Search

Many materials and structures consist of numerous slender struts or fibers. Due to the manufacturing processes of different types of struts and the growth processes of natural fibers, their mechanical response frequently fluctuates from strut to strut, as well as locally within each strut. In associated mechanical models each strut is often represented by a string of beam elements, since the use of conventional three-dimensional finite elements renders the simulations computationally inefficient. The parameter input fields of each string of beam elements are ideally such that the local fluctuations and fluctuations between individual strings of beam elements are accurately captured. The goal of this study is to capture these fluctuations in several intercorrelated bounded random fields. Two formulations to describe the intercorrelations between each random field, as well as the case without any intercorrelation, are investigated. As only a few sets of input fields are available (due to time constraints of the supposed experimental techniques), the identification of the random fields’ parameters involves substantial uncertainties. A probabilistic identification approach based on Bayes’ theorem is employed to treat the involved uncertainties.

Variations in quantities, qualities and time availability of input materials pose a major risk to circular supply chains (CSC) and require new models for creating and evaluating adaptive and resilient CSC in the circular economy (CE). This can be achieved by consistently modelling the overarching relationship between resource input- and output streams, without neglecting the associated risks. The model proposed below consists of five components which are based on five resilience requirements for SCs and provides a data-based recommended course of action for managers at a low entry-barrier. It consists of a CSC visualization, a safety stock calculation, a risk monitoring for each SC node, a reporting logic, and a measurement catalogue. The inspiration for this model came from an innovative case study (“Zirkelmesser”) in the metal processing industry in which secondary products and materials are used to produce new products. Here, the problem of maintaining the resource supply arose and led to resilience issues. The mentioned case study is used as an application example for the model application and helps to make emerging circular supply chains predictable and better manageable, thus increasing their resilience.

Steel wire ropes are used for numerous industrial applications such as ships, elevators, cranes and bridges. A wire rope consists of numerous thin, steel wires and its geometrical construction can be explained in two steps. First, several wires are wrapped together in a helical shape called a strand. Second, several strands are wrapped together in a helical shape to form the final wire rope. In most cases, each strand is compacted before they are wrapped together to form the final wire rope. Compaction generally reduces contact stresses and thereby, extends ropes’ service life. Not many models have been proposed to predict the compaction process and its influence on the strand’s mechanical behavior during service. This contribution proposes a computationally efficient approach that consists of two elastoplastic mechanical models. The first model, describing the compaction process, is of a 2D plane strain nature and is therefore fast. Subsequently, the 2D geometry and plastic variables predicted by the compaction model are used to generate the initial geometry and initial plastic state of a 3D model, that is subsequently used to describe the strand’s mechanical behavior during service (we limit ourselves to tension). The results of the approach, with and without the mapping of the plastic variables, are compared to experimental measurements and the results without compaction. This is investigated for two real world strands.

The concept of Circular Economy (CE) is gaining increasing attention as an indispensable renewal of linear economy without neglecting sustainable development goals. Closing resource loops and keeping resources in the system at the highest level of use for as long as possible are cited as the main goals of CE. However, due to missing information exchange, the lack of consistency between the existing end-of-life (EOL) infrastructure and the respective product designs hinders a successful circularity of resources. This research presents a method to collect, process, and apply EOL process data to provide the beginning-of-life (BOL) with important EOL-knowledge through a CE-adapted product design assessment. EOL-data is collected using a Circular Value Stream Mapping (C-VSM), EOL-information is processed using a digital state flow representation, and EOL-knowledge is applied by providing a decision-support tool for product designers in the context of a PET bottle case study in Luxembourg. The goal is to anticipate a circular flow of resources by reflectively aligning product design with the relevant EOL infrastructure. In contrast to the linear economy, the developed method makes it possible to consider not only the requirements of users but also the actual end users, the EOL process chains, when designing products.

Computer Aided Design (CAD) is widely used in the creation and optimization of various industrial systems and processes. Transforming a CAD geometry into a computational discretization that be used to solve PDEs requires care and a deep knowledge of the selected computational method. In this article, we present a novel integrated collocation scheme based on smart clouds. It allows us to transform a CAD geometry into a complete point collocation model, aware of the base geometry, with minimum effort. For this process, only the geometry of the domain, in the form of a STEP file, and the boundary conditions are needed. We also introduce an adaptive refinement process for the resultant smart cloud using an \textit{a posteriori} error indication. The scheme can be applied to any 2D or 3D geometry, to any PDE and can be applied to most point collocation approaches. We illustrate this with the meshfree Generalized Finite Difference (GFD) method applied to steady linear elasticity problems. We further show that each step of this process, from the initial discretization to the refinement strategy, is connected and is affected by the approach selected in the previous step, thus requiring an integrated scheme where the whole solution process should be considered at once.

The human brain is the most structurally and biochemically complex organ, and its broad spectrum of diverse functions is accompanied by high energy demand. In order to address this high energy demand, brain cells of the central nervous system are organised in a complex and balanced ecosystem, and perturbation of brain energy metabolism is known to be associated with neurodegenerative diseases such as Alzheimer's (AD) and Parkinson's disease. Among all cells composing this ecosystem, astrocytes contribute metabolically to produce the primary energy substrate of life, $\ATP$, and lactate, which can be exported to neurons to support their metabolism. Astrocytes have a star-shaped morphology, allowing them to connect on the one side with blood vessels to uptake glucose and on the other side with neurons to provide lactate. Astrocytes may also exhibit metabolic dysfunctions and modify their morphology in response to diseases. A mechanistic understanding of the morphology-dysfunction relation is still elusive. This thesis developed and applied a mechanistic multiscale modelling approach to investigate astrocytic metabolism in physiological morphologies in healthy and diseased human subjects. The complexity of cellular systems is a significant obstacle in investigating cellular behaviour. Systems biology tackles biological unknowns by combining computational and biological investigations. In order to address the elusive connection between metabolism and morphology in astrocytes, we developed a computational model of central energy metabolism in realistic morphologies. The underlying processes are described by a reaction-diffusion system that can represent cells more realistically by considering the actual three-dimensional shape than classical ordinary differential equation models where the cells are assumed to be spatially punctual, i.e. have no spatial dimension. Thus, the computational model we developed integrates high-resolution microscopy images of astrocytes from human post-mortem brain samples and simulates glucose metabolism in different physiological astrocytic human morphologies associated with AD and healthy conditions. The first part of the thesis is dedicated to presenting a numerical approach that includes complex morphologies. We investigate the classical finite element method (FEM) and cut finite element method (\cutfem{}) for simplified metabolic models in complex geometries. Establishing our image-driven numerical method leads to the second part of this thesis, where we investigate the crucial role played by the locations of reaction sites. We demonstrate that spatial organisation and chemical diffusivity play a pivotal role in the system output. Based on these new findings, we subsequently use microscopy images of healthy and Alzheimer's diseased human astrocytes to build simulations and investigate cell metabolism. In the last part of the thesis, we consider another critical process for astrocytic functionality: calcium signalling. The energy produced in metabolism is also partially used for calcium exchange between cell compartments and mainly can drive mitochondrial activity as a main ATP generating entity. Thus, the active cross-talk between glucose metabolism and calcium signalling can significantly impact the metabolic functionality of cells and requires deeper investigation. For this purpose, we extend our established metabolic model by a calcium signalling module and investigate the coupled system in two-dimensional geometries. Overall, the investigations showed the importance of spatially organised metabolic modelling and paved the way for a new direction of image-driven-meshless modelling of metabolism. Moreover, we show that complex morphologies play a crucial role in metabolic robustness and how astrocytes' morphological changes to AD conditions lead to impaired energy metabolism.

Global pandemics, devastating wars and natural disasters with increasing frequency and impact are disrupting previously carefully balanced manufacturing networks. All industrial companies are required to examine their operations and adjust accordingly. The increasing cost of resources require enterprises to re-design their value creation processes to be more sustainable, to optimize the supplier network to become more resilient and to accelerate digitizing of operations to enhance operational effectiveness. This year's WGAB research seminar is themed around Digitization of the work environment for sustainable production and seeks to contribute solutions to the current challenges. The scientific discourse aims to advance the sustainable and data-based organization of value creation processes. Exemplary efforts for the sustainable production of 3D printed footwear and the circular supply chain of energy production will be discussed. With advances in sensory data collection in cyber-physical production systems (CPPS), there are new opportunities for sensing the status of manufacturing systems, which enable advanced data analytics to contribute to a sustainable production. Intelligent processes enable sustainable value creation and bi-directional knowledge exchange between humans and machines. With people at the centre of the CPPS, production systems shall be both adaptive and personalized for every worker. People need to be involved in the technological and organizational changes. Simulating the migration from a linear economy to a circular economy supports the trend of regionalized production networks. Digital assistance systems are tested to back up resilient manufacturing. We would like to thank all authors for their efforts in preparing the contributions, which are valuable inputs to the discourse to solve the current challenges.

Suspensions of particles in a fluid domain could be seen in different natural and industrial applications, ranging from food production to blood flow. For this reason, many researchers studied this topic to get a better insight into the physics of the problem. One of the main topics in this field is to understand the inter-particle forces. In which, particles in the fluid domain face three main forces, which are hydrodynamic long-range interaction, a collision between particles, and lubrication forces. [1] Treatment of first and second forces is straightforward because they could be modeled accurately with the computational fluid dynamics (CFD) method coupled with the Discrete Element Method (CFD-DEM). However, this strategy becomes less accurate for calculating lubrication force when two particles approach each other in a gap distance smaller than the grid size. This is because the grid resolution is not fine enough to capture the correct hydrodynamic interaction. Among different CFD methods that could be implemented to consider the physics of the suspensions of particles, Immersed Boundary (IB) method has provided a better description of the nature of the topic since it could be used to provide fully resolved CFD simulation. However, the results of the previous researchers also have shown the IB methods also face difficulty in correctly capturing the lubrication effects. Although some researchers have proposed to add a corrective force term in the IB method, this strategy faces a stability problem when there are many particles inside the simulation domain. For this reason, Naoki Hori et al [2] proposed a new strategy to use IB without considering any correction term. In their work, the C dt parameter is defined as a function of time step size, fluid viscosity, and mesh resolution. By keeping this parameter in a specific range, IB could simulate lubrication force with high accuracy, meaning that the grid resolution and time step size are the key parameters in determining the lubrication force. [2] In the present work, a variant of the IB method named the hybrid immersed-boundary/fictitious domain (HFD-IB) method was selected as the CFD solver. [3] Then, it was coupled with the XDEM code to consider the collision forces between the particles. After successful validation of this CFD-DEM solver, the problem of falling two inline spherical particles in the fluid domain is considered. Our solver could get the interaction forces between two-particle correctly by keeping the C dt in a specific range mentioned by the reference articles. As seen in Figure 1, drafting, kissing, and tumbling of particles are illustrated. References [1] Kroupa, M., Vonka, M., Soos, M. and Kosek, J., 2016. Utilizing the discrete element method for the modeling of viscosity in concentrated suspensions. Langmuir, 32(33), pp.8451-8460. [2]Hori, N., Rosti, M.E. and Takagi, S., 2022. An Eulerian-based immersed boundary method for particle suspensions with implicit lubrica [3] Municchi, F. and Radl, S., 2017. Consistent closures for Euler-Lagrange models of bi-disperse gas-particle suspensions derived from particle-resolved direct numerical simulations. International Journal of Heat and Mass Transfer, 111, pp.171-190.

The Simulation Open Framework Architecture (SOFA) is a software environment for building simulations with a particular focus on real-time medical applications, e.g. surgery. Its scope is far broader than the FEniCS Project, encompassing e.g. rigid body dynamics, interfacing with haptic devices, contact and visualisation. Naturally, it also includes some finite element models of soft tissue mechanics, but these capabilities are currently ‘pre-baked’ and limited to a few simple constitutive models. The goal of this work is to incorporate state-of-the-art code generation tools from the FEniCS Project into SOFA in order to hugely increase SOFA’s capabilities in terms of soft tissue mechanics. To this end we have developed a new SOFA plugin named SOniCS. For adding a new material model in SOniCS, the user describes its strain energy density function using UFL (Unified Form Language) syntax. Then, using FFCx (FEniCSx Form Compiler) we generate the C code associated with the kernels corresponding to the automatically differentiated cell-local residual and stiffness forms. Finally, we assemble these kernels in SOFA into global tensors and solve the resulting non-linear systems of equations. The result is that it is now possible to straightforwardly implement complex material models such as the Holzapfel-Ogden anisotropic model into SOFA, and to use them alongside SOFA’s existing strong feature set in medical simulation.

Metal Additive Manufacturing (MAM) offers many advantages such as fast product manufacturing, nearly zero material waste, prototyping of complex large parts and the automatization of the manufacturing process in the aerospace, automotive and other sectors. In the MAM, several parameters influence the product creation steps, making the MAM challenging. In this thesis, we modelize and control the deposition process for a type of MAM where a laser beam is used to melt a metallic wire to create the metal parts called the Laser Wire Additive Manufacturing Process (LWAM). In the dissertation, first, a novel parametric modeling approach is created. The goal of this approach is to use parametric product design features to simulate and print 3D metallic objects for the LWAM. The proposed method includes a pattern and the robot toolpath creation while considering several process requirements of LWAM, such as the deposition sequences and the robot system. This technique aims to develop adaptive robot toolpaths for a precise deposition process with nearly zero error in the product creation process. Second, a layer geometry (width and height) prediction model to improve deposition accuracy is proposed. A machine learning regression algorithm is applied to several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. The experimental results shows that the model has an error rate of (i.e., 2∼4%). Third, a physics-based model of the bead geometry including known process parameters and material properties was created. The model developed for the first time includes critical process parameters, the material properties and the thermal history to describe the relationship between the layer height with different process inputs (i.e., the power, the standoff distance, the temperature, the wire-feed rate and the travel speed). The numerical results show a match of the model with the experimental measurements. Finally, a Model Predictive Controller (MPC) was designed to keep the layer height trajectory constant, considering the constraints and the operating range of the parameters of the process inputs. The model simulation result shows an acceptable tracking of the reference height.

In breast cancer treatment, surgery is one of the most common practices [DeSantis et al., 2019]. The surgery involves a complex pipeline, principally due to the difference between the imaging and the surgical posture [Mazier et al., 2021]. Indeed, because of the stance difference, the surgeon has to rely on radioactive or invasive markers to predict the tumor position in the surgical setup. Biomechanical simulations could predict such complex tumor displacements but often require patient-specific data (material properties, organs geometries, or loading and boundary conditions). Full-field acquisitions coupled with landmark identifications allow obtaining relative deformation between the different configurations. Having this information and assuming a finite element model, an identification procedure of the model parameters can be carried out. Finally, finding a suitable computational model allowing for a compromise between accuracy and speed, one may consider surrogate models for real-time simulations (20 to 50 FPS). In this work, we obtained the patient-specific geometry through micro-computed tomography in 8 different configurations, including 15 bio-markers. Assessing the displacement of the bio-markers enabled us to infer the relative strains between the different configurations. A heterogeneous neo-Hookean model was assumed for simulating soft tissue behavior. Based on the displacements and the position of the biomarkers, model parameters identification was performed to calibrate the experimental data with the finite element method results. To overcome speed performance issues, Convolutional Neural Network (CNN) trained with a synthetic simulation-based dataset generated by applying different gravity directions is used. Preliminary results show that CNN can predict the displacement of anatomical landmarks to millimetric precision and is 100 times faster than the finite element method, satisfying our real-time objective. Plus, the use of Bayesian inferences involves a longer prediction time but allows a 95% confidence interval of the biomarkers' displacements. For a given precision, contrary to CNNs, optimization methods are computationally expensive and depend on an initialization point. Although CNNs require new training for each patient, optimization algorithms can be applied regardless of the patient's geometry. Through this study, we observed that material properties were playing an essential role but not as much as the anatomical structures e.g. infra-mammary or Copper’s ligaments.

Investigating neurodegenerative diseases can be done complementary through biological and computational experiments. A good computational approach describing a simplification of the reality and focusing only on some features of the problem can help getting insights on the field. The question addressed in our work is the role of astrocytes in neurodegeneration. These cells have two interesting characteristics that we want to investigate in our model: first, their role as metabolic mediator between neurons and blood vessels and second, their peculiar morphology. In fact, metabolic dysfunctions and morphological changes have been noticed in astrocyte affected by neuropathology. Computationally the main difficulty arising from solving a metabolic model into cellular shape comes from the complexity of the domain. The shape of astrocytes are very ramified, with thin branches and sharp edges. As shown in our previous work \cite{Farina}, a \cutfem{} \cite{Burman} approach is a suitable tool to deal with this issue. In our latest work we use real human three-dimensional astrocyte morphologies obtained via microscopy \cite{Salamanca} as domain to solve our system. The performed simulations highlight the effect of morphological changes on the system output. Suggesting that our model can be crucial in understanding the morphological-dependency in neuropathologies and that the spatial component cannot be neglected.

Augmented reality (AR) is a technology that allows overlaying of virtual elements on top of the physical environment. This enhances the perception and conveys additional information to the user. With the emergence of industry 4.0 concepts in manufacturing landscape, AR found its way to improve existing Human-Machine Interfaces (HMI) on the shop-floor. The industrial setting has a wide variety of application opportunities from AR, ranging from training and digital work instructions to quality inspection and remote maintenance. Even though its implementation in the industry is rising in popularity, it is still mainly restricted to large companies due the limited availability of resources in Small and Medium Size Enterprises (SME). However, SMEs can benefit from AR solutions in its production processes. Therefore, this research aims to develop and present the results of comparison of two simple and cost-effective AR software frameworks for Hand Held Device (HHD) and a Head Mounted Device (HMD), which can be applied for developing AR applications for manufacturing. Two AR applications are developed using these software frameworks which are presented in the case study section. Android device is chosen as a HHD and HoloLens is the HMD used in the case study. The development structure can be reproduced by a wider range of enterprises with diverse needs and resource availability.

Membrane distillation (MD) is a process to desalinate sea water. Pilot plants are operated aiming at increasing the modules’ efficiency for large-scale applications. In air-gap membrane distillation (AGMD) the state-of-the-art modeling of mass and heat transfer is one-dimensional, combining evaporation and diffusion through the membrane and the condenser channel in one correlation. In this work, a numerical model is developed which computes AGMD modules in three dimen- sions. For evaporation and condensation, energy conservation equations at the interfaces are solved. Simulation results are compared to experimental data and a good agreement is found. The model is then employed to compare numerically two air-gap MD module configurations and evaluate their performance at different feed inlet temperatures, velocities and air-gap thicknesses. In the upside configuration, the hot feed flows above the membrane, while in the downside configura- tion it flows below the air-gap and membrane. In the latter, the feed solution is not in contact with the membrane but separated by the air-gap which is expected to improve the fouling resistance of the membrane. The three-dimensional computational fluid dynamic computation allows the visu- alization of the velocity profile in the air-gap due to buoyancy in the downside configuration

Experimental and field investigations for solution mining processes have improved intensely in recent years. Due to today’s computing capacities, three-dimensional simulations of potential salt solution caverns can further enhance the understanding of these processes. They serve as a “virtual prototype” of a projected site and support planning in reasonable time. In this contribution, we present a meshfree generalized finite difference method (GFDM) based on a cloud of numerical points that is able to simulate solution mining processes on microscopic and macroscopic scales, which differ significantly in both the spatial and temporal scales. Focusing on anticipated industrial requirements, Lagrangian and Eulerian formulations including an Arbitrary Lagrangian–Eulerian (ALE) approach are considered.

Predictive analysis of poroelastic materials typically require expensive and time-consuming multiscale and multiphysics approaches, which demand either several simplifications or costly experimental tests for model parameter identification.This problem motivates us to develop a more efficient approach to address complex problems with an acceptable computational cost. In particular, we employ artificial neural network (ANN) for reliable and fast computation of poroelastic model parameters. Based on the strong-form governing equations for the poroelastic problem derived from asymptotic homogenisation, the weighted residuals formulation of the cell problem is obtained. Approximate solution of the resulting linear variational boundary value problem is achieved by means of the finite element method. The advantages and downsides of macroscale properties identification via asymptotic homogenisation and the application of ANN to overcome parameter characterisation challenges caused by the costly solution of cell problems are presented. Numerical examples, in this study, include spatially dependent porosity and solid matrix Poisson ratio for a generic model problem, application in tumour modelling, and utilisation in soil mechanics context which demonstrate the feasibility of the presented framework.

Computer Aided Design (CAD) software packages are used in the industry to design mechanical systems. Then, calculations are often performed using simulation software packages to improve the quality of the design. To speed up the development costs, companies and research centers have been trying to ease the integration of the computation phase in the design phase. The collocation methods have the potential of easing such integration thanks to their meshless nature. The geometry discretization step which is a key element of all computational method is simplified compared to mesh-based methods such as the finite element method. We propose in this thesis a unified workflow that allows the solution of engineering problems defined by partial differential equations (PDEs) directly from input CAD files. The scheme is based on point collocation methods and proposed techniques to enhance the solution. We introduce the idea of “smart clouds”. Smart clouds refer to point cloud discretizations that are aware of the exact CAD geometry, appropriate to solve a defined problem using a point collocation method and that contain information used to improve locally the solution. We introduce a unified node selection algorithm based on a generalization of the visibility criterion. The proposed algorithm leads to a significant reduction of the error for concave problems and does not have any drawback for convex problems. The point collocation methods rely on many parameters. We select in this thesis parameters for the Generalized Finite Difference (GFD) method and the Discretization-Corrected Particle Strength Exchange (DC PSE) method that we deem appropriate for most problems from the field of linear elasticity. We also show that solution improvement techniques, based on the use of Voronoi diagrams or on a stabilization of the PDE, do not lead to a reduction of the error for all of the considered benchmark problems. These methods shall therefore be used with care. We propose two types of a posteriori error indicators that both succeed in identifying the areas of the domain where the error is the greatest: a ZZ-type and a residual-type error indicator. We couple these indicators to a h-adaptive refinement scheme and show that the approach is effective. Finally, we show the performance of Algebraic Multigrid (AMG) preconditions on the solution of linear systems compared to other preconditioning/solution methods. This family of preconditioners necessitates the selection of a large number of parameters. We assess the impact of some of them on the solution time for a 3D problem from the field of linear elasticity. Despite the performance of AMG preconditions, ILU preconditioners may be preferred thanks to their ease of usage and robustness to lead to a convergence of the solution.

We propose the modeling of glioblastoma isocitrate dehydrogenase wild-type (GBMwt) build on the following hypotheses: the brain tissue is a porous medium, the coupling of hypoxia consequences and mechanical interplay between extra-cellular matrix and tumor cells is the driver of the malignant evolution of the disease. In this thesis, a poromechanical model is developed with the aim of a clinical application in oncology. A review, with a large scope, is done on mechanical applications in clinical management of cancer. The model is first validated on in vitro experimental data of encapsulated multi-cellular spheroids. Then, a clinical collaboration is initiated with the Neuro-imaging center of Toulouse, and the targeted clinical application is the modeling of non- operable GBMwt. To this end, the model is first adapted to the specificity of brain tissue mechanics. Characteristic features of the disease are modeled: necrotic core, modified extra-cellular matrix production, emerging malignant phenotype and invasion. Clinical imaging data are pre- treated to inform the model in a patient specific basis. A proposition of modeling is provided with an evaluation against clinical data.

In the context of Industry 4.0 (I 4.0), one of the most important aspects is data, followed by the capital required to deploy advanced technologies. However, most Small and Medium Enterprises (SMEs) are neither data ready nor have the capital to upgrade their existing machinery. In SMEs, most of the legacy machines do not have data gathering capabilities. In this scenario, the concept of retrofitting the existing machinery with sensors and building an Industrial Internet of Things (IIoT) is more beneficial than upgrading the equipment to newer machinery. The current research paper proposes a simple architecture on retrofitting a legacy machine with external sensors for data collection and feeding the cloud-based databases for analysis/monitoring purposes. The design and functional aspects of the architecture are then tested in a laboratory environment on a drilling machine with no embedded sensors. Data related to the speed of the drill head and the bore depth are collected using newly retrofitted sensors to validate the proposed architecture.

Welding of Aluminium (Al) and Copper (Cu) in a dissimilar fashion is required for the manufacturing of solar thermal absorbers, battery modules and refrigeration applications. The high strength, thermal and electrical conductivity of Cu combined with the lightweight property of Al material enable the high performance of the product. A laser is a precise tool, which can increase the productivity and quality of the welding process. Welding Al and Cu is considered difficult because of the formation of complex intermetallic phases which reduce the strength of the joint. Laser brazing from low melting Al sheet to Cu sheet is the traditional technique to reduce the intermetallic phases. This thesis focuses on irradiation of laser beam from copper sheet to aluminium sheet in overlapped configuration. The approach is to form a large amount of intermixing to obtain (Cu) solid solution and Al-rich phase Al+Al2Cu in the interface. By this approach, it was found that a fusion zone with a large number of good phases was formed. The intermetallic compounds Al2Cu,Al3Cu4,Al4Cu9 are intermixed and small. Such a microstructure is beneficial for joint strength. The characterization was done by light optical microscopy and scanning electron microscope. EDS analysis was used to estimate the composition and identify the phases. It was found that a beneficial Cu solid solution phase is present in the joint. To qualify the joint and identify the weld status, melting characteristics during laser welding by observation of the optical emission in Ultraviolet-visible wavelength was studied. The Al melting peak at 396 nm and Cu melting peak at 578 nm was found to correlate to the welding process parameters. The signals correlate to the actual melting of Cu and Al sheets, which was investigated by the cross-sectional images and the weld images on the top of the Cu-Al weld. Therefore, the possibility for real-time analysis to identify different welding conditions is shown.

In this paper, we present a new model to simulate the formation, evolution, and break-up of a thin film of fluid flowing over a curved surface. Referred to as the discrete droplet method (DDM), the model captures the evolution of thin fluid films by tracking individual moving fluid droplets. In contrast to existing thin-film models that solve a PDE to determine the film height, here, we compute the film height by numerical integration based on the aggregation of droplets. The novelty of this approach in using droplets makes it suitable for simulating the formation of fluid films, and modelling thin film flows on partially wetted surfaces. The DDM is a Lagrangian approach, with a force balance on each droplet governing the motion, and derivatives approximated using a smoothed particle hydrodynamics (SPH) like approach. The proposed model is thoroughly validated by comparing results against analytical solutions, against the results of the shallow-water equations for thin film flow, and also against results from a full 3-D resolved Navier–Stokes model. We also present the use of the DDM on an industrial test case. The results highlight the effectiveness of the model for simulations of flows with thin films.

Woody biomass energy is a kind of renewable energy that contributes to the reduction of greenhouse gas emissions, the creation of healthier forests, and the reduction of wildfire danger. Simulations of biomass combustion, in general, are time-consuming simulations with a large number of input particles. We use a deep hidden physics-based neural network model to predict the behavior of particles throughout the simulation based on the equations of motion to achieve an efficient simulation and reduce the processing effort. We replace discrete element methods with inverse methods, which have the advantage of simulating velocity fields without knowing the simulation's boundary and initial conditions. Reconstruction of the velocity fields is done using a recurrent neural network in conjunction with a physics-based loss function. The proposed model is suitable for modeling problems that involve moving particles in a fixed bed. The number of neurons and activation functions in the artificial neural network are optimized, and the effect of the sampling method and the number of outputs are studied.

In this paper, we introduce a mesh-free numerical framework using the finite pointset method for the modeling and simulation of injection molding processes. When compared to well-established mesh-based methods, which have been widely applied for these applications, our approach avoids the need for extensive preprocessing and enables accurate treatment of free surfaces and other associated phenomena. To accurately model the polymer injections, we consider a detailed material model, with temperature dependent viscosity and density, while also considering shear thinning behavior with a strain rate dependent viscosity. Our numerical investigations show that injection molding-specific problems such as the modeling of viscous flows and the fountain flow effect can be successfully implemented using our presented framework. For a thorough validation of our proposed model, we compare the simulated flow behavior with injection molding experiments, which are also performed in this work. The experimental setup considers the injection of a polymer melt into a spiral mold. The flow behavior is investigated experimentally at varying melt injection and wall temperature, with different threshold pressures. Our numerical simulations show a good comparison with these experimental results, both qualitatively and quantitatively. We also introduce a correction mechanism to ensure energy conservation, which has often been challenging in mesh-free approaches. This is the first time that the flow behavior in a mesh-free injection molding method has been experimentally validated and successfully applied to the simulation of an actual industrial vehicle component.

The quasicontinuum method is a multiscale method that combines locally supported coarse-grained domains, with small regions in which the microstructural model is fully resolved. This contribution proposes the first adaptive formulation of the method for microstructural elastoplasticity. The microstructural model uses an elastoplastic beam description. The indicator for refinement is the occurrence of plastic deformation, such that plasticity can only occur in fully resolved regions. An illustrative numerical example of a scratch test of an elastoplastic Kelvin lattice demonstrates the capabilities of the resulting framework.

Background: Breast-conserving surgery is the most acceptable option for breast cancer removal from an invasive and psychological point of view. During the surgical procedure, the imaging acquisition using Magnetic Image Resonance is performed in the prone configuration, while the surgery is achieved in the supine stance. Thus, a considerable movement of the breast between the two poses drives the tumor to move, complicating the surgeon's task. Therefore, to keep track of the lesion, the surgeon employs ultrasound imaging to mark the tumor with a metallic harpoon or radioactive tags. This procedure, in addition to an invasive characteristic, is a supplemental source of uncertainty. Consequently, developing a numerical method to predict the tumor movement between the imaging and intra-operative configuration is of significant interest. Methods: In this work, a simulation pipeline allowing the prediction of patient-specific breast tumor movement was put forward, including personalized preoperative surgical drawings. Through image segmentation, a subject-specific finite element biomechanical model is obtained. By first computing an undeformed state of the breast (equivalent to a nullified gravity), the estimated intra-operative configuration is then evaluated using our developed registration methods. Finally, the model is calibrated using a surface acquisition in the intra-operative stance to minimize the prediction error. Findings: The capabilities of our breast biomechanical model to reproduce real breast deformations were evaluated. To this extent, the estimated geometry of the supine breast configuration was computed using a corotational elastic material model formulation. The subject-specific mechanical properties of the breast and skin were assessed, to get the best estimates of the prone configuration. The final results are a Mean Absolute Error of 4.00 mm for the mechanical parameters E_breast = 0.32 kPa and E_skin = 22.72 kPa. The optimized mechanical parameters are congruent with the recent state-of-the-art. The simulation (including finding the undeformed and prone configuration) takes less than 20 s. The Covariance Matrix Adaptation Evolution Strategy optimizer converges on average between 15 to 100 iterations depending on the initial parameters for a total time comprised between 5 to 30 min. To our knowledge, our model offers one of the best compromises between accuracy and speed. The model could be effortlessly enriched through our recent work to facilitate the use of complex material models by only describing the strain density energy function of the material. In a second study, we developed a second breast model aiming at mapping a generic model embedding breast-conserving surgical drawing to any patient. We demonstrated the clinical applications of such a model in a real-case scenario, offering a relevant education tool for an inexperienced surgeon.

Since the mitigation of climate change is one of the biggest challenges to face on a global scale, the topic has become more relevant also in industrial context. Learning factories have proven to be suitable environments to address and convey competencies to tackle industrial challenges in an interactive way. Hence, several learning factories are already dealing with sustainability topics in various use cases. This paper strives to present a state of the art of sustainability and circular economy in learning factories. Therefore, a classification framework is developed based on the state of the art of several learning factories and existing literature regarding the topic. This framework is then used to systematically describe the different activities regarding sustainability and circular economy that are currently ongoing in learning factories worldwide. This can be used to get an idea about the different aspects of the topic and how to address them, but furthermore also offers assistance to identify “blind spots” which could and should be addressed in learning factories in the future.

The Robotix-Academy Conference for Industrial Robotics (RACIR) is held on September 22, 2021 at Trier University of Applied Sciences, Environmental Campus Birkenfeld, Germany as a hybrid conference. The topics concerned by RACIR are: robot design, robot kinematics/dynamics/control, system integration, sensor/ actuator networks, distributed and cloud robotics, bio-inspired systems, service robots, robotics in automation, biomedical applications, autonomous vehicles (land, sea and air), robot perception, manipulation with multifinger hands, micro/nano systems, sensor information, robot vision, multimodal interface and human-robot interaction.

The presented article shows an estimation method of optimum autofrettage pressure taking into consideration subsequent cyclic loading. An autofrettage process is used in pressure vessel applications for strength improvement. The process relies on applying massive pressure that causes internal portions of the part to yield plastically, resulting in internal compressive residual stresses when pressure is released. Later applied working pressure (much lower than autofrettage pressure) creates stress reduced by the residual compressive stress improving the structural performance of the pressure vessels. The optimum autofrettage pressure is a load that maximizes the fatigue life of the structure at the working load. The estimation method of that pressure of a hydrogen valve is the subject of the presented work. Finite element and fatigue analyses were employed to investigate the presented problem. An automated model was developed to analyze the design for various autofrettage pressures. As the results of the procedure, the optimum autofrettage pressure is determined. The research has shown that the developed method can profitably investigate the complex parts giving the autofrettage load that maximizes the fatigue life. The findings suggest that the technique can be applied to a large group of products subjected to the autofrettage process.

We present a stochastic approach combining Bayesian Inference (BI) with homogenization theories in order to identify, on the one hand, the parameters inherent to the model assumptions and, on the other hand, the composite material constituents behaviors, including their variability. In particular, we characterize the model parameters of a Mean-Field Homogenization (MFH) model and the elastic matrix behavior, including the inherent dispersion in its Young’s modulus, of non-aligned Short Fibers Reinforced Polymer (SFRP) composites. The inference is achieved by considering as observations experimental tests conducted at the SFRP composite coupons level. The inferred model and material law parameters can in turn be used in Mean-Field Homogenization (MFH)-based multi-scale simulations and can predict the confidence range of the composite material responses.

Phase-field models have the advantage in that no geometric descriptions of cracks are required, which means that crack coalescence and branching can be treated without additional effort. Miehe and Schanzel, 2014 introduced a rate-independent phase-field damage model for finite strains in which a viscous damage regularization was proposed. We extend the model to depend on the loading rate and time by incorporating rubber’s strain-rate dependency in the constitutive description of the bulk, as well as in the damage driving force. The parameters of the model are identified using experiments at different strain rates. Local strain fields near the crack tip, obtained with digital image correlation (DIC), are used to help identify the length scale parameter. Three different degradation functions are assessed for their accuracy to model the rubber’s rate-dependent fracture. An adaptive time-stepping approach with a corrector scheme is furthermore employed to increase the computational efficiency with a factor of six, whereas an active set method guarantees the irreversibility of damage. Results detailing the energy storage and dissipation of the different model constituents are included, as well as validation results that show promising capabilities of rate-dependent phase-field modeling.

In this paper, we present a new multi-physics computational framework that enables us to capture and investigate complex fracture behavior in cement-based materials at early-age. The present model consists of coupling the most important chemo-thermo-mechanical processes to describe temperature evolution, variation of hydration degree, and mechanical behavior. The changes of material properties are expressed as a function of the hydration degree, to capture the age effects. Fracture analysis of these processes is then accommodated by a versatile phase field model in the framework of smeared crack models, addressing the influence of cracks on hydration and thermal transfer. We additionally describe a stable and robust numerical algorithm, which aims to solve coupled problems by using a staggered scheme. The developed approach is applied to study the fracture phenomena for both homogeneous and heterogeneous concrete structures. Especially, in the second case, all microstructural heterogeneities of sand and cement matrix are explicitly accounted. Nucleation, initiation, and propagation of complex crack network are simulated in an efficient way demonstrating the potential of the proposed approach to assess the early-age defects in concrete structures and materials.

Gas-dynamic pressure surges and adiabatic compression phenomena are generally hard to predict numerically. In this contribution, we study the effect of the pressure reserve capacity on the compressible gas-dynamics pressure surge and adiabatic compression in a fitted respirator oxygen valve geometry. A three-dimensional remeshed smoothed particle hydrodynamics method for the simulation of isotropic turbulence is used, the method is coupled with Brinkman penalisation technique for flow simulation inside the complex valve geometry. Simulations are carried out for three different pressure reserve quantities, to replicate the opening of the valve, two time-based pressure inlet boundary condition functions were simulated along with an impulsively started scenario. A geometrical sensitivity analysis is provided, where the simulation is performed on a modified valve design which exhibits a damping effect on the gas dynamics and flow characteristics, which has a favourable effect on the valve functionality and safety. It is found that the capacity of the pressure reserve has a considerable effect on the simulated flow fields (velocity, temperature), as the temperature could rise 6.0X the reference temperature, and up to 2.7X the reference velocity. The numerical results are compared with a previous study carried out by Rotarex S.A., demonstrating that the remeshed particle-mesh method coupled with Brinkman penalisation provides a good quality simulation and the results are in agreement with the reference solution.

The present paper aims to study the phenomena of droplet entrainment from water pool. This phenomena could be either a consequence of boiling or depressurization. In a bubble column, droplets are released at the surface of the pool by bursting (in bubbly flow regime) or by detachment (in churn turbulent flow regime) depending of the hydrodynamics inside the pool. Eventually, these droplets are entrained by the streaming gas (superficial gas velocity) or settling down due to gravity. Many experimental studies have been conducted, and several numerical simulations were performed to a better understanding of the phenomena of entrainment. Numerical simulation are a good tool to simulate an experiment due to limitations of data. To that end, CFD showed to be a good candidate to perform such a simulation, yet demand high computational performance and are time consuming. However, Lumped Parameter codes (LP) are widely used due to their simplicity and fast running, nevertheless, only provides qualitative and quantitative data. This paper gives a detailed review on the phenomena of entrainment covering experimental, analytical work. Relevant details on pool scrubbing are also provided for completeness purposes.

The concept of the circular economy (CE) is receiving encouraging attention among scholars and practitioners, as a convenient solution to move away from the linear economy concept without neglecting the goals of sustainable development. The main goals of the CE are the closing of resource loops and the keeping of resources in the system for as long as possible at the highest utility level. However, as a result of the lack of internationally accepted definitions of the CE and several unsolved barriers, an excessive and inconsistent number of different CE applications exist. Most fields are mainly focusing on making a linear system circular instead of applying the CE principles in a holistic way. This paper presents a strategy to close the mentioned inconsistency gap, by contrasting currently discussed CE barriers and goals and thereof deriving two areas with a need for action (1. identifying the needed collection, sorting, and recovery infrastructure, and 2. developing circular product design guidelines). The strategy itself consists of connecting these two areas through an improved information exchange between the end-of-life (EOL) and beginning-of-life (BOL) of products. The result is CE design guidelines which are in accordance with the available or needed collection, sorting, and recovery infrastructure. The proposed strategy presents an innovative solution to apply CE principles in a holistic manner, based on EOL-driven product design.

A large number of concrete bridges show increasing damage due to corrosion and fatigue. The traditional visual inspection and subsequent assessment of concrete bridges is carried out regularly by an experienced engineer. This type of inspection can be time-consuming, costly and leading to errors. Hence, there is a great interest in complementary, alternative and easy-to-implement methods for condition monitoring of bridges. The University of Luxembourg tested different approaches on a part of a real bridge. Various tests were performed in the healthy reference state, e.g. loading tests with a movable test load according to today’s standards. The measurements in the reference state were repeated several times under outdoor conditions to monitor and document the real temperature influence. Displacement transducers were set up in the vertical and horizontal directions. Simultaneously, a new approach was used for determining the deflection with a laser-based system, that measured the displacements in the bridge’s longitudinal direction by an oblique reflector panel under a well-defined angle. The data gained from the laser-based measurement system were compared to the data from classical displacement transducers. In general, the comparison of the two measuring systems shows quite similar results.

This contribution presents the extensions of beam-to-beam and beam-inside-beam contact schemes of the same authors towards frictional interactions. Since the schemes are based on the beams’ true surfaces (instead of surfaces implicitly deduced from the beams’ centroid lines), the presented enhancements are not only able to account for frictional sliding in the beams’ axial directions, but also in the circumferential directions. Both the frictional beam-to-beam approach as well as the frictional beam-inside-beam approach are applicable to shear-deformable and shear-undeformable beams, as well as to beams with both circular and elliptical cross-sections (although the cross-sections must be rigid). A penalty formulation is used to treat unilateral and frictional contact constraints. FE implementation details are discussed, where automatic differentiation techniques are used to derive the implementations. Simulations involving large sliding displacements and large deformations are presented for both beam-to-beam and beam-inside-beam schemes. All simulation results are compared to those of the frictionless schemes.

The aim of this work is to study the phenomena of droplet entrainment from water pool. This phenomenon could be either a consequence of boiling or depressurization. In a bubble column, droplets are released from the surface of the pool by bubble burst (in bubbly flow regime) or by detachment from liquids sheets (in churn turbulent flow regime) depending on the hydrodynamics inside the pool. Eventually, these droplets will be entrained by the streaming gas (superficial gas velocity) or fall back due to gravity. Many experimental studies have been conducted, and several numerical simulations were performed for a better understanding of the phenomena of entrainment. Numerical simulation are a good tool to simulate an experiment due to limitations of data. To that end, CFD showed to be a good candidate to perform such a simulation, yet these it demand high computational performance and are time consuming. However, Lumped Parameter codes (LP) are widely used due to their simplicity and fast running. The number of correlations that quantify the entrainment previously developed based on empirical, semi-empirical and theoretical approaches are limited to a specific regime in the water pool, thermal hydraulic conditions or even to a specific geometry For this purpose, after an extensive study, an empirical correlation is proposed to cover the flow regimes from bubbly to churn turbulent, and could be applied to a wide range of geometries. The current correlation shows an increase until a maximum entrainment of about 2.10-4, corresponding to gas velocity of 0.05 m/s for bubbly flow regime, a slight decrease to 2.10-5, for the transition regime for superficial gas velocities up to 0.1 m/s, and a sharp increase as the superficial gas velocity goes up to 5 m/s The experimental database used to develop the present empirical correlation covers a broader range of boundary conditions, namely pressure [1 bar – 15 bar], water pool thermal condition [subcooled – boiling], vessel diameter[0.19m- 3.2 m], pool diameter [0.1 m – 1.4 m], superficial gas velocity up to 5.0 m/s and for soluble and insoluble aerosols. Therefore, the proposed empirical correlation aims to constitute an important tool to transfer the experimental results to reactor application.

Increasing product variance and individualisation lead to increasing demands for flexibility in produc-tion and production control. In the context of remanufacturing, these demands are further intensified by unknown conditions of the used products. Each product to be remanufactured may therefore require an individual route through the remanufacturing system. This process, which puts used products into an "as good as new or better" condition, is receiving increasing attention due to its high ecological and economic potential and legal regulations. In order to meet these requirements, a hybrid control archi-tecture will be presented. This consists of centralised and decentralised components. At the decentral-ised level, all physical production participants are networked with software components and controlled by these. These components can acquire the status and availability of the corresponding manufacturing participants. They can communicate with each other as well as with the central level. The central level is where the scheduling of machines and automated guided vehicle (AGVs) takes place. This is carried out simultaneously and not sequentially as is the case with the currently available control systems. A method based on Constraint Programming (CP) is being developed to optimise scheduling. Simulation results show that a simultaneous, as opposed to a sequential, scheduling enables a reduction of makespan by 35.6 %. Compared to other state of the art methods, the CP-based approach provides the best results and this in a significantly shorter computing time. The control architecture is able to react adequately to unexpected events such as machine failures or new orders. It uses real-life feedback from the shop floor for this purpose. The architecture is implemented as a multi-agent system. The approach can be validated by successfully controlling a model factory in a realistic environment. Steigende Produktvarianz und –individualisierung führen zu steigenden Flexibilitätsansprüchen ge-genüber der Fertigung und der Fertigungssteuerung. Zusätzlich verschärfen sich diese Anforderungen im Rahmen der Refabrikation durch unbekannte Zustände der gebrauchten Produkte nochmals. Jedes zu refabrizierende Produkt kann daher eine individuelle Route durch das Refabrikationssystem benö-tigen. Dieser Prozess, in welchem gebrauchte Produkte in einen neuwertigen Zustand transformiert werden, gewinnt durch hohe ökologische und ökonomische Potenziale sowie gesetzliche Vorschriften zunehmende Aufmerksamkeit. Um entsprechenden Anforderungen gerecht zu werden, erfolgt die Vor-stellung einer hybriden Steuerungsarchitektur. Diese besteht aus zentralen und dezentralen Kompo-nenten. In der dezentralen Ebene werden alle physischen Fertigungsteilnehmer mit softwaretechni-schen Komponenten vernetzt und von diesen gesteuert. Diese Komponenten können den Status sowie die Verfügbarkeit der zugehörigen Fertigungsteilnehmer akquirieren. Sie können sowohl untereinan-der als auch mit der zentralen Ebene kommunizieren. In der zentralen Ebene erfolgt die Ablaufplanung von Maschinen und fahrerlosen Transportsystemen (FTS). Diese wird simultan und nicht, wie bei den derzeit verfügbaren Steuerungssystemen sequenziell durchgeführt. Zur Optimierung der Ablaufpla-nung wird ein auf Constraint Programming (CP) basierendes Verfahren entwickelt. Simulationsergeb-nisse zeigen, dass eine simultane, gegenüber einer sequenziellen, Ablaufplanung eine Durchlaufzeit-reduzierung von 35,6 % ermöglicht. Der CP-basierte Ansatz liefert im Vergleich zu anderen State of the Art-Verfahren, die besten Ergebnisse. Zusätzlich werden diese in einer deutlich kürzeren Rechen-zeit generiert. Die Steuerungsarchitektur ist in der Lage, adäquat auf unerwartet auftretende Ereignisse, wie Maschinenausfälle oder neue Aufträge zu reagieren. Sie nutzt hierzu Echtzeit-Feedback vom Shop-Floor. Die Implementierung der Architektur erfolgt als Multi-Agenten-System. Der Ansatz kann durch die erfolgreiche Steuerung einer Modellfabrik in einer realitätsnahen Umgebung validiert wer-den.

The quasicontinuum method is a concurrent multiscale approach in which lattice models are fully resolved in small regions of interest and coarse-grained elsewhere. Since the method was originally proposed to accelerate atomistic lattice simulations, its refinement criteria – that drive refining coarse-grained regions and/or increasing fully-resolved regions – are generally associated with quantities relevant to the atomistic scale. In this contribution, a new refinement indicator is presented, based on the energies of dedicated cells at coarse-grained domain surfaces. This indicator is incorporated in an adaptive scheme of a generalization of the quasicontinuum method able to consider periodic representative volume elements, like the ones employed in most computational homogenization approaches. However, this indicator can also be used for conventional quasicontinuum frameworks. Illustrative numerical examples of elastic indentation and scratch of different lattices demonstrate the capabilities of the refinement indicator and its impact on adaptive quasicontinuum simulations., info:eu-repo/semantics/published

We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The algorithm is executed by the modular robot itself and based on a distributed iterative solution of mechanical equilibrium equations derived from a simplified model of the robot. The model treats intermodular connections as beams and assumes no-sliding contact between the modules and the ground. We also provide a procedure for simplified instability detection. The algorithm is verified in the Programmable Matter simulator VisibleSim, and in real-life experiments on the modular robotic system Blinky Blocks. © 2004-2012 IEEE.

Computational techniques have been widely used to tackle problems in the biological sciences. A com- promise between high quality simulations and simple but accurate models can help to understand un- known aspects of this field. In this work, we will show how the Cut Finite Element Method (CutFEM) [1] can be a powerful tool to solve a reaction diffusion PDE system that models the energy metabolism of a cell. The main difficulty to approach this problem is dealing with the morphology of the cell that can have sharp edges and evolves over time. While classical FEM requires the mesh conform to the domain boundary, CutFEM allows a non-conforming discretisation of the domain, and thus is especially suited for modeling complex and evolving cellular geometries. First, we introduce our simplified model for metabolic pathways taking place in a region small enough to consider the material property as homogeneous. The results obtained with FEM (FENICS Project [2][3]) and CutFEM suggest that the two methods are equivalent. This allows us to use CutFEM to increase the complexity of the domain, from a spherical shaped cell to an irregular astrocyte. We conclude that CutFEM is a robust method for tackling biological problems with complex geometries, opening the possibility to extend the complexity of our mathematical model including more features and to consider real cellular shapes that evolve in time in future work.

Augmented Reality (AR) systems have seen a rapid adaptation in both training and in virtual assembly instructions. AR systems assist the operator by enhancing user perception of reality, reducing the defects, and lead-time. However, there is a significant lack in the existing literature to compare AR systems and conventional work instructions. The aim of this research is to provide an empirical evidence of comparing task completion time, number of errors, workload index and system usability of AR and conventional paper-based work instructions. For this purpose, we designed an experiment where participants use paper based instructions and AR instructions to assemble a planetary gearbox. The task was assessed using NASA-TLX and System Usability Scale (SUS) tests, which allowed further analysis using descriptive statistics. Moreover, the qualitative interview at the end of the experiment gave more insights about participant’s overall experience.