On the crashworthiness optimisation of a new multi-corner tube under axial loading

Najibi, Amir; Ghazifard, Payman; Torkian, Jahangir

doi:10.1080/17445302.2022.2162754

Cited by 7 publications

(3 citation statements)

References 50 publications

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“…95 All these parameters could be studied in a future work. Najibi et al 96,97 proved that Multi-objective optimization with Pareto fronts is effective for crashworthiness analysis. Therefore, this method will be adapted in future studies to analyze the crushing of GFRP perforated tubes.…”

Section: Future Workmentioning

confidence: 99%

Multi-response optimization of crashworthy performance of perforated thin walled tubes

Alshahrani

Sebaey

Allah

et al. 2023

Journal of Composite Materials

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It is advised to add cuts as circular holes to constructions’ walls for technical requirements such as the assembly and to reduce the weight. Since these induced cutouts have a substantial influence on crashworthiness and failure mechanisms, the size, location, and number of these cutouts should be carefully selected for energy absorption components. This research concentrates on the optimization of process parameters for the crushing performance of perforated thin-walled glass/epoxy (GFRP) square tubes. Taguchi model was adapted to formulate the design of the experiment while desirability function analysis (DFA) was used for process parameters optimization in terms of crashworthiness indicators. Test specimens were fabricated via wet warping by hand lay-up technique and tested under quasi-static axial crushing. Finally, the specimen yielding the optimum process parameter combination was compared with the intact sample. GFRP samples with circular holes exhibit optimum initial peak force ([Formula: see text]) and total absorbed energy [Formula: see text] with values of 38.35 and 4.47 % which are smaller than those of the intact samples. On contrary, the optimum specific absorbed energy [Formula: see text] and crush force efficiency ([Formula: see text]) present, respectively, 31.08 and 80.28 % greater than those of intact one. The considered process parameters were noticed to be of substantial influences on the crush behavior of perforated GFRP tubes exposed to axial compression.

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Section: Future Workmentioning

confidence: 99%

Multi-response optimization of crashworthy performance of perforated thin walled tubes

Alshahrani

Sebaey

Allah

et al. 2023

Journal of Composite Materials

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“…Also, the proposed hybrid network was compared with the well-known traditional machine learning methods, such as k-NN [66], RF [33], and GBM [44]. Additionally, one of the earliest deep learning methods, namely, the group method of data handling (GMDH) [67][68][69], was used for comparison. Each experiment was repeated ten times to alleviate the impact of randomization, and the mean values were reported.…”

Section: Prediction Performance Comparison Of Different Neural Networ...mentioning

confidence: 99%

Remaining Useful Life Estimation of Turbofan Engines with Deep Learning Using Change-Point Detection Based Labeling and Feature Engineering

Ensarioğlu,

İnkaya,

Emel

2023

Applied Sciences

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Accurate remaining useful life (RUL) prediction is one of the most challenging problems in the prognostics of turbofan engines. Recently, RUL prediction methods for turbofan engines mainly involve data-driven models. Preprocessing the sensor data is essential for the performance of the prognostic models. Most studies on turbofan engines use piecewise linear (PwL) labeling, which starts with a constant initial RUL value in normal/healthy operating time. In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN-LSTM (one-dimensional–convolutional neural network–long short-term memory) hybrid neural network model for RUL prediction. The procedure was evaluated on the subset FD001 of the C-MAPSS dataset. The proposed procedure was compared with machine learning and deep learning models with and without the new difference feature. Also, the results were compared with the studies that used similar labeling approaches. Our analysis of the numerical results underscores the clear superiority of the proposed 1D-CNN-LSTM model with the difference feature in RUL prediction, with a score of 437.2 and an RMSE value of 16.1. This result illustrates the superior predictive capability of the 1D-CNN-LSTM model, which outperformed traditional machine learning methods and one of the earliest deep learning methods. These findings emphasize the superior predictive capability of the 1D-CNN-LSTM model and underline the potential of the feature engineering process for more accurate and robust RUL prediction in the context of turbofan engine prognostics.

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“…In this way, the input vector can be mapped to the hidden space without weight, while the mapping from the hidden layer to the output space is linear; that is, the output of RBF is a linear weighted sum of unit outputs. Different from traditional data processing methods such as group method of data handling (GMDH), GMDH increases the complexity of the research due to the interrelationships between research data [30,31]. RBF has the advantages of simple structure, simple training, fast learning convergence, and can approximate any nonlinear function.…”

Section: 𝜑(‖𝑥 − 𝑥mentioning

confidence: 99%

Radial Basis Function Based Meta-Heuristic Algorithms for Parameter Extraction of Photovoltaic Cell

He¹,

Xi²,

Li³

et al. 2023

Processes

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Accurate parameter estimation of photovoltaic (PV) cells is crucial for establishing a reliable cell model. Based on this, a series of studies on PV cells can be conducted more effectively to improve power output; an accurate model is also crucial for the operation and control of PV systems. However, due to the high nonlinearity of the cell and insufficient measured current and voltage data, traditional PV parameter identification methods are difficult to solve this problem. This article proposes a parameter identification method for PV cell models based on the radial basis function (RBF). Firstly, RBF is used to de-noise and predict the data to solve the current problems in the parameter identification field of noise data and insufficient data. Then, eight prominent meta-heuristic algorithms (MhAs) are used to identify the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM) parameters of PV cells. By comparing the identification accuracy of the three models in two datasets in detail, the final results show that this method can effectively achieve parameter extraction, with advantages such as high extraction accuracy and stability, greatly improving the accuracy and reliability of parameter identification. Especially in the TDM, the I-V data and P-V data obtained from the PV model established through the identified parameters have very high fitting accuracy with the measured I-V and P-V data, reaching 99.58% and 99.65%, respectively. The research can effectively solve the low accuracy problem caused by insufficient data and noise data in the traditional method of identifying PV cells and can greatly improve the accuracy of PV cell modeling. It is of great significance for the operation and control of PV systems.

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On the crashworthiness optimisation of a new multi-corner tube under axial loading

Cited by 7 publications

References 50 publications

Multi-response optimization of crashworthy performance of perforated thin walled tubes

Multi-response optimization of crashworthy performance of perforated thin walled tubes

Remaining Useful Life Estimation of Turbofan Engines with Deep Learning Using Change-Point Detection Based Labeling and Feature Engineering

Radial Basis Function Based Meta-Heuristic Algorithms for Parameter Extraction of Photovoltaic Cell

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