Search Results

Abstract This study aims to explore the wear characteristics of fused deposition modeling (FDM) printed automotive parts and techniques to improve wear performance. The surface roughness of the parts printed from this widely used additive manufacturing technology requires more attention to reduce surface roughness further and subsequently the mechanical strength of the printed geometries. The main aspect of this study is to examine the effect of process parameters and annealing on the surface roughness and the wear rate of FDM printed acrylonitrile butadiene styrene (ABS) parts to diminish the issue mentioned above. American Society for Testing and Materials (ASTM) G99 specified test specimens were fabricated for the investigations. The parameters considered in this study were nozzle temperature, infill density, printing velocity, and top/bottom pattern.

Abstract Biogas (60% methane–40% CO2 approximately) can be used in the reactivity-controlled compression ignition (RCCI) mode along with a high-reactivity fuel (HRF). In this work dimethyl ether (DME) that can also be produced from renewable sources was used as the HRF as a move toward sustainable power generation. The two-cylinder turbocharged diesel engine modified to work in the DME–biogas RCCI (DMB-RCCI) mode was studied under different proportions of methane (45–95%) in biogas since the quality of this fuel can vary depending on the feedstock and production method. Only a narrow range of biogas to DME ratios could be tolerated in this mode at each output without misfire or knock. Detailed experiments were conducted at brake mean effective pressures (BMEPs) of 3 and 5 bar at a speed of 1500 rpm and comparisons were made with the diesel–biogas dual-fuel and diesel–biogas RCCI modes under similar methane flow rates while the proportion of CO2 was varied.

Abstract This study demonstrates the defossilized operation of a heavy-duty port-fuel-injected dual-fuel engine and highlights its potential benefits with minimal retrofitting effort. The investigation focuses on the optical characterization of the in-cylinder processes, ranging from mixture formation, ignition, and combustion, on a fully optically accessible single-cylinder research engine. The article revisits selected operating conditions in a thermodynamic configuration combined with Fourier transform infrared spectroscopy. One approach is to quickly diminish fossil fuel use by retrofitting present engines with decarbonized or defossilized alternatives. As both fuels are oxygenated, a considerable change in the overall ignition limits, air–fuel equivalence ratio, burning rate, and resistance against undesired pre-ignition or knocking is expected, with dire need of characterization.

Abstract Automotive subframe is a critical chassis component as it connects with the suspension, drive units, and vehicle body. All the vibration from the uneven road profile and drive units are passed through the subframe to the vehicle body. OEMs usually have specific component-level drive point dynamic stiffness (DPDS) requirements for subframe suppliers to achieve their full vehicle NVH goals. Traditionally, the DPDS improvement for subframes welded with multiple stamping pieces is done by thickness and shape optimization. The thickness optimization usually ends up with a huge mass penalty since the stamping panel thickness has to be changed uniformly not locally. Structure shape and section changes normally only work for small improvements due to the layout limitations. Tuned rubber mass damper (TRMD) has been widely used in the automotive industry to improve the vehicle NVH performance thanks to the minimum mass it adds to the original structure.

Abstract Fossil fuel reserves are swiftly depleting when consumer demand for these fuels continues to rise. In order to meet the demand and diminish the pollution derived through conventional fuels, it is crucial to employ cleaner fuels made from substitutes such as waste biomass. Also, converting waste biomass to fuel can lower usage of landfills. There are many biomass resources that are suitable for fuel production, out of which groundnut is also a potential feedstock. Groundnut shell biomass was chosen for this study, as it is a waste leftover during shelling of groundnuts for various commercial applications. The procured groundnut shells were converted to oil using pyrolysis process and was distilled. Both the pyrolysis oil and the distilled oil were analyzed using Fourier transform infrared instrument wherein the presence of functional groups such as alcohols, amines, and carboxylic acids were identified.

Abstract The expansion of the internet has made everyone’s personal and professional lives more transparent. There are network security issues because people like sharing resources under the right conditions. Academics have demonstrated significant interest in situation awareness, which includes situation prediction, situation appraisal, and event detection, rather than focusing on the security of a single device in the network. Multi-stage attack forecasting and security situation awareness are two significant issues for network supervisors because the future usually is unknown. Hence, this study suggests combined intuitionistic fuzzy sets and deep neural network (CIFS-DNN) for network security situation prediction. The goal is to provide network administrators with a resource they can use as a point of reference while they formulate and carry out preventive actions in the event of a network assault.

Abstract TOC

Abstract Various feedstocks can be employed for biodiesel production, leading to considerable variation in composition and engine fuel characteristics. Using biodiesels originating from diverse feedstocks introduces notable variations in engine characteristics. Therefore, it is imperative to scrutinize the composition and properties of biodiesel before deployment in engines, a task facilitated by predictive models. Additionally, the international commercialization of biodiesel fuel is contingent upon stringent regulations. The traditional experimental measurement of biodiesel properties is laborious and expensive, necessitating skilled personnel. Predictive models offer an alternative approach by estimating biodiesel properties without depending on experimental measurements. This research is centered on building models that correlate mid-infrared spectra of biodiesel and critical fuel properties, encompassing kinematic viscosity, cetane number, and calorific value.

Abstract Driving schedule of every vehicle involves transient operation in the form of changing engine speed and load conditions, which are relatively unchanged during steady-state conditions. As well, the results from transient conditions are more likely to reflect the reality. So, the current research article is focused on analyzing the biofuel-like lemon peel oil (LPO) behavior under real-world transient conditions with fuel injection parameter MAP developed from steady-state experiments. At first, engine parameters and response MAPs are developed by using a response surface methodology (RSM)-based multi-objective optimization technique. Then, the vehicle model has been developed by incorporating real-world transient operating conditions. Finally, the developed injection parameters and response MAPs are embedded in the vehicle model to analyze the biofuel behavior under transient operating conditions.

Abstract With the rapid development and the growing deployment of autonomous ground vehicles (AGVs) worldwide, there is an increasing need to design reliable, efficient, robust, and scalable motion-planning algorithms. These algorithms are crucial for fulfilling the desired goals of safety, comfort, efficiency, and accessibility. To design optimal motion-planning algorithms, it is beneficial to explore existing techniques and make improvements by addressing the limitations of associated techniques, utilizing hybrid algorithms, or developing novel strategies. This article categorizes and overviews numerous motion-planning algorithms for AGVs, shedding light on their strengths and weaknesses for a comprehensive understanding.

Abstract A multiple output dynamically adjustable capacity system (MODACS) is developed to provide multiple voltage output levels while supporting varying power loads by switching multiple battery strings between serial and parallel connections. Each module of the system can service either a low voltage bus by placing its strings in parallel or a high voltage bus with its strings in series. Since MODACS contains several such modules, it can produce multiple voltages simultaneously. By switching which strings and modules service the different output rails and by varying the connection strategy over time, the system can balance the states of charge (SOC) of the strings and modules. A model predictive control (MPC) algorithm is formulated to accomplish this balancing. MODACS operates in various power modes, each of which imposes unique constraints on switching between configurations.

Abstract After a severe lane change, a wind gust, or another disturbance, the driver might be unable to recover the intended motion. Even though this fact is known by any driver, the scientific investigation and testing on this phenomenon is just at its very beginning, as a literature review, focusing on SAE Mobilus® database, reveals. We have used different mathematical models of car and driver for the basic description of car motion after a disturbance. Theoretical topics such as nonlinear dynamics, bifurcations, and global stability analysis had to be tackled. Since accurate mathematical models of drivers are still unavailable, a couple of driving simulators have been used to assess human driving action. Classic unstable motions such as Hopf bifurcations were found. Such bifurcations seem almost disregarded by automotive engineers, but they are very well-known by mathematicians. Other classic unstable motions that have been found are “unstable limit cycles.”

Abstract Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time.

Abstract The two-branch exhaust of an asymmetric twin-scroll turbocharged engine are asymmetrically and periodically complicated, which has great impact on turbine matching. In this article, a matching effect of turbine speed parameter on asymmetric twin-scroll turbines based on the exhaust pulse energy weight distribution of a heavy-duty diesel engine was introduced. First, it was built as an asymmetric twin-scroll turbine matching based on exhaust pulse energy distribution. Then, by comparing the average matching point and energy matching points on the corresponding turbine performance map, it is revealed that the turbine speed parameter of energy matching points was a significant deviation from the turbine speed parameter under peak efficiency, which leads to the actual turbine operating efficiency lower than the optimal state.

Abstract Asymmetric twin-scroll turbocharging technology, as one of the effective technologies for balancing fuel economy and nitrogen oxide emissions, has been widely studied in the past decade. In response to the ever-increasing demands for improved fuel efficiency and reduced exhaust emissions, extensive research efforts have been dedicated to investigating various aspects of this technology. Researchers have conducted both experimental and simulation studies to delve into the intricate flow mechanism of asymmetric twin-scroll turbines. Furthermore, considerable attention has been given to exploring the optimal matching between asymmetric twin-scroll turbines and engines, as well as devising innovative flow control methods for these turbines. Additionally, researchers have sought to comprehend the impact of exhaust pulse flow on the performance of asymmetric twin-scroll turbines.

Abstract Heavy-duty on-road engines are expected to conform to an ultralow NOx (ULNOx) standard of 0.027 g/kWh over the composite US heavy-duty transient federal test procedure (HD-FTP) cycle by 2031, a 90% reduction compared to 2010 emissions standards. Additionally, these engines are expected to conform to Phase 2 greenhouse gas regulations, which require tailpipe CO2 emissions under 579 g/kWh. This study experimentally demonstrates the ability of high fuel stratification gasoline compression ignition (HFS-GCI) to satisfy these emissions standards. Steady-state and transient tests are conducted on a prototype multi-cylinder heavy-duty GCI engine based on a 2010-compliant Cummins ISX15 diesel engine with a urea-SCR aftertreatment system (ATS). Steady-state calibration exercises are undertaken to develop highly fuel-efficient GCI calibration maps at both cold-start and warmed up conditions.

Abstract Throughout the automobile industry, the electronic brake boost technologies have been widely applied to support the expansion of the using range of the driver assist technologies. The electronic brake booster (EBB) supports to precisely operate the brakes as necessary via building up the brake pressure faster than the vacuum brake booster. Therefore, in this article a novel control strategy for the EBB based on fuzzy logic control (FLC) is developed and studied. The configuration of the EBB is established and the system model including the permanent magnet synchronous motor (PMSM), a two-stage reduction transmission (gears and a ball screw), a servo body, reaction disk, and the hydraulic load are modeled by MATLAB/Simulink. The load-dependent friction has been compensated by using Karnopp friction model. Due to the strong nonlinearity on the EBB components and the load-dependent friction, FLC has been used for the control algorithm.

Abstract A time domain analysis method of ride comfort and energy dissipation characteristics is proposed for automotive vibration proportional–integral–derivative (PID) control. A two-degrees-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined, and the Routh stability criterion method was introduced to assess the impact of PID control on vehicle stability. The PID control parameters are tuned using the differential evolution algorithm, and to improve the algorithm’s adaptive ability, an adaptive operator is introduced, so that the mutation factor of differential evolution algorithm can change with the number of iterations.

Abstract This article presents surrogate mixtures that simulate the physical and chemical properties in the auto-ignition of hydrotreated vegetable oil (HVO). Experimental investigation was conducted in the Ignition Quality Tester (IQT) to validate the auto-ignition properties with respect to those of the target fuel. The surrogate development approach is assisted by artificial neural network (ANN) embedded in MATLAB optimization function. Aspen HYSYS is used to calculate the key physical and chemical properties of hundreds of mixtures of representative components, mainly alkanes—the dominant components of HVO, to train the learning algorithm. Binary and ternary mixtures are developed and validated in the IQT. The target properties include the derived cetane number (DCN), density, viscosity, surface tension, molecular weight, and volatility represented by the distillation curve. The developed surrogates match the target fuel in terms of ignition delay and DCN within 6% error range.

Abstract This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.