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The purpose of this paper is to describe how a 3D/1D transient heat transfer model has been developed for getting accurate thermal boundary conditions when investigating the heat transfer in the TiAl castings and also for reducing the computational cost and simplifying the mesh generation.

Heat transfer in the mould is assumed to take place only in a direction perpendicular to the mould wall, called 1D heat transfer. The coordinates of cell centre and the temperature in the mould wall can be calculated by the model instead of meshing mould. Heat transfer in the mould is computed via the FD solution of a 1D heat transfer equation.

For some types of geometry, the model works very well. However, for some, which contain the geometric feature called “dead corner”, the model can't cover. There is some impact on the accuracy of the model.

In the casting industry, the geometry of the casting is usually very complex and contains different features. This leads to difficult meshing when using numerical model to predict the casting process. Furthermore, an accurate calculation is very important on the thermal boundary during filling and solidification, to support practice, to improve the process and minimise the casting defects.

In this paper, a novel method is developed to calculate the heat transfer through the casting‐mould interface to the mould wall in a casting.

Insight in the temperature distribution on the internal and external surface of the nacelle is of great importance during the design phase of an aircraft. However, detailed information is not always needed. In a preliminary project stage or during parametric optimization, short analysis times are often more crucial than high accuracy. In such cases, the global insight in the temperature levels suffices to gain understanding of the relevance and influence of certain parameters. Nevertheless, estimating the maximum temperature for the most adverse conditions should also be done before a prototype is built. Therefore, this study aims to present and compare a simplified and an advanced methodology for the analysis of engine bay cooling and ventilation systems as well as heat transfer in the nacelle in a small airplane equipped with a turboprop engine in the tractor arrangement.

Both approaches included conductive, convective and radiative heat transfer in the engine bay of the small airplane I-23 as well as heat conduction in the nacelle made of material with anisotropic thermal conductivity. The one-dimensional (1D) model assumed that the nacelle with the air flow and engine was represented by a lumped thermal model in which heat was exchanged between the different lumped segments (the nodes) and the flowing air and engine. The three-dimensional (3D) model was based on the continuous control volume approach for heat, fluid flow and thermal radiation as well as on realizable k-ε turbulence model. Both models used commercial software.

The temperature distribution at the internal and external surface of the top nacelle was calculated. The 1D model predicted a temperature per node (per segment) while the 3D model was able to determine its values accurately and find the location of hot spots. Considering the complex geometry of the engine bay and nacelle and the assumed simplification, the obtained 1D and 3D results agreed quite well.

Both models will help in the development of new ventilation and cooling systems of the engine bay and nacelle as well as in the selection of materials for parts of the top cowling in the newly redesigned airplane I-23 equipped with a turboprop engine. In addition, the methodology presented in this paper might be applied in the development of other airplanes.

The 1D and 3D models of complex heat transfer inside the engine bay and in the nacelle of the newly re-designed airplane I-23 were elaborated and compared.

The purpose of this paper is to develop a concurrent multiscale computational method for granular materials in the quasi-static loading regime.

Overlapped-coupling between a micropolar linear elastic one-dimensional (1D) mixed finite element (FE) model and a 1D chain of Hertzian nonlinear elastic, glued, discrete element (DE) spheres is presented. The 1D micropolar FEs and 1D chain of DEs are coupled using a bridging-scale decomposition for static analysis.

It was found that an open-window DE domain may be coupled to a micropolar continuum FE domain via an overlapping region within the bridging-scale decomposition formulation for statics. Allowing the micropolar continuum FE energy in the overlapping region to contribute to the DE energy has a smoothing effect on the DE response, especially for the rotational degrees of freedom (dofs).

The paper focusses on 1D examples, with elastic, glued, DE spheres, and a linear elastic micropolar continuum implemented in 1D.

A concurrent computational multiscale method for granular materials with open-window DE resolution of the large shearing region such as at the interface with a penetrometer skin, will allow more efficient computations by reducing the more costly DE domain calculations, but not at the expense of generating artificial boundary effects between the DE and FE domains.

Open-window DE overlapped-coupling to FE continuum domain, accounting for rotational dofs in both DE and FE methods. Contribution of energy from micropolar FE in overlap region to underlying DE particle energy.

This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.

This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.

The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.

Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.

To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.

The purpose of this study is to evaluate the effectiveness of two-dimensional (2D) cyclic softened membrane model (CSMM)-based non-linear finite element (NLFE) model in predicting the complete non-linear response of shear critical bridge piers (with walls having aspect ratios greater than 2.5) under combined axial and reversed cyclic uniaxial bending loads. The effectiveness of the 2D CSMM-based NLFE model has been compared with the widely used one-dimensional (1D) fiber-based NLFE models.

Three reinforced concrete (RC) hollow rectangular bridge piers tested under reversed cyclic uniaxial bending and sustained axial loads at the National Centre for Research on Earthquake Engineering (NCREE) Taiwan have been simulated using both 1D and 2D models in the present study. The non-linear behavior of the bridge piers has been studied through various parameters such as hysteretic loops, energy dissipation, residual drift, yield load and corresponding drift, peak load and corresponding drift, ultimate loads, ductility, specimen stiffness and critical strains in concrete and steel. The results obtained from CSMM-based NLFE model have been critically compared with the test results and results obtained from the 1D fiber-based NLFE models.

It has been observed from the analysis results that both 1D and 2D simulation models performed well in predicting the response of flexure critical bridge pier. However, in the case of shear critical bridge piers, predictions from 2D CSMM-based NLFE simulation model are more accurate. It has, thus, been concluded that CSMM-based NLFE model is more accurate and robust to simulate the complete non-linear behavior of shear critical RC hollow rectangular bridge piers.

In this study, a novel attempt has been made to provide a rational and robust FE model for analyzing shear critical hollow RC bridge piers (with walls having aspect ratios greater than 2.5).

In recent years, innovative aircraft designs have been investigated by researchers to address the environmental and economic issues for the purpose of green aviation. To keep air transport competitive and safe, it is necessary to maximize design efficiencies of the aircrafts in terms of weight and cost. The purpose of this paper is to focus on the research which has led to the development of a novel lattice fuselage design of a forward-swept wing aircraft in the conceptual phase by topology optimization technique.

In this paper, the fuselage structure is modelled with two different types of elements – 1D beam and 2D shell – for the validation purpose. Then, the finite element analysis coupled with topology optimization is performed to determine the structural layouts indicating the efficient distributed reinforcements. Following that, the optimal fuselage designs are obtained by comparison of the results of 1D and 2D models.

The topological results reveal the need for horizontal stiffeners to be concentrated near the upper and lower extremities of the fuselage cross section and a lattice pattern of criss-cross stiffeners should be well-placed along the sides of the fuselage and near the regions of window locations. The slight influence of windows on the optimal reinforcement layout is observed. To form clear criss-cross stiffeners, modelling the fuselage with 1D beam elements is suggested, whereas the less computational time is required for the optimization of the fuselage modelled using 2D shell elements.

The authors propose a novel lattice fuselage design in use of topology optimization technique as a powerful design tool. Two types of structural elements are examined to obtain the clear reinforcement detailing, which is also in agreement with the design of the DLR (German Aerospace Center) demonstrator. The optimal lattice layout of the stiffeners is distinctive to the conventional semi-monocoque fuselage design and this definitely provides valuable insights into the more efficient utilization of composite materials for novel aircraft designs.

The purpose of this paper is to analyze the bulk energy transport processes in the build chamber environment before and during laser sintering (LS) to provide a basis for effective and accurate thermal control for the LS process. This leads to improved mechanical properties and geometrical tolerances for LS products and may be applied to optimize operation cycle times for the LS process.

Computational models with two levels of complexity were built to explore the heat transfer mechanisms in the LS process. In a one-dimensional model (1D), the powder performed as a semi-infinite solid and heater flux to the powder surface was modeled with a heater control law. A two-dimensional (2D) fluid/solid finite element model of the build chamber and powder bins provided insight into the thermal processes in the build chamber.

Numerical 1D simulations were verified with measurements from sensors embedded in the build chamber powder bed. Using a 2D model, computed powder surface temperatures during the warm up and build phases were verified with an infrared camera. Convective currents in the build chamber and non-uniformities in the distribution of temperature over the radiant heater surface were found to be substantial contributors to non-uniformities in the powder bed surface temperature.

Limited heat sources were analyzed. No three-dimensional model was built. Assumptions to decrease the part bed temperature difference were not tested.

These simulation and experimental results may be used to enhance thermal control and operation efficiency during the LS process and to improve LS product mechanical properties.

The purpose of this paper is to develop a sub‐domain perturbation technique for refining magnetic circuit models with finite element (FE) models of different dimensions.

A simplified problem considering ideal flux tubes is first solved, as either a 1D magnetic circuit or a simplified FE problem. Its solution is then corrected via FE perturbation problems considering the actual flux tube geometry and the exterior regions, that allow first 2D and then 3D leakage fluxes. Each of these sub‐problems requires an appropriate proper volume mesh, with no need of interconnection. The solutions are transferred from one problem to the other through projections of source fields between meshes.

The developed perturbation FE method allows to split magnetic circuit analyses into subproblems of lower complexity with regard to meshing operations and computational aspects. A natural progression from simple to more elaborate models, from 1D to 3D geometries, is thus possible, while quantifying the gain given by each model refinement and justifying its utility.

Approximate problems with ideal flux tubes are accurately corrected when accounting for leakage fluxes via surface sources of perturbations. The constraints involved in the subproblems are carefully defined in the resulting FE formulations, respecting their inherent strong and weak nature. As a result, an efficient and accurate computation of local fields and global quantities, i.e. flux, MMF, reluctance, is obtained. The method is naturally adapted to parameterized analyses on geometrical and material data.

This paper aims to study a commercially available industrial part feeder that uses an industrial robot arm and computer vision system. Three conveyor belts are arranged to singulate and circulate parts, bringing them under a camera where their pose is recognized and subsequently manipulated by the robot arm. The problem is addressed of optimizing belt speeds and hence throughput of this feeder that avoid: starvation, where no parts are visible to the camera and saturation, where too many parts prevent part pose detection or grasping.

Models are developed for intermittent and continuous motion feeding based on a 2D Poisson process. Renewal theory is applied to model intermittent motion and an M/G/1 queue with customer impatience to model continuous motion feeding. These models are verified using discrete event simulation.

The models predict and optimize feeder behaviour very accurately and it is possible to compute optimal settings for different part sizes and throughput sensitivity.

Feeder belt velocities are currently estimated based on intuition and ad hoc trial and error. The results provide a scientific alternative. The models are straightforward to implement and can provide velocity settings for feeders in industrial use.

This paper advances the scientific understanding of automation and part feeding.

The purpose of this paper is the development of a new turbocharger compressor is a challenging task particularly when both wider operating range and higher efficiency are required. However, the cumbersome design effort and the inherent calculus burden can be significantly reduced by using appropriate design optimization approaches as an alternative to conventional design techniques.

This paper presents an optimization-based preliminary-design (OPD) approach based on a judicious coupling between evolutionary optimization techniques and a modified one-dimensional mean-line model. Two optimization strategies are considered. The first one is mono-objective and is solved using genetic algorithms. The second one is multi-objective and it is handled using the non-dominated sorting genetic algorithm-II. The proposed approach constitutes an automatic search process to select the geometrical parameters of the compressor, ensuring the most common requirements of the preliminary-design phase, with a minimum involvement of the designer.

The obtained numerical results demonstrate that the proposed tool can rapidly produce nearly optimal designs as an excellent basis for further refinement in the phase by using more complex analysis methods such as computational fluid dynamics and meta-modeling.

This paper outlines a new fast OBPD approach for centrifugal compressor turbochargers. The proposal adopts an inverse design method and consists of two main phases: a formulation phase and a solution phase. The complexity of the formulated problem is reduced by using a sensitivity analysis. The solution phase requires to link, in an automatic way, three processes, namely, optimization, design and analysis.