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Browse AllThis study is to use the renewable fuels such as bioethanol and biobutanol as performance improving additives into diesel fuel. Nano-alumina is added in three proportions into diesel, diesel–bioethanol, and diesel–biobutanol blends for further enhancement of performance. The novelty of this study is the utilization of the bio-alcohols manufactured from the waste vegetables and fruits, which are reducing the land pollution, disposal cost, and the decrease in the dependency of diesel fuel. Blends of diesel–bioethanol and diesel–biobutanol are prepared and tested for the homogeneity in the controlled temperature of 25°C. The blends after the homogeneity test are tested for the required properties and compared with the base of commercial Bharat Stage VI diesel. One blend from three base fuels such as diesel, diesel–bioethanol, and diesel–biobutanol is being chosen and further blended with three proportions of nano-alumina particles (50 mg/L, 75 mg/L, and 100 mg/L) and further tested for
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
With the global issue of fossil fuel scarcity and the greenhouse effect, interest in electric vehicles (EVs) has surged recently. At that stage, because of the constraints of the energy density and battery performance degradation in low-temperature conditions, the mileage of EVs has been criticized. To guarantee battery performance, a battery thermal management system (BTMS) is applied to ensure battery operates in a suitable temperature range. Currently, in the industry, a settled temperature interval is set as criteria of positive thermal management activation, which is robust but leads to energy waste. BTMS has a kilowatt-level power usage under high- and low-temperature environments. Optimizing the BTMS control strategy becomes a potential solution to reduce energy consumption and overcome mileage issues. An appropriate system simulation model provides an effective tool to evaluate different BTMS control strategies. In this study, a predictive BTMS control strategy, which adjusts
Fuel cell vehicles (FCVs) offer a promising solution for achieving environmentally friendly transportation and improving fuel economy. The energy management strategy (EMS), as a critical technology for FCVs, faces significant challenges of achieving a balanced coordination among the fuel economy, power battery life, and durability of fuel cell across diverse environments. To address these challenges, a learning-based EMS for fuel cell city buses considering power source degradation is proposed. First, a fuel cell degradation model and a power battery aging model from the literature are presented. Then, based on the deep Q-network (DQN), four factors are incorporated into the reward function, including comprehensive hydrogen consumption, fuel cell performance degradation, power battery life degradation, and battery state of charge deviation. The simulation results show that compared to the dynamic programming–based EMS (DP-EMS), the proposed EMS improves the fuel cell durability while
Increasing global pressure to reduce anthropogenic carbon emissions has inspired a transition from conventional petroleum-fueled internal combustion engines to alternative powertrains, including battery electric vehicles (EVs) and hybrids. Hybrids offer a promising solution for emissions reduction by addressing the limitations of pure EVs such as slow recharge and range anxiety. In a previous research endeavor, a prototype high-power density generator was meticulously designed, fabricated, and subjected to testing. This generator incorporated a compact permanent magnet brushless dynamo and a diminutive single-cylinder two-stroke engine with low-technology constructions. This prototype generated 8.5 kW of electrical power while maintaining a lightweight profile at 21 kg. This study investigates the performance and emissions reduction potential by adapting the prototype to operate on methanol fuel. Performance and emissions were experimentally evaluated under varying operating conditions
The Automated Mobility Partnership (AMP) is a consortium of industry and academic stakeholders dedicated to advancing Automated Driving Systems (ADS) through a comprehensive suite of tools, datasets, and methodologies. The AMP portal integrates events from over 35 million miles of naturalistic driving data including thousands of annotated crashes and near-crashes and a decade of U.S. police-reported crash data curated by the Virginia Tech Transportation Institute. The portal enables data discovery, visualization, processing, and analysis through secured web access. This paper briefly describes the AMP portal and examines its utility in developing and evaluating the safety of ADS using standardized processes. For the examination, we provide examples based on generic automated driving functions, guided by the Safety of the Intended Functionality (SOTIF) framework. The results show that AMP is instrumental in identifying recorded real-world cases in which the hazardous behavior of a
Toyota Motor Corporation pursuing an omnidirectional strategy that includes battery electric vehicle (BEV), plug-in hybrid electric vehicle (PHEV), and fuel cell electric vehicle (FCEV) to accelerate electrification. One of the technical challenges with our xEV batteries which feature good degradation resistance and long battery life, is that regenerative braking cannot be fully effective due to the decrease in regenerative power in some situations, such as low battery temperature. For the electrified vehicles with an internal combustion engine such as PHEVs, the solution has been running the engine to increase deceleration through engine braking during coasting. PHEVs are expected to extend their cruising range and enhance EV driving experience as "Practical BEVs". While increasing battery capacity and enhancing convenience, the restrictions on EV driving opportunity due to low battery temperature may negatively affect PHEV’s appealing. As an alternative, introducing a battery heater
Honda Motor Corporation has developed a new naturally aspirated in-line 4-cylinder direct injection gasoline engine for C segment sedans that combines high environmental performance and power output. Development time and cost were greatly reduced by utilizing basic structures and components that had previously been developed engine for hybrid vehicles. In addition to the environmental performance at which hybrid engines excel, the driving performance required from a pure gasoline engine for C segment sedans with a low environmental impact was aimed to achieve by optimizing the shape of the combustion chamber to obtain rapid combustion, adjusting intake and exhaust valve timing, employing fuel injection control and adopting a two-piece water jacket that protects the exhaust system component by lowering the exhaust gas temperature at high load. As a result, the newly developed engine achieves a maximum thermal efficiency of 40% with knock suppression effect through rapid combustion
The Rotating Liner Engine (RLE) is a design concept where the cylinder liner of a heavy-duty Diesel engine rotates at about 2-4 m/s surface speed to eliminate the piston ring and skirt boundary friction near the top and bottom dead center. Two single cylinder engines are prepared using the Cummins 4BT 3.9 platform, one is RLE, the other is baseline (BSL), i.e. conventional. In 2022, we published the test results of the RLE under load, but we lacked detail test data for the baseline. In this new set of experiments, we compare the RLE performance at idle and under load of up to about 7 bar IMEP (indicated mean effective pressure) to the baseline under similar conditions. It has been proven that the elimination of metallic contact between the compression rings and cylinder wall takes place with a liner speed of 1.5-2.3 m/s surface speed (283-426 rpm for the 102 mm bore) for the 850-1280 rpm crankshaft speed. The RLE FMEP is substantially reduced under load, which is a trend opposite to
This research experimentally investigates the spray vaporization of high-pressure dimethyl ether (DME) using a single-hole research injector focusing on nominal operating conditions from the Engine Combustion Network (ECN). DME is a synthetic alternative to diesel fuel, offering both high reactivity and potential reductions in particulate emissions. Because DME only features half of the energy density of diesel fuel, a specifically designed fuel system with a high mass flow rate to meet the energy delivery requirements is needed. The unique physical properties of DME, including higher vapor pressure and lower viscosity, introduce challenges like cavitation and unique evaporation characteristics that deviate from typical diesel fuel. These features are likely to lead to differences in fuel mixing and combustion. This study aims to provide detailed experimental data on DME spray characteristics under engine-like conditions, helping the development of predictive CFD models for optimal
In internal combustion engines, hydrogen is considered as one of the most promising alternatives to replace fossil fuels and reduce CO2 emissions. In such a context, traditional injectors for hydrocarbon fuels are currently being tailored to be used with hydrogen, or a single-hole/multi-hole cap mounted at the injector tip was used to obtain better mixing and air utilization. Nevertheless, the hydrogen injection can be accompanied by the formation of highly under-expanded jets and will significantly influence the downstream mixing process. Therefore, in order to achieve a better understanding on hydrogen-air mixture, this work aims to numerically investigate the influence of the nozzle geometry on the jet behaviors in the near nozzle region. The nozzle diameter ranges from 0.1 mm to 2.0 mm and the nozzle length is from 1mm to 2mm. The injection pressure ranges from 10 bar to 70 bar. As the boundary condition varied, differences in both the internal flow of different nozzle structures
Decarbonized or low carbon fuels, such as hydrogen/methane blends, can be used in internal combustion engines to support ambitious greenhouse gas (GHG) emission reduction goals worldwide, including achieving carbon neutrality by 2045. However, as the volumetric concentration of H2 in these fuel blends surpasses 30%, the in-cylinder flame propagation and combustion rates increase significantly, causing an unacceptable increase in nitrogen oxides (NOx) emissions, which is known to have substantial negative effects on human health and the environment. This rise in engine-out NOx emissions is a major concern, limiting the use of H2 fuels as a means to reduce GHG emissions from both mobile and stationary power generation engines. In this study, an experimental investigation of the combustion performance and emissions characteristics of a 4th generation Tour split-cycle engine was undertaken while operating on 100% methane and various hydrogen/methane fuel blends (30%, 40%, and 50% by volume
In this paper, based on the cylindrical flow theory of incompressible viscous fluids and the equivalent circuit model of resonant sensing elements, a theoretical model for the measurement of liquid viscosity with a U-Shaped tungsten wire resonance sensor was established. This model can measure the liquid viscosity independently without liquid density or coupled detection of liquid density. The experimental results show that the decoupling of liquid viscosity and its density can be achieved at Re<1. The liquid viscosity is strongly linear with the resonant conductance. The viscosity measurement error is less than 7.24% in the viscosity range of 7.235cP to 85.2cP.
Selective catalytic oxidation/reduction catalysts coated on diesel particulate filters (SDPF) are an important technology route to meet next-stage emission regulations. The previous research of the research group showed that compared with SDPF coated with Cu-SSZ-13, the SDPF coated with novel selective catalytic oxidation-selective catalytic reduction (SCO-SCR) catalyst, which combined MnO2-CeO2/Al2O3 and Cu-SSZ-13, can simultaneously improve NOx reduction and soot oxidation performance. Catalyst coating strategy is an important parameter affecting the performance of SDPF. In this study, the effects of different coating strategies of SCO-SCR catalysts (C25, C50, C75, and C100) on the performance of NOx reduction and soot oxidation in SDPF were investigated. The results show that, as the inlet gas temperature increases, NO emissions first decrease and then increase, NOx conversion efficiency first increases and then decreases, and the rich-NO2 area, NH3 oxidation rate, N2O, CO, CO2
The integrated vehicle crash safety design provides longer pre-crash preparation time and design space for the in-crash occupant protection. However, the occupant’s out-of-position displacement caused by vehicle’s pre-crash emergency braking also poses challenges to the conventional restraint system. Despite the long-term promotion of integrated restraint patterns by the vehicle manufacturers, safety regulations and assessment protocols still basically focus on traditional standard crash scenarios. More integrated crash safety test scenarios and testing methods need to be developed. In this study, a sled test scenario representing a moderate rear-end collision in subsequence of emergency braking was designed and conducted. The bio-fidelity of the BioRID II ATD during the emergency braking phase is preliminarily discussed and validated through comparison with a volunteer test. The final forward out-of-position displacement of the BioRID II ATD falls within the range of volunteer
It is common practice in the automotive industry to explore the knock limits of fuels on an engine by a comparison of the knock limited spark advance (KLSA) at threshold knock intensity. However, the knock propensity of gasolines can be rated by changing one of three metrics on a variable compression ratio Cooperative Fuels Research (CFR) octane rating engine while holding the other two variables constant: knock intensity, spark timing, and critical compression ratio. The operational differences between the standard research octane number (RON) rating and modern engine operation have been explored in three parts. The first part focused on the effects of lambda and knock characterization. The second part studied the effects of spark timing. This third part explores the knock ratings of several gasolines by comparing the critical compression ratios at constant combustion phasing and knock intensity. The threshold knock intensity was based on the standard octane rating D1 pickup or by
Wankel rotary engines are renowned for having lower efficiency than classic reciprocating engines. One of the factors affecting the efficiency is an unfavourable surface area-to-volume ratio given by the particular geometry of the engine, which increases the heat loss during the combustion phase. A novel and specific study on this aspect was carried out in this work by implementing a general parametric routine in Octave/Matlab. It was able to compute the surface area-to-volume ratio and execute a sensitivity analysis on specific engine geometrical parameters (e.g. housing width “b”) in order to determine the geometrical configuration with the minimum surface area-to-volume ratio for a given swept volume, compression ratio and K factor (i.e. the ratio between generating radius and eccentricity). The aforementioned procedure was then applied considering the geometry of the Advanced Innovative Engineering 225CS rotary engine. Three virtual geometrical configurations with the same
Technology development for enhancing passenger experience has gained attention in the field of autonomous vehicle (AV) development. A new possibility for occupants of AVs is performing productive tasks as they are relieved from the task of driving. However, passengers who execute non-driving-related tasks are more prone to experiencing motion sickness (MS). To understand the factors that cause MS, a tool that can predict the occurrence and intensity of MS can be advantageous. However, there is currently a lack of computational tools that predict passenger's MS state. Furthermore, the lack of real-time physiological data from vehicle occupants limits the types of sensory data that can be used for estimation under realistic implementations. To address this, a computational model was developed to predict the MS score for passengers in real time solely based on the vehicle's dynamic state. The model leverages self-reported MS scores and vehicle dynamics time series data from a previous
This study focuses on the dynamic behavior and ride quality of three different modes of oil-gas interconnected suspension systems: fully interconnected mode, left-right interconnected mode, and independent mode. A multi-body dynamics model and a hydraulic model of the oil-gas suspension were established to evaluate the system's performance under various operating conditions. The research includes simulations of pitch and roll excitations, as well as ride comfort tests on different road surfaces, such as Class B roads and gravel roads. The analysis compares the effectiveness of the modes in suppressing pitch and roll movements and their impact on overall ride comfort. Results show that the independent mode outperforms the other two in minimizing roll, while the fully interconnected mode offers better pitch control but at the cost of reduced comfort. These findings provide valuable insights for the future design and optimization of oil-gas interconnected suspension systems, especially in
Precise state estimation during a lateral maneuver is not just a theoretical concept but a practical necessity. The performance of the Kalman filter is directly impacted by the comprehensive research and innovative approaches to counter nonlinearity and uncertainty. The use of machine learning in control theory is one such development that has significantly enhanced the effectiveness of our work. This paper provides an enhanced adaptive Kalman filter architecture with a neural network for a rapid obstacle avoidance maneuver. The proposed design exemplifies not just its effectiveness in terms of better state estimation in the presence of complex nonlinear vehicle dynamics and disturbances but also its potential downsides sometimes. Simulation results verify the same by ensuring a significant improvement to the traditional design, demonstrating better accuracy and the need for such advances in vehicle dynamics and control.