Search Results

Valve train system is one major contributor to engine overall friction loss and is approximately 30% of total engine friction at lower speed and approximately 20 % at higher engine speed. Valve spring loads (preload and working) are proportional to friction loss of valve train. To optimizing the valve spring design main requirement is valve train perform it function safely at maximum engine cutoff RPM with minimum preload and working load. Robustness and frictional power loss are contradicting requirement, robustness demand high stiffness spring for better valve jump and bounce performance with dynamic safe valve spring design, on the other hand low frictional power loss demand for use of low stiffness spring. To optimize the valve spring stiffness for meeting both the requirement we need accurate prediction of valve spring in design stage and good correlation with testing data to reduce the number of iterations.

In recent years, world-wide automotive manufacturers have been continuously working in the research of suitable technical solutions to meet upcoming stringent carbon dioxide (CO2) reduction and Real Driving Emission (RDE) targets, as set by international regulatory authorities. Many frictions reduction and lambda=1 (λ=1) technologies have been already developed or are currently under study by automotive manufacturer for gasoline engines, to meet legislated targets.

When a vehicle moves over uneven ground, motion of the sprung and unsprung masses causes dynamic shifting in the load transmitted to the ground, making the normal reaction in the tire-soil patch a continuously changing wheel parameter that may affect vehicle performance. At high loads, sinkage of the wheel can become high as the wheel digs into the soil. At low loads, the wheel can have difficulty acquiring sufficient traction. Additionally, steerability of the wheel can be diminished at very low loads. Controlling the damping forces in the suspension that is usually used to improve ride quality and stabilize motion of the sprung mass can result in an increase in the dynamic variation of the wheel normal reaction and cause vehicle performance deterioration. In this paper, a method is developed to establish boundary constraints on the dynamic normal reaction to maintain reasonable tire-terrain mobility characteristics.

In the recent years world-wide automotive manufacturers are continuously working in the research of the suiTable technical solutions to meet upcoming stringent carbon dioxide (CO2) emission targets, defined by regulatory authorities across the world. Many technologies have been already developed, or are currently under study, to meet the legislated targets. To meet this objective, the generation of tumble at intake stroke and the conservation of turbulence intensity at the end of compression stroke inside the combustion chamber have a significant role in the contribution towards accelerating the burning rate, increasing the thermal efficiency and reducing the cyclic variability [1]. Tumble generation is mainly attained by intake port design, and conservation is achieved during the end of compression stroke 690 ~ 720 crank angles (CA) which is strictly affected by the piston bowl geometry and pentroof combustion chamber shape.

In recent years, worldwide automotive manufacturers have been continuously working in the research of suitable technical solutions to meet upcoming stringent Real Driving Emission (RDE) and Corporate Average Fuel Economy (CAFÉ) targets, as set by international regulatory authorities. Many technologies have been already developed, or are currently under study by automotive manufacturer for gasoline engines, to meet legislated targets. In-line with the above objective, there are many technologies available in the market to expand lambda 1 (λ=1) region by reducing fuel enrichment at high load-high revolutions per minute (RPM) by reducing exhaust gas temperature (for catalyst protection) for RDE regulation [1]. Integrated Exhaust Manifold (IEM) is the key technology for the Internal Combustion (IC) for the subjected matter as catalyst durability protection is done by reducing exhaust gas temperatures instead of injecting excess fuel for cooling catalyst.

IC Engine Timing belt is a major noise prone area and it takes time during development to achieve acceptable NVH characteristics. In an existing engine under series production noise problem observed due to excitation of timing belt span by crank timing sprocket tooth. From vehicle perspective noise was heard in vehicle cabin at around idling RPM and a second peak observed around twice the initial RPM. This paper includes a methodology for use of computer based analytical simulation methods to predict timing belt dynamic behavior and NVH characteristics. Along with development of computer based multi body dynamic model for timing belt, validation of simulation model with actual testing was done and after correlation of testing and simulated results countermeasure were finalized based on iterations in multi body simulation model.

With more stringent emission norm coming in future, add more pressure on IC engine to improve fuel efficiency for survival in next few decades. In gasoline SI (spark ignition) engine, valve events have major influence on fuel economy, performance and exhaust emissions. Optimization of valve event demands for extensive simulation and testing to achieve balance between conflicting requirement of low end torque, maximum power output, part load fuel consumption and emission performance. Balance between these requirements will become more critical when designing low cost valve train without VVT (Variable valve timing) to reduce overall cost of engine. Higher CR (Compression ratio) is an important low cost measure to achieve higher thermal efficiency but creates issue of knocking thereby limiting low speed high load performance. The effective CR reduction by means of late intake valve closing (LIVC) is one way to achieve higher expansion ratio while keeping high geometric CR.

In recent years, world-wide automotive manufacturers have been continuously working in the research of suitable technical solutions to meet upcoming stringent carbon dioxide (CO2) emission targets, as defined by international regulatory authorities. Many technologies have been already developed, or are currently under study, to meet legislated targets. In-line with above objective, the enhancement of turbulence intensity inside the combustion chamber has a significant importance which contributes to accelerating the burning rate, to increase the thermal efficiency and to reduce the cyclic variability [9]. Turbulence generation is mainly achieved during the intake stroke which is strictly affected by the intake port geometry, orientation and to certain extends by combustion chamber masking. Conservation of turbulence intensity till 700~720 crank angle (CA) is achieved by optimized shape of combustion chamber geometry and piston bowl shape.

To achieve acceptable valve train lubrication with low viscosity oil is challenging. Understanding the effect of valve train design parameter on valve train lubrication is important. In this study rocker arm valve train mechanism is analyzed to check the influence of valve train design parameters on lubrication at cam and rocker face interface. In valve train lubrication, lubrication oil film thickness play important role in valve train wear and friction. Main objective is to maintain acceptable oil film thickness at peak valve lift and minimizing the duration of critical minimum oil film thickness at cam flank to avoid wear at cam/ roller rocker follower interface when using low viscosity oil. Cam nose wear effect the engine performance and wear at cam flank change the dynamic characteristics of valve train.

This paper outlines a real-time hierarchical control allocation algorithm for multi-axle land vehicles with independent hub motor wheel drives. At the top level, the driver’s input such as pedal position or steering wheel position are interpreted into desired global state responses based on a reference model. Then, a locally linearized rigid body model is used to design a linear quadratic regulator that generates the desired global control efforts, i.e., the total tire forces and moments required track the desired state responses. At the lower level, an optimal control allocation algorithm coordinates the motor torques in such a manner that the forces generated at tire-road contacts produce the desired global control efforts under some physical constraints of the actuation and the tire/wheel dynamics. The performance of the proposed control system design is verified via simulation analysis of a 3-axle heavy vehicle with independent hub-motor drives.

Using the total probability theorem, we propose a method to calculate the failure rate of a linear vibratory system with random parameters excited by stationary Gaussian processes. The response of such a system is non-stationary because of the randomness of the input parameters. A space-filling design, such as optimal symmetric Latin hypercube sampling or maximin, is first used to sample the input parameter space. For each design point, the output process is stationary and Gaussian. We present two approaches to calculate the corresponding conditional probability of failure. A Kriging metamodel is then created between the input parameters and the output conditional probabilities allowing us to estimate the conditional probabilities for any set of input parameters. The total probability theorem is finally applied to calculate the time-dependent probability of failure and the failure rate of the dynamic system. The proposed method is demonstrated using a vibratory system.

Assessment of braking performance that includes brake fade is a critical part of the evaluation of military light tactical vehicles as it is for conventional light cars and trucks. These vehicles are sometimes called upon to operate in severe mountain regions that challenge the braking performance well beyond the environment in which these vehicles are normally operated. The U.S. Army Test Operating Procedure (TOP) 2-2-608 includes a test schedule conducted in the mountainous region near Jennerstown, Pennsylvania. While this test procedure represents a typical mountain environment, it does not represent the most severe mountain descents that can be encountered across the United States. As a preliminary step to developing a representative severe mountain descent braking test, mountain roads throughout the United States were evaluated analytically to identify potential test venues.

Ride control of military vehicles is challenging due to varied terrain and mission requirements such as operating weight. Achieving top speeds on rough terrain is typically considered a key performance parameter, which is always constrained by ride discomfort. Many military vehicles using passive suspensions suffer with compromised performance due to single tuning solution. To further stretch the performance domain to achieving higher speeds on rough roads, semi-active suspensions may offer a wide range of damping possibilities under varying conditions. In this paper, various semi-active control strategies are examined, and improvements have been made, particularly, to the acceleration-driven damper (ADD) strategy to make the approach more robust for varying operating conditions. A seven degrees of freedom ride model and a quarter-car model were developed that were excited by a random road process input modeled using an auto-regressive time series model.

Design optimization often relies on computational models, which are subjected to a validation process to ensure their accuracy. Because validation of computer models in the entire design space can be costly, we have previously proposed an approach where design optimization and model validation, are concurrently performed using a sequential approach with variable-size local domains. We used test data and statistical bootstrap methods to size each local domain where the prediction model is considered validated and where design optimization is performed. The method proceeds iteratively until the optimum design is obtained. This method however, requires test data to be available in each local domain along the optimization path. In this paper, we refine our methodology by using polynomial regression to predict the size and shape of a local domain at some steps along the optimization process without using test data.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. It also affects the scheduling for preventive maintenance. Reliability usually degrades with time increasing therefore, the lifecycle cost due to more frequent failures which result in increased warranty costs, costly repairs and loss of market share. In a lifecycle cost based design, we must account for product quality and preventive maintenance using time-dependent reliability. Quality is a measure of our confidence that the product conforms to specifications as it leaves the factory. For a repairable system, preventive maintenance is scheduled to avoid failures, unnecessary production loss and safety violations. This article proposes a methodology to obtain the optimal scheduling for preventive maintenance using time-dependent reliability principles.

Real-time driving simulations are an important tool for verifying vehicle and vehicle component designs with a driver in the loop. They not only provide a cost effective solution but also an ability to verify designs in a safe and controlled operating environment. A real-time driving experiment has been developed for Stryker to compare the ride and handling performance of a baseline passive suspension to that of a Magneto-Rheological (MR) semi-active damper suspension. The Tank Automotive Research Development and Engineering Center (TARDEC) has integrated this new suspension into a real time vehicle dynamics model of the Stryker using the MR suspension model developed by the Original Equipment Manufacturer (OEM). Using this real-time model and the TARDEC Ride Motion Simulator (RMS), TARDEC associates, along with associates from the Stryker Program Management office and the suspension OEM were able to drive and compare the passive and MR Stryker in a virtual environment.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. As time progresses, the product may fail due to time-dependent operating conditions and material properties, component degradation, etc. The reliability degradation with time may increase the lifecycle cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended function successfully for a specified time interval. In this work, we consider the first-passage reliability which accounts for the first time failure of non-repairable systems. Methods are available in the literature, which provide an upper bound to the true reliability which may overestimate the true value considerably. Monte-Carlo simulations are accurate but computationally expensive.

Understanding reliability is critical in design, maintenance and durability analysis of engineering systems. A reliability simulation methodology is presented in this paper for vehicle fleets using limited data. The method can be used to estimate the reliability of non-repairable as well as repairable systems. It can optimally allocate, based on a target system reliability, individual component reliabilities using a multi-objective optimization algorithm. The algorithm establishes a Pareto front that can be used for optimal tradeoff between reliability and the associated cost. The method uses Monte Carlo simulation to estimate the system failure rate and reliability as a function of time. The probability density functions (PDF) of the time between failures for all components of the system are estimated using either limited data or a user-supplied MTBF (mean time between failures) and its coefficient of variation.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. As time progresses, the product may fail due to time-dependent operating conditions and material properties, component degradation, etc. The reliability degradation with time may increase the lifecycle cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended function successfully for a specified time interval. In this work, we consider the first-passage reliability which accounts for the first time failure of non-repairable systems. Methods are available in the literature, which provide an upper bound to the true reliability which may overestimate the true value considerably. This paper proposes a methodology to calculate the cumulative probability of failure (probability of first passage or upcrossing) of a dynamic system, driven by an ergodic input random process.

The system response of many engineering systems depends on time. A random process approach is therefore, needed to quantify variation or uncertainty. The system input may consist of a combination of random variables and random processes. In this case, a time-dependent reliability analysis must be performed to calculate the probability of failure within a specified time interval. This is known as cumulative probability of failure which is in general, different from the instantaneous probability of failure. Failure occurs if the limit state function becomes negative at least at one instance within a specified time interval. Time-dependent reliability problems appear if for example, the material properties deteriorate in time or if random loading is involved which is modeled by a random process. Existing methods to calculate the cumulative probability of failure provide an upper bound which may grossly overestimate the true value.