Tenneco (Germany)

Abstract Pipeline operators sometimes face challenges in finding an optimum decision while executing both capital and operational projects. Utility- and risk-based decision-making approaches could create a framework for devising an optimum solution, especially in cases involving multi-parameter decision making. From a utility function perspective, an optimum solution would be the one with the least amount of cost and the highest benefit. In other words, an optimum solution could be achieved by maximizing a utility function. Accordingly, as an optimization problem, constraints related to safety, risk, and reliability for a given scenario could limit or dictate the space of feasible solutions. Addressing the problem of arriving at an optimum solution for a multi-parameter decision is the core topic of this paper. Moreover, optimized utility functions — decision making analyses based on probabilistic risk assessment — are discussed. In addition, the paper introduces a conceptual approach that has been utilized for infrastructure decision making: the life quality index (LQI) approach.

Exhaust systems need to increase their after treatment efficiency in order to meet the future emissions regulations. This involves complex systems containing multiple catalysts and a filter, which damp the engine order pulsations. Consequently, the flow-induced noise is increasing its contribution to the emitted sound. These noise sources are based on multiple physics phenomena, and therefore require new measurement methods and complex flow simulation with acoustic propagation: the CAA (Computational Aeroacoustics). Currently the Mach number or similar analogies are commonly used in industry with defined targets in order to prevent flow noise issues. Even if this easy simulation process helps to find the best geometry among others, it is not accurate enough and does not permit a good understanding of the noise sources. The DNS (Direct Numerical Simulation) is a CAA approach based on a compressible and transient calculation of Navier-Stokes equations that can directly model the pressure fluctuation and therefore predict the SPL (Sound Pressure Level) and its spectrum. It requires high computational resources, which are today available thanks to faster and cheaper processors along with optimized CFD algorithms.