Search Results

Effectively obtaining physical parameters for vehicle dynamic model is the key to successfully performing any computer-based dynamic analysis, control strategy development or optimization. For a spring and lump mass vehicle model, which is a type of vehicle model widely used, its physical parameters include sprung mass, unsprung mass, inertial properties of the sprung mass, stiffness and damping coefficient of suspension and tire, etc. To minimize error, the paper proposes a method to estimate these parameters from vehicle modal parameters which are in turn obtained through full-car dynamic testing. To verify its effectiveness, a visual vehicle with a set of given parameters, build in the Adams(Automatic Dynamic Analysis of Mechanical Systems)/Car environment, is used to perform the dynamic testing and provide the testing data for the parameter estimation.

With the development of advanced ABE fermentation technology, the volumetric percentage of acetone, butanol and ethanol in the bio-solvents can be precisely controlled. To seek for an optimized volumetric ratio for ABE-diesel blends, the previous work in our team has experimentally investigated and analyzed the combustion features of ABE-diesel blends with different volumetric ratio (A: B: E: 6:3:1; 3:6:1; 0:10:0, vol. %) in a constant volume chamber. It was found that an increased amount of acetone would lead to a significant advancement of combustion phasing whereas butanol would compensate the advancing effect. Both spray dynamic and chemistry reaction dynamic are of great importance in explaining the unique combustion characteristic of ABE-diesel blend. In this study, a semi-detailed chemical mechanism is constructed and used to model ABE-diesel spray combustion in a constant volume chamber.

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.

Increasing regulatory demand for efficiency has led to development of novel combustion modes such as HCCI, GCI and RCCI for gasoline light duty engines. In order to realize HCCI as a compression ignition combustion mode system, in-cylinder compression temperatures must be elevated to reach the autoignition point of the premixed fuel/air mixture. This should be co-optimized with appropriate fuel formulations that can autoignite at such temperatures. CFD combustion modeling is used to model the auto ignition of gasoline fuel under compression ignition conditions. Using the fully detailed fuel mechanism consisting of thousands of components in the CFD simulations is computationally expensive. To overcome this challenge, the real fuel is represented by few major components of create a surrogate fuel mechanism. In this study, 9 variations of gasoline fuel sets were chosen as candidates to run in HCCI combustion mode.

Past experimental studies conducted by the current authors on a 13 liter 16.7:1 compression ratio heavy-duty diesel engine have shown that diesel-Compressed Natural Gas (CNG) Reactivity Controlled Compression Ignition (RCCI) combustion targeting low NOx emissions becomes progressively difficult to control as the engine load is increased. This is mainly due to difficulty in controlling reactivity levels at higher loads. For the current study, CFD investigations were conducted in CONVERGE using the SAGE combustion solver with the application of the Rahimi mechanism. Studies were conducted at a load of 5 bar BMEP to validate the simulation results against RCCI experimental data. In the low load study, it was found that the Rahimi mechanism was not able to predict the RCCI combustion behavior for diesel injection timings advanced beyond 30 degCA bTDC. This poor prediction was found at multiple engine speed and load points.

This paper intends to present a novel optimal trajectory planning method for obstacle avoidance on highways. Firstly, a mapping from the road Cartesian coordinate system to the road Frenet-based coordinate system is built, and the path lateral offset in the road Frenet-based coordinate system is represented by a function of quintic polynomial respecting the traveled distance along the road centerline. With different terminal conditions regarding its position, heading and curvature of the endpoint, and together with initial conditions of the starting point, the path planner generates a bunch of candidate paths via solving nonlinear equation sets numerically. A path selecting mechanism is further built which considers a normalized weighted sum of the path length, curvature, consistency with the previous path, as well as the road hazard risk.

Gasoline compression ignition (GCI) technology shows the potential to obtain high thermal efficiencies while maintaining low soot and NOx emissions in light-duty engine applications. Recent experimental studies and numerical simulations have indicated that high reactivity gasoline-like fuels can further enable the benefits of GCI combustion. However, there is limited empirical data in the literature studying the gasoline compression ignition process at relevant in-cylinder conditions, which are required for further optimizing combustion system designs. This study investigates the temporal and spatial evolution of the compression ignition process of various high reactivity gasoline fuels with research octane numbers (RON) of 71, 74 and 82, as well as a conventional RON 97 E10 gasoline fuel. A ten-hole prototype gasoline injector specifically designed for GCI applications capable of injection pressures up to 450 bar was used.

In this paper, a model-based linear estimator and a non-linear control law for an Fe-zeolite urea-selective catalytic reduction (SCR) catalyst for heavy duty diesel engine applications is presented. The novel aspect of this work is that the relevant species, NO, NO2 and NH3 are estimated and controlled independently. The ability to target NH3 slip is important not only to minimize urea consumption, but also to reduce this unregulated emission. Being able to discriminate between NO and NO2 is important for two reasons. First, recent Fe-zeolite catalyst studies suggest that NOx reduction is highly favored by the NO 2 based reactions. Second, NO2 is more toxic than NO to both the environment and human health. The estimator and control law are based on a 4-state model of the urea-SCR plant. A linearized version of the model is used for state estimation while the full nonlinear model is used for control design.

Aerodynamic noise generated in a miniature car had been evaluated using numerical simulation. Large Eddy Simulation (LES) was applied to analyze the transient flow field and the Ffowcs Williams-Hawkings (FW-H) acoustic analogy was employed to conduct acoustic analysis. The time accurate flow data was obtained using a finite volume flow solver on an unstructured grid. The flow field around the rear view mirror was obtained by numerical for two cases with different side view mirrors. Moreover, the distribution of acoustic source was predicted on side windows, and the aerodynamic noise was lowed through optimizing the shape of the rear view mirror and some experiments were done to validate the effect. Present study ascertained the feasibility and applicability of finite volume method (FVM) with SGS model towards prediction of aerodynamic noise generated in production vehicle.

Research into the estimation of diesel engine combustion metrics via non-intrusive means, typically referred to as “remote combustion sensing” has become an increasingly active area of combustion research. Success in accurately estimating combustion metrics with low-cost non-intrusive transducers has been proven and documented by multiple sources on small scale diesel engines (2-4 cylinders, maximum outputs of 67 Kw, 210 N-m). This paper investigates the application of remote combustion sensing technology to a larger displacement inline 6-cylinder diesel with substantially higher power output (280 kW, 1645 N-m) than previously explored. An in-depth frequency analysis has been performed with the goal of optimizing the estimated combustion signature which has been computed based upon the direct relationship between the combustion event measured via a pressure transducer, and block vibration measured via accelerometers.

Exhaust emissions were characterized from a Cummins LTA10 heavy-duty diesel engine operated at two EPA steady-state modes with and without an uncatalyzed Corning ceramic particulate trap. The regulated emissions of nitrogen oxides (NOx), hydrocarbons (HC), and total particulate matter (TPM) and its components as well as the unregulated emissions of PAH, nitro-PAH, mutagenic activity and particle size distributions were measured. The consistently significant effects of the trap on regulated emissions included reductions of TPM and TPM-associated components. There were no changes in NOx and HC were reduced only at one operating condition. Particle size distribution measurements showed that nuclei-mode particles were formed downstream of the trap, which effectively removed accumulation-mode particles. All of the mutagenicity was direct-acting and the mutagenic activity of the XOC was approximately equivalent to that of the SOF without the trap.

Optimization design for vehicle front-end structures has proven rather essential and been extensively used to improve the vehicle performance. Nevertheless, the front-end structure needs to meet the requirement of both pedestrian safety and structural stiffness which are somewhat contradicting to each other. Furthermore, an optimal design could become less meaningful or even unacceptable when some uncertainties present. In the paper, a multi-objective discrete robust optimization (MODRO) algorithm is used to minimize the injury of head and maximize the structural stiffness involving uncertainties. MODRO algorithm is achieved by coupling grey relational analysis (GRA) and principal component analysis (PCA) with Taguchi method. The optimized result shows that the MODRO algorithm improved performance of pedestrian head injury and robustness of the vehicle front-end structure.

This paper describes a study to demonstrate the feasibility of developing an acoustic encapsulation to reduce airborne noise from a commercial diesel engine. First, the various sources of noise from the engine were identified using Nearfield Acoustical Holography (NAH). Detailed NAH measurements were conducted on the four sides of the engine in an engine test cell. The main sources of noise from the engine were ranked and identified within the frequency ranges of interest. Experimental modal analysis was conducted on the oil pan and front cover plate of the engine to reveal correlations of structural vibration results with the data from the NAH. The second phase of the study involved the design and fabrication of the acoustical encapsulation (noise covers) for the engine in a test cell to satisfy the requirements of space, cost and performance constraints. The acoustical materials for the enclosure were selected to meet the frequency and temperature ranges of interest.

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.

The capability to detect combustion in a diesel engine has the potential of being an important control feature to meet increasingly stringent emission regulations and for the development of alternative combustion strategies such as HCCI and PCCI. In this work, block mounted accelerometers are investigated as potential feedback sensors for detecting combustion characteristics in a high-speed, high pressure common rail (HPCR), 1.9L diesel engine. Accelerometers are positioned in multiple placements and orientations on the engine, and engine testing is conducted under motored, single and pilot-main injection conditions. Engine tests are then conducted at varying injection timings to observe the resulting time and frequency domain changes of both the pressure and acceleration signals.

Transient engine operations are modeled and simulated with a 1-D code (GT Power) using heat release and emission data computed by a 3-D CFD code (Kiva3). During each iteration step of a transient engine simulation, the 1-D code utilizes the 3-D data to interpolate the values for heat release and emissions. The 3-D CFD computations were performed for the compression and combustion stroke of strategically chosen engine operating points considering engine speed, torque and excess air. The 3-D inlet conditions were obtained from the 1-D code, which utilized 3-D heat release data from the previous 1-D unsteady computations. In most cases, only two different sets of 3-D input data are needed to interpolate the transient phase between two engine operating points. This keeps the computation time at a reasonable level. The results are demonstrated on the load response of a generator which is driven by a medium-speed diesel engine.

Steady-state particulate loading experiments were conducted on an advanced production catalyzed particulate filter (CPF), both with and without a diesel oxidation catalyst (DOC). A heavy-duty diesel engine was used for this study with the experiments conducted at 20, 40, 60 and 75 % of full load (1120 Nm) at rated speed (2100 rpm). The data obtained from these experiments were used and are necessary for calibrating the MTU 1-D 2-Layer CPF model. These experimental and modeling results were compared to previous research conducted at MTU that used the same engine but an earlier development version of the combination of DOC and CPF. The motivation for the comparison of the two systems was to determine whether the reformulated production catalysts performed as good or better than the early development catalysts. The results were compared to understand the filtration and oxidation differences between the two DOC+CPF and the CPF-only aftertreatment systems.