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This paper discusses simplified lumped parameter thermal modeling of power train components. In particular, it discusses the tradeoff between model complexity and the ability to correlate the predicted temperatures and flow rates with measured data. The benefits and problems associated with using a three lumped mass model are explained and the value of this simpler model is promoted. The process for correlation and optimization using modern software tools is explained. Examples of models for engines and transmissions are illustrated along with their predictive abilities over typical driving cycles.

The development of the AUTO TEMP II Temperature Control System used in Chrysler Corp. vehicles is summarized. A description of the design, development, function, and manufacturing aspects of the control system is presented, with emphasis on unique control parameters, reliability, serviceability, and check-out of production assemblies. Auto Temp II was developed by Chrysler in conjunction with Ranco Incorporated. The servo-controlled, closed-loop system, which has a sensitivity of 0.5 F, utilizes a water-flow control valve for temperature control, along with a cold engine lockout. The basic components are: sensor string, servo, and amplifier. All automatic functions involving control of mass flow rate, temperature, and distribution of the air entering the vehicle, are encompassed in one control unit. All components are mechanically linked through the gear train and are responsive to the amplifier through the feedback potentiometer.

TFC/IW, total fuel consumption divided by inertia (test) weight is a useful concept in analyzing the total or composite fuel economy generated in thousands of tests using the carbon balance technique in EPA Federal Test Procedure and Highway Driving Cycle. TFC/IW is a measure of drive train efficiency that requires no additional complicating assumptions. It is applicable to one test or a fleet representing many tests.

To understand how the passenger compartment cavity interacts with the surrounding panels (roof, windshield, dash panel, etc) a numerical panel contribution analysis was performed using FEA and BEA techniques. An experimental panel contribution analysis was conducted by Reiter Automotive Systems. Test results showed good correlation with the simulation results. After gaining some insight into panel contributions for power train noise, an attempt was made to introduce beads in panels to reduce vibration levels. A fully trimmed body structural-acoustic FEA model was used in this analysis. A network of massless beam elements was created in the model. This full structural-acoustic FEA model was then used to determine the optimal location for the beads, using the added beams as optimization variables.

Frames are important structures found in many transportation applications such as automotive bodies and train cars. They are also widely employed in buildings, bridges, and other load bearing designs. When a frame is carrying multiple loads, it can potentially risk a catastrophic buckling failure. The loads on the frame may be non-proportional in that one force stays constant while the other is increased until buckling occurs. In this study the buckling problem is formulated as a constrained eigenvalue problem (CEVP). As opposed to other CEVP in which the eigenvectors are forced to comply with a number of the constraints, the eigenvalues in the current CEVP are subject to some equality constraints. A numerical algorithm for solving the constrained eigenvalue problem is presented. The algorithm is a simple trapping scheme in which the computation starts with an initial guess and a window containing the potential target for the eigenvalue is identified.