Search Results (2)

The front wall acoustic package system plays a crucial role in automotive design, and its performance directly affects the quality and comfort of the interior noise. In response to the limitations of traditional experimental and simulation methods in terms of accuracy and efficiency, this paper proposes a convolutional neural network (AFWL-CNN) based on adaptive weighted feature learning. Using a data-driven method, the sound insulation performance of the entire vehicle’s front wall acoustic package system was predicted and analyzed based on the original parameters of the front wall acoustic package components, thereby effectively avoiding the shortcomings of traditional TPA and CAE methods. Compared to the traditional CNN model (RMSE = 0.042, MAE = 3.89 dB, I-TIME = 13.67 s), the RMSE of the proposed AFWL-CNN model was optimized to 0.031 (approximately 26.19% improvement), the mean absolute error (MAE) was reduced to 2.84 dB (approximately 26.99% improvement), and the inference time (I-TIME) increased to 17.16 s (approximately 25.53% increase). Although the inference time of the AFWL-CNN model increased by 25.53% compared to the CNN model, it achieved a more significant improvement in prediction accuracy, demonstrating a reasonable trade-off between efficiency and accuracy. Compared to AFWL-LSTM (RMSE = 0.039, MAE = 3.35 dB, I-TIME = 19.81 s), LSTM (RMSE = 0.044, MAE = 4.07 dB, I-TIME = 16.71 s), and CNN–Transformer (RMSE = 0.040, MAE = 3.74 dB, I-TIME = 19.55 s) models, the AFWL-CNN model demonstrated the highest prediction accuracy among the five models. Furthermore, the proposed method was verified using the front wall acoustic package data of a new car model, and the results showed the effectiveness and reliability of this method in predicting the acoustic package performance of the front wall system. This study provides a powerful tool for fast and accurate performance prediction of automotive front acoustic packages, significantly improving design efficiency and providing a data-driven framework that can be used to solve other vehicle noise problems. Full article

For determining the effect of illumination on nutrient removal in an artificial food web (AFW) system, we launched a pilot continuous-flow system. The system consisted of a storage basin, a phytoplankton growth chamber, and a zooplankton growth chamber. A 25,000 Lux AFW-light emitting diode (LED) on system and an AFW-LED off system were separately operated for 10 days. In the AFW-LED on system, the maximum chlorophyll-a concentration of the phytoplankton chamber was four times higher than that of the AFW-LED off system. With artificial nighttime illumination, the microalgae became both smaller and more nutritious; the microalgae became high quality food for the zooplankton, Daphnia magna. Consequently, this zooplankton became more efficient at extracting nutrients and grew more densely than in the AFW-LED off system condition. In the LED-on condition, the amounts of total nitrogen (TN) and total phosphorus (TP) flowing into the system for 10 days were 84.7 g and 20.4 g, and the amounts flowing out were 19.5 g (23%) and 4.0 g (20%), respectively. In contrast, in the LED-off condition, 83.8 g and 20.6 g of TN and TP flowed into the system while 38.8 g (46%) and 6.8 g (33%) flowed out, respectively. Artificial illumination significantly improves the removal rate of nutrients in an AFW system. Full article