Modelling, Volume 2, Issue 1 (March 2021) – 8 articles

The utilization of sacrificial layers to strengthen civilian structures against terrorist attacks is of great interest to engineering experts in structural retrofitting. The sacrificial cladding structures are designed to be attached to the façade of structures to absorb the impact of the explosion through the facing plate and the core layer progressive plastic deformation. Therefore, blast load striking the non-sacrificial structure could be attenuated. The idea of this study is to construct a sacrificial cladding structure from multicellular hybrid tubes to protect the prominent bearing members of civil engineering structures from blast hazard. The hybrid multi-cell tubes utilized in this study were out of staking composite layers (CFRP) around thin-walled tubes; single, double, and quadruple (AL) thin-walled tubes formed a hybrid single cell tube (H-SCT), a hybrid double cell tube (H-DCT), and a hybrid quadruple cell tube (H-QCT). An unprotected reinforced concrete (RC) panel under the impact of close-range free air blast detonation was selected to highlight the effectiveness of fortifying structural elements with sacrificial cladding layers. To investigate the proposed problem, Eulerian–Lagrangian coupled analyses were conducted using the explicit finite element program (Autodyn/ANSYS). The numerical models’ accuracy was validated with available blast testing data reported in the literature. Numerical simulations showed a decent agreement with the field blast test. The proposed cladding structures with different core topologies were applied to the unprotected RC slabs as an effective technique for blast loading mitigation. Mid-span deflection and damage patterns of the RC panels were used to evaluate the blast behavior of the structures. Cladding structure achieved a desired protection for the RC panel as the mid-span deflection decreased by 62%, 78%, and 87% for H-SCT, H-DCT, and H-QCT cores, respectively, compared to the unprotected panels. Additionally, the influence of the skin plate thickness on the behavior of the cladding structure was investigated. Full article

The manufacturing industry is confronted with increasing demands for digitalization. To realize a digital twin of the cutting process, an increase of the model reliability of the virtual representation becomes necessary. Thereby, different models are required to represent the experimental behavior of the workpiece material or frictional interactions. One of the most utilized material models is the Johnson–Cook material model. The material model parameters are determined either by conventional or by non-conventional material tests, or inversely from the cutting process. However, the inverse parameter determination, where the model parameters are iteratively modified until a sufficient agreement between experimental and numerical results is reached, is not robust and requires a high number of iterations. In this paper, an approach for the inverse determination of material model parameters based on the Particle Swarm Optimization (PSO) is presented. The approach was investigated by the inverse re-identification of an initial parameter set. The conducted investigations showed that a material model parameter set can be determined within a small number of iterations. Thereby, the determined material model parameters resulted in deviations of approximately 1% in comparison to their target values. It was shown that the PSO is suitable for the inverse material parameter determination from cutting simulations. Full article

Dimensioning and assessment of a specific wind turbine imply iterative steps for design optimization, as well as load calculations and performance analyses of the system in various environmental conditions. However, due to the complexity of wind turbine systems, fully coupled aero-hydro-servo-elastic codes are indispensable to represent and simulate the non-linear system behavior. To cope with the large number of simulations to be performed during the design process of a wind turbine system, automation of simulation executions and optimization procedures are required. In this paper, such a holistic simulation and optimization framework is presented, by which means iterative simulations within the wind turbine design assessment and development processes can be managed and executed in an automated and high-performance manner. The focus lies on the application to design load case simulations, as well as the realization of automated optimizations. The proper functioning and the high flexibility of the framework tool is shown based on three exemplary optimization tasks. Full article

Every day, new data must be analysed as well as possible in all areas of applied science, which requires the development of attractive statistical models, that is to say adapted to the context, easy to use and efficient. In this article, we innovate in this direction by proposing a new statistical model based on the functionalities of the sinusoidal transformation and power Lomax distribution. We thus introduce a new three-parameter survival distribution called sine power Lomax distribution. In a first approach, we present it theoretically and provide some of its significant properties. Then the practicality, utility and flexibility of the sine power Lomax model are demonstrated through a comprehensive simulation study, and the analysis of nine real datasets mainly from medicine and engineering. Based on relevant goodness of fit criteria, it is shown that the sine power Lomax model has a better fit to some of the existing Lomax-like distributions. Full article

In this work, the performance of reliability methods for the probabilistic analysis of local scour at a bridge pier is investigated. The reliability of bridge pier scour is one of the important issues for the risk assessment and safety evaluation of bridges. Typically, the depth prediction of bridge pier scour is estimated using deterministic equations, which do not consider the uncertainties related to scour parameters. To consider these uncertainties, a reliability analysis of bridge pier scour is required. In the recent years, a number of efficient reliability methods have been proposed for the reliability-based assessment of engineering problems based on simulation, such as Monte Carlo simulation (MCS), subset simulation (SS), importance sampling (IS), directional simulation (DS), and line sampling (LS). However, no general guideline recommending the most appropriate reliability method for the safety assessment of bridge pier scour has yet been proposed. For this purpose, we carried out a comparative study of the five efficient reliability methods so as to originate general guidelines for the probabilistic assessment of bridge pier scour. In addition, a sensitivity analysis was also carried out to find the effect of individual random variables on the reliability of bridge pier scour. Full article

Accurate modelling and simulation of a nuclear power plant are important factors in the strategic planning and maintenance of the plant. Several nonlinearities and multivariable couplings are associated with real-world plants. Therefore, it is quite challenging to model such cyberphysical systems using conventional mathematical equations. A visual analytics approach which addresses these limitations and models both short term as well as long term behaviour of the system is introduced. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the data, k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of cyberphysical systems using system states and state transitions. In this paper, the indicated methodology is deployed over time-series data collected from a nuclear power plant for nine years. It is observed that this approach of combining the machine learning principles with the finite state machine capabilities facilitates feature exploration, visual analysis, pattern discovery, and effective modelling of nuclear power plant data. In addition, finite state machine representation supports identification of normal and abnormal operation of the plant, thereby suggesting that the given approach captures the anomalous behaviour of the plant. Full article

Understanding the operation of filters used to remove particulates from fluids is important in many practical industries. Typically the particles are larger than the pores in the filter so a cake layer of particles forms on the filter surface. Here we extend existing models for filter blocking to account for deformation of the filter material and the cake layer due to the applied pressure that drives the fluid. These deformations change the permeability of the filter and the cake and hence the flow. We develop a new theory of compressible-cake filtration based on a simple poroelastic model in which we assume that the permeability depends linearly on local deformation. This assumption allows us to derive an explicit filtration law. The model predicts the possible shutdown of the filter when the imposed pressure difference is sufficiently large to reduce the permeability at some point to zero. The theory is applied to industrially relevant operating conditions, namely constant flux, maximising flux and constant pressure drop. Under these conditions, further analytical results are obtained, which yield predictions for optimal filter design with respect to given properties of the filter materials and the particles. Full article

The digital factory provides undoubtedly great potential for future production systems in terms of efficiency and effectivity. A key aspect on the way to realize the digital copy of a real factory is the understanding of complex indoor environments on the basis of three-dimensional (3D) data. In order to generate an accurate factory model including the major components, i.e., building parts, product assets, and process details, the 3D data that are collected during digitalization can be processed with advanced methods of deep learning. For instance, the semantic segmentation of a point cloud enables the identification of relevant objects within the environment. In this work, we propose a fully Bayesian and an approximate Bayesian neural network for point cloud segmentation. Both of the networks are used within a workflow in order to generate an environment model on the basis of raw point clouds. The Bayesian and approximate Bayesian networks allow us to analyse how different ways of estimating uncertainty in these networks improve segmentation results on raw point clouds. We achieve superior model performance for both, the Bayesian and the approximate Bayesian model compared to the frequentist one. This performance difference becomes even more striking when incorporating the networks’ uncertainty in their predictions. For evaluation, we use the scientific data set S3DIS as well as a data set, which was collected by the authors at a German automotive production plant. The methods proposed in this work lead to more accurate segmentation results and the incorporation of uncertainty information also makes this approach especially applicable to safety critical applications aside from our factory planning use case. Full article