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In an automotive air-conditioning (AC) system, upfront prediction of the cabin cool down rate in the initial design stage will help in reducing the overall product development (PD) time. Vehicle having higher seating capacity will have higher thermal load and providing thermal comfort to all passengers uniformly is a challenging task for the automotive HVAC (Heating Ventilation and Air conditioning) industry. Dual HVAC unit is generally used to provide uniform cooling to a large cabin volume. One dimensional (1D) simulation is being extensively used to predict the HVAC performance during the initial stage of PD. The refrigerant loop with components such as compressor, condenser, TXV and evaporator was modeled. The complicated vehicle cabin including the glazing surfaces and enclosures were modeled as a three row duct system using 1D tool AMESim®. The material type, density, specific heat capacity and thermal conductivity of the material were specified.

An automobile door is a complex module, which consists of various fixed and movable subassemblies and components. Parameters such as safety, vehicle dynamics, aesthetic and strength are critical while designing the door assembly. Apart from the above, the design of door trim should minimize BSR (buzz squeak and rattle) at vehicle running conditions. Stiffness is one of the key engineering requirements which if not optimized will result in higher BSR levels and failure of the door trim components. In this study, more importance is given to optimize the stiffness of door trim. As per DVP (design verification and planning) standards of the OEMs, the range of deflection for the plastic trim parts is defined considering the conditions, comfort level and location of use. If stiffness is higher than the requirement, the door trim plastic parts are harder and will violate the quality and safety norms. If it is lower, then trim parts will not meet the functional requirements and safety norms.

Cooling system is one of the important systems of an engine to maintain the optimum coolant temperature across engine and its components. Analysis of cooling system at initial phase of product development will help in optimum design of the system and there by achieving better performance of engine. For this purpose the traditional method followed is to run several bench tests and to analyze the engine performance and repeat the bench tests for validating any design changes. This results in increased lead time of engine development and overall cost. To reduce the lead time as well as reduce the overall cost, 1D (one dimension) simulation tools place a major role. Simulation of engine cooling system with special kind of engine coolant water jacket is challenging. It is difficult to achieve the simulation results close to bench test due to complexity of the system.

The Charge Air Cooler (CAC) is designed to cool the charge air after being boosted by the Turbocharger. In order to maintain the optimum temperature and to further improve the charge air density entering to the engine the CAC is used. This makes the combustion more efficient and better engine performance and fuel economy. The performance of the CAC is highly affected by the plumbing lines which transport the compressed charge air from turbocharger to the intake manifold of the Engine. It consists of tube, hose, duct and resonator. Designing the optimum CAC plumbing lines with lesser pressure drop is the major requirement of the CAC system considering the complex packaging. In such scenarios, one-dimensional (1D) simulation is a good way to compute the pressure drop for faster and economical solution.

An expansion tank is an integral part of an automotive engine cooling system. The primary function of the expansion tank is to allow the thermal expansion of the coolant. The expansion tank will be referred as hot bottle in this paper. In the System level modeling of the engine internal flow, it is imperative to accurately model and characterize the components in the system. It is often challenging to define the hot bottle accurately with limited parameters in the 1D modeling. Currently it is very difficult to optimize the system by testing. Since testing consumes a lot of time and changes in development stage. If the hot bottle component is not defined properly in the system network, then the system flow balancing cannot be predicted accurately. In this paper, the approach of creating a 1D modeling tool for hot bottle flow prediction is discussed and the simulation results are compared with the physical test data.

In an Automotive air conditioning system, the air flow distribution in the cabin from the HVAC (Heating, ventilation and air conditioning), ducts and outlets is evaluated by the velocity achieved at driver and passenger mannequin aim points. Multiple simulation iterations are being carried out before finalizing the design of HVAC panel duct and outlets until the target velocity is achieved. In this paper, a parametric modeling of the HVAC outlet is done which includes primary and secondary vane creation using CATIA. Java macro files are created for simulation runs in STAR CCM+. ISIGHT is used as an interface tool between CATIA and STARCCM+. The vane limits of outlet and the target velocity to be achieved at mannequin aim points are defined as the boundary conditions for the analysis. Based on the optimization technique and the number of iterations defined in ISIGHT, the vane angle model gets updated automatically in CATIA followed by the simulation runs in STARCCM+.

The performance of the Transmission Oil Cooler (TOC) is influenced significantly by the TOC plumbing lines which transmit the oil from transmission system to the oil cooler and back. Designing the optimum TOC plumbing line with lesser pressure drop is the need of the hour considering the complex nature of the vehicle packaging. Reducing the pressure drop increases the oil flow rate through the transmission which results in optimum performance. Improved transmission efficiency in turn shall improve the engine efficiency and performance. The improvements obtained from increased transmission and engine efficiency shall result in an overall increase in vehicle fuel economy. Optimization solutions are required in the early product development cycle where the components are not readily available and/or are prohibitively expensive to do testing. In such scenarios, one-dimensional (1D) simulations shall be employed to compute the pressure drop for faster and economical solutions.

In an automotive air-conditioning (AC) system, the amount of work done by the compressor is also influenced by the suction line which meters the refrigerant flow. Optimizing the AC suction line routing has thus become an important challenge and the plumbing designers are required to come up with innovative packaging solutions. These solutions are required in the early design stages when prototypes are not yet appropriate. In such scenarios, one-dimensional (1D) simulations shall be employed to compute the pressure drop for faster and economical solution. In this paper, an approach of creating a modeling tool for suction line pressure drop prediction is discussed. Using DFSS approach L12 design iterations are created and simulations are carried out using 1D AMESim software. Prototypes are manufactured and tested on HVAC bench calorimeter. AC suction line pressure drop predicted using the 1D modeling co-related well with the test data and the error is less than 5%.

Simulation has become an integral part in the design and development of an automotive air-conditioning (AC) system. Simulation is widely used for both system level and component level analyses and are carried out with one-dimensional (1D) and Computational Fluid Dynamics (CFD) tools. This paper describes a 1D approach to model refrigerant loop and vehicle cabin to simulate the soak and cool down analysis. Soak and cool down is one of the important tests that is carried out to test the performance of a heating, ventilation and air-conditioning (HVAC) system of a vehicle. Ability to simulate this cool down cycle is thus very useful. 1D modeling is done for the two-phase flow through the refrigerant loop and air flow across the heat exchangers and cabin with the commercial software AMESim. The model is able to predict refrigerant pressure and temperature inside the loop at different points in the cycle.