Documents published in Scipedia

• Simultaneous, Dynamical Analysis of Structural Ropes and Membranes on all Level-sets

  T. Fries, M. Kaiser

  ECCOMAS 2024.

  
  

  Abstract

  Thelevel sets of scalar functions may imply the geometries of individual ropes and membranes. All level sets within an interval, considered in some bulk domain, define infinitely many geometries at once. A mechanical model is proposed which enables the simultaneous, dynamic analysis of all such geometries. For the solution of the governing equations, a tailored numerical method coined Bulk Trace FEM is employed for the spatial discretization, using higher-order background meshes in the bulk domains. The HHT-α method is used for the temporal discretization. Numerical results are presented that demonstrate the potential of the proposed mechanical model and numerical method

  Abstract
  Thelevel sets of scalar functions may imply the geometries of individual ropes and membranes. All level sets within an interval, considered in some bulk domain, define infinitely [...]

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• Micro-scale analysis of rough surfaces using non-conforming meshes

  M. Oliveira, D. Neto, L. Menezes

  ECCOMAS 2024.

  
  

  Abstract

  This work presents a finite element model of the contact between a flat rigid surface and a rough deformable elastoplastic body, enabling the micro-scale analysis of the contact conditions. To assure the computational efficiency a non-conforming mesh is adopted to describe the deformable body. The results show that the model can capture the main effects of the bulk material constitutive behaviour, which is known to have a significant impact on the problem, as well on local changes on the friction conditions.

  Abstract
  This work presents a finite element model of the contact between a flat rigid surface and a rough deformable elastoplastic body, enabling the micro-scale analysis of the contact [...]

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• Offline and Online Performance Contrast of Optimal Control

  M. Miah, W. Lienhart

  ECCOMAS 2024.

  
  

  Abstract

  Civil structures are quite vulnerable to extreme dynamic loads as well as to nat ural disasters. The aforementioned problem is well-known and interestingly, unavoidable as it is almost impossible to know when that type of loads or disasters are going to hit the existing structures. Due to such unpredictability, many are interested to adopt the online or real-time control and monitoring strategy instead of conventional approach. However, still offline moni toring and vibration control strategies are useful to understand the overall performance of the investigated dynamical problem as it might not be an option to go for online due to feasibility or other constrains. In order to hold a debate, herein, the controlled performance of a multi degree-of-freedom system has investigated both adopting offline and online approaches. The linear-quadratic regulator (LQR) algorithm has been employed as the control law and it is as sumed that controller will behave as an active control system. In order to understand the effect of the optimal control, the displacements and velocities at different degree-of-freedom’s level have considered and compared. The outcome suggests that both approaches have advantages and disadvantages such as offline approach is quite useful to understand at the design phase of the project, while, online approach might be very effected after the construction.

  Abstract
  Civil structures are quite vulnerable to extreme dynamic loads as well as to nat ural disasters. The aforementioned problem is well-known and interestingly, unavoidable as [...]

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• Determination of Neutral Axis and Effect of the Variation of Cross Sectional Area on the Mechanical Properties of Smart Meta-Structure

  S. Sahana, A. Singh, B. Bhattacharya

  ECCOMAS 2024.

  
  

  Abstract

  In recent years, manufacturing has paved the way to enhance structural properties using 3D printed structures by constructing complex shapes. The properties of such structures depend on the arrangement of the internal lattices. Honeycomb is one such simple lattice struc ture that is widely used by researchers as it exhibits a high strength-to-weight ratio. However, the elastic properties of the lattice structures are intrinsic functions of the material properties and the geometric shape. Hence, it is impossible to modulate the elastic properties once constructed. Recent studies have shown that the active modulation of the elastic properties can be achieved by incorporating smart materials over the substrate layers of the lattice. The analytical expressions have been developed for honeycomb/ auxetic honeycomb lattice considering the Euler-Bernoulli bi-layer beam to determine its elastic properties. The expression is well valid for lattices where the thickness of the smart material is relatively less compared to the substrate thickness. How ever, it does not produce consistent results as the thickness of the smart material increases due to the shift of the position of the neutral axis, which was earlier assumed to be at the geomet ric centre of the substrate beam. This paper presents a modified formulation that considers the change in the position of the neutral axis as the thickness of the smart material patches varies. This modification allows the use of the analytical expression for beams with higher thickness ratios and can be used to understand the impact of forces in shear deformation. In addition, the variation in the elastic properties has also been investigated for different cross-sectional shapes such as I-section, T-section, and rectangular cross-section. The formulation presented here is generic, and the concept can be used in various futuristic multi-functional structural systems and devices across different length scales

  Abstract
  In recent years, manufacturing has paved the way to enhance structural properties using 3D printed structures by constructing complex shapes. The properties of such structures [...]

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• ψ − flow: A Novel Physics-Constrained Architecture to Enforce Incompressibility and Boundary Conditions for Fast and Accurate Flow Predictions

  M. Cabral, B. Font, G. Weymouth

  ECCOMAS 2024.

  
  

  Abstract

  Deeplearning models have demonstrated remarkable capabilities at producing fast predictions of complex flow fields. However, incorporating known physics is essential to ensure that physical solutions can generalize to flow regimes not used for training. In this study, a formulation that, by construction, enforces flow incompressibility and respects the invariance of physical laws across different unit systems is introduced. We demonstrate that this approach can achieve performance improvements of up to 100 times compared to purely data-driven methods, all while maintaining fidelity to other crucial physical quantities. Moreover, we show that for canonical flow test cases, such a physics-constrained model can yield accurate results even with training datasets as small as a few hundred points and neural networks containing only a handful of neurons. It is also shown, however, that physics-constrained machine learning models are not silver bullets out of the box, and require careful consideration in their application and integration with other constraints. Specifically, this study addresses how a problem that is mathematically simple may not necessarily be straightforward in machine learning terms, and discusses ongoing efforts to bridge this gap. We conclude by discussing the place of physics-constrained machine learning models within a landscape primarily dominated by physics-informed approaches, in particular in the context of real-world problems where data and computational resources are often limited

  Abstract
  Deeplearning models have demonstrated remarkable capabilities at producing fast predictions of complex flow fields. However, incorporating known physics is essential to ensure [...]

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• On the Usage of PANS for the CFD Simulation of Wind Turbines

  T. Gomes, G. Vaz, A. Maximiano, L. Sileo, V. Krasilnikov

  ECCOMAS 2024.

  
  

  Abstract

  With the rapid evolution of o↵shore wind energy, engineering tools are crucial to catalyze technological developments and increase their maturity, therefore leading to lower costs. Complex turbine-turbine interactions require a good knowledge of the physics of the flow on, around and down/upstream of each turbine, which can be provided using high-fidelity CFD simulations. Turbulence models play a critical role on this matter and an adequate balance between accuracy and computational e ort is necessary. While RANS approaches are quite e cient, LES should provide the most accurate result. Yet, even nowadays, LES blade-resolved simulations are still computationally prohibitive for industrial purposes. A middle-ground exists in SRS formulations, such as hybrid ones as DDES, or bridging ones such as PANS. In the present work emphasis is placed on PANS, since numerical and modelling errors can be studied and quantified independently, as opposite to other SRS approaches. Using as a benchmark the UNAFLOWwindturbine, it is found that traditional RANS and DDES turbulence formulations are able to predict integral forces, but partially fail in capturing wake mixing. Nevertheless, PANS, while enabling the user to select the ratio of turbulent quantities modelled, is not able to properly capture the integral forces due to premature separation in the blades. Several causes are discussed, including insu cient mesh refinement in the near-wall region and lack of turbulent content of the numerical inlet, preventing laminar to turbulent flow transition. Future work should focus on inlet synthetic turbulence generation, in line with existent literature, in order to improve the shortcomings faced in properly resolving the near-wall flow.

  Abstract
  With the rapid evolution of o↵shore wind energy, engineering tools are crucial to catalyze technological developments and increase their maturity, therefore leading [...]

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• Cas Ametller Origen: cap a una cadena de subministrament centralitzada, circular i semiautomàtica

  A. Boar, E. Palau Pinyana

  4rceec (2025). Vol. La musculatura del sistema empresarial català, 133

  
  

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• Research on similarity law of nonlinear shock response of ship plate frame structure under underwater explosion

  J. Wang, D. Shi, X. Yao, Z. Wang

  WCCM2024.

  
  

  Abstract

  The anti-explosion ability of ship grillage structure is an important index to evaluate the vitality of ships. Its model test is a low-cost and effective method to evaluate the vitality of ships and guide the design of ship anti impact structures. In view of the nonlinear and nonstationary process of underwater explosion damage to ship grillage, this paper breaks through the nonlinear effect of transient explosion impact that is not considered in the traditional scale model design, focuses on the one-dimensional nonlinear impact response of ship grillage structure, and carries out the characterization study of the similarity between model experiments and real ships. Considering that the vertical motion of the prototype and the model grillage structure in the model test obey the random walking model, the vertical impact response of the deck grillage is characterized as one-dimensional nonlinear non-stationary Brownian motion, which is described by Hurst index. Based on the classical similarity law, the similarity transformation relationship between the range R and the mean square deviation S is derived, and the Hurst index of the model and the prototype meets the equal relationship; Take a section of grillage structure on a real ship and conduct prototype, 1/2, 1/3, 1/4 and 1/5 one-dimensional nonlinear explosion impact scale simulation tests respectively. The numerical response results show obvious nonlinear characteristics, and the Hurst index of displacement, velocity and acceleration response of the model within the pulse width range is less than 5% compared with the prototype. According to the scale invariance of fractional Brownian motion, the similarity conversion relationship of multiple parameters (displacement, velocity, acceleration and mean square response) is obtained. With the mean square response as the characteristic parameter, the response value of the prototype is converted through this relationship, and compared with the model simulation results, the multi parameter response error under each scale ratio is less than 20%. It provides theoretical and technical support for conducting similar experiments on nonlinear response of underwater explosion shock of ships

  Abstract
  The anti-explosion ability of ship grillage structure is an important index to evaluate the vitality of ships. Its model test is a low-cost and effective method to evaluate [...]

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• A new approach to combine physics-based and data-driven models using a localised trustworthiness metric

  L. Bogaerts, A. Persoons, M. Faes, D. Moens

  WCCM2024.

  
  

  Abstract

  Artificial Neural Networks (ANNs) can solve many (un)supervised learning tasks by virtue of the universal approximation theorem. In the context of on-line process control for manufacturing processes, ANNs are an ideal approach for e.g., on-line monitoring or prediction tasks. However, since they are trained on experimental input-output pairs, the governing physical relations are only implicitly included. This, for instance, can cause inaccuracies when extrapolating to out-of-sample data-points [1]. On the other hand, the numerical approximation of the governing physical laws via numerical methods holds strong potential for the accurate simulation of physical phenomena that occur during manufacturing processes. However, the corresponding computational effort is an impediment that arises with the need for numerous simulations [2]. This makes the application of such numerical schemes computationally intractable within an on-line monitoring context. As such, it is clear that ANNs and numerical simulation models have strong potential, but are fundamentally different models. However, their combination serves as a potentially efficient and accurate aggregated predictor, the so called grey-box model. Such grey-box model is based on highly efficient machine learning algorithms, the black-box member, and backed by validated data with respect to physics generated by the numerical model, the white-box member. A grey-box model capable of defining a trustworthy prediction, including a measurement of uncertainty on the estimator, remains challenging.

  Abstract
  Artificial Neural Networks (ANNs) can solve many (un)supervised learning tasks by virtue of the universal approximation theorem. In the context of on-line process control [...]

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• Enhancing predictive modeling in reactor building dose distribution: A neural network-aided approach

  J. Liu, K. Koyamada, H. Natsukawa, S. Kamioka

  WCCM2024.

  
  

  Abstract

  Ensuring the safety of nuclear reactor decommissioning workers requires accurate, real-time predictions of radiation dose rates within reactor buildings. However, due to the complexity of these structures, such predictions are computationally intensive and time consuming. In this paper, we propose constructing a surrogate model using deep learning to predict radiation dose rates based on simulation results in a space containing a square pillar and a radiation source. The accuracy of the surrogate model's predictions was verified and visualized. Additionally, by applying the principle of superposition, we demonstrated that the distribution of radiation dose rates in spaces with a pillar and multiple radiation sources can be obtained by summing the surrogate model results for each radiation source. We also examined the application of the surrogate model to predicting radiation dose rates in spaces containing multiple square pillars and multiple radiation sources. This approach shows the potential for surrogate models to accurately and efficiently predict radiation dose rates in reactor buildings with complex structures and multiple radiation sources in real time.

  Abstract
  Ensuring the safety of nuclear reactor decommissioning workers requires accurate, real-time predictions of radiation dose rates within reactor buildings. However, due to the [...]

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