MPI to Coarray Fortran: experiences with a CFD solver for unstructured meshes

High-resolution numerical methods and unstructured meshes are required in many applications of Computational Fluid Dynamics (CFD). These methods are quite computationally expensive and hence bene t from being parallelized. Message Passing Interface (MPI) has been utilized traditionally as a parallelization strategy. However, the inherent complexity of MPI contributes further to the existing complexity of the CFD scienti c codes. The Partitioned Global Address Space (PGAS) parallelization paradigm was introduced in an attempt to improve the clarity of the parallel implementation. We present our experiences of converting an unstructured high-resolution compressible Navier-Stokes CFD solver from MPI to PGAS Coarray Fortran. We present the challenges, methodology and performance measurements of our approach using Coarray Fortran. With the Cray compiler, we observe Coarray Fortran as a viable alternative to MPI. We are hopeful that Intel and open-source implementations could be utilized in the future

A bidirectional deep LSTM machine learning method for flight delay modelling and analysis

Flight delays can be prevented by providing a reference point from an accurate prediction model because predicting flight delays is a problem with a specific space. Only a few algorithms consider predicted classes\u27 mutual correlation during flight delay classification or prediction modelling tasks. None of these existing methods works for all scenarios. Therefore, the need to investigate the performance of more models in solving the problem of flight delay is vast and rapidly increasing. This paper presents the development and evaluation of LSTM and BiLSTM models by comparing them for a flight delay prediction. The LSTM does the feature extraction in both models, except that the BiLSTM maintains an equilibrium with a forward and backward hidden sequences model training. The experimental results show that the BiLSTM model accuracy improved by 97.56%, with a 21.11% accuracy increase. Furthermore, the models\u27 performance results and confusion matrix shows how the BiLSTM model outperforms the LSTM model. In evaluating the MCC, the BiLSTM model offers a better mutual correlation among the predicted classes with 0.9944. Our findings suggest that for predicting flight delays, the BiLSTM model utilises the advantages of the bidirectional hidden sequences and the deep neural network for exploitation and exploration of best performance given a high accuracy, precision, recall and F1-Score results. Hence, we can recommend the BiLSTM in developing a decision support system for flight delays and related applications

Measuring airport service quality using machine learning algorithms

The airport industry is a highly competitive market that has expanded quickly during the last two decades. Airport management usually measures the level of passenger satisfaction by applying the traditional methods, such as user surveys and expert opinions, which require time and effort to analyse. Recently, there has been considerable attention on employing machine learning techniques and sentiment analysis for measuring the level of passenger satisfaction. Sentiment analysis can be implemented using a range of different methods. However, it is still uncertain which techniques are better suited for recognising the sentiment for a particular subject domain or dataset. In this paper, we analyse the sentiment of air travellers using five different algorithms, namely Logistic Regression, XGBoost, Support Vector Machine, Random Forest and Naïve Bayes. We obtain our data set through the SKYTRAX website which is a collection of reviews of around 600 airports. We apply some pre-processing steps, such as converting the textual reviews into numerical form, by using the term frequency-inverse document frequency. We also remove stopwords from the text using the NLTK list of stopwords. We evaluate our results using the accuracy, precision, recall and F1_score performance metrics. Our analysis shows that XGBoost provides the most accurate results when compared with other algorithms

A deep BiLSTM machine learning method for flight delay prediction classification

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features to train and test the models. The performance evaluation of the models and Confusion matrix shows that BiLSTM outperforms the LSTM model. In evaluating the models using the Mathews Correlation Coefficient (MCC), the BiLSTM model offers a better correlation of 0.99 between the original and predicted classes. Our experiment shows that for predicting flight delays, the BiLSTM model takes advantage of the forward and backward hidden sequences and the deep neural network for performance exploration and exploitation to achieve high accuracy, recall, and F1-Score. Our findings suggest that the BiLSTM model can effectively predict flight delays and provide valuable information for airlines, passengers, and airport managers

A hybrid ensemble machine learning approach for arrival flight delay classification prediction using voting aggregation technique

The number of flights keeps increasing with the development of civil aviation, and flight delays have become a severe issue of concern to the aviation industry. The need for reliable flight delay classification and prediction cannot be overemphasised because of its importance. In this research, we classify arrival flight delay using a hybrid ensemble machine learning algorithm based on voting aggregation. Each ensemble’s architecture consists of four supervised machine learning algorithms: Logistic Regression, Decision Tree, Support Vector Classifier and Random Forest. We conducted a comparative experiment using the United States Bureau of Statistics dataset to verify the efficacy of our proposed approach against other benchmark approaches. We employ multiple evaluation metrics to check the performance of our proposed model comprehensively. Accuracy, Recall, Precision, F1-Score, AUC Score, and ROC curve. Our experimental results show that our model outperforms the benchmark techniques on the binary classification of flight delays task. Hence, the hybrid ensemble model presented in this research can be used as a decision-support model to improve the flight scheduling management system design to assist the airline and airport operation managers and passengers with improved travel itineraries management

Validation and verification of a 2D lattice Boltzmann solver for incompressible fluid flow

The lattice Boltzmann method (LBM) is becoming increasingly popular in the fluid mechanics society because it provides a relatively easy implementation for an incompressible fluid flow solver. Furthermore the particle based LBM can be applied in microscale flows where the continuum based Navier-Stokes solvers fail. Here we present the validation and verification of a two-dimensional in-house lattice Boltzmann solver with two different collision models, namely the BGKW and the MRT models [1]. Five different cases were studied, namely: (i) a channel flow was investigated, the results were compared to the analytical solution, and the convergence properties of the collision models were determined; (ii) the lid-driven cavity problem was examined [2] and the flow features and the velocity profiles were compared to existing simulation results at three different Reynolds number; (iii) the flow in a backward-facing step geometry was validated against experimental data [3]; (iv) the flow in a sudden expansion geometry was compared to experimental data at two different Reynolds numbers [4]; and finally (v) the flow around a cylinder was studied at higher Reynolds number in the turbulent regime. The first four test cases showed that both the BGKW and the MRT models were capable of giving qualitatively and quantitatively good results for these laminar flow cases. The simulations around a cylinder highlighted that the BGKW model becomes unstable for high Reynolds numbers but the MRT model still remains suitable to capture the turbulent von Karman vortex street. The in-house LBM code has been developed in C and has also been parallelised for GPU architectures using CUDA [5] and for CPU architectures using the Partitioned Global Address Space model with UPC [6

Simulate cavitation bubble with single component multi-phase Lattice Boltzmann method

Cavitation occurs when the pressure drops below a critical value at which point it can cause great damage to the machines such as propellers. In this study, a two-dimensional single bubble with different pressure differences between the boundary and the bubble will be studied based on the single component Shan-Chen model with the Carnahan-Starling (C-S) Equation of State (EOS) incorporated, which is similar to the model in [1-2]. Firstly, the model with the C-S EOS will be validated based on Maxwell’s equal area construction. The equilibrium density of liquid and vapor is obtained using a flat interface simulation according to [3]. It was demonstrated that the model has great thermal consistency according to this validation. Furthermore, we show results for a single bubble case for which its growth and collapse can be validated against the RayleighPlesset (R-P) equation with various pressure differences. Results show good agreement with the R-P equation and literature

Illuminating the neural landscape of pilot mental states: a convolutional neural network approach with Shapley Additive explanations interpretability

Predicting pilots’ mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenue for detection. However, the interpretability of machine learning and deep learning models, which are often used for such tasks, remains a significant issue. This study aims to address these challenges by developing an interpretable model to detect four mental states—channelised attention, diverted attention, startle/surprise, and normal state—in pilots using EEG data. The methodology involves training a convolutional neural network on power spectral density features of EEG data from 17 pilots. The model’s interpretability is enhanced via the use of SHapley Additive exPlanations values, which identify the top 10 most influential features for each mental state. The results demonstrate high performance in all metrics, with an average accuracy of 96%, a precision of 96%, a recall of 94%, and an F1 score of 95%. An examination of the effects of mental states on EEG frequency bands further elucidates the neural mechanisms underlying these states. The innovative nature of this study lies in its combination of high-performance model development, improved interpretability, and in-depth analysis of the neural correlates of mental states. This approach not only addresses the critical need for effective and interpretable mental state detection in aviation but also contributes to our understanding of the neural underpinnings of these states. This study thus represents a significant advancement in the field of EEG-based mental state detection

Validation and verification of a 2D lattice Boltzmann solver for incompressible fluid flow

The lattice Boltzmann method (LBM) is becoming increasingly popular in the fluid mechanics society because it provides a relatively easy implementation for an incompressible fluid flow solver. Furthermore the particle based LBM can be applied in microscale flows where the continuum based Navier-Stokes solvers fail. Here we present the validation and verification of a two-dimensional in-house lattice Boltzmann solver with two different collision models, namely the BGKW and the MRT models [1]. Five different cases were studied, namely: (i) a channel flow was investigated, the results were compared to the analytical solution, and the convergence properties of the collision models were determined; (ii) the lid-driven cavity problem was examined [2] and the flow features and the velocity profiles were compared to existing simulation results at three different Reynolds number; (iii) the flow in a backward-facing step geometry was validated against experimental data [3]; (iv) the flow in a sudden expansion geometry was compared to experimental data at two different Reynolds numbers [4]; and finally (v) the flow around a cylinder was studied at higher Reynolds number in the turbulent regime. The first four test cases showed that both the BGKW and the MRT models were capable of giving qualitatively and quantitatively good results for these laminar flow cases. The simulations around a cylinder highlighted that the BGKW model becomes unstable for high Reynolds numbers but the MRT model still remains suitable to capture the turbulent von Karman vortex street. The in-house LBM code has been developed in C and has also been parallelised for GPU architectures using CUDA [5] and for CPU architectures using the Partitioned Global Address Space model with UPC [6

Advancing aviation safety through machine learning and psychophysiological data: a systematic review

In the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, under-researched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior