Search Results

Large-scale flows in internal combustion engines directly affect combustion duration and emissions production. These benefits are significant given increasingly stringent emissions and fuel economy requirements. Recent efforts by engine manufacturers to improve in-cylinder flows have focused on the design of specially shaped intake ports. Utility engine manufacturers are limited to simple intake port geometries to reduce the complexity of casting and cost of manufacturing. These constraints create unique flow physics in the engine cylinder in comparison to automotive engines. An experimental study of intake-generated flows was conducted in a four-stroke spark-ignition utility engine. Steady flow and in-cylinder flow measurements were made using three simple intake port geometries at three port orientations. Steady flow measurements were performed to characterize the swirl and tumble-generating capability of the intake ports.

A manual task can be performed based on alternative movement techniques. Ergonomic human motion simulation requires consideration of alternative movement techniques, because they could bring different biomechanical, physiological, and psychophysical consequences. A method for identifying movement techniques from existing motion data was developed. The method is based on a JCV (Joint Contribution Vector) index and statistical clustering. A JCV quantifies a motion's underlying movement technique by computing contributions of individual body joint DOFs (degree-of-freedom) to the achievement of the task goal. Given a set of motions (motion capture data) achieving the same or similar task goals, alternative movement techniques can be identified by 1) representing the motions in terms of JCV and 2) performing a statistical clustering analysis. Performance of this movement technique identification method was evaluated based on a set of stoop and squat lifting motions.

This paper presents a new composite drive cycle used to evaluate and test the performance of Class 4-6 heavy-duty hybrid electric vehicles (HEVs). The new cycle is being used in the ongoing Advanced Heavy Hybrid Propulsion Systems (AHHPS) Program, sponsored by the U.S. Department of Energy. The goal was to select a cycle that is acceptable to all involved parties, has an achievable speed-time trace for target applications, represents the typical driving pattern of these applications, and is practical for testing and state-of-charge correction. These criteria were applied to numerous element and composite cycles. Ultimately, a new composite cycle was developed and selected-the Combined International Local and Commuter Cycle (CILCC). Various activities conducted under the AHHPS Program are based on this cycle, including energy auditing, modeling and simulation, system optimization, and vehicle testing.

The power management control system development and vehicle test results for a medium-duty hybrid electric truck are reported in this paper. The design procedure adopted is a model-based approach, and is based on the dynamic programming technique. A vehicle model is first developed, and the optimal control actions to maximize fuel economy are then obtained by the dynamic programming method. A near-optimal control strategy is subsequently extracted and implemented using a rapid-prototyping control development system, which provides a convenient environment to adjust the control algorithms and accommodate various I/O configurations. Dynamometer-testing results confirm that the proposed algorithm helps the prototype hybrid truck to achieve a 45% fuel economy improvement on the benchmark (non-hybrid) vehicle. It also compares favorably to a conventional rule-based control method, which only achieves a 31% fuel economy improvement on the same hybrid vehicle.

Pick up and delivery vehicle applications such as parcel handling trucks represent an ideal duty cycle for Hybrid Electric Powertrains. The low speed, frequent stopping and starting operation provides good opportunities for enhancing engine behavior and recovering braking energy by adding an electric drive system to the vehicle. FedEx Express collaborated with the environmental advocacy group Environmental Defense to announce the Future Vehicle Program, with the goal of developing significant improvements in emissions and fuel economy for the familiar FedEx Express W700 parcel delivery vehicle. This paper describes the objectives, development activities, and test results for one of the vehicles submitted to this program. A team led by Eaton Corporation prepared the Direct Hybrid Electric Powertrain system, which received the highest ranking in the Future Vehicle Program evaluation.

A multi-coordinate FRF-based inverse substructuring approach is proposed to partition a vehicle system into two or more substructures, which are coupled at discrete interface points. The joint and free substructure dynamic characteristics are then extracted from the coupled system response spectra. Depending on the actual form of the structural coupling terms, three forms of the coupling matrix are assumed here. The most general one constitutes the non-diagonal form, and the other two simpler cases are the block-diagonal and purely diagonal representations that can be used to simplify testing process and overcome computational problems. The paper is focused on the investigation of the durability of these three formulations when the input FRFs are noise contaminated. A finite element model of a simplified vehicle system is used as the case study.

A spectral-based substructuring approach applying linear frequency response functions (FRF) is proposed for improving the accuracy of simulating the dynamics of coupled systems. The method also applies a least square singular value decomposition (SVD) scheme to overcome the inherent computational deficiency in the basic substructuring formulation. The computational problem is caused by the magnification of measurement errors during any one of the matrix inversion calculations required for this method. The primary objective of applying this approach is to examine the possibility of analyzing higher frequency response that is normally not possible using conventional modeling technique such as the direct finite and boundary element, and lumped parameter techniques. In this study, additional concepts are also evaluated to quantify the limitations and range of applicability of the proposed substructuring approach for simulating the vibration response of complex powertrain structures.

Using FEM (Finite Element Method) and other analytical approaches, a systematic methodology was developed to predict an engine valve's fatigue life. In this study, a steel (SAE 21-2N) exhaust valve on an engine with a type 2 valve train configuration was used as a test case. Temperature and stress/strain responses of each major event phase of the engine cycle were analytically simulated. CFD models were developed to simulate the exhaust gas flow to generate boundary conditions for a thermal model of the valve. FEM simulations accounted for thermal loads, temperature dependent material properties, thermal stresses, closing impact stresses and combustion load stresses. An estimated fatigue life was calculated using Miner's rule of damage accumulation in conjunction with the Modified Goodman approach for fluctuating stresses. Predicted life results correlated very well with empirical tests.

A special vehicle dynamometer has been developed that allows engineers to evaluate driveline components and control algorithms for advanced, electrically-assisted drive systems on commercial vehicles. This dynamometer allows objective measurements of performance, fuel economy, and exhaust emissions, while the full vehicle is operated over a specified driving cycle. This system can be used to exercise the electric motor, engine, transmission and battery systems on Medium Duty Hybrid Trucks - in regeneration as well as power mode - all indoors and in a controlled, repeatable environment. This paper will provide descriptions of the operating goals, control features, and results of testing with this dynamometer. Once the various parameters have been optimized for fuel and emissions performance in this facility, the vehicle can be evaluated where it counts - on the road.

Sound transmission through the sidewall of an automotive muffler has been studied theoretically and experimentally. Three wall structures: a single shell, double shell and porous-cored shell constructions are considered. Transmission losses through the sidewalls were measured using the two microphone method. Experimental results are compared to one another, and to the corresponding theoretical analysis results, which shows that the mean flow effect is not a significant factor in designing the muffler sidewall.

Test sensors are evaluated for noise path analysis applications. Newly developed ICP™ piezo-electric strain gages are used with accelerometers and microphones in a conventional noise path analysis test on the front body/suspension attachment points of a vehicle. In a less conventional application, a steering knuckle is converted into a 6-DOF force transducer using an array of strain gages and using an array of 3-DOF load cells. The two sensor arrays are both calibrated with a 6-DOF load cell. The result is an estimate of the three translation force and three moment operating inputs entering the steering knuckle from the wheel.

The perturbed boundary condition (PBC) model updating procedure has been developed to correct the finite element model [1]. The use of additional structural configurations adds more experimental information about the system and so better updating results can be expected. While it works well for simulated examples, practical limitations and additional requirements arise when it is used to update engineering structures. In this paper, the merits and the practical limitations of the techmques will be discussed in depth through the updating of a simulated system where the “measured” data is generated by computer and a real test structure where the experimentally measured data is noisy and distorted due to leakage. Useful suggestions and recommendations are drawn to guide the model updating of practical engineering structures.

A new method to estimate a structure's inertia properties using a prototype load cell designed to measure all loads and moments applied to a structure is presented. This prototype six-axis transducer approach employs 32 piezoelectric sensing elements which are arranged to form the load cell. These redundant measurements are used to determine the principal forces and moments from an overdetermined set of equations. Calibration of this multi-crystal load cell is performed with a fixture that utilizes a calibration mass and quasi-free-free boundary conditions. The resulting calibration matrix is a 6×32 transformation from the coupled measurements to a decoupled set of pseudo measurements consisting of the forces acting on a structure. With this transducer and its calibration matrix, a system's inertia properties can be estimated. A thorough discussion of both the calibration and inertia estimation procedure with a experimental test case is presented.

In this work, the goal of enhanced reliability through redundancy is explored. Two levels of fusion have been defined: the first is a fusion of sensors, redundant in both number and type, and the second is a statistical fusion of the resulting data at a software level. An intermediate preprocessing level is required to connect both fusions. The various types of sensors which are included are bulk micromachined flow, pressure and hydrogen sensors and a thin film poly-crystalline silicon temperature sensor. Individual sensors have been fabricated and packaged in arrays. Associated preprocessing has been designed to be able to handle all of the signals coming from each sensor and prepare them for statistical analysis. Data fusion algorithms have been written and tested.

The High Pressure Oxidizer Turbine (HPOT) discharge temperature of the Space Shuttle Main Engine (SSME) was estimated using Radial Basis Function Neural Networks (RBFNN) during the startup transient. Estimation was performed for both nominal engine operation and during simulated input sensor failures. The K-means clustering algorithm was used on the data to determine the location of the basis function centers. The performance of the RBFNN is compared with that of a feedforward neural network trained with the Quickprop learning algorithm.

Efforts have been made to utilize Al constructs to identify flaws in the Space Shuttle Main Engine (SSME) faceplate regions. In order to expand the applicability of these algorithms to a larger problem domain, the automatic visual inspection system(AVIS) has been modified to enable a user with little or no image processing background to define a system capable of identifying flaws on a given set of imagery. This system requires the user to simply identify flawed regions and the selection of processing and feature descriptors is performed automatically. This paper explicates the motivations, definitions, and performance issues associated with the AVIS paradigm.

Building and operating dependable systems is fundamental to many critical applications, such as designing integrated hardware and software systems for vehicles or satellites. Dependable systems techniques, methods, and tools are developed and used by researchers and practitioners working in widely varying disciplines. In order to provide a unifying framework for the successful dissemination and sharing of dependability results, the development of a dependable systems knowledge base is underway.1 Two database support subsystems are under development: one that manages the storage and retrieval of document information, as well as communicating between the user interface layer and the physical database layer, and another that manages the lexicon of dependability terminology for the user interface layer. The system will provide access to information in a sophisticated, intelligent manner that enables a human user to function more effectively in learning and decision-making capacities.

This paper provides several examples of silicon “micromachined” semiconductor sensors with which the authors are involved for aerospace condition monitoring. This and related work in MEMS (Micro Electro Mechanical Systems) has the potential to revolutionize condition monitoring in aerospace condition and “health monitoring” by (1) moving “smart” electronics out to the sensor chip itself and (2) combining a vast quantity and types of, not only electronic, but micromechanical sensing schemes into the silicon chip . Precisely formed cantilevers, gears, valves, microplumbing and even micro motors of the cross-section of a human hair can be fabricated on a single silicon microchip. Silicon is an excellent mechanical material with a yield strength several times that of stainless steel. Also silicon has excellent thermal properties , whereas compatible silicon dioxide (which we typically use in connection with silicon microelectronics patterning) is virtually a thermal insulator.

Modeling of liner cavitation corrosion is of increasing significance since new engine design trends could aggravate the problem. Cavitation corrosion is of a complex nature and is affected by numerous coupled factors. A system approach to analyze and assess cavitation corrosion damage is deemed necessary. The approach accounts for the macroscopic and microscopic aspects of the phenomenon that include modeling of piston dynamics, liner transient vibration, pressure wave propagation, bubble dynamics and their effect on material damage. Though detection methods can provide crucial insight of factors that influence the cavitation problem, analysis methods are required at the initial design stage to provide overall engine design optimization and reduce prototype development cost and time. This analytical diagnostic approach provides a powerful tool to give valuable and relatively quick insight in solving engine liner cavitation corrosion problems.