Comparative study of conventional and unconventional designs of cathode flow fields in PEM fuel cell

The choice of an appropriate flow field distributor is crucial to circumvent mass and charge transfer resistance-related issues in proton exchange membrane fuel cells (PEMFCs). In this work, incorporating all the anisotropic nature of the gas diffusion layers (GDLs), a three-dimensional, multiphase CFD model is built to perform a comparative study of several types of cathode flow field designs. Three conventional (i.e. parallel, serpentine and interdigitated) and two recently-introduced (i.e. parallel with blocks and the metal foam) flow field designs were considered for the cathode side. The results showed that the best fuel cell performance is obtained with the metal foam flow field as it induces the lowest water saturation, the lowest values and more uniform distribution of current density and temperature as well as relatively medium pressure drop. Compared with the parallel flow field case, the peak power density increases by about 50% when using the metal foam flow field and by about 10% when using the other three investigated flow fields (i.e. serpentine, interdigitated and parallel with blocks). The parametric analysis reveals that the metal foam outperforms other designs at intermediate and high humidity conditions whereas the interdigitated flow field design outperforms other designs at low humidity conditions

On the computational fluid dynamics of PEM fuel cells (PEMFCs): an investigation on mesh independence analysis

Mesh independence analysis is one of the most crucial steps in any CFD problem. The aim of this study was to investigate the most commonly used variables employed for grid independency studies in a typical PEMFC and to find possibly the most effective variables that may be decisive in a PEMFC grid independence test. Herein, a three-dimensional (3-D), steady state, non-isothermal computational fluid dynamics (CFD) model of a serpentine channel proton exchange membrane fuel cell (PEMFC) was developed. The present model includes various conservation equations that are dominant in a typical PEMFC: the mass, momentum, species, charge, and energy equations, which are coupled with the electrochemical model. The numerical results indicate that much more care should be taken while obtaining a mesh independence solution for CFD studies in PEMFC systems. Based on our findings in this study, it was demonstrated that employment of merely the polarization curve (current-voltage), especially only in a given specific point, was not sufficient to carry out the mesh independence tests for CFD studies in PEMFCs. In addition, it was observed that the average volumetric hydrogen concentration inside the catalyst layer on the anode side has a more significant role to check the grid independency tests

Fuzzy-Based Intelligent Model for Rapid Rock Slope Stability Analysis Using Qslope

Artificial intelligence (AI) applications have introduced transformative possibilities within geohazard analysis, particularly concerning the assessment of rock slope instabilities. This study delves into the amalgamation of AI and empirical techniques to attain highly precise outcomes in the evaluation of slope stability. Specifically, our primary objective is to propose innovative and efficient methods by investigating the integration of AI within the well-regarded Qslope system, renowned for its efficacy in analyzing rock slope stability. Given the complexities inherent in rock characteristics, particularly in coastal regions, the Qslope system necessitates adjustments and harmonization with other geomechanical methodologies. Uncertainties prevalent in rock engineering, compounded by water-related factors, warrant meticulous consideration during all calculations. To address these complexities, we present a novel approach through the infusion of fuzzy set theory into the Qslope classification, leveraging fuzziness to effectively quantify and accommodate uncertainties. Our approach employs a sophisticated fuzzy algorithm encompassing six inputs, three outputs, and 756 fuzzy rules, thereby enabling a robust assessment of rock slope stability in coastal regions. The implementation of this method capitalizes on the high-level programming language Python, enhancing computational efficiency. To validate the potency of our AI-based approach, we conducted preliminary tests on slope instabilities within coastal zones, indicating a promising initial direction. The results underwent thorough evaluation, affirming the precision and dependability of the proposed method. However, it is crucial to emphasize that this work represents a first attempt to apply AI to the evaluation of rock slope stability. Our findings underscore a high degree of concurrence and expeditious stability assessment, vital for timely and effective hazard mitigation. Nonetheless, we acknowledge that the reliability of this innovative method must be established through broader applications across diverse scenarios. The proposed AI-based approach’s effectiveness is validated through a preliminary survey on a slope instability case within a coastal region, and its potential merits must be substantiated through broader validation efforts

Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which aim to address global challenges, professionals in the field have developed diverse methodologies to analyze, assess, and predict the occurrence of landslides, including quantitative, qualitative, and semi-quantitative approaches. With the advent of computer programs, quantitative techniques have gained prominence, with computational intelligence and knowledge-based methods like artificial neural networks (ANNs) achieving remarkable success in landslide susceptibility assessments. This article offers a comprehensive review of the literature concerning the utilization of ANNs for landslide susceptibility assessment, focusing specifically on riverside areas, in alignment with the SDGs. Through a systematic search and analysis of various references, it has become evident that ANNs have emerged as the preferred method for these assessments, surpassing traditional approaches. The application of ANNs aligns with the SDGs, particularly Goal 11: Sustainable Cities and Communities, which emphasizes the importance of inclusive, safe, resilient, and sustainable urban environments. By effectively assessing riverside landslide susceptibility using ANNs, communities can better manage risks and enhance the resilience of cities and communities to geohazards. While the number of ANN-based studies in landslide susceptibility modeling has grown in recent years, the overarching objective remains consistent: researchers strive to develop more accurate and detailed procedures. By leveraging the power of ANNs and incorporating relevant SDGs, this survey focuses on the most commonly employed neural network methods for riverside landslide susceptibility mapping, contributing to the overall SDG agenda of promoting sustainable development, resilience, and disaster risk reduction. Through the integration of ANNs in riverside landslide susceptibility assessments, in line with the SDGs, this review aims to advance our knowledge and understanding of this field. By providing insights into the effectiveness of ANNs and their alignment with the SDGs, this research contributes to the development of improved risk management strategies, sustainable urban planning, and resilient communities in the face of riverside landslides

The predictive model for COVID‑19 pandemic plastic pollution by using deep learning method

Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predicting the environmental impacts of these wastes can be used to provide situational management, conduct control procedures, and reduce the COVID-19 effects. In this regard, the presented study attempted to provide a deep learning-based predictive model for forecasting the expansion of the pandemic plastic in the megacities of Iran. As a methodology, a database was gathered from February 27, 2020, to October 10, 2021, for COVID-19 spread and personal protective equipment usage in this period. The dataset was trained and validated using training (80%) and testing (20%) datasets by a deep neural network (DNN) procedure to forecast pandemic plastic pollution. Performance of the DNN-based model is controlled by the confusion matrix, receiver operating characteristic (ROC) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, Gaussian naïve Bayes, logistic regression, and multilayer perceptron methods. According to the comparative modelling results, the DNN-based model was found to predict more accurately than other methods and have a significant predominance over others with a lower errors rate (MSE = 0.024, RMSE = 0.027, MAPE = 0.025). The ROC curve analysis results (overall accuracy) indicate the DNN model (AUC = 0.929) had the highest score among others

Application of the modified Q-slope classification system for sedimentary rock slope stability assessment in Iran

Abstract(#br)The Q-slope system is an empirical method for discontinuous rock slope engineering classification and assessment. It has been introduced recently to provide an initial prediction of rock slope stability assessment by applying simple assumptions which tend to reflect different failure mechanisms. This study offers a correlation relationship between Q-slope and slope stability degree using case studies of sedimentary rock slopes from 10 regions of Iran. To this end, we have investigated 200 areas from these regions, gathered the necessary geotechnical data, have classified the slopes from a Q-slope perspective, and have estimated their stability relationships. Based on artificial intelligence techniques including k-nearest neighbours, support vector machine, Gaussian process, Decision tree, Random-forest, Multilayer perceptron, AdaBoost, Naive Bayes and Quadratic discriminant analysis, the relationships and classifications were implemented and revised in the Python high-level programming language. According to the results of the controlled learning models, the Q-slope equation for Iran has indicated that the stability-instability class distributions are limited to two linear states. These limits refer to the B-Line (lower limit) as

Comparative Analysis for Slope Stability by Using Machine Learning Methods

Featured Application: The presented paper conducted a comparative analysis based on well-known MLP, SVM, DT, and RF learning methods to assess/predict the safety factor (F.S) of earthslopes. Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) plays an essential part in the stability assessment, which guarantees operations’ success. Providing accurate and reliable F.S can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate F.S calculations. The presented study focused on the F.S estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate F.S for the earth slopes. These machine learning classifiers have a strong capability predict the F.S under certain conditions for slope failures and uncertainties. These models were implemented on a dataset containing 100 earth slopes’ stabilities, recorded based on F.S from various locations in the provinces of Fars, Isfahan, and Tehran in Iran, which were randomly divided into the training and testing datasets. These predictive models were validated by Janbu’s limit equilibrium analysis method (LEM) and GeoStudio commercial software. Regarding the study’s results, MLP (accuracy = 0.901/precision = 0.90) provides more accurate results to predict the F.S than other classifiers, with good agreement with LEM results. The SVM algorithm follows MLP (accuracy = 0.873/precision = 0.85). Regarding the estimated loss function, MLP obtained a 0.29 average loss in the F.S prediction process, which is the lowest rate. The SVM, DT, and RF obtained 0.41, 0.62, and 0.45 losses, respectively. This article tried to fill the gap in traditional analysis procedures based on advanced procedures in slope stability assessments

Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis

Slope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under various loading conditions. Thus, the application of an accurate procedure to estimate the FS can lead to a fast and precise decision during the stabilization process. In this regard, using computational models that can be operated accurately is strongly needed. The performance of five different machine learning models to predict the slope safety factors was investigated in this study, which included multilayer perceptron (MLP), support vector machines (SVM), k-nearest neighbors (k-NN), decision tree (DT), and random forest (RF). The main objective of this article is to evaluate and optimize the various machine learning-based predictive models regarding FS calculations, which play a key role in conducting appropriate stabilization methods and stabilizing the slopes. As input to the predictive models, geo-engineering index parameters, such as slope height (H), total slope angle (β), dry density (γd), cohesion (c), and internal friction angle (φ), which were estimated for 70 slopes in the South Pars region (southwest of Iran), were considered to predict the FS properly. To prepare the training and testing data sets from the main database, the primary set was randomly divided and applied to all predictive models. The predicted FS results were obtained for testing (30% of the primary data set) and training (70% of the primary data set) for all MLP, SVM, k-NN, DT, and RF models. The models were verified by using a confusion matrix and errors table to conclude the accuracy evaluation indexes (i.e., accuracy, precision, recall, and f1-score), mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). According to the results of this study, the MLP model had the highest evaluation with a precision of 0.938 and an accuracy of 0.90. In addition, the estimated error rate for the MLP model was MAE = 0.103367, MSE = 0.102566, and RMSE = 0.098470

Application of an image processing-based algorithm for river-side granular sediment gradation distribution analysis

Determining grain-size and grading distribution of river-side sediments is very important for issues related to lateral embankment drift, river-side nourishment, management plans, and riverbank stability. In this regard, experimental procedures such as sieve analysis are used in regular assessments which require special laboratory equipment that are quite time consuming to perform. The presented study provides a machine vision and image processing-based approach for determining coarse grained sediment size and distribution that is relatively quick and effective. In this regard, an image image processing-based method was used to determine the particle size of sediments as justified by screening tests which were conducted on samples taken from the riverside granular sediments. As a methodology, different grain identification stages were applied to extract sediment features such as pre-processing, edge detection, granular size classification and post-processing. According to the results of the grain identification stages, the applied technique identified about 35% sand, 55% gravel and 7% cobble which is approximately near to the screen test results which were determined as 30% sand, 52% gravel, and 5% cobble. These results obtained from computer-based analyses and experiments indicated that the utilised processing technique provided satisfactory results for gradation distribution analysis regarding riverside granular sediments