Search Results

The development of PM and NOx reduction system with the combination of DOC included DPF and SCR catalyst in addition to the AOC sub-assembly for NH3 slip protection is described. DPF regeneration strategy and manual regeneration functionality are introduced with using ITH, HCI device on the EUI based EGR, VGT 12.3L diesel engine at the CVS full dilution tunnel test bench. With this system, PM and NOx emission regulation for JPNL was satisfied and DPF regeneration process under steady state condition and transient condition (JE05 mode) were successfully fulfilled. Manual regeneration process was also confirmed and HCI control strategy was validated against the heat loss during transient regeneration mode. Presenter Seung-il Moon

For fracture cracks that occurred in the tight coupling exhaust manifold durability test of a four-cylinder gasoline engine with EGR channel, causes and solutions for fracture failure were found with the help of CFD and FEA numerical simulations. Wall temperature and heat transfer coefficient of the exhaust manifold inside wall were first accurately obtained through the thermal-fluid coupling analysis, then thermal modal and thermoplastic analysis were acquired by using the finite element method, on account of the bolt pretightening force and the contact relationship between flange face and cylinder head. Results showed that the first-order natural frequency did not meet the design requirements, which was the main reason of fatigue fracture. However, when the first-order natural frequency was rising, the delta equivalent plastic strain was increasing quickly as well.

In order to predict the thermal fatigue life of the internal combustion engine exhaust manifold effectively, it was necessary to accurately obtain the unsteady heat transfer process between hot streams and exhaust manifold all the time. This paper began with the establishment of unsteady coupled heat transfer model by using serial coupling method of CFD and FEA numerical simulations, then the bidirectional thermal coupling analysis between fluid and structure was realized, as a result, the difficulty that the transient thermal boundary conditions were applied to the solid boundary was solved. What's more, the specific coupling mode, the physical quantities delivery method on the coupling interface and the surface mesh match were studied. On this basis, the differences between strong coupling method and portioned treatment for solving steady thermal stress numerical analysis were compared, and a more convenient and rapid method for solving static thermal stress was found.

Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

In order to ensure the safety of a structure, adequate strength for structural elements must be provided. Moreover, catastrophic deformations such as buckling must be prevented. Using the linear finite element method, deterministic buckling analysis is completed in two main steps. First, a static analysis is performed using an arbitrary ordinate applied loading pattern. Using the obtained element axial forces, the geometric stiffness of the structure is assembled. Second, an eigenvalue problem is performed between structure's elastic and geometric stiffness matrices, yielding the structure's critical buckling loads. However, these deterministic approaches do not consider uncertainty the structure's material and geometric properties. In this work, a new method for finite element based buckling analysis of a structure with uncertainty is developed. An imprecise probability formulation is used to quantify the uncertainty present in the mechanical characteristics of the structure.

Recreational vehicles have a lot of potential consumers in China, especially the type C recreational vehicle is popular among consumers due to its advantages, prompting an increase in the production and sales volumes. The type C vehicle usually has a higher air drag than the common commercial vehicles due to its unique appearance. It can be reduced by optimizing the structural parameters, thus the energy consumed by the vehicle can be decreased. The external flow field of a recreational vehicle is analyzed by establishing its computational fluid dynamic (CFD) model. The characteristic of the RV’s external flow field is identified based on the simulation result. The approximation models of the vehicle roof parameters and air drag and vehicle volume are established by the response surface method (RSM). The vehicle roof parameters are optimized by multi-objective particle swarm optimization (MO-PSO).

The lightweight design of a vehicle can save manufacturing costs and reduce greenhouse gas emissions. For the off-road vehicle and truck, the chassis frame is the most important load-bearing assembly of the separate frame construction vehicle. The frame is one of the most assemblies with great potential to be lightweight optimized. However, most of the vehicle components are mounted on the frame, such as the engine, transmission, suspension, steering system, radiator, and vehicle body. Therefore, boundaries and constraints should be taken into consideration during the optimal process. The finite element (FE) model is widely used to simulate and assess the frame performance. The performance of the frame is determined by the design parameters. As one of the largest components of the vehicle, it has a lot of parameters. To improve the optimum efficiency, sensitivity analysis is used to narrow the range of the variables.

This paper presents a combined numerical and experimental investigation of the characteristics of spark discharge in a spark-ignition engine. The main objective of this work is to gain insights into the spark discharge process and early flame kernel development. Experiments were conducted in an inert medium within an optically accessible constant-volume combustion vessel. The cross-flow motion in the vessel was generated using a previously developed shrouded fan. Numerical modeling was based on an existing discharge model in the literature developed by Kim and Anderson. However, this model is applicable to a limited range of gas pressures and flow fields. Therefore, the original model was evaluated and improved to predict the behavior of spark discharge at pressurized conditions up to 45 bar and high-speed cross-flows up to 32 m/s. To accomplish this goal, a parametric study on the spark channel resistance was conducted.

In recent years gasoline compression ignition (GCI) has been shown to offer an attractive combination of low criteria pollutants and high efficiency. However, enabling GCI across the full engine load map poses several challenges. At high load, the promotion of partial premixing of air and fuel is challenging due to the diminished ignition-delay characteristics at high temperatures, while under low load operations, maintaining combustion robustness is problematic due to the low reactivity of gasoline. Variable valve actuation (VVA) offers a means of addressing these challenges by providing flexibility in effective compression ratio. In this paper, the effects of VVA were studied at high loads in a prototype heavy-duty GCI engine using a gasoline research octane number (RON) 93 at a geometric compression ratio (CR) of 15.7. Both late intake valve closing (LIVC) and early intake valve closing (EIVC) strategies were analyzed as a measure to reduce the effective compression ratio.

Correct shifting schedule of vehicle coasting mode play a vital role in improving vehicle comfort and economy. At present, the calibration of the transmission shifting schedule ignores the impact of vehicle’s dynamic mass. This paper proposes a method for optimizing the shifting schedule of the coasting modes with gear based on the dynamic mass identification of the vehicle. This method identifies the dynamic mass of the vehicle during driving and substitute them into the process of solving the shifting schedule parameters. Then we get the optimal shifting schedule. At first, establish the Extended Kalman Filter to Pre-process the experimental data, reducing errors caused by excessive data fluctuations. Then, establishing a weighted squares estimation model based on particle swarm optimization to identify the dynamic mass of the vehicle.

In this study, the combustion system of a light-duty compression ignition engine running on a market gasoline fuel with Research Octane Number (RON) of 91 was optimized using computational fluid dynamics (CFD) and Machine Learning (ML). This work was focused on optimizing the piston bowl geometry at two compression ratios (CR) (17 and 18:1) and this exercise was carried out at full-load conditions (20 bar indicated mean effective pressure, IMEP). First, a limited manual piston design optimization was performed for CR 17:1, where a couple of pistons were designed and tested. Thereafter, a CFD design of experiments (DoE) optimization was performed where CAESES, a commercial software tool, was used to automatically perturb key bowl design parameters and CONVERGE software was utilized to perform the CFD simulations. At each compression ratio, 128 piston bowl designs were evaluated.

The hydraulic retarder is the most stabilized auxiliary braking system [1-2] of heavy-duty vehicles. When the hydraulic retarder is working during auxiliary braking, all of the braking energy is transferred into the thermal energy of the transmission medium of the working wheel. Theoretically, the residual heat-sinking capability of the engine could be used to cool down the transmission medium of the hydraulic retarder, in order to ensure the proper functioning of the hydraulic retarder. Never the less, the hydraulic retarder is always placed at the tailing head of the gearbox, far from the engine, long cooling circuits, which increases the risky leakage risk of the transmission medium. What's more, the development trend of heavy load and high speed vehicle directs the significant increase in the thermal load of the hydraulic retarder, which even higher than the engine power.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

In the field of Electric Vehicle (EV), what the driver is most concerned with is that whether the value of the battery's capacity is less than the failure threshold because of the degradation. And the failure threshold means instability of the battery, which is of great danger for drives and passengers. So the capacity is an important indicator to monitor the state of health (SOH) of the battery. In laboratory environment, standard performance tests can be carried out to collect a number of related data, which are available for regression prediction in practical application, such as the on-board battery pack. Firstly, we make use of the NASA battery data set to form the observed data sequence for regression prediction. And a practical method is proposed to determine the minimum embedding dimension and get the recurrence formula, with which a capacity model is built.

Computational fluid dynamics of gas-fueled large-bore spark ignition engines with pre-chamber ignition can speed up the design process of these engines provided that 1) the reliability of the results is not affected by poor meshing and 2) the time cost of the meshing process does not negatively compensate for the advantages of running a computer simulation. In this work a flame propagation model that runs with arbitrary hybrid meshes was developed and coupled with the KIVA4-MHI CFD solver, in order to address these aims. The solver follows the G-Equation level-set method for turbulent flame propagation by Tan and Reitz, and employs improved numerics to handle meshes featuring different cell types such as hexahedra, tetrahedra, square pyramids and triangular prisms. Detailed reaction kinetics from the SpeedCHEM solver are used to compute the non-equilibrium composition evolution downstream and upstream of the flame surface, where chemical equilibrium is instead assumed.

With the rapidly developing automotive industry and stricter environmental protection laws and regulations, lightweight materials, advanced manufacturing processes and structural optimization methods are widely used in body design. Therefore, in order to evaluate and improve the pedestrian protection during a collision, this paper presents an impact simulation modeling and structural optimization method for a sport utility vehicle engine hood made of carbon fiber reinforced plastic (CFRP). Head injury criterion (HIC) was used to evaluate the performance of the hood in this regard. The inner panel and the outer panel of CFRP hood were discretized by shell elements in LS_DYNA. The Mat54-55 card was used to define the mechanical properties of the CFRP hood. In order to reduce the computational costs, just the parts contacted with the hood were modeled. The simulations were done in the prescribed 30 impact points.

Gradient based topology optimization method is difficult used to optimization of crashworthiness structures due to the expensive computational cost of sensitivity analysis and complex nonlinear behaviors (geometric nonlinearity, material nonlinearity and contact nonlinearity) of structures during a collision. Equivalent static loads (ESLs) method is one of the methods for nonlinear dynamic response optimization. However, this method ignores the material nonlinearity. Thus this paper proposes an improved topology optimization method for crashworthiness structure based on a modified ESLs method. A new calculation of ESLs considering material nonlinearity is proposed. The improved ESLs method is employed to transform the nonlinear dynamic response optimization into a nonlinear static response optimization with multiple load cases. Each element in the design domain is assigned with a design variable.

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.

In order to extend the operability limit of the gasoline compression ignition (GCI) engine, as an avenue for low temperature combustion (LTC) regime, the effects of parametric variations of engine operating conditions on the performance of six-stroke GCI (6S-GCI) engine cycle are numerically investigated, using an in-house 3D CFD code coupled with high-fidelity physical sub-models along with the Chemkin library. The combustion and emissions were calculated using a skeletal chemical kinetics mechanism for a 14-component gasoline surrogate fuel. Authors’ previous study highlighted the effects of the variation of injection timing and split ratio on the overall performance of 6S-GCI engine and the unique mixing-controlled burning mode of the charge mixtures during the two additional strokes. As a continuing effort, the present study details the parametric studies of initial gas temperature, boost pressure, fuel injection pressure, compression ratio, and EGR ratio.