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A new index for evaluating load path dispersion is proposed, using a structural load path analysis method based on the concept of U* , which expresses the connection strength between a load point and an arbitrary point within the structure enables the evaluation of the load path dispersion within the structure by statistical means such as histograms and standard deviations. Presenter Tadashi Naito, Honda R&D Co., Ltd.

Premixed Charge Compression Ignition (PCCI), a Low Temperature Combustion (LTC) strategy for diesel engines is of increasing interest due to its potential to simultaneously reduce soot and NOx emissions. However, the influence of mixture preparation on combustion phasing and heat release rate in LTC is not fully understood. In the present study, the influence of injection timing on mixture preparation, combustion and emissions in PCCI mode is investigated by experimental and computational methods. A sequential coupling approach of 3D CFD with a Stochastic Reactor Model (SRM) is used to simulate the PCCI engine. The SRM accounts for detailed chemical kinetics, convective heat transfer and turbulent micro-mixing. In this integrated approach, the temperature-equivalence ratio statistics obtained using KIVA 3V are mapped onto the stochastic particle ensemble used in the SRM.

A Dual Piston Variable Compression Ratio (VCR) engine has been newly developed. In order to ensure the strength of the Dual Piston, the design guidelines were established. There are two advantages of this design. One is the compactness and the compatibility with a mass production engine block. Another is less power consumption required during compression ratio switching. However, the durability is a challenge for this design because of the impact load during the switching driven by the inertial force of a reciprocating piston. In order to achieve a durable configuration, it was necessary to consider the dynamics of the stress after impact, from analysis of the impacting process during the switching. The analysis of stress and deformation mode was improved in accuracy by using Computer Aided Engineering (CAE) in the designing process.

For a series plug-in hybrid electric vehicle (PHEV), it is critical that batteries be sized to maximize vehicle performance variables, such as fuel efficiency, gasoline savings, and zero emission capability. The wide range of design choices and the cost of prototype vehicles calls for a development process to quickly and systematically determine the design characteristics of the battery pack, including its size, and vehicle-level control parameters that maximize the net present value (NPV) of a vehicle during the planning stage. Argonne National Laboratory has developed Autonomie, a modeling and simulation framework. With support from The MathWorks, Argonne has integrated an optimization algorithm and parallel computing tools to enable the aforementioned development process. This paper presents a study that utilized the development process, where the NPV is the present value of all the future expenses and savings associated with the vehicle.

A new index U* for evaluating load path dispersion is proposed, using a structural load path analysis method based on the concept of U*, which expresses the connection strength between a load point and an arbitrary point within the structure. U* enables the evaluation of the load path dispersion within the structure by statistical means such as histograms and standard deviations. Different loading conditions are applied to a body structure, and the similarity of the U* distributions is evaluated using the direction cosine and U* 2-dimensional correlation diagrams. It is shown as a result that body structures can be macroscopically grasped by using the U* distribution rather than using the stress distribution. In addition, as an example, the U* distribution of torsion loading condition is shown to comprehensively include characteristics of the U* distribution of other loading conditions.

This paper proposes a new motor design procedure for reducing motor loss in hybrid vehicles (HEV) and electric vehicles (EV). To find an optimum design in a short time, a non-linear magnetic circuit model was developed for interior permanent magnet synchronous motors (IPMSM). Speed-torque curves and motor losses were calculated based on this model. Combined with Energy Management Simulation, this model makes it possible to find an optimum motor design with minimum loss.

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

In this research, a simulation method was developed in which it was able to estimate, in the early stage of design, the strains that potentially lead to damages to motorcycle engine parts when tipped over from a stationary state. Splitting a series of phenomena from the start of tilting of motorcycle from the upright position up to the end of collision of engine parts after the contact on the ground to two groups by before and after the contact of engine parts on the ground, we applied the multi body dynamics analysis to the first group, and the elastro-plastic FEM analysis to the latter one. In the computer simulation of collision using the elastro-plastic FEM analysis, we minimized the FEM models from the entire motorcycle models and treated others as a solid model to shorten the computation period. It is also realized that the strains occurring in the engine parts can be simulated by considering only the mass of the parts which are rigidly mounted on the engine.

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).

A full understanding and characterization of the near-field of diesel sprays is daunting because the dense spray region inhibits most diagnostics. While x-ray diagnostics permit quantification of fuel mass along a line of sight, most laboratories necessarily use simple lighting to characterize the spray spreading angle, using it as an input for CFD modeling, for example. Questions arise as to what is meant by the “boundary” of the spray since liquid fuel concentration is not easily quantified in optical imaging. In this study we seek to establish a relationship between spray boundary obtained via optical diffused backlighting and the fuel concentration derived from tomographic reconstruction of x-ray radiography. Measurements are repeated in different facilities at the same specified operating conditions on the “Spray A” fuel injector of the Engine Combustion Network, which has a nozzle diameter of 90 μm.

Cavitation plays a significant role in high pressure diesel injectors. However, cavitation is difficult to measure under realistic conditions. X-ray phase contrast imaging has been used in the past to study the internal geometry of fuel injectors and the structure of diesel sprays. In this paper we extend the technique to make in-situ measurements of cavitation inside unmodified diesel injectors at pressures of up to 1200 bar through the steel nozzle wall. A cerium contrast agent was added to a diesel surrogate, and the changes in x-ray intensity caused by changes in the fluid density due to cavitation were measured. Without the need to modify the injector for optical access, realistic injection and ambient pressures can be obtained and the effects of realistic nozzle geometries can be investigated. A range of single and multi-hole injectors were studied, both sharp-edged and hydro-ground. Cavitation was observed to increase with higher rail pressures.

This paper implements a coupled approach to integrate the internal nozzle flow and the ensuing fuel spray using a Volume-of-Fluid (VOF) method in the CONVERGE CFD software. A VOF method was used to model the internal nozzle two-phase flow with a cavitation description closed by the homogeneous relaxation model of Bilicki and Kestin [1]. An Eulerian single velocity field approach by Vallet et al. [2] was implemented for near-nozzle spray modeling. This Eulerian approach considers the liquid and gas phases as a complex mixture with a highly variable density to describe near nozzle dense sprays. The mean density is obtained from the Favreaveraged liquid mass fraction. The liquid mass fraction is transported with a model for the turbulent liquid diffusion flux into the gas.

Shock waves have been recently observed in high-pressure diesel sprays. In this paper, three-dimensional numerical simulations of supersonic diesel spray injection have been performed to investigate the underlying dynamics of the induced shock waves and their interactions with the spray. A Volume-of-Fluid based method in the CFD software (CONVERGE) is used to model this multiphase phenomena. An adaptive Mesh Refinement (AMR) scheme is employed to capture the front of the spray and the shock waves with high fidelity. Simulation results are compared to the available experimental observations to validate the numerical procedure. Parametric studies with different injection and ambient conditions are conducted to examine the effect of these factors on the generation of shock waves and their dynamics.

It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.

With demands for enhanced environmental performance such as fuel economy, the tendency has been to reduce the amount of wind introduced to the engine room to reduce drag. Meanwhile, exhaust gas temperatures are increasing in order to reduce emissions concentrations. As a result, the temperature environments for parts inside the engine room and underfloor parts are becoming harsher, and accurately understanding the temperature environments of parts is crucial in determining Engine room component layout during vehicle development and applying effective thermal countermeasures. Computational fluid dynamics (CFD) are effective for understanding complex phenomena such as heat generation and cooling. However, this paper reports the development of a method for accurately calculating the vehicle temperature distribution through identification from test results.

When utilizing large models containing numerous uncertain parameters, model calibration becomes a critical step in the analysis. Traditional methods of calibration involve adjusting uncertain parameters based on expert opinion or best estimates. While this traditional calibration may lead to better model predictions, it usually only yields better estimates for certain specific conditions. This drastically reduces the functionality of the model in question. Bayesian calibration is an alternative to traditional calibration methods which utilizes available information (simulation results and/or real world measured values) to iteratively refine uncertain parameters (either assumed or measured uncertainty) while considering not only parametric uncertainty, but also model, observational, and residual uncertainties at every step of the calibration process.

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.

Designing a lightweight and durable engine is universally important from the standpoints of fuel economy, vehicle dynamics and cost. However, it is challenging to theoretically find an optimal solution which meets both requirements in products such as the cylinder head, to which various thermal loads and mechanical loads are simultaneously applied. In our research, we focused on “non-parametric optimization” and attempted to establish a new design approach derived from the weight reduction of a cylinder head. Our optimization process consists of topology optimization and shape optimization. In the topology optimization process, we explored an optimal structure with the theoretically-highest stiffness in the given design space. This is to provide an efficient structure for pursuing both lightweight and durable characteristics in the subsequent shape optimization process.

In order to reduce automobile body weight and improve crashworthiness, the use of high-strength steels has increased greatly in recent years. An optimal combination of both crash safety performance and lightweight structure has been a major challenge in automobile body engineering. In this study, the Cockcroft-Latham fracture criterion was applied to predict the fracture of high-strength steels. Marciniak-type biaxial stretching tests for high-strength steels were performed to measure the material constant of the Cockcroft-Latham fracture criterion. Furthermore, in order to improve the simulation accuracy, local anisotropic parameters based on the plastic strain (strain dependent model of anisotropy) were measured using the digital image grid method and were incorporated into Hill's anisotropic yield condition by the authors. In order to confirm the validity of the Cockcroft-Latham fracture criterion, uniaxial tensile tests were performed.

The strength characteristic of CFRP composite materials is often dependent on the internal micro-structural fracture mode. When performing a simulation on composite structures, it is necessary to take the fracture mode into account, especially in an automobile body structure with a complex three-dimensional shape, where inter-ply fractures tend to appear due to out-of-plane load inputs. In this paper, an energy-based inter-ply fracture model with fracture toughness criteria, and an intra-ply fracture model proposed by Ladeveze et al. were explained. FEM analyses were performed on three-dimensional test specimens applying both fracture models and the simulated results were compared with experimental ones. Reproducibility of the fracture mode was confirmed and the importance of combining both models was discussed.