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A CAE-based optimization method is developed for Lube Oil Consumption (LOC) analysis of the piston-ring system. With accurate thermodynamic boundary conditions from 1D engine combustion simulation, piston motion, dynamics of piston ring, and characteristics of oil consumption are simulated using AVL Piston&Ring. The model is validated by comparing with available test data. Good match is achieved. The model is then applied to a diesel engine. The root cause of excessive LOC of the engine has been identified through CAE. The improved understanding has been applied to optimize the piston and piston ring. Engine dyno test, 1200-hour engine durability test, and 45000-kilometer vehicle test have been conducted to validate the optimized design. The experiment results are in good agreement with CAE predictions, and the oil consumption has been improved over the original design.

Experimental studies of soot particles showed that the intensity ratio of amorphous and graphite layers measured by Raman spectroscopy correlates to soot oxidation reactivities, which is very important for regeneration of the diesel particulate filters and gasoline particulate filters. This physical mechanism is absent in all soot models. In the present paper, a novel two-layer soot model was proposed that considers the amorphous and graphite layers in the soot particles. The soot model considers soot inception, soot surface growth, soot oxidation by O2 and OH, and soot coagulation. It is assumed that amorphous-type soot forms from fullerene. No soot coagulation is considered in the model between the amorphous- and graphitic-types of soot. Benzene is taken as the soot precursor, which is formed from acetylene. The model was implemented into a commercial CFD software CONVERGE using user defined functions. A diesel engine case was simulated.

Texas Tech University's FutureTruck team has converted a stock 2002 Ford Explorer™ to a fuel cell powered hybrid-electric vehicle to enter into the 2002 FutureTruck Challenge. The internal combustion engine was replaced with a Honeywell manufactured hydrogen fuel cell. The fuel cell requires hydrogen, oxygen, and de-ionized (DI) water systems to produce power. Two AC induction motors drive the vehicle and there are two 270VDC nickel metal hydride (NiMH) battery packs to store energy. Water, heat, air and small amounts of hydrogen are the only emissions of the vehicle, which is the main benefit of the conversion. The team also sought to maintain, as much as possible, the comforts and performance options a driver would enjoy on a stock vehicle.

A valid human biodynamic model is very useful for studying the human body's response to whole body vibration. Whole body vibration is one of the important factors in the study of vehicle ride comfort. The environmental vibrations are transferred to the human body through floor and seat. Seated posture is the most commonly used position in automobiles. Therefore, studying the human body response in a seated position has attracted a lot of attention. Because the human body is in direct contact with the seat, its design plays a very important role in vibration transmission. In seat design, two important components are seat suspension and cushion. The mechanical properties of these components are stiffness, damping and mass. These properties can be changed by adjusting cushion material and seat suspension linkages. In this paper, three types of seat models are used. The first one is a hard seat.

Pressure ringing phenomena in internal combustion engine are often observed in cylinder pressure measurement, which may be due to combustion dynamics, pressure oscillation inside the combustion chamber and/or inside a drilled probe hole for cylinder pressure sensor installation. In the present study, combustion process in a production DI diesel engine instrumented with pressure sensors in the cylinder head was analyzed using 3D combustion CFD simulation. Three combustion models (the CTC model with the Shell autoignition model, the Sage model with detailed chemistry, and the ECFM-3Z model) and three reaction mechanisms (the Shell autoignition model, the Chalmers reduced n-heptane mechanism, and the IFP PRF mechanism) were employed to validate their capability in capturing pressure ringing phenomena. Grid size within the drilled hole and speed of sound CFL number were varied to evaluate the effects on pressure ringing prediction.

NASA's mission statement includes the protection of the home planet and a goal to inspire the next generation of explorers. NASA's current vision also includes human exploration of the Moon and Mars. Typically, residents of low-income communities are not directly involved in the space exploration process. Parents of children in low-income communities are inclined to be more interested in the educational components of NASA's activities rather than the technological accomplishments. This paper describes the approach taken to start and support a space and science club in a colonia near the U.S. - Mexico border in South Texas. The club provided a new organizational structure for linking NASA's goals with a low-income community. The structure of the club evolved over the course of three years to reflect the interests and resources of the youth that lived in the colonia.

Hybrid electric vehicle with hydrogen fueled internal combustion engine could be an interim between current vehicles and fuel cell vehicles which are thought to be more of far term reality. This paper introduces the hydrogen engine powered parallel hybrid electric vehicle developed by Texas Tech University Advanced Vehicle Engineering Lab. It presents the innovative use of LabVIEW Real-Time controllers to develop the prototype vehicle powertrain control system. Then it discusses the hydrogen engine control issues and presents the development of the engine control unit by LabVIEW FPGA. Vehicle communication issues among the distribution management system are presented. The vehicle test results are also shown in this paper.

Interest in electric vehicles (EVs) has significantly increased from the last decade, as the whole world is concerned about the reduction of emission of greenhouse gas by reducing the use of fossil fuel in transportation. The primary issue for electric vehicles is to develop an energy storage system i.e battery that can enable high mileage, rapid charging, and high-performance driving. Hence, battery management is required to get maximum, safe, and consistent performance of electric vehicles when running in a variety of conditions. To get the most out of a battery, it's important to keep an eye on its operating conditions, especially temperature, which has been shown to have a direct impact on battery performance and life. So, a battery thermal management system (BTMS) is crucial in the control of the thermal behavior of the battery. A good system simulation tool can minimize the time and cost of designing such a complicated thermal management system.

Machine learning methods, such as decision trees and deep neural networks, are becoming increasingly important and useful for data analysis in various scientific fields including dynamics and control, signal processing, pattern recognition, fluid mechanics, and chemical synthesis, etc. For future engine design and performance optimization, there is an urgent need for a robust predictive model which could capture the major combustion properties such as autoignition and flame propagation of multicomponent fuels under a wide range of engine operating conditions, without massive experimental measurement or computational efforts. It will be shown that these long-held limitations and challenges related to complex fuel combustion and engine research could be readily solved by implementing machine learning methods.

The application of Computational Fluid Dynamics (CFD) for three-dimensional (3D) combustion analysis coupled with detailed chemistry in engine development is hindered by its expensive computational cost. Chemistry computation may occupy as much as 90% of the total computational cost. In the present paper, a new two-step iso-octane combustion model was developed for spark-ignited (SI) engine to maximize computational efficiency while maintaining acceptable accuracy. Starting from the model constants of an existing global combustion model, the new model was developed using an approach based on sensitivity analysis to approximate the results of a reference skeletal mechanism. The present model involves only five species and two reactions and utilizes only one uniform set of model constants. The validation of the new model was performed using shock tube and real SI engine cases.

In recent years, study on the ride comfort of vehicles has attracted wide attention. The vibration caused by the road is transmitted to the human body through the tire, suspension, vehicle body, and the seat. Since the human body is in contact with the seat and the vibration is transmitted directly to the human body through the seat, the seat pan angle plays an important role on the vibration response of the human body. Previous studies have explored the effects of different backrest designs on human vibration response, but ignored the effects of different seat pan angles. Therefore, this paper will use a human biodynamic model combined with a 6-DOF seat model to study the effect of seat pan angles and feet-floor interaction on human vibration response. Three cases are proposed: Case 1 has a seat pan angle 8°, Case 2 has a seat pan angle 13°, and Case 3 has a seat pan angle 17°.

Flame speed data reported in most literature are acquired in conventional apparatus such as the spherical combustion bomb and counterflow burner, and are limited to atmospheric pressure and ambient or slightly elevated unburnt temperatures. As such, these data bear little relevance to internal combustion engines and gas turbines, which operate under typical pressures of 10-50 bar and unburnt temperature up to 900K or higher. These elevated temperatures and pressures not only modify dominant flame chemistry, but more importantly, they inevitably facilitate pre-ignition reactions and hence can change the upstream thermodynamic and chemical conditions of a regular hot flame leading to modified flame properties. This study focuses on how auto-ignition chemistry affects flame propagation, especially in the negative-temperature coefficient (NTC) regime, where dimethyl ether (DME), n-heptane and iso-octane are chosen for study as typical fuels exhibiting low temperature chemistry (LTC).

In vehicle driving environment, the driver is subjected to the vibrations in horizontal, vertical, and fore-aft directions. The human body is very much sensitive to whole body vibration and this vibration transmission to the body depends upon various factors including road irregularities, vehicle suspension, vehicle dynamics, tires, seat design and the human body's properties. The seat design plays a vital role in the vibration isolation as it is directly in contact with human body. Vibration isolation properties of a seat depend upon its dynamic parameters which include spring stiffness and damping of seat suspension and cushion. In this paper, an optimization-based method is used to determine the optimal seat dynamic parameters for seat suspension, and cushion based on minimizing occupant's body fatigue (occupant body absorbed power). A 14-degree of freedom (DOF) multibody biodynamic human model in 2D is selected from literature to assess three types of seat arrangements.

With the depletion of petroleum resources around the world, the need to have fuel-efficient mobility solutions and sustainable alternative fuels for automobiles has become prominent. Hybrid Electric Vehicles (HEV) and Battery Electric Vehicles have recently gained attention in terms of fuel-efficient mobility solutions. Recent research work by the authors of these articles and many other research groups have demonstrated the suitability of ammonia as a sustainable alternative power for automobiles [1, 2, 3, 4, 5, 6, 7, 9, 18]. Ammonia has been used for a long period of time mainly as an agricultural chemical and as a sustainable and carbon-free fuel and has substantial potential as a liquid fuel for mobile applications [1, 2, 3, 4, 5, 6, 7]. Ammonia-rich fuels can be used to run HEVs equipped with an Internal Combustion Engine (ICE) as the primary power source and a battery as the secondary energy source.

There is a growing interest in ammonia as a potential carbon-free fuel due to the current trend of decarbonization in ground transportation. Benefits of ammonia as a fuel include its high volumetric energy density, ease of storage and transportation, and mature manufacturing infrastructure. On the other hand, ammonia suffers from a low flame speed, long ignition delay times and NOx formation. In this work, a computational investigation of ammonia and hydrogen blends in a 0-D homogeneous charge compression ignition reactor is conducted using different blends under a range of engine-relevant conditions. Iso-contours of the crank angle corresponding to 50% of total heat release (CA50) are developed to assess the reactivity of the different blends under different engine speeds and equivalence ratios. The results show that ammonia requires a high inlet temperature to achieve a CA50 close to top dead center (TDC).

Understanding the fundamental details of drop/wall interactions is important to improving engine performance. Most of the drop-wall interactions studies are based on the impact of a single drop on the wall. To accurately mimic and model the real engine conditions, it is necessary to characterize spray/wall interactions with different impingement frequencies at a wide range of wall temperatures. In this study, a numerical method, based on Smoothed Particle Hydrodynamics (SPH), is used to simulate consecutive droplet impacts on a heated wall both below and above the Leidenfrost temperature. Impact regimes are identified for various impact conditions by analyzing the time evolution of the post-impingement process of n-heptane drops at different impingement frequencies and wall surface temperatures. For wall temperature below the Leidenfrost temperature, the recoiled film does not leave the surface.

The phenomenon of drop-wall interaction plays a crucial role in a wide range of industrial applications. When liquid droplets come into contact with a high-temperature surface, it can lead to thermal shock due to rapid temperature fluctuations. This abrupt temperature change can generate thermal stress within the solid wall material. If the thermal stress exceeds the material's strength in that specific stress mode, it can result in material failure. Therefore, it is imperative to delve into the evolving temperature patterns on high-temperature surfaces to optimize material durability. This study focuses on investigating drop-wall interactions within the context of engine environments. To achieve this, the Smoothed Particle Hydrodynamics (SPH) method is employed to simulate the impact of fuel droplets on a silicon nitride wall. The goal is to understand the heat transfer mechanisms, thermal penetration depths, and temperature distributions within the heated wall.