No abstract

A test bench has been designed to assess condensation formation produced on the interior of a low-pressure exhaust gas recirculation cooler working with hot stream of humid air representing an engine warm-up stage, when its coolant starts from very cold conditions. An experimental campaign has been conducted with three different exhaust gas recirculation mass flow rates, four exhaust gas recirculation inlet temperatures and three different coolant initial temperatures, covering common conditions found in the low-pressure exhaust gas recirculation system of internal combustion engines under cold starts. The transient experimental results are analyzed and compared with a simple psychrometric condensation model, obtaining a good correlation and reproducing the trends of the condensation, even though an overprediction of the condensates of around 20%–40% exists due to the strong hypotheses assumed. The warm-up tests are most sensitive to the initial coolant temperature. For example, an engine starting at –10 °C ambient temperature could require 10 min to stop producing water in the low-pressure exhaust gas recirculation cooler, with an accumulated quantity during the warm-up of about 100 mL of condensates.

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The automation of lifespan assays with C. elegans in standard Petri dishes is a challenging problem because there are several problems hindering detection such as occlusions at the plate edges, dirt accumulation, and worm aggregations. Moreover, determining whether a worm is alive or dead can be complex as they barely move during the last few days of their lives. This paper proposes a method combining traditional computer vision techniques with a live/dead C. elegans classifier based on convolutional and recurrent neural networks from low-resolution image sequences. In addition to proposing a new method to automate lifespan, the use of data augmentation techniques is proposed to train the network in the absence of large numbers of samples. The proposed method achieved small error rates (3.54% ± 1.30% per plate) with respect to the manual curve, demonstrating its feasibility.

In engineering problems, design space approximation using accurate computational models may require conducting a simulation for each explored working point, which is often not feasible in computational terms. For problems with numerous parameters and computationally demanding simulations, the possibility of resorting to multi-fidelity surrogates arises as a means to alleviate the effort by employing a reduced number of high-fidelity and expensive simulations and predicting a much cheaper low-fidelity model. A multi-fidelity approach for design space approximation is therefore proposed, requiring two different designs of experiments to assess the best combination of surrogate models and an intermediate meta-modeled variable. The strategy is applied to the prediction of condensation that occurs when two humid air streams are mixed in a three-way junction, which occurs when using low-pressure exhaust gas recirculation to reduce piston engine emissions. In this particular case, most of the assessed combinations of surrogate and intermediate variables provide a good agreement between observed and predicted values, resulting in the lowest normalized mean absolute error (3.4%) by constructing a polynomial response surface using a multi-fidelity additive scaling variable that calculates the difference between the low-fidelity and high-fidelity predictions of the condensation mass flow rate.