PLATE-FIN HEAT EXCHANGER FOULING IDENTIFICATION USING D-OPTIMAL EXPERIMENTAL DESIGN

Fouling is a notable problem for aircraft heat exchangers in high-debris environments. Monitoring heat exchanger fouling levels is essential for aircraft maintenance scheduling and safety. In this document, methods for offline fouling identification during aircraft ground handling are explored to utilize a broader range of inputs than what is available during flight. Optimal experimental design is applied to a cross-flow plate-fin heat exchanger model of an environmental cooling system to find input trajectories that can maximize fouling identification capabilities. A model is developed for this analysis that accounts for mass, momentum and energy balances, validated with reported data and then applied to a dynamic sensitivity analysis framework. The sensitivities of the measured outputs with respect to fouling-related uncertainties are maximized by adjusting controllable inputs within specified design constraints. This method is assessed by a comparison of the confidence intervals of the estimated thermal fouling resistance through a series of case studies that examine uncertain environmental conditions. INTRODUCTION An environmental control system (ECS) serves the essential purpose of providing fresh air at appropriate conditions (determined by factors such as current altitude, aircraft flight path, etc.) to the passengers and crew. It is also responsible for additional tasks such as component heating and cooling, anti-icing, de-misting and rain dispersion. ECSs control the hot, compressed “bleed” air stream coming from the aircraft engines in traditional designs or from a “ram” air stream compressor in more modern designs (Moir & Seabridge, 2008). The majority of aircraft ECSs control the hot air stream with cross-flow plate fin heat exchangers due to their excellent heat transfer efficiency in relation to their small weight and volume (Kays & London, 1984). Using ambient ram air as the cold side fluid, these air-cooled heat exchangers of an ECS decrease the temperature of the hot stream. However, as a result of using ambient air as the cold side fluid, the ECS, and in particular the cold side of the heat exchanger, is exposed to fouling contaminants like sand, dust and salt (Moir & Seabridge, 2008; Wright et al., 2009). Aircraft ECS fouling typically occurs through particle deposition onto components when the aforementioned contaminants are present in the inlet airflow streams. The accumulation of these particles are functions of the inlet stream airflow rate, temperature, pressure and the contaminant concentration (Shah & Sekuli, 2003). Particle deposition onto the ECS heat exchanger surface over time results in the reduction of heat transfer efficiency and performance, and the increase in pressure drop, leading to potentially severe maintenance costs from component and system failures (Shah et al., 2009; Wright et al., 2009). To alleviate these costs, identification of ECS fouling has been the subject of many studies (Litt et al., 2004; Shang & Liu, 2010). Focusing on ECS heat exchanger fouling identification, typical practice within the aerospace industry is to implement a system health check, known as a built-in test (BIT). Prior to flight, manually initiated built-in tests (iBIT) can be run to control the ECS with a much wider operating region (Al-Asaad & Shringi, 2000). The noteworthy time scale difference between iBITs (several minutes) and fouling (hundreds of hours) allows fouling related properties, like thermal fouling resistance and deposit thickness, to be handled as parameters, warranting the use of parameter estimation for fouling identification. Here, we propose a method that optimizes a set of system inputs to minimize the uncertainty in iBIT fouling identification, based on the Optimal Experimental Design (OED) techniques reported by Federov (2010). The objective of the model-based approach of OED is to decrease the uncertainty of estimated model parameters while taking into account the system measurements and their variances (Bruwer & MacGregor, 2006). OED is wellestablished in the field of statistical design of experiments, commonly applied to enhance parameter estimation. Modelbased OED uses an explicit mathematical model, which reflects our general understanding of a process with given parametric uncertainty, and can be cast as an optimization problem that maximizes future experiment information to minimize uncertainty in model parameters and system conditions. Developing an optimal iBIT can thus be formulated as an OED problem. There exist ample applications of model-based OED. They can be applied across all engineering disciplines to any system (linear, non-linear, steady-state or dynamic). The only requirement of the model-based approach is that there must exist a model to describe the physical phenomena of the process through first principles/fundamental equations and empirical correlations, given uncertainties that are known or generally understood. This work uses a comprehensive plate fin heat exchanger model where the empirical correlations and model equations are assumed accurate and the model input uncertainty is taken into consideration. Using dynamic heat transfer analysis, the proposed method applies this OED framework to develop an optimal iBIT for fouling Proceedings of International Conference on Heat Exchanger Fouling and Cleaning 2015 (Peer-reviewed) June 07 12, 2015, Enfield (Dublin), Ireland Editors: M.R. Malayeri, H. Müller-Steinhagen and A.P. Watkinson Published online www.heatexchanger-fouling.com