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This paper aims to describe and investigate the mathematical models and numerical modeling of how a cell membrane is affected by a transient ice freezing front combined with the influence of thermal fluctuations and anisotropy.

The study consists of mathematical modeling, validation with an analytical solution, and shows the influence of thermal noises on phase front dynamics and how it influences the freezing process of a single red blood cell. The numerical calculation has been modeled in the framework of the phase field method with a Cahn–Hilliard formulation of a free energy functional.

The results show an influence scale on directional phase front propagation dynamics and how significant are stochastic thermal noises in micro-scale freezing.

The numerical calculation has modeled in the framework of the phase field method with a Cahn–Hilliard formulation of a free energy functional.

The purpose of this paper is to present a new simplified local remeshing procedure for the study of discrete crack propagation in finite element (FE) mesh. The proposed technique accounts for the generation and propagation of crack‐like failure within an FE‐model. Beside crack propagation, the technique enables the analysis of fragmentation of initially intact continuum. The capability of modelling fragmentation is essential in various structure‐structure interaction analyses such as projectile impact analysis and ice‐structure interaction analysis.

The procedure combines continuum damage mechanics (CDM), fictitious crack approach and a new local remeshing procedure. In the approach a fictitious crack is replaced by a discrete crack by applying delete‐and‐fill local remeshing. The proposed method is independent of mesh topology unlike the traditional discrete crack approach. The procedure is implemented for 3‐D solid elements in commercial finite element software Abaqus/Explicit using Python scripting. The procedure is completely automated, such that crack initiation and propagation analyses do not require user intervention. A relatively simple constitutive model was implemented strictly for demonstrative purposes.

Well known examples were simulated to verify the applicability of the method. The simulations revealed the capabilities of the method and reasonable correspondence with reference results was obtained. Material fragmentation was successfully simulated in ice‐structure interaction analysis.

The procedure for modelling discrete crack propagation and fragmentation of initially intact quasi‐brittle materials based on local remeshing has not been presented previously. The procedure is well suited for simulation of fragmentation and is implemented in a commercial FE‐software.

The Standing Committee of the House of Commons on Trade, presided over by LORD E. FITZMAURICE, met again on July 16th and proceeded with the Sale of Adulterated Butter Bill.

The purpose of this paper is to describe a methodology for predicting the effects of glaze ice geometry on airfoil aerodynamic coefficients by using neural network (NN) prediction. Effects of icing on angle of attack stall are also discussed.

The typical glaze ice geometry covers ice horn leading‐edge radius, ice height, and ice horn position on airfoil surface. By using artificial NN technique, several NNs are developed to study the correlations between ice geometry parameters and airfoil aerodynamic coefficients. Effects of ice geometry on airfoil hinge moment coefficient are also obtained to predict the angle of attack stall.

NN prediction is feasible and effective to study the effects of ice geometry on airfoil performance. The ice horn location and height, which have a more evident and serious effect on airfoil performance than ice horn leading‐edge radius, are inversely proportional to the maximum lift coefficient. Ice accretions on the after‐location of the upper surface of the airfoil leading edges have the most critical effects on the airfoil performance degradation. The catastrophe of hinge moment and unstable hinge moment coefficient can be used to predict the stall effectively.

Since the simulation results of NNs are shown to have high coherence with the tunnel test data, it can be further used to predict coefficients at non‐experimental conditions.

The simulation method by using NNs here can lay the foundation of the further research about the airfoil performance in different ice cloud conditions through predicting the relations between the ice cloud conditions and ice geometry.

The purpose of this paper is to present a new technique based on the combination of wavelet packet transform (WPT) and artificial neural networks (ANNs) for predicting the ice accretion on the surface of an airfoil.

Wavelet packet decomposition is used to reduce the number of input vectors to ANN and to improve the training convergence. An ANN is developed with five variables (velocity, temperature, liquid water content, median volumetric diameter and exposure time) taken as input data and one dependent variable (the decomposed ice shape) given as the output. For the purpose of comparison, three different ANNs, back-propagation network (BP), radial basis function network (RBF) and generalized regression neural network (GRNN), are trained to simulate the wavelet packet coefficients as a function of the in-flight icing conditions.

The predicted ice accretion shapes are compared with the corresponding results from previously published NASA experimentation, LEWICE and the Fourier-expansion-based method. It is found that the BP network has an advantage on predicting the rime ice, and the RBF network is relatively suitable for the glaze ice, while the GRNN can be applied for both without classifying the specimens. Results also show an advantage of WPT in performing the analysis of ice accretion information and the prediction accuracy is improved as well.

The proposed method is open to further improvement and investment due to its small computational resource requirement and efficient performance.

The simulation method combining ANN and WPT outlined here can lay the foundation for further research relating to ice accretion prediction under different ice cloud conditions.

The internal combustion engine operated on gaseous fuels shows great potential in terms of integration of the renewable and traditional sources for an effective solution for clean energy production challenge. Different fuel mixtures that can be used to power the engine are characterized by various combustion properties, which can affect its overall efficiency. The purpose of this paper is to provide reasonable answer, how the operation condition can change due to different fuel, without enormous cost of prototyping processes using physical models a digital model can be seen as promising technique.

Presented work discusses the application, and extensive description of two commercial codes Ansys Fluent and Forte for modeling stationary engine fueled by compressed natural gas (CNG) and biogas. To check the model accuracy, all carried out numerical results were compared against experimental data collected at in-house test rig of single cylinder four stroke engine. The impacts of tested gaseous fuel on the engine working conditions and emission levels were investigated.

Carried out simulations showed good agreement with experimental data for investigated cases. Application on numerical models give possibility to visualize flame front propagation and pollutant formation for tested fuels. The biogas fuel has shown the impaired early flame phase, which led to longer combustion, lower efficiency, power output, repeatability and in some cases higher HC and carbon monoxide (CO) emissions as a result of combustion during the exhaust stroke. Looking at the CO formation it was observed that it instantly accrue with flame front propagation as a result of methane oxidation, while for NOx formation revers effect was seen.

The application of new approach for modeling combustion process in stationary engines fueled by CNG and alternative biogas fuel has been discussed. The cons and pros of the Forte code in terms of its application for engine prototaping process has been discussed.

This paper is concerned with the determination of the transient stress and deformational state of plate‐like discontinua subject to flexural cracking. Such a phenomenon can be easily visualized as the type of fragmentation to floating sea ice impacted by an ice‐breaker or offshore platform. The discrete element method is used to solve the dynamic equilibrium equations for each distinct deformable body and the interaction between bodies. Each body may deform elastically and fracture into further pieces if a brittle failure criterion for flexure is exceeded. The discrete plate element is a hybrid thin‐plate (Kirchhoff) mode lumped at element boundaries with transverse shear deformation computed at element centroids. Errors in computed stresses near point loads and cracks by the current element warrant the use of an improved mixed mode plate element. A three‐dimensional application of the discrete element method is presented for the case of fragmentation of floating sea ice impacting an arctic offshore platform. A semi‐implicit solution scheme is introduced to overcome the stringent explicit time step stability conditions due to stiff members in the discrete element formulation.

The purpose of this study is to establish a novel airfoil icing prediction model using deep learning with geometrical constraints, called geometrical constraints enhancement neural networks, to improve the prediction accuracy compared to the non-geometrical constraints model.

The model is developed with flight velocity, ambient temperature, liquid water content, median volumetric diameter and icing time taken as inputs and icing thickness given as outputs. To enhance the icing prediction accuracy, the model involves geometrical constraints into the loss function. Then the model is trained according to icing samples of 2D NACA0012 airfoil acquired by numerical simulation.

The results show that the involvement of geometrical constraints effectively enhances the prediction accuracy of ice shape, by weakening the appearance of fluctuation features. After training, the airfoil icing prediction model can be used for quickly predicting airfoil icing.

This work involves geometrical constraints in airfoil icing prediction model. The proposed model has reasonable capability in the fast assessment of aircraft icing.

The purpose of this paper is to study the efficiency of different oil and gas markets. Most previous studies examined the issue using low frequency date sampled at monthly, weekly, or daily frequencies. In this study, 30-minute intraday data are used to explore efficiency in energy markets.

Sophisticated statistical analysis techniques such as Granger-causality regressions, augmented Dickey-Fuller tests, cointegration tests, vector autoregressions are used to explore the transmission of information between oil and gas energy markets.

This study provides evidence for efficiency in energy markets. The new information that arrives either to futures markets or spot markets is digested correctly, completely, and in a fast manner, and is propagated to the other market. The evidence indicates high efficiency.

This study is one of the first papers that uses 30-minute interval intraday data to investigate efficiency in oil and gas commodity markets.

This paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.

The adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.

This integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.

The combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.

The novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.