial Intelligence (AI) and Machine Learning (ML) are trending transformative technologies reshaping numerous industries. AI enables machines to replicate human cognitive functions, such as reasoning and decision-making, while ML, a branch of AI, employs algorithms that allow systems to learn and improve from data over time. The purpose of the paper is to show an approach of using machine learning techniques to analyze the impact of variations in structural design parameters on vehicle NVH responses. The study begins by executing the Design of
Miskin, Atul R.Parmar, AzanRaj, SoniaHimakuntla, Uma Maheswar
In the modern automotive industry, squeak and rattle issues are critical factors affecting vehicle perceived quality and customer satisfaction. Traditional approaches to predicting and mitigating these problems heavily rely on physical testing and simulation technologies, which can be time-consuming and resource-intensive, especially for larger models. In this study, a data-driven machine learning approach was proposed to mitigate rattle risks more efficiently. This study evaluated a floor console model using the traditional simulation-based E-line method to pinpoint high-risk areas. Data generation is performed by varying material properties, thickness, and flexible connection stiffness using the Hammersley sampling algorithm, creating a diverse and comprehensive dataset for generating a machine learning (ML) model. Utilizing the dataset, the top contributing variables were identified for training the ML models. Various machine-learning models were developed and evaluated, and the
Parmar, AzanRao, SohanReddy, Hari Krishna
High-frequency whine noise in electric vehicles (EVs) is a significant issue that impacts customer perception and alters their overall view of the vehicle. This undesirable acoustic environment arises from the interaction between motor polar resonance and the resonance of the engine mount rubber. To address this challenge, the proposal introduces an innovative approach to predicting and tuning the frequency response by precisely adjusting the shape of rubber flaps, specifically their length and width. The approach includes the cumulation of two solutions: a precise adjustment of rubber flap dimensions and the integration of ML. The ML model is trained on historical data, derived from a mixture of physical testing conducted over the years and CAE simulations, to predict the effects of different flap dimensions on frequency response, providing a data-driven basis for optimization. This predictive capability is further enhanced by a Python program that automates the optimization of flap
Hazra, SandipKhan, Arkadip
Squeak and Rattle (S&R) issues present significant challenges in the automotive industry, negatively affecting the perceived quality of vehicles. Early identification of these issues through rigorous testing protocols—such as auditory assessments and dynamic simulations—enables the development of more robust systems while optimizing resource use. Finite Element Method (FEM) simulations are crucial for identifying S&R issues during the design phase, allowing engineers to address potential problems before the creation of physical prototypes. By developing high-fidelity virtual models and accurately simulating flexible connections, these simulations effectively capture rattle effects, enhancing prediction reliability. Traditional snap stiffness calculations typically employ a cantilever-based formulation, which is suitable for simple snap-fit designs but insufficient for more complex geometries that require enhanced stiffness. To address this limitation, the proposed methodology utilizes
Rao, SohanElangovan, PraneshReddy, Hari
As India’s economy expands and road infrastructure improves, the number of car owners is expected to grow substantially in the coming years. This market potential has intensified competition among original equipment manufacturers (OEMs) to position their products with a focus on cost efficiency while delivering a premium user experience. Noise and Vibration (NV) performance is a critical differentiator in conveying a vehicle's premiumness, and as such, NV engineers must strategically balance the achievement of optimal acoustic performance with constraints on cost, mass, and development timelines. Traditionally, NV package optimization occurs at the prototype or advanced prototype stage, relying heavily on physical testing, which increases both cost and time to market. Furthermore, late-stage design changes amplify these challenges. To address these issues, this paper proposes the integration of Hybrid Statistical Energy Analysis (HSEA) into the early stages of vehicle development
Rai, NiteshMehta, MakrandRavindran, Mugundaram
In this work, Genetic Algorithm (GA) optimized Proportional Integral Derivative (PID) controller is employed in the active suspension. The PID gain values are optimally tuned based on the objective function by the Integral Time Absolute Error (ITAE) criteria of various suspension measures like vehicle body displacement, suspension and tire deflections. The proposed GAPID controller is experimentally validated through the 3-DOF quarter-car (QC) test rig model. The fabricated model with passive suspension system (PASS) and active suspension system (ACSS) with an electrical actuator is presented. The schematic representation of the fabricated test set-up with and without ACSS is also illustrated. Further, simulation and experimental response of the fabricated model with and without ACSS are compared. It is identified that the proposed GAPID controller attenuates the sprung mass acceleration by about 41.64 % and 29.13 % compared with PASS for the theoretical as well as experimental cases
A, ArivazhaganKandavel, Arunachalam
This study focuses on the numerical analysis of weather-strip contact sealing performance with a variable cross-sectional design, addressing both static and dynamic behaviors, including the critical issue of stick-slip phenomena. By employing finite element modeling (FEM), the research simulates contact pressures and deformations under varying compression loads, DCE (Door Closing Efforts) requirements, typical in automotive applications. The analysis evaluates how changes in the cross-sectional shape of the weather-strip affect its ability to maintain a consistent sealing performance, especially under dynamic vehicle operations. The study also delves into stick-slip behavior, a known cause of noise and vibration issues, particularly improper/ loosened door-seal contact during dynamic driving condition. This study identifies key parameters influencing stick-slip events, such as friction coefficients, material stiffness, surface interactions, sliding velocity, wet/dry condition
Ganesan, KarthikeyanSeok, Sang HoSun, Hyang Sun
The process of producing aircraft parts involves the drilling of aluminum alloys. This creates a large amount of chips, which are removed using air, but sometimes they still remain within the holes. This is checked by inspectors through visual inspection. However, the quality of human inspection varies based on skill level and fatigue. Thus, image-based inspection should be used to stabilize and further improve inspection quality. This study aims to build a framework for chip detection based on image processing. Taking into account on-site implementation, the system must have low installation and running costs and be standalone. Therefore, we adopt the KIZKI algorithm, which satisfies these conditions. KIZKI means awareness in Japanese. This is a model of human peripheral vision and saccades. It does not require training like AI and can achieve high-speed and high-performance detection using a low-performance computer. In other words, there is no need for a computer with an expensive
Iinuma, MarinSato, JunyaTsuji, Masahiko
This research optimizes sheet metal gusset geometry to support a suspension pickup point in a Formula SAE racecar. The sheet metal gusset design incorporates an external radial cut-out and an internal triangular cut-out, each of which can be adjusted in size to optimize the stiffness to mass ratio. A finite element analysis was set up using a heavy braking load case, which applied 3900 N to the suspension point being supported by the gusset. A parametric optimization (finite element analysis) was run in SolidWorks to gather mass and stiffness data for each of the 143 designs under the prescribed load case. The parametric optimization was run on both a simulated front hoop and a test fixture, which showed a similar trend in their results. Experimental testing was performed on three designs. The gusset profiles were waterjet and TIG welded to the test fixture tube frames. The results of the test agreed with the simulation results with a discrepancy of less than 10% in all cases. The
Burggraf, JacobWillerth, Stephanie MichelleYu, Bosco
With the development of intelligent transportation systems and the increasing demand for transportation, traffic congestion on highways has become more prominent. So accurate short-term traffic flow prediction on these highways is exceedingly crucial. However, because of the complexity, nonlinearity, and randomness of highway traffic flows, short-term prediction of its flows can be difficult to achieve the desired accuracy and robustness. This article presents a novel architectural model that harmoniously fuses bidirectional long–short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and multi-head attention (MHA) components. Bayesian optimization (BO) is also used to determine the optimal set of hyperparameters. Based on the PeMS04 dataset from California, USA, we evaluated the performance of the proposed model across various prediction intervals and found that it performs best within a 5-min prediction interval. In addition, we have conducted comparison and ablation
Chen, PengWang, TaoMa, ChangxiChen, Jun
Modern aircraft, ships, and offshore structures are increasingly constructed using fiber-reinforced composite materials. However, when subjected to lightning strikes, these materials can suffer significant structural and functional damage due to their electrical and thermal properties. This study aims to develop a novel finite element (FE) model to minimize the error in estimating the thermal damage caused during lightning strikes. This will aid in design and optimization of lightning protection systems. The developed model introduces a simplified numerical approach to model the lightning arc interaction with CFRP laminate. The existing FE model includes idealized loading conditions, leading to high error in estimation of severe damage area and in-depth damage. The proposed methodology incorporates a more realistic lightning-induced loading pattern to improve accuracy. Several cases are analyzed using available FE methods and compared to the proposed model (case 6) to evaluate the
Sontakkey, AkshayKotambkar, MangeshKaware, Kiran
The rise of electric vehicles (EVs) highlights the need to transition to a renewable energy society, where power is generated from sustainable sources. This shift is driven by environmental, economic, and energy security concerns. However, renewable energy sources like wind and solar are intermittent, necessitating extensive energy storage systems. Vanadium redox flow batteries (VRFBs) are promising for large-scale energy storage due to their long cycle life, scalability, and safety. In VRFBs, cells are typically connected in series to increase voltage, with electrolytes introduced through parallel flow channels using a single manifold. This design, while simple and low in pressure drop, often leads to imbalanced flow rates among cells, affecting performance. Balancing flow rates is crucial to minimize uneven overpotential and enhance durability, presenting an optimization challenge between achieving uniform flow and minimizing pressure drop. This study developed numerical models to
Suwanpakdee, NutAiemsathit, PorametCharoen-amornkitt, PatcharawatSuzuki, TakahiroTsushima, Shohji
Topology optimization (TO) in electrochemical systems has recently attracted many researchers. Previous studies suggested minimal performance differences between 2D and 3D designs, indicating that 2D models suffice to enhance performance, especially in unidirectional flow scenarios. A later study found that the concentration distribution in an optimized 2D flow system differed from that in a unidirectional flow system. We posited that pulsating flow could further enhance the performance of such systems. First, we initiated TO for a diffusion-reaction system in a steady state. The optimized structure obtained from this process served as the foundation for subsequent investigations involving a pulsating flow source in convection-diffusion-reaction systems. We introduced two different systems with distinct flow natures: one characterized by a flow nature of 1D and the other by a flow nature of 2D. The results demonstrated that the optimized structure with a heterogeneous distribution
Long, MenglyAlizadeh, MehrzadSun, PengfeiCharoen-amornkitt, PatcharawatSuzuki, TakahiroTsushima, Shohji
Flex fuel vehicles (FFV) can operate effectively from E5 (Gasoline 95%, ethanol 5%) fuel to E100 (Gasoline 0%, ethanol 100%) fuel. It is necessary to meet the performance, drivability, emission targets and regulatory requirements irrespective of fuel mixture combination. This research work focuses on optimizing the combustion efficiency and conversion efficiency of catalytic converter of a spark-ignited less than 200 cc engine for FFV using Taguchi methods robust optimization technique. The study employs an eight-step robust optimization approach to simultaneously minimize engine out emissions and maximize catalytic converter efficiency. Six control factors including type of fuel, catalyst heating rpm, lambda (excess-air ratio), injection end angle, lambda controller delay, and ignition timing are optimized. Four noise factors like compression ratio, clearance volume, catalyst noble metal loading, and catalyst aging are also considered. Through approximately 100 physical experiments on
Vaidyanathan, BalajiArunkumar, PraveenkumarShunmugasundaram, PalaniMurugesan, ManickamJayajothijohnson, Vedhanayagam
As the automotive sector shifts towards cleaner and more sustainable technologies, fuel cells and batteries have emerged as promising technologies with revolutionary potential. Hydrogen fuel cell vehicles offer faster refueling times, extended driving ranges, and reduced weight and space requirements compared to battery electric vehicles, making them highly appealing for future transportation applications. Despite these advantages, optimizing electrode structures and balancing various transport mechanisms are crucial for improving PEFCs’ performance for widespread commercial viability. Previous research has utilized topology optimization (TO) to identify optimal electrode structures and attempted to establish a connection between entropy generation and topographically optimized structures, aiming to strengthen TO numerical findings with a robust theoretical basis. However, existing studies have often neglected the coupling of transport phenomena. Typically, it is assumed that a single
Tep, Rotanak Visal SokLong, MenglyAlizadeh, MehrzadCharoen-amornkitt, PatcharawatSuzuki, TakahiroTsushima, Shohji
The rear swing arm, a crucial motorcycle component, connects the frame and wheel, absorbing the vehicle’s load and various road impacts. Over time, these forces can damage the swing arm, highlighting the need for robust design to ensure safety. Identifying potential vulnerabilities through simulation reduces the risk of failure during the design phase. This study performs a detailed fatigue analysis of the swing arm across different road conditions. Data for this research were collected from real-vehicle experiments and simulation analyses, ensuring accuracy by comparing against actual performance. Following CNS 15819-5 standards, road surfaces such as poorly maintained, bumpy, and uneven roads were tested. Using Motion View, a comprehensive multi-body dynamic model was created for thorough fatigue analysis. The results identified the most stress-prone areas on the swing arm, with maximum stress recorded at 109.6N on poorly maintained roads, 218.3N on bumpy surfaces, and 104.8N on
Chiou, Yi-HauHwang, Hsiu-YingHuang, Liang-Yu
In recent years, researchers have increasingly focused on ammonia–diesel dual-fuel engines as a means of reducing CO2 emissions. Analyzing in-cylinder combustion processes is essential for optimizing the performance of ammonia–diesel dual-fuel engines. However, there is currently a lack of suitable reaction kinetics models for ammonia–diesel engine conditions. In this study, the ignition delay of ammonia/n-heptane mixtures was measured, and a reduced chemical mechanism was developed. Using rapid compression machine (RCM) experiments, the ignition delays of ammonia/n-heptane mixtures with different ammonia energy fractions (AEFs) (40%, 60%, and 80%) were measured. The test pressure ranged from 1.5 to 3.0 MPa, while the temperature ranged from 667 to 919 K, with an equivalence ratio of 1. The results showed that as the AEFs increased, the ignition delay of the premixed mixture also increased. When the AEF was 40%, the ammonia/n-heptane premixed mixture exhibited the negative temperature
Cai, KaiyuanLiu, YiChen, QingchuQi, YunliangLi, LiWang, Zhi
Different approaches are undertaken to mitigate the impact of the transport sector on climate change. Alongside electrifying powertrains, sustainable e-fuels such as polyoxymethylene dimethyl ethers (OME) are considered a promising bridging technology for different applications. However, this requires that the engines are optimized for the new fuels. Accordingly, this study aims to optimize the numerical spray modeling of OME in CONVERGE. Based on the KH–RT break-up model, the spray simulations of three different commercial injectors for heavy-duty applications are analyzed regarding the predictability of the liquid and gaseous penetration lengths and the total simulation time. A sensitivity analysis is conducted for the turbulence model, mesh size, and spray parameters prior to optimizing the spray model and validating it with experimental results. While each parameter individually influences the different phases of the injection event, the sensitivity analysis reveals that the break
Zepf, AndreasHärtl, MartinJaensch, Malte
The liquid-cooled plate is a critical component of the liquid-cooled system for battery packages.Battery overheating problem under charging and discharging can be solved effectively by a liquid-cooled plate with superior thermal performance.A liquid-cooled plate with a leaf-vein-like structure aiming to enhance the cooling performance was proposed and designed. A test bench of the battery pack cooling system was built,and an experimental for battery pack cooling system was carried. The maximum temperature,temperature difference of batteries inside a module and flow resistance the liquid-cooled plate were measured. A model for thermal analysis of the liquid-cooled plate was established,and the model was validated by the data obtained from the experiment.The effects of parameters such as the number of main and secondary flow channels,the inclination angle of the secondary flow channels and the inlet flow distribution on the fluid and temperature characteristics of the battery pack
Liao, ChangshengWang, XihuiZhou, FupengLi, KunyuanShangguan, Wen-Bin
This paper addresses the need for improved material selection in parcel shelves, a key component in passenger vehicles used to conceal the trunk area. The focus is on weight optimization, structural integrity, and perceived quality improvement using sustainable and ultra-lightweight composite materials. Traditional materials such as PET Woodstock, while durable, contribute significantly to vehicle weight, which is a drawback in the context of electric vehicles (EVs). The proposed composite material alternatives offer a high strength-to-weight ratio and have been shown to improve the load vs. deflection ratio, enhance aesthetics, and reduce manufacturing complexity and costs. This study outlines the testing and evaluation process of varying GSM and thicknesses of composite materials, demonstrating superior stiffness, reduced deflection under load, and enhanced ease of assembly. This work contributes to the ongoing efforts to achieve lightweighting, cost efficiency, and sustainability in
Kinthala, Nareen KumarPatnaik, MangaKhandelwal, MohitKakani, Phani KumarPalaniappan, Elavarasan
An efficient and safe aircraft scheduling scheme is of great significance to the construction of smart airports. The towbarless aircraft taxiing system (TLATS) is a common dispatching method, which is composed of the towbarless towing vehicle (TLTV) and the aircraft. The system’s trajectory planning and autonomous steering control are being researched in order to improve steering accuracy, dispatching efficiency, and safety. In this article, the towbarless aircraft taxiing system is transformed into tractor-trailer system, the kinematic model and the dynamic model of the aircraft-tractor are established. Taking TLTV as a virtual subsystem of TLATS, and it is regarded as the controlled object of path planning and tracking. In response to the operational requirements of TLTV, an advanced A-star(A*) path planning algorithm is proposed to perform collision avoidance and turn radius restrictions during path planning resulting in a reference path for TLATS. Considering the estimation
Zhu, HengjiaZhao, ZhouqiaoXu, YitongZhang, Wei
In future planetary exploration missions, the Eight-Wheeled Planetary Laboratory (EWPL) will have sufficient capacity for tasks but will experience significant lateral slips during high-speed turns due to its large inertia. Modern technology allows for independent steering of all eight wheels, but controlling each wheel's steering angle is key to improving stability during turns. This paper introduces a novel rear-axle steering feed-forward controller to reduce sideslip. First, a mathematical model for the vehicle's steering is established, including kinematic equations based on Ackermann steering. Feed-forward zero side-slip control is applied to the third and fourth axles to counteract the side-slip angle of the center of mass. A multi-body dynamics model of the EWPL is then built in Chrono to evaluate the turning radius and optimize steering angle ratios for the rear axles. Finally, a steady-state cornering simulation on loose terrain compares the performance of the proposed
Liu, JunZhang, KaidiShi, JunweiYang, WenmiaoZhang, YunqingWu, Jinglai
The integrated bracket is a plastic part that packages functional components such as the ADAS (Advanced Driver Assistance System) camera, rain light sensor, and the mounting provisions of the auto-dimming IRVM (Inner Rear View Mirror). This part is fixed on the windshield of an automobile using double-sided adhesive tapes and glue. ADAS, rain light sensors, and auto-dimming IRVM play an important part in the safety of the driver and everyone present in the automobile. This makes proper functioning of the integrated bracket very integral to occupant safety. Prior to this work, the following literature; Integrated Bracket for Rain Light Sensor/ADAS/Auto-Dimming IRVM with provision of mounting for Aesthetic Cover [1] outlines the design considerations and advantages of mounting several components on the same bracket. It follows the theme where the authors first define the components packaged on the integrated bracket and then the advantages of packaging multiple components on a single
Chandravanshi, PriyanshDharmatti, Girish
In this work, design optimization for the lightweight of the body frame of a commercial electric bus with the requirements of stiffness, strength and crashworthiness is presented. The technique for order preference by similarity to ideal solution (TOPSIS) is applied to calculate the components that have a great impact on the output response of the static modal model and the rear-end collision model. The thickness of the five components with the highest contribution in the two models is determined as the final design variable. Design of experiment (DOE) is carried out based on the Latin Hypercube sampling method, and then the surrogate models are fitted by the least squares regression (LSR) method based on the DOE sampling data. The error analysis of the surrogate model is carried out to determine whether it can replace the finite element (FE) model for optimization, then the optimization scheme for lightweight optimization of electric bus frame is implemented based on the algorithm of
Yang, XiujianTian, DekuanLiu, JiaqiCui, YanLin, Qiang
The final step in manufacturing high-precision parts for internal combustion engines, such as cylinder heads and blocks, is the removal of machining chips from the finished parts. This step is crucial because the machining chips and cutting oil left on the surface after machining can cause quality issues in the downstream engine assembly and affect the cooling system’s performance during engine operation. This chip removal step is especially critical for parts with internal cavities, such as the water jackets in cylinder heads, due to the difficulty of removing chips lodged in the narrow passages of these internal channels. To effectively remove chips from the water jacket, machining chip washing systems typically utilize multiple high-velocity water jets directed into the water jacket, creating flows with substantial kinetic energy to dislodge and evacuate the machining chips. For machining chip washing systems equipped with dozens of water nozzles, optimizing washing efficiency
Jan, JamesTorcellini, SabrinaKhorran, AaronHall, Mark
In this paper, the topology and shape optimization of a vehicle Heating, Ventilation, and Air Conditioning (HVAC) system is presented. The CFD and optimization methodologies are implemented within AcuSolve™ software. The topology optimization algorithm computes the geometry, where the design domain is parameterized with a field of porosity design variables which indicates the material, fluid or solid, throughout the domain. The optimization is performed using the continuous adjoint approach by the Galerkin Least Squares solver on which the AcuSolve™ solver is based. The design is further improved by using shape optimization. To optimize the geometrical shape, a combination of smooth perturbations, in terms of so called morph shapes, are used to deform the geometrical shape in the optimization algorithm. To this end, a parameterization of the design space is done using a moderate number of design variables, each associated with a morph shape. The two optimization phases are connected by
Papadimitriou, DimitriosSandboge, Robert
Optimizing engine mounting systems is a complex task that requires balancing the isolation of vehicle vibrations with controlling powertrain movement within a limited dynamic envelope. Six Degrees of Freedom (6DOF) optimization is widely used for mounting stiffness and location optimization. This study investigates the application of various optimization algorithms for 6DOF analysis in engine mount design, where the system’s stochastic behaviour and probabilistic characteristics present additional challenges. Selecting an appropriate optimization framework is essential for achieving accurate and efficient NVH results. Recent advancements in research have introduced several 6DOF optimization algorithms to determine the optimal stiffness and location of engine mounts. The study evaluates a range of optimization methods, including Simultaneous Hybrid Exploration that is Robust, Progressive and Adaptive (SHERPA), Quadratic Programming (QP), Genetic Algorithm (GA), Particle Swarm
Hazra, SandipKhan, Arkadip
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