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Modern VVA systems offer new potentialities in improving the fuel consumption for spark-ignition engines at low and medium load, meanwhile they grant a higher volumetric efficiency and performance at high load. Recently introduced systems enhance this concept through the possibility of concurrently modifying the intake valve opening, closing and lift leading to the development of almost "throttle-less" engines. However, at very low loads, the control of the air-flow motion and the turbulence intensity inside the cylinder may require to select a proper combination of the butterfly throttling and the intake valve control, to get the highest BSFC (Brake Specific Fuel Consumption) reduction. Moreover, a low throttling, while improving the fuel consumption, may also produce an increased gas-dynamic noise at the intake mouth. In highly "downsized" engines, the intake valve control is also linked to the turbocharger operating point, which may be changed by acting on the waste-gate valve.

Growing concerns about the emissions of internal combustion engines have forced the adoption of aftertreatment devices to reduce the adverse impact of diesel engines on health and environment. Diesel particulate filters are considered as an effective means to reduce the particle emissions and comply with the regulations. Research activity in this field focuses on filter configuration, materials and aging, on understanding the variation of soot layer properties during time, on defining of the optimal strategy of DPF management for on-board control applications. A model was implemented in order to simulate the filtration and regeneration processes of a wall-flow particulate filter, taking into account the emission characteristic of the engine, whose architecture and operating conditions deeply affect the size distribution of soot particles.

The increasing limitations in engine emissions and fuel consumption have led researchers to the need to accurately predict combustion and related events in gasoline engines. In particular, knock is one of the most limiting factors for modern SI units, severely hindering thermal efficiency improvements. Modern CFD simulations are becoming an affordable instrument to support experimental practice from the early design to the detailed calibration stage. To this aim, combustion and knock models in RANS formalism provide good time-to-solution trade-off allowing to simulate mean flame front propagation and flame brush geometry, as well as “ensemble average” knock tendency in end-gases. Still, the level of confidence in the use of CFD tools strongly relies on the possibility to validate models and methodologies against experimental measurements.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

A previously developed CFD-based optimization tool is utilized to find optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses of a split injection system, the duration of each pulse, the exhaust gas recirculation rate, the boost pressure and the compression ratio.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

This paper attempts to integrate a derivative-free probability analysis method to Reliability-Based Robust Design Optimization (RBRDO). The Eigenvector Dimension Reduction (EDR) method is used for the probability analysis method. It has been demonstrated that the EDR method is more accurate and efficient than the Second-Order Reliability Method (SORM) for reliability and quality assessment. Moreover, it can simultaneously evaluate both reliability and quality without any extra expense. Two practical engineering problems (vehicle side impact and layered bonding plates) are used to demonstrate the effectiveness of the EDR method.

In the last decade, considerable advances have been made in reliability-based design optimization (RBDO). One assumption in RBDO is that the complete information of input uncertainties are known. However, this assumption is not valid in practical engineering applications, due to the lack of sufficient data. In practical engineering design, information concerning uncertainty parameters is usually in the form of finite samples. Existing methods in uncertainty based design optimization cannot handle design problems involving epistemic uncertainty with a shortage of information. Recently, a novel method referred to as Bayesian Reliability-Based Design Optimization (BRBDO) was proposed to properly handle design problems when engaging both epistemic and aleatory uncertainties [1]. However, when a design problem involves a large number of epistemic variables, the computation task for BRBDO becomes extremely expensive.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

This paper describes the design, development and validation of a muffler for reducing exhaust noise from a commercial tele-handler. It also describes the procedure for modeling and optimizing the exhaust muffler along with experimental measurement for correlating the sound transmission loss (STL). The design and tuning of the tele-handler muffler was based on several factors including overall performance, cost, weight, available space, and ease of manufacturing. The analysis for predicting the STL was conducted using the commercial software LMS Virtual Lab (LMS-VL), while the experimental validation was carried out in the laboratory using the two load setup. First, in order to gain confidence in the applicability of LMS-VL, the STL of some simple expansion mufflers with and without extended inlet/outlet and perforations was considered. The STL of these mufflers were predicted using the traditional plane wave transfer matrix approach.

Diesel particulate trap regeneration is a complex process involving the interaction of phenomena at several scales. A hierarchy of models for the relevant physicochemical processes at the different scales of the problem (porous wall, filter channel, entire trap) is employed to obtain a rigorous description of the process in a multidimensional context. The final model structure is validated against experiments, resulting in a powerful tool for the computer-aided study of the regeneration behavior. In the present work we employ this tool to address the effect of various spatial non-uniformities on the regeneration characteristics of diesel particulate traps. Non-uniformities may include radial variations of flow, temperature and particulate concentration at the filter inlet, as well as variations of particulate loading. In addition, we study the influence of the distribution of catalytic activity along the filter wall.

Internal combustion engines for vehicle propulsion are more and more sophisticated due to increasingly restrictive environmental regulations. In case of heavy-duty engines, Compressed Natural Gas (CNG) fueling coupled with Three-Way Catalyst (TWC) and Exhaust Gas Recirculation (EGR) can help in meeting the imposed emission limits and preventing from thermal stress of engine components. To cope with the new issues associated with the more complex hardware and to improve powertrain performance and reliability and after-treatment efficiency, the engine control strategies must be reformulated. The paper focuses on the steady-state optimization of control parameters for a heavy-duty engine fueled by CNG and equipped with turbocharger and EGR. The optimization analysis is carried out to design EGR, spark timing and wastegate control, aimed at increasing fuel economy while reducing in-cylinder temperature to prevent from thermal stress of engine components.

Power split in Fuel Cell Hybrid Electric Vehicles (FCHEVs) has been controlled using different strategies ranging from rule-based to optimal control. Dynamic Programming (DP) and Model Predictive Control (MPC) are two common optimal control strategies used in optimization of the power split in FCHEVs with a trade-off between global optimality of the solution and online implementation of the controller. In this paper, both control strategies are developed and tested on a FC/battery vehicle model, and the results are compared in terms of total energy consumption. In addition, the effects of the MPC prediction horizon length on the controller performance are studied. Results show that by using the DP strategy, up to 12% less total energy consumption is achieved compared to MPC for a charge sustaining mode in the Urban Dynamometer Driving Schedule (UDDS) drive cycle.

The characteristics of the intake system affect both engine power output and gas-dynamic noise emissions. The latter is particularly true in downsized VVA engines, where a less effective attenuation of the pressure waves is realized, due to the intake line de-throttling at part-load. For this engine architecture, a refined air-box design is hence requested. In this work, the Transmission Loss (TL) of the intake air-box of a commercial VVA engine is numerically computed through a 3D FEM approach. Results are compared with experimental data, showing a very good correlation. The validated model is then coupled to an external optimizer (ModeFRONTIERTM) to increase the TL parameter in a prefixed frequency range. The improvement of the acoustic attenuation is attained through a shape deformation of the inner structure of the base device, taking into account constraints related to the device installation inside the engine bay.

The recent interest in alternative non-fossil fuels has led researchers to evaluate several alcohol-based formulations. However, one of the main requirements for innovative fuels is to be compatible with existing units’ hardware, so that full replacement or smart flexible-fuel strategies can be smoothly adopted. n-Butanol is considered as a promising candidate to replace commercial gasoline, given its ease of production from bio-mass and its main physical and chemical properties similar to those of Gasoline. The compared behavior of n-butanol and gasoline was analyzed in an optically-accessible DISI engine in a previous paper [1]. CFD simulations explained the main outcomes of the experimental campaign in terms of combustion behavior for two operating conditions. In particular, the first-order role of the slower evaporation rate of n-butanol compared to gasoline was highlighted when the two fuels were operated under the same injection phasing.

The interest of the vehicle producers in fulfillment emission legislations without adopting after treatment systems is driving to the use of non-conventional energy sources for modern engines. A previous test campaign dealing with the use of blends of diesel and propane in a CI engine has pointed out the potential of this non-conventional fuel for diesel engines. The soft adaptation of the common rail injection system and the potential benefits, in terms of engine performances and pollutant emissions, encourage the use of propane-diesel blends if an optimization of the injection strategies is performed. In this work, the performances of a propane-diesel mixture in a research diesel engine have been investigated. The injection strategies of Euro 5 calibration have been used as reference for the development of optimized strategies. The aim of the optimization process was to ensure the same engine power output and reduce the pollutant emissions.

Downsizing by turbo charging is a current approach for the reduction of fuel consumption of Spark Ignition (SI) engines. For downsized engines compression ratio has to be set as high as possible to achieve substantial gains in thermodynamic efficiency. Unfortunately, the possibility to take full advantages offered by downsizing is limited by knock phenomenon, which imposes constraints both on supercharging and compression ratios. Quasi-dimensional and multidimensional simulation can play a role of increasing importance for the design and the optimization of future engine prototypes more and more based on advanced combustion concepts, provided that well proven tools for knock simulation may be available.

The paper deals with experimental testing of a natural gas fueled engine. Break Specific fuel Consumption (BSFC), Average Mass Flow Rate, Instantaneous Cylinder Pressure and some wall temperatures have been measured at some full and part load operating conditions. The results of this experimental activity, still in progress, have been used to calibrate a 1D-flow engine's model. Then the effects of some VVA strategies have been theoretically studied through the validated model. With the aim of maximizing the full load engine's torque, a genetic algorithm was used to calculate the optimized intake and exhaust valves timing angles. Various VVA strategies were compared at part-load in order to reduce brake specific fuel consumption.

The objective of this study is the development of a computationally efficient CFD-based tool for finding optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses, the duration of each pulse, the duration of the dwell, the exhaust gas recirculation rate and the boost pressure.

Higher carbon number alcohols offer an opportunity to meet the Renewable Fuel Standard (RFS2) and improve the energy content, petroleum displacement, and/or knock resistance of gasoline-alcohol blends from traditional ethanol blends such as E10 while maintaining desired and regulated fuel properties. Part II of this paper builds upon the alcohol selection, fuel implementation scenarios, criteria target values, and property prediction methodologies detailed in Part I. For each scenario, optimization schemes include maximizing energy content, knock resistance, or petroleum displacement. Optimum blend composition is very sensitive to energy content, knock resistance, vapor pressure, and oxygen content criteria target values. Iso-propanol is favored in both scenarios' suitable blends because of its high RON value.