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The steering assembly is a part of an automotive suspension system that provides control and stability. It provides control of direction, stability, and control over placement of the car. Optimization of the vehicle in weight results in enhanced performance and low fuel consumption, more so for an all-terrain race car. Optimization in this paper loosely refers to weight reduction and achieving the optimum stiffness to weight ratio of each component. This research encompasses various aspects linked to conceptualizing, designing, analysing, optimizing, and finally manufacturing the steering sub-system. Analytical calculations for mechanical design were performed using data from various experiments and jigs. CAD was developed using SolidWorks, and various analyses were performed using Altair HyperWorks. Finite Element Analysis (FEA) was primarily used to build stress plots and locate weak spots aiding optimization.

The EcoCAR Mobility Challenge (EMC) is the latest edition of the Advanced Vehicle Technology Competition (AVTC) series sponsored by the US Department of Energy. This competition challenges 11 North American universities to redesign a stock 2019 Chevrolet Blazer into an energy-efficient, SAE level 2-autonomous mild hybrid electric vehicle (mHEV) for use in the Mobility as a Service (MaaS) market. The Mississippi State University (MSU) team designed a P4 electric powertrain with an 85kW (113.99 HP) permanent magnet synchronous machine (PMSM) powered by a custom 5.4 kWh lithium-ion energy storage system. To maximize energy efficiency, Model Based Design concepts were leveraged to optimize the overall gear ratio for the P4 system. To accommodate this optimized ratio in the stock vehicle, a custom offset gearbox was designed that links the PMSM to the rear drive module.

The advancement in embedded systems and positional accuracy with base station GPS modules created opportunity to develop high performance autonomous ground vehicles. However, the development of vehicle model and making accurate state estimations play vital role in reducing the cross track error. The present research focus on developing Linear Quadratic Gaussian (LQG) with Kalman estimator for autonomous ground vehicle to track various routes, that are made with the series of waypoints. The model developed in the LQG controller is a kinematic bicycle model, which mimics 1/5th scale truck. Further, the cubic spline fit has been used to connect the waypoints and generate the continuous desired/target path. The testing and implementation has been done at APS labs, MTU on the mentioned vehicle to study the performance of controller. Python has been used for simulations, controller coding and interfacing the sensors with controller.

An automobile seat’s thermal performance can be challenging to quantify since it requires comprehensive human subject testing. Seat manufacturers must rely on subjective ratings to understand how the construction of a seat and its underlying heating and cooling technology may compare to other seats. Other factors may influence seat ratings published by global marketing information services companies (e.g., JD Power and Associates). In particular, occupants may be biased by the vehicle class in which a seat is installed and by how much the contribution of a specific vehicle’s HVAC system performance affects the perception of seat thermal comfort. Therefore, there is a need for an objective testing methodology that does not rely on human participants but is still capable of producing a thermal performance rating in terms of established thermal comfort scales.

Off road navigation demands ground robots to traverse complex and often changing terrain. Classification and assessment of terrain can improve path planning strategies by reducing travel time and energy consumption. In this paper we introduce a terrain classification and assessment framework that relies on both exteroceptive and proprioceptive sensor modalities. The robot captures an image of the terrain it is about to traverse and records corresponding vibration data during traversal. These images are manually labelled and used to train a support vector machine (SVM) in an offline training phase. Images have been captured under different lighting conditions and across multiple locations to achieve diversity and robustness to the model. Acceleration data is used to calculate statistical features that capture the roughness of the terrain whereas angular velocities are used to calculate roll and pitch angles experienced by the robot.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

There has been a great effort expended in identifying causes of Hydro-Carbon (HC) and Particulate Matter (PM) emissions resulting from poor spray preparation, leading to characterization of fueling behavior near nozzle. It has been observed that large droplet size is a primary contributor to HC and PM emission. Imaging technologies have been developed to understand the break-up and consistency of fuel spray. However, there appears to be a lack of studies of the spray characteristics at the End of Injection (EOI), near nozzle, in particular, the effect that tip deposits have on the EOI characteristics. Injector tip deposits are of interest due to their effect on not only fuel spray characteristics, but also their unintended effect on engine out emissions. Using a novel imaging technique to extract near nozzle fuel characteristics at EOI, the impact of tip deposits on Gasoline Direct Injection (GDI) fuel injectors at the EOI is being examined in this work.

The proliferation of small silicon micro-chips has led to a large assortment of low-cost transducers for data acquisition. Production vehicles on average exploit more than 60 on board sensors, and that number is projected to increase beyond 200 per vehicle by 2020. Such a large increase in sensors is leading the fourth industrial revolution of connectivity and autonomy. One major downfall to installing many sensors is compromises in their accuracy and processing power due to cost limitations for high volume production. The same common errors in data acquisition such as sampling, quantization, and multiplexing on the CAN bus must be accounted for when utilizing an entire array of vehicle sensors. A huge advantage of onboard sensors is the ability to calculate vehicle parameters during a daily drive cycle to update ECU calibration factors in real time. One such parameter is driveline damping, which changes with gear state and drive mode. A damping value is desired for every gear state.

Sound intensity measurement techniques that used a two-microphone configuration, were first developed in the late 1970s. Originally, the focus was on improving precision during testing or post-processing. However, with the advent of modern, sophisticated equipment, the focus has shifted to the apparatus. Availability of phase-matched microphones has made post-test correction obsolete as the microphones eliminate a majority of the errors before the data is even collected. This accuracy, however, comes at a cost, as phase-matched microphones are highly priced. This paper discusses employing the method of improving post-processing precision, using inexpensive, current equipment. The phase error of the system is corrected using a simple calibration technique and a handheld phase calibrator that is similar to the one used for amplitude calibration of microphones.

When fuel at elevated temperatures is injected into an ambient environment at a pressure lower than the saturation pressure of the fuel, the fuel vaporizes in the nozzle and/or immediately upon exiting the nozzle; that is, it undergoes flash boiling. It is characterized by a two-phase flow regime co-located with primary breakup, which significantly affects the spray characteristics. Under flash boiling conditions, the near nozzle spray angle increases, which can lead to shorter penetration because of increased entrainment. In a multi-hole injector this can cause other impacts downstream resulting from the increased plume to plume interactions. To study the effect of injector temperature and injection pressure with real fuels, an experimental investigation of the spray characteristics of a summer grade gasoline fuel with 10% ethanol (E10) was conducted in an optically accessible constant volume spray vessel.

Michigan Tech Formula SAE is a student-led team that designs and builds an open-wheel race car to compete with similar teams from other universities in early May each year. The team has adopted a vehicle development process where the design, build, and test/compete phases happen in consecutive years. This process is motivated by the need to perform validation testing in the fall prior to competition due to Houghton winters lingering well into April. In order to compete every year, all three phases are always in-process to ensure the consecutive completion vehicles. As a student organization, Formula SAE membership has a two to three year turnover rate. This limited organizational memory results in redesign rather than re-use of parts. Simple parts are easier to re-model than manually search a directory structure for an existing design. This redundant work is wasted effort and is often results in repeating poor design features that had been improved by previous team members.

High fidelity engine simulation requires realistic fuel models. Although typical automotive fuels consist of more than few hundreds of hydrocarbon species, researches show that the physical and chemical properties of the real fuels could be represented by appropriate surrogate fuel models. It is desirable to represent the fuel using the same set of physical and chemical surrogate components. However, when the reaction mechanisms for a certain physical surrogate component is not available, the chemistry of the unmatched physical component is described using that of a similar chemical surrogate component at the expense of accuracy. In order to reduce the prediction error while maintaining the computational efficiency, a method of on-the-fly reactivity adjustment (ReAd) of chemical reaction mechanism along with fuel re-distribution based on reactivity is presented and tested in this study.

Modeling ignition kernel development in spark ignition engines is crucial to capturing the sources of cyclic variability, both with RANS and LES simulations. Appropriate kernel modeling must ensure that energy transfer from the electrodes to the gas phase has the correct timing, rate and locations, until the flame surface is large enough to be represented on the mesh by the G-Equation level-set method. However, in most kernel models, geometric details driving kernel growth are missing: either because it is described as Lagrangian particles, or because its development is simplified, i.e., down to multiple spherical flames. This paper covers the geometric aspects of kernel development, which makes up the core of a Triangulated Lagrangian Ignition Kernel model. One (or multiple, if it restrikes) spark channel is initialized as a one-dimensional Lagrangian particle thread.

The effect of direct-injected fuel on particle size distributions (PSDs) of particulate matter emitted from dual-fuel combustion strategies was investigated. The PSD data were acquired from a light-duty single-cylinder diesel engine operated using conventional diesel combustion (CDC) and two diesel/natural gas dual-fuel combustion strategies. Three different direct-injection (DI) fuels (diesel, 2,6,10-trimethyldodecane, and a primary reference fuel blend) and two different injector nozzles were studied. The DI fuels were chosen to have similar energy and ignition characteristics (heat of combustion and cetane number) but different physical and chemical properties (volatility, aromatics %, viscosity, density). The two nozzles (with different orifice diameter and spray angle) allowed a wide range in DI fuel quantity for the dual-fuel combustion strategies.

Objective of this work is the incorporation of the flame stretch effects in an Eulerian-Lagrangian model for premixed SI combustion in order to describe ignition and flame propagation under highly inhomogeneous flow conditions. To this end, effects of energy transfer from electrical circuit and turbulent flame propagation were fully decoupled. The first ones are taken into account by Lagrangian particles whose main purpose is to generate an initial burned field in the computational domain. Turbulent flame development is instead considered only in the Eulerian gas phase for a better description of the local flow effects. To improve the model predictive capabilities, flame stretch effects were introduced in the turbulent combustion model by using formulations coming from the asymptotic theory and recently verified by means of DNS studies. Experiments carried out at Michigan Tech University in a pressurized, constant-volume vessel were used to validate the proposed approach.

Cycle-to-cycle variation in combustion phasing and combustion rate cause knock to occur differently in every cycle. This is found to be true even if the end gas thermo-chemical time history is the same. Three cycles are shown that have matched combustion phasing, combustion rate, and time of knock onset, but have knock intensity that differs by a factor of six. Thus, the prediction of knock intensity must include a stochastic component. It is shown that there is a relationship between the maximum possible knock intensity and the unburned fuel energy at the time of knock onset. Further, for a small window of unburned energy at knock onset, the probability density function of knock intensity is self similar when scaled by the 95th percentile of the cumulative distribution, and log-normal in shape.

This paper aims to extend the existing Volume of Fluid (VOF) model by implementing an evaporation sub-model in an open source Computational Fluid Dynamics (CFD) software, OpenFOAM. The paper applies the new model to numerically study the evaporation of spherical n-heptane droplets impinging on a hot wall at atmospheric pressure and a temperature above the Leidenfrost temperature. Volume of Fluid (VOF) method is chosen to track the liquid gas interface and the capability of VOF method implemented in interDyMFoam solver of OpenFOAM to simulate hydrodynamics during droplet-droplet interaction and droplet-film interaction is explored. Firstly, the in-built solver is used to simulate problems in isothermal conditions and the simulation results are compared qualitatively with the published results to validate the solver. A numerical method for modeling heat and mass transfer during evaporation is implemented in conjunction with the VOF.

In this paper, large eddy simulation (LES) coupled with two uncertainty quantification (UQ) methods, namely latin-hypercube sampling (LHS) and polynomial chaos expansion (PCE), have been used to quantify the effects of model parameters and spray boundary conditions on diesel and gasoline spray simulations. Evaporating, non-reacting spray data was used to compare penetration, mixture fraction and spray probability contour. Two different sets of four uncertain variables were used for diesel and gasoline sprays, respectively. UQ results showed good agreement between experiments and predictions. UQ statistics indicated that discharge coefficient has stronger impact on gasoline than diesel sprays, and spray cone angle is important for vapor penetration of both types of sprays. Additionally, examination of the gasoline spray characteristics showed that plume-to-plume interaction and nozzle dribble are important phenomena that need to be considered in high-fidelity gasoline spray simulations.

Cylinder liners exert a major influence on engine performance, reliability, durability and maintenance. Various combinations of nonmetallic reinforcements and coatings have been used to improve the tribological performance of sleeves or surfaces used in compressors and internal combustion engines in four stroke, two stroke and rotary configurations. In this paper we report the use of a hybrid composite containing silicon carbide and graphite in an aluminum alloy matrix to improve the performance of various small engines and compressors. Material properties of the base material, as well as comparative dynamometer testing, are presented.

A correlation was developed to predict the ignition delay of PRF blends at a wide range of engine-relevant operating conditions. Constant volume simulations were performed using Cantera coupled with a reduced reaction mechanism at a range of initial temperatures from 570-1860K, initial pressures from 10-100atm, oxygen mole percent from 12.6% to 21%, equivalence ratios from 0.30-1.5, and PRF blends from PRF0 to PRF100. In total, 6,480 independent ignition delay simulations were performed. The correlation utilizes the traditional Arrhenius formulation; with equivalence ratio (φ), pressure (p), and oxygen mole percentage (xo2) dependencies. The exponents α, β, and γ were fitted to a third order polynomial with respect to temperature with an exponential roll-off to a constant value at low temperatures to capture the behavior expressed by the reaction mechanism. The location and rate of the roll-off functions were modified by linear functions of PRF.