Optimising cure cycle of unsaturated polyester nanocomposites using directed grid search method

2019 ◽  
Vol 27 (5) ◽  
pp. 253-261
Author(s):  
Mehdi Poorabdollah ◽  
Arezoo Kamran
Keyword(s):  
Unsaturated Polyester ◽  
Mechanical Analysis ◽  
Search Method ◽  
Cycle Duration ◽  
Grid Search ◽  
Cure Cycle ◽  
Grid Search Method ◽  
Cloisite 20A

In this research, a method is proposed for optimising cure cycle of neat unsaturated polyester (UP) resins, and UP resins containing Cloisite 20A (UP/20A), in thin parts. Using the proposed optimisation method, optimised cure cycles for both neat UP resin system and UP/20A were calculated. Results of dynamic mechanical analysis tests show that samples cured by the optimised cure cycles are slightly different from those samples cured using a long-term cure cycle in terms of structural relaxation. This confirms that the proposed optimisation method not only reduces the cure cycle duration efficiently but also preserves the quality of the samples.

Moho depth determination in the Eastern Alps-Pannonian basin-Carpathian mountains region based on H-K grid search method and CCP migration of receiver functions

2020 ◽  
Author(s):  
Dániel Kalmár ◽  
György Hetényi ◽  
István Bondár ◽  
Keyword(s):  
Pannonian Basin ◽  
Eastern Alps ◽  
Receiver Functions ◽  
Search Method ◽  
Moho Depth ◽  
Grid Search ◽  
Carpathian Mountains ◽  
Entire Study ◽  
Grid Search Method ◽  
Private Network

<p>We perform P-to-S receiver function analysis to determine a detailed map of the crust-mantle boundary in the Eastern Alps–Pannonian basin–Carpathian mountains junction. We use data from the AlpArray Seismic Network, the Carpathian Basin Project and the South Carpathian Project temporary seismic networks, the permanent stations of the Hungarian National Seismological network, stations of a private network in Hungary as well as selected permanent seismological stations in neighbouring countries for the time period between 2004.01.01. and 2019.03.31. Altogether 221 seismological stations are used in the analysis. Owing to the dense station coverage we can achieve so far unprecedented resolution, thus extending our previous work on the region. We applied three-fold quality control, the first two on the observed waveforms and the third on the calculated radial receiver functions, calculated by the iterative time-domain deconvolution approach. The Moho depth was determined by two independent approaches, the common conversion point (CCP) migration with a local velocity model and the H-K grid search. We show cross-sections beneath the entire investigated area, and concentrate on major structural elements such as the AlCaPa and Tisza-Dacia blocks, the Mid-Hungarian Fault Zone and the Balaton Line. Finally, we present the Moho map obtained by the H-K grid search method and pre-stack CCP migration and interpolation over the entire study area, and compare results of two independent methods to prior knowledge.</p>

scholarly journals Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1384
Author(s):  
Shuang Song ◽  
Shugang Li ◽  
Tianjun Zhang ◽  
Li Ma ◽  
Shaobo Pan ◽  
...  
Keyword(s):  
Prediction Model ◽  
Search Method ◽  
Evaluation Index ◽  
Batch Size ◽  
Grid Search ◽  
Gas Concentration ◽  
Grid Search Method ◽  
Performance Evaluation Index ◽  
The Mean ◽  
Concentration Prediction

The effective prediction of gas concentration and the reasonable formulation of corresponding safety measures have important significance for improving the level of coal mine safety. To improve the accuracy of gas concentration prediction and enhance the applicability of the models, this paper starts with actual coal mine production monitoring data, improves the accuracy of gas concentration prediction through multi-parameter fusion prediction, and constructs a recurrent neural network (RNN)-based multi-parameter fusion prediction of coal face gas concentration. We determined the performance evaluation index of the model’s prediction method; used the grid search method to optimize the hyperparameters of the batch size; and used the number of neurons, the learning rate, the discard ratio, the network depth, and the early stopping method to prevent overfitting. The gas concentration prediction models—based on RNN and PSO-SVR and PSO-Adam-BP neural networks—were compared and analyzed experimentally with the mean absolute percentage error (MAPE) as the performance evaluation index. The result show that using the grid search method to adjust the batch size, the number of neurons, the learning rate, the discard ratio, and the network depth can effectively find the optimal hyperparameter combination. The training error can be reduced to 0.0195. Therefore, Adam’s optimized RNN gas concentration prediction model had higher accuracy and stability than the BP neural network and SVR. During training, the mean absolute error (MAE) could be reduced to 0.0573, and the root mean squared error (RMSE) could be reduced to 0.0167; however, the MAPE could be reduced to 0.3384% during prediction. The RNN gas concentration prediction model and parameter optimization method based on Adam optimization can effectively predict gas concentration. This method shows high accuracy in the prediction of gas concentration time series and can be used as a reference model for predicting mine gas concentration.

Optimizing cure cycle of nanoclay-reinforced unsaturated polyester resins considering various curing kinetic models

2017 ◽  
Vol 52 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Mehdi Poorabdollah ◽  
Arezoo Kamran
Keyword(s):  
Kinetic Models ◽  
Polyester Resin ◽  
Unsaturated Polyester ◽  
Optimization Method ◽  
Unsaturated Polyester Resin ◽  
Maximum Temperature ◽  
Cycle Duration ◽  
Exponential Factor ◽  
Diffusion Factor ◽  
Cure Cycle

In this research, a method has been presented for optimizing cure cycle of neat unsaturated polyester resin and polyester resin containing 3 wt% nanoclay Cloisite® 10 A (UP/10 A) and nanoclay Cloisite® 30B (UP/30B) in thin components. We refer to this optimization method as the directed grid search method. To model cure behaviour of unsaturated polyester resin, four different kinetic models were considered, including advanced iso-conversional method, Kamal method, Kamal method with Chern diffusion factor, and Kamal method with Sbirrazzuoli diffusion factor. The comparison of our proposed optimized cure cycles indicates that addition of nanoclay to the resin causes the cure cycle duration and also the maximum temperature during the cure to decrease. In addition, by adding nanoclay to the resin, the optimal cure cycle is transformed from a five-stage cure cycle to a four-stage one. Our investigations indicate that optimal cure cycles for the UP/10 A and UP/30B systems do not significantly differ. This is because the pre-exponential factor can influence the optimal cure cycle as much as the activation energy. Our results show that the optimal cure cycle obtained from the Kamal model is much shorter than the optimal cure cycle obtained from the advanced iso-conversional method, while the optimal cure cycle obtained from the Kamal model with Chern diffusion factor is very similar to the cure cycle resulting from the advanced iso-conversional method.

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