Flash. Flash. Ing. Stefano Odorizzi EnginSoft CEO and President


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3 Newsletter EnginSoft Year 9 n 43 Flash Virtual Simulation creates more and more interest in diverse industrial fields and among the young; it is a driving force for innovation, for employment and creates opportunities for companies. With the CAE Poster Award, EnginSoft fosters and promotes collaborations between industry and universities. At the International CAE Conference 2012, the Award was presented for the first time to six outstanding Young Researchers and businesses for their highly innovative work in the field of simulation. Many of our guests with whom I spoke at the Conference shared the enthusiasm for innovative research and to create new business, to realize visions, to make the most out of the enormous resources we have available in our network. While the year turns to an end, we are building on these foundations, on the opportunities and new activities we have created together with our customers and partners. Success is possible together. It is in this spirit that we are approaching the New Year at EnginSoft. We invite our readers to enjoy the articles in this Newsletter and to contact us with feedback and ideas for collaboration. This issue presents contributions on the use of modefrontier for the optimization of a boomerang shape, the analysis work performed for a frequencyreconfigurable microstrip antenna and the Particle Finite Element Method, PFEM. The latter is an effective numerical technique for multidisciplinary engineering problems which involve fluidsoilstructure interaction. Alenia Aermacchi, Politecnico di Torino and the Università del Salento inform us about ECS System Simulation for architecture and performance optimization. Further case studies cover the development work of Lovato Electric, the Feat Group, the Department of Information Engineering of University of Pisa as well as the use of the Grapheur technology for material selection. We introduce the RuBeeCOMP, the INTERCER2 and the MUSIC (which stands for: Multilayer control & cognitive System to drive metal and plastic production line for Injected Components) Research Projects. Our Software Updates feature the latest ANSYS Workbench 14.5 release and SIMPACK, a multibody simulation tool. We report from the TechNet Alliance Fall Meeting in Germany, the Round Table Meeting of companies from Venetia and offer a comprehensive review of the International CAE Conference to which EnginSoft had the great pleasure to welcome more than 700 attendees. We encourage our readers to download the Conference Proceedings and to look at the inspiring work of the awarded young researchers, the six Posters we also highlight in this Newsletter. Please stay tuned to the EnginSoft Training Program and Event Calendar. We hope to welcome many of you to our CAE courses and events in 2013 and beyond. EnginSoft and the Editorial Team wish you and your families a very happy, healthy and a prosperous New Year! Stefano Odorizzi Editor in chief Ing. Stefano Odorizzi EnginSoft CEO and President Flash
4 4 Newsletter EnginSoft Year 9 n 4 Sommario  Contents CASE STUDIES 6 Optimization of a Boomerang shape using modefrontier 10 The Particle Finite Element Method. An effective numerical technique for multidisciplinary engineering problems involving fluidsoilstructure interaction 14 FrequencyReconfigurable Microstrip Antenna for Software Defined Radio 16 ECS System Simulation  Architecture and Performance Optimization from the Early Phases of the System Design 24 How Geometrical Dimensioning & Tolerancing influence the performances of an electromechanical contactor 26 Research Activities on SlotCoupled Patch Antenna Excited by a Square Ring Slot 30 Grapheur for Material Selection 33 Studio di fattibilità produttiva attraverso simulazione numerica di processo di forgiatura RESEARCH & TECHNOLOGY TRANSFER 41 Multidisciplinary Optimization for an IEEE RuBee tag integrated in a fiberreinforced composite structure through the RuBeeCOMP Numerical Platform 44 Meeting conclusivo del progetto "RuBeeCOMP" SOFTWARE UPDATES 45 Le Novità in ambito Mechanical della nuova Release ANSYS Workbench Simulating Gear Pairs within SIMPACK RESEARCH & TECHNOLOGY TRANSFER 50 EnginSoft coordinates the new MUSIC European Project 52 Modellazione e Progettazione Ottimale di Strutture Ceramiche 53 EnginSoft ed il progetto INTERCER2 TRAINING 54 Corsi di Addestramento Software 2013 EVENTS 56 International CAE Conference: like never before! 58 CAE Poster Award. A reward to the genius of young researchers 59 EnginSoft sostiene le attività di Ricerca dell Istituto Mario Negri di Milano 66 Le reti d impresa? Serve un cambio di mentalità 68 CAE Conference 2012 welcomed Sponsors from Japan. Postconference interviews 70 Trainer europei di ANSYS alla scuola EnginSoft 71 Event Calendar Contents
5 Newsletter EnginSoft Year 9 n 45 PAGE 16 ECS SYSTEM SIMULATION OF AN ALENIA AIRCRAFT PAGE 24 HOW GEOMETRICAL DIMENSIONING & TOLERANCING INFLUENCES THE PERFORMANCES OF AN ELECTROMECHANICAL CONTACTOR Newsletter EnginSoft Year 9 n 4  Winter 2012 To receive a free copy of the next EnginSoft Newsletters, please contact our Marketing office at: All pictures are protected by copyright. Any reproduction of these pictures in any media and by any means is forbidden unless written authorization by EnginSoft has been obtained beforehand. Copyright EnginSoft Newsletter. Advertisement For advertising opportunities, please contact our Marketing office at: EnginSoft S.p.A BERGAMO c/o Parco Scientifico Tecnologico Kilometro Rosso  Edificio A1, Via Stezzano 87 Tel Fax FIRENZE Via Panciatichi, 40 Tel Fax PADOVA Via Giambellino, 7 Tel Fax MESAGNE (BRINDISI) Via A. Murri, 2  Z.I. Tel Fax TRENTO fraz. Mattarello  Via della Stazione, 27 Tel Fax TORINO Corso Moncalieri, 223 Tel Fax PAGE 41 MULTIDISCIPLINARY OPTIMIZATION FOR AN IEE RUBEE TAG PAGE 56 INTERNATIONAL CAE CONFERENCE 2012: MORE THAN 700 PARTICIPANTS The EnginSoft Newsletter editions contain references to the following products which are trademarks or registered trademarks of their respective owners: ANSYS, ANSYS Workbench, AUTODYN, CFX, FLUENT and any and all ANSYS, Inc. brand, product, service and feature names, logos and slogans are registered trademarks or trademarks of ANSYS, Inc. or its subsidiaries in the United States or other countries. [ICEM CFD is a trademark used by ANSYS, Inc. under license]. (www.ansys.com) modefrontier is a trademark of ESTECO srl (www.esteco.com) Flowmaster is a registered trademark of Menthor Graphics in the USA (www.flowmaster.com) MAGMASOFT is a trademark of MAGMA GmbH. (www.magmasoft.de) Forge and Coldform are trademarks of Transvalor S.A. (www.transvalor.com) LSDYNA is a trademark of Livermore Software Technology Corporation (www.lstc.com) Grapheur is a product of Reactive Search SrL, a partner of EnginSoft (www.grapheur.com) Simpack is a product of SIMPACK AG (www.simpack.com) For more information, please contact the Editorial Team COMPANY INTERESTS ESTECO CONSORZIO TCN EnginSoft GmbH  Germany EnginSoft UK  United Kingdom EnginSoft France  France EnginSoft Nordic  Sweden Aperio Tecnologia en Ingenieria  Spain Cascade Technologies Reactive Search SimNumerica M3E Mathematical Methods and Models for Engineering ASSOCIATION INTERESTS NAFEMS International TechNet Alliance RESPONSIBLE DIRECTOR Stefano Odorizzi  PRINTING Grafiche Dal Piaz  Trento The EnginSoft NEWSLETTER is a quarterly magazine published by EnginSoft SpA Autorizzazione del Tribunale di Trento n 1353 RS di data 2/4/2008 Contents
6 6 Newsletter EnginSoft Year 9 n 4 Optimization of a Boomerang shape using modefrontier A boomerang is a flying object apparently simple but particularly challenging for the complex physics modeling, since it should indeed involve: six degrees of freedom body dynamics; aerodynamics of rotational blades; personal capabilities of the thrower; In this paper we show how the design optimization software modefrontier, developed by ESTECO, can be employed for a nonstandard problem consisting in the numerical simulation of the boomerang flight and the final optimization of its shape. The boomerang trajectory is obtained by means of a dynamic model integrated to a CFD analysis able to compute aerodynamic coefficients. To steer the complete optimization process modefrontier is coupled to Catia v5 for the boomerang shape modification, to MATLAB for the dynamic simulation, and to StarCCM+ for aerodynamic analysis. Moreover, dedicated RSM (Response Surfaces Methods) available in modefrontier are used to extrapolate the aerodynamic coefficients as a function of the boomerang angle of incidence and velocity, as required by the dynamic model, allowing a reduced number of CFD analyses for each geometric configuration. Different design simulations are therefore automatically executed by modefrontier, following a dedicated optimization strategy until the optimal geometry of the boomerang is found accordingly to the specified requirements, such as minimum energy for the launch and desired accuracy in returning. 1. Equations of the boomerang motion Considering that a boomerang spins fast, it is possible to write the socalleds moothed boomerang equations in which the different quantities (velocities, angles, forces) are timeaveraged over a boomerang rotation: where: I z is the maximum boomerang principal moment of inertia; V is the velocity magnitude of the boomerang center of mass; m the boomerang mass; ψ is the angle of incidence of the boomerang; ϑ, φ, ψ are the Euler angles of a xyz reference system partially fixed on the boomerang (such that the boomerang center of mass is always placed in the xyz origin, the z axis is always directed as the maximum boomerang moment of inertia axis, namely normal to the section plane shown in Fig.3, and the projection of the boomerang center of mass velocity on the xy planes is directed as the x axis); ω z the boomerang angular velocity around the z axis; T x, T y, T z, F x, F y, F z, are torque and force components in the xyz reference system, basically due to the interaction between the boomerang and the air and the gravity force. The gravity force can be expressed in the xyz reference system as: The absolute position of the boomerang center of mass can be found as function of the previous parameters by: The equations of motion can be integrated numerically (high order RungeKutta method) once the initial conditions are provided and the forces and torques are available at any time step. A candidate boomerang trajectory can therefore be simulated through the flowing steps: i) for a certain number of Ψ and U pairs (where U=V/ω z a), with a distance between the boomerang center of mass and the farthest boomerang point from the center of mass) the corresponding notdimensional values of F and T are computed by CFD simulations: a dimensional analysis can prove indeed that F and T depend only on Ψ and U for a given boomerang geometry and for a Reynolds number range typical of boomerang flights; ii) response surfaces for F(Ψ,U) and T(Ψ,U) are built; iii) equations of motion are integrated starting from given initial conditions and using the response surfaces computed previously to express forces and torques at any position and time step. The trajectory of the boomerang is affected by the initial conditions, namely by the way the boomerang is launched. Case Histories
7 Newsletter EnginSoft Year 9 n 47 Four launching parameters are considered (they will be automatically tuned for each candidate boomerang by the optimization methodology described in section 4): V: initial boomerang translational velocity; Spin: initial boomerang spin; Aim: angle between the initial boomerang translational velocity and the horizontal plane; Tilt: angle between the initial boomerang rotational plane and the vertical axis (0 tilt corresponds to a vertical boomerang plane of rotation). 2. Boomerang Parameterization The boomerang geometry chosen for the optimization will be the classical two arms V and Ω shape type. The most important parameters that affect the boomerang behavior are linked to the blades profile, the angle between the two arms and the dihedral of the arms. A total number of 9 input parameters has been defined. A. Blade profiles Changing the profile by playing with the angle of attack and cutting on the top of the leading and trailing edge can change a lot the lift provided by the arm. The lift in particular affects the turn capability of the boomerang (precession effect). The arc blades Fig. 1  Effect on blade profile of Bezier control points are in general designed with a positive angle of attack; this helps the boomerang plane to lay down and to float in air. For the parametric boomerang geometry a flat bottom airfoil has been chosen. The blade profiles are built by a Bezier parametric curve, with 4 control points. The profile shape is modified by the changing vertical and horizontal position of the Bezier control points. In this way it is possible to change the angle of attack and the thickness of the blades (see Fig.1). The profiles of the leading and of the trailing arm are controlled by the same parameters, in order to reduce their total number. In particular the vertical position of the trailing arm is set as a fraction of the vertical position of the leading arm. B. Dihedral angle Boomerang arms usually have a positive dihedral angle of about ; the dihedral affects both the lift and the lay down velocity of the rotation plane, keeping practically unchanged the mass of the boomerang. The boomerang parametric model is provided with the two parameters α and d that allow to change the dihedral by removing a small amount of material from the boomerang arms tips (Fig.2). The α parameter is basically the stabilizer s angle of attack. Fig. 2  Leading and trailing edges; dihedral angle. C. Angle between arms This angle usually ranges between 70 and 140. In fact, this parameter has an important effect on the boomerang stability. The length of the arms is fixed to keep a constant overall size of the boomerang. 3. Aerodynamic forces computation details by CFD The CFD software employed is StarCCM+. The approach we considered consists in using two reference systems  one external and inertial, the other fixed with respect to the boomerang and having its origin placed in the boomerang center of mass. Also, two domains and two grids are used: the first is spherical, having its origin placed in the boomerang center of mass and associated to the boomerang reference system; the second corresponds to an external parallelepiped shape associated to the external reference system. The internal spherical domain is provided with a rotation velocity around an axis normal to the boomerang plane and passing through the boomerang center of mass. The information exchange between the two domains is provided by an interface boundary that allows to interpolate the field values. In StarCCM+, a polyhedral mesh with prisms layers at the boomerang walls is defined within the sphere around the boomerang and an hexahedral mesh is defined in the rest of the domain (Fig.3). The twoequations RANS SST (Shear Stress Transport) turbulent model, with wall functions, is chosen and a segregated solver with constant density is employed. A mesh size of about 2.5 millions of cells has been defined, this being a good tradeoff between Fig. 3  Particular of a mesh section Case Histories
8 8 Newsletter EnginSoft Year 9 n 4 Fig. 4  CFD results on different revolution frames accuracy and computational efforts. Fig.4 shows the pressure field on a boomerang surface in different time steps during a rotation. It is possible to notice that the pressure force on each arm changes a lot during the rotation according to the relative position of the blades with respect to the translational velocity. At the end of the numeric simulation (for a given Ψ,U pair) the averaged forces and torques acting during the rotation are computed and then the corresponding F and T are available. 4. Process flow automation in modefrontier The whole process aiming at evaluating and optimizing the performances of the boomerang has been completely automatized through the software modefrontier. In this modular environment, the complete process flow is defined by the user, who can select among several available optimization algorithms, including Genetic and Evolutionary Algorithms, Game Strategies, Gradientbased Methodologies, MetaModels and Robust Design Optimization. Fig. 5  modefrontier main workflow modefrontier effectively automates the computation of the boomerang trajectory through the following steps: 1. modify the boomerang Catia model parameters; 2. obtain the updated geometry (stl file) from Catia and transfer it to StarCCM+ execution module; 3. launch StarCCM+ to build the computational mesh; 4. launch different StarCCM+ simulations using the same mesh prepared as above varying U and Ψ parameters for an appropriate number of samples; for each U and Ψ pair the corresponding forces and torques F and T are obtained; 5. use the set of simulations computed in iv) as training set to build in modefrontier response surfaces to obtain F(Ψ,U) and T(Ψ,U) over the whole range of variation of Ψ,U 6. pass the response surfaces and the boomerang inertia data to a MATLAB script to compute the trajectory by integrating equations of motion using a 4th order Runge Kutta method; 7. run an internal optimization for the given configuration to tune the four launching parameters (by minimizing the arrival distance); 8. the main multiobjective algorithm assesses how good the trajectory is with respect to specified objectives (total energy needed for the launch to be minimized) 9. the steps i)viii) are repeated automatically by the algorithm until one or more optimal configurations are obtained. The modefrontier workflow is shown in Fig.5. In particular, on the top we find the nodes (green subsystem) that define the range of variations of all the geometrical parameters, then the process flow (black line) starts with the interfaces to select the optimization algorithms and set their options, to continue with the CATIA direct interface that allows to automatically update the geometric model at the variation of the parameters, obtaining as results the updated Stl model, which is transferred to the following script node used to run StarCCM+ to create the mesh for the proposed geometry. The mesh (.sim file) is then transferred to the following application node, which basically launches in batch mode another modefrontier project file, running a set of CFD computations through StarCCM+ on the same mesh varying U and Ψ parameters, as described at point iv) above. The output of the internal modefrontier project is a Response Surface (RSM) or Metamodel, based on the available training set, able to extrapolate F(Ψ,U) and T(Ψ,U) over the whole range of variation of the two parameters (fig.6). The algorithm used for the RSM training is Kriging and the model is automatically exported as a Cscript, which can be read by MATLAB. The last application node in the process flow is another modefrontier project node, called launch_parameters_tuning. This node actually runs another optimization project in batch mode, using as input variables the four launch parameters described in section 1. The boomerang shape is fixed and the objective is defined by the minimization of the distance from the arrival position and the launching position. For this purpose, a fast monoobjective algorithm is used (Simplex) and the project just executes a MATLAB script through the corresponding direct interface for each set of launching parameters; basically the script drives a RungeKutta integration to compute the Case Histories
9 Newsletter EnginSoft Year 9 n 49 Fig. 6  Response surfaces of boomerang aerodynamic forces boomerang trajectory (retrieving the needed F(Ψ,U) and T(Ψ,U) values for each integration time step directly from the Response surface available for each boomerang geometry). The final outcome of the modefrontier Batch node in the main process flow for each boomerang geometry is therefore its tuned trajectory, whose performances are to be optimized in the external loop. For this purpose, from this node the following outputs are extracted: Range: this is the maximum distance reached by the boomerang during its flight; it has just been considered as a constraint in the optimization, to penalize configurations of too small range;; Accuracy: this is the difference between the position from which the boomerang is launched and the position where the boomerang returns (optimized by the internal loop as described above for each boomerang candidate solution) Energy: this is the energy (translational plus rotational) necessary to launch the boomerang, that is a quantity to be minimized (to reduce the effort for the thrower). 5. Optimization Strategy and Optimization Results Several tests were performed in order to find the proper number of simulations required to create enough accurate response surfaces. It has been found that a matrix of 12 points guarantees an error of approximation lower than 5% and this was the size of the training set finally selected. This means that each boomerang trajectory computation needs 12 CFD simulations. For this reason a classical multiobjective optimization algorithm that may require hundreds of designs evaluations is not practically feasible, therefore a different strategy, based on the Game Theory (Hierarchical Games), has been chosen. As indicated in the previous chapter, two different objectives (returning accuracy and launch global energy) have to be considered. Actually, any candidate solution is first optimized by the internal workflow in order to tune the launching parameters (follower player); then, the identified optimal solution is evaluated by the external optimization workflow which handles the energy objective minimization by changing properly the geometrical parameters (leader player). Note that for both the internal and external optimizer the same modefrontier algorithm, Simplex, has been used due to its efficiency to solve singleobjective problems in few iterations. Fig.7 reports the global results of the optimization process, in the space of the objectives and constraints considered. In particular the abscissa reports the launch energy (Joule), the ordinate indicates the range (meters), and the color of the bubbles reports the returning accuracy for each design (distance in meters). At the end of the process, one of the optimal boomerang configuration has been chosen and its geometry and trajectory are also reproduced in Fig.8. The energy required to launch the boomerang is 3.5 J, the ratio of rotational with total energy is only 7% that corresponds to an initial spin of about 4 Hz and to an initial translational velocity equal to 15 m/s; the tilt angle is 0, while the aim is about 20. This set should make the boomerang launch pretty easy, with a range of 14.5 m. In conclusion, this paper has described an automatic and efficient methodology for the multiobjective optimization of a boomerang shape, resulting an interesting benchmark and proof of concept to illustrate the multiobjective and multidisciplinary capabilities of the optimization environment modefrontier. Rosario Russo, Alberto Clarich  ESTECO Spa Enrico Nobile, Carlo Poloni  Università di Trieste For more information: Francesco Franchini, EnginSoft Fig. 7  Optimization results Fig. 8  Optimal boomerang configuration and trajectory Case Histories
10 10  Newsletter EnginSoft Year 9 n 4 The Particle Finite Element Method. An effective numerical technique for multidisciplinary engineering problems involving fluidsoilstructure interaction Introduction The analysis of problems involving the interaction of fluids, soil/rocks and structures is relevant in many areas of engineering. Examples are common in the study of landslides and their effect on reservoirs and adjacent structures, offshore and harbour structures under large waves, constructions hit by floods and tsunamis, soil erosion and stability of rockfill dams in overspill situations, excavation and drilling problems in civil and petroleum engineering, etc. The author and his group have developed in previous works a particular class of Lagrangian formulation for solving problems involving complex interactions between (free surface) fluids and solids. The socalled particle finite element method (PFEM, treats the mesh nodes in the fluid and solid domains as particles which can freely move and even separate from the main fluid domain representing, for instance, the effect of water drops. A mesh connects the nodes discretizing the domain where the governing equations are solved using a stabilized FEM. An advantage of the Lagrangian formulation used in PFEM is that the nonlinear and non symmetric convective terms disappear from the fluid equations. The difficulty is however transferred to the problem of adequately (and efficiently) moving the mesh nodes. In the next section the key ideas of the PFEM are outlined. Next the basic equations for a general continuum using a Lagrangian description and the formulation are schematically presented. We present several examples of application of the PFEM to solve multidisciplinary FSSI problems such as the motion of rocks by water streams, the stability of breakwaters and constructions under sea waves, the study of a landslide falling into a reservoir, the sinking of ships and the collision of ships with ice blocks. The basis of the particle finite element method In the PFEM both the fluid and the solid domains are modelled using an updated Lagrangian formulation. That is, all variables are assumed to be known in the current configuration at time t. The new set of variables in both domains is sought for in the next or updated configuration at time t + Δt. The finite element method (FEM) is used to solve the equations of continuum mechanics for each of the subdomains. Hence a mesh discretizing these domains must be generated in order to solve the governing equations for each subdomain in the standard FEM fashion. The quality of the numerical solution depends on the discretization chosen as in the standard FEM. Adaptive mesh refinement techniques can be used to improve the solution. Fig. 1  Scheme of a typical solution with PFEM. Sequence of steps for moving a cloud of nodes representing a domain containing a fluid and a solid part from time n (t=t n ) to time n+2 (t=t n + 2Δt) Case Histories
11 Newsletter EnginSoft Year 9 n 411 For clarity purposes we will define the collection or cloud of nodes (C) pertaining to the analysis domain (V) containing the fluid and solid subdomains and the mesh (M) discretizing both domains. A typical solution with the PFEM involves the following steps. 1. The starting point at each time step is the cloud of points in the fluid and solid domains. For instance n C denotes the cloud at time t = t n (Figure 1). 2. Identify the boundaries for both the fluid and solid domains defining the analysis domain n V in the fluid and the solid. This is an essential step as some boundaries (such as the free surface in fluids) may be severely distorted during the solution, including separation and reentering of nodes. The Alpha Shape method is used for the boundary definition. 3. Discretize the fluid and solid domains with a finite element mesh. n M We use an effect mesh generation scheme based on the extended Delaunay tesselation. 4. Solve the coupled Lagrangian equations of motion for the overall continuum. Compute the state variables in at the next (updated) configuration for t + Δt: velocities, pressure and viscous stresses in the fluid and displacements, stresses and strains in the solid. 5. Move the mesh nodes to a new position n n+1 C where n+1 denotes the time t n + Δt, in terms of the time increment size. This step is typically a consequence of the solution process of step Go back to step 1 and repeat the solution for the next time step to obtain n+2 C (Figure 1). We emphasize that the key differences between the PFEM and the classical FEM are the remeshing technique and the identification of the domain boundary at each time step. Generation of a new mesh A key point for the success of the PFEM is the fast regeneration of a mesh at every time step on the basis of the position of the nodes in the space domain. In our work the mesh is generated using the socalled extended Delaunay tesselation (EDT). As a general rule for large 3D problems meshing consumes around 15% of the total CPU time per time step, while the solution of Fig. 2  Modelling of contact conditions at a solidsolid interface with the PFEM the equations (with typically 3 iterations per time step) and the system assembly consume approximately 70% and 15% of the CPU time per time step, respectively. These figures refer to analyses in a single processor Pentium IV PC and prove that the generation of the mesh has an acceptable cost in the PFEM. Indeed considerable speed can be gained using parallel computing techniques. Identification of boundary surfaces One of the main tasks in the PFEM is the correct definition of the boundary domain. Boundary nodes are sometimes explicitly identified. In other cases, the total set of nodes is the only information available and the algorithm must recognize the boundary nodes (Figure 2). In our work we use a Delaunay partition for recognizing boundary nodes and, hence, boundary surfaces. This is performed by using the socalled Alpha Shape method. This method also allows one to identify isolated fluid particles outside the main fluid domain. These particles are treated as part of the external boundary where the pressure is fixed to the atmospheric value. We recall that each particle is a material point characterized by the density of the solid or fluid domain to which it belongs. The mass lost when a boundary element is eliminated due to departure of a node from the analysis domain containing a fluid is regained when the node falls down and a new boundary element is created by the Alpha Shape algorithm. The boundary recognition method is useful for detecting contact conditions between the fluid domain and a boundary, as well as between different solids as detailed in the next section. Treatment of contact conditions in the PFEM Known velocities at boundaries in the PFEM are prescribed in strong form to the boundary nodes. These nodes might belong to fixed external boundaries or to moving boundaries. Contact between fluid particles and fixed boundaries is accounted for by the incompressibility condition which naturally prevents fluid nodes to penetrate into the solid boundaries. The contact between two solid interfaces is treated by introducing a layer of contact elements between the two interacting solid interfaces. This layer is automatically created during the mesh generation step by prescribing a minimum distance (h c ) between two solid boundaries. If the distance exceeds the minimum value (h c ) then the generated elements are treated as fluid elements. Otherwise the elements are treated as contact elements where a relationship between the tangential and normal forces and the corresponding displacement is introduced (Figure 2). This algorithm allows us to identify and model complex frictional contact conditions between two or more interacting bodies moving in water in an extremely simple manner. The algorithm can also be used effectively to model frictional contact conditions between rigid or elastic solids in structural mechanics applications. Modeling of bed erosion Prediction of bed erosion and sediment transport in open channel flows are important tasks in river and environmental Case Histories
12 12  Newsletter EnginSoft Year 9 n 4 engineering. Bed erosion can lead to instabilities of the river basin slopes. It can also undermine the foundation of bridge piles thereby favouring structural failure. Modeling of bed erosion is also relevant for predicting the evolution of surface material dragged in earth dams in overspill situations. Bed erosion is one of the main causes of environmental damage in floods. In a recent work we have proposed an extension of the PFEM to model bed erosion. The erosion model is based on detaching elements belonging to the bed surface in terms of the frictional work at the surface originated by the shear stresses in the fluid. The resulting erosion model resembles Archard law typically used for modeling abrasive wear in surfaces under frictional contact conditions. Sediment deposition can be modeled by an inverse process. Hence, a suspended node adjacent to the bed surface with a velocity below a threshold value is attached to the bed surface. Fig. 5  Erosion of a soil mass due to sea waves and the subsequent falling into the sea of an adjacent lorry Fig. 6  Simulation of landslide falling on constructions using PFEM Fig. 3  Breaking waves on breakwater slopes containing reinforced concrete blocks Examples Impact of sea waves on piers and breakwaters Figure 3 shows the analysis of the effect of breaking waves on two different sites of a breakwater containing reinforced concrete blocks (each one of 4x4x4 mts). The figures correspond to the study of Langosteira harbour in A Coruña, Spain using PFEM. Soil erosion problems Figure 4a shows the capacity of the PFEM for modelling soil erosion, sediment transport and material deposition in a river bed. The soil particles are first detached from the bed surface under the action of the jet stream. Then they are transported by the flow and eventually fall down due to gravity forces into the bed surface at a downstream point. Figure 4b shows the progressive erosion of the unprotected part of a breakwater slope in the Langosteira harbour in A Coruña, Spain. The non protected upper shoulder zone is progressively eroded under the sea waves. Falling of a lorry into the sea by erosion of the road slope due to sea waves Figure 5 shows a representative example of the progressive Fig. 7  Lituya Bay landslide. Left: Geometry for the simulation. Right: Landslide direction and maximum wave level erosion of a soil mass adjacent to the shore due to sea waves and the subsequent falling into the sea of a 2D object representing the section of a lorry. The object has been modeled as a rigid solid. This example and the previous ones, although still quite simple and schematic, show the possibilities of the Fig. 4  (a) Erosion, transport and deposition of soil particles at a river bed due to an impacting jet stream (b) Erosion of an unprotected shoulder of a breakwater due to sea waves Fig. 8  Lituya Bay landslide. Evolution of the landslide into the reservoir obtained with the PFEM. Maximum level of generated wave (551 mts) in the north slope Case Histories
13 Fig. 92D simulation of the sinking of a cargo vessel due to a breach in the bow region. (a) Water streamline at different times. (b) Water velocity pattern at different times during sinking PFEM for modeling complex FSSI problems involving soil erosion, free surface waves and rigid/deformable structures. Modelling of landslides The PFEM is particularly suited for modelling landslide motion and its interaction with structures and the environment. Figure 6 shows a simulation using PFEM of a soil mass representing a landslide falling on four constructions modelled as rigid body solids. A case of much interest is when a landslide occurs in the vicinity of a reservoir. The fall of debris material into the reservoir typically induces large waves that can overtop the dam originating an unexpected flooding that can cause severe damage to the constructions and population in the downstream area. We present some results of the 3D analysis of the landslide produced in Lituya Bay (Alaska) on July 9th 1958 (Figure 7). The landslide was originated by an earthquake and mobilized 90 millions tons of rocks that fell on the bay originating a large wave that reached a hight on the opposed slope of 524 mts. Figure 8 shows images of the simulation of the Lituya Bay landslide with PFEM. PFEM results have been compared with observed values of the maximum water level in the north hill adjacent to the reservoir. The maximum water level in this hill obtained with PFEM was 551 mts. This is 5% higher than the value of 524 mts. observed experimentally. The maximum height location differs in 300 mts from the observed value. In the south slope the maximum water Newsletter EnginSoft Year 9 n 413 height observed was 208 mts, while the PFEM result (not shown here) was 195 mts (6% error). Simulation of sinking of ships The PFEM can be effectively applied for simulating the sinking of ships under a variety of scenarios. Figure 9 shows images of the 2D simulation of the sinking of a cargo vessel induced by a breach in the bow region. Figure 10 displays a 3D simulation of the skinking of a simple fisherman boat induced by a hole in the side of the hull. These examples evidence the potential of PFEM for the study of the sinking of ships. Colision of boat with ice blocks Figures 11 shows an example of the application of PFEM to the study of the collision of a ship with floating ice blocks. The boat and the ice blocks have been modelled as rigid bodies in this example. Indeed, the deformation of the ship structure due to the iceship interaction forces can be accounted for in the analysis. Conclusions The particle finite element method (PFEM) is a promising numerical technique for solving fluidsoilstructure interaction (FSSI) problems involving large motion of fluid and solid particles, surface waves, water splashing, frictional contact situations between fluidsolid and solidsolid interfaces and bed erosion, among other complex phenomena. The success of the PFEM lies in the accurate and efficient solution of the equations of an incompressible continuum using an updated Lagrangian formulation and a stabilized finite element method allowing the use of low order elements with equal order interpolation for all the variables. Other essential solution ingredients are the efficient regeneration of the finite element mesh, the identification of the boundary nodes using the AlphaShape technique and the simple algorithm to treat frictional contact conditions and erosion/wear at fluidsolid and solidsolid interfaces via mesh generation. The examples presented have shown the potential of the PFEM for solving a wide class of practical FSSI problems in engineering. Eugenio Oñate International Center for Numerical Methods in Engineering (CIMNE), Spain Universitat Politècnica de Catalunya (UPC), Spain Fig. 103D simulation of the sinking of a boat induced by a hole in the side of the hull Fig. 113D simulation of a boat colliding with five ice blocks Case Histories
14 14  Newsletter EnginSoft Year 9 n 4 FrequencyReconfigurable Microstrip Antenna for SoftwareDefined Radio The increasing demand for portable devices with wireless connectivity within a wide frequency spectrum presents an ambitious challenge for the designer of the RF frontend who has to manage different wireless standards (GSM, UMTS, WiMAX, WiFi, Bluetooth, LTE). Covering several frequency bands simultaneously with a single antenna can be a very demanding task, which is why the employment of many different antennas integrated in the device and the use of multiband or broadband antennas might be a feasible solution for the problem. The use of different antennas implies an increase of the overall cost and space requirements. Broadband antennas transmit and receive signals within a large bandwidth but they may suffer an unbearable deterioration of the signal to noise ratio and thus a reduction of the overall efficiency of the system. Moreover, the electromagnetic spectrum is a shared resource that is more and more congested with the increasing number of users of wireless devices and the further exploitation of the available frequencies by other services poses practical and regulatory difficulties. To cope with this problem, the employment of an unused part of the spectrum or the opportunistic and temporary use of a shared portion may offer new resources. The Cognitive Radio (CR) concept has been proposed as a solution since the related CR radio network is able to evaluate the instant occupancy of spectrum and decides on this basis how to allocate services on temporarily unoccupied parts of the EM spectrum. This recent paradigm of communication allows an efficient spectrum usage but also poses some challenges, with regard to hardware and software, which have motivated the rise of the Software Defined Radio (SDR) concept during the last years. A device based on SDR is an integrated system which must exhibit extreme hardware performance to support the necessary softwarebased signal processing and guarantee the desired flexibility. The final goal is therefore to implement most of the radio system in software, easy to update or Fig. 1  Top view of the frequencyreconfigurable microstrip patch antenna. The continuous circles indicate group#1 whereas dashed circles designate group#2. upgrade, without changing the controlled hardware. This ambitious objective imposes strict requirements to the capabilities of the device radio frontend especially in terms of the requested frequency agility necessary for the smart and dynamic adaptation to the wireless environment. In particular, severe constraints are placed on critical components such as filters, matching networks and antennas. The SDR architecture requires a reconfigurable antenna which is able to modify one, or a combination, of its fundamental radiation properties depending on the adopted scheme [6]. A radiating device can exhibit a frequency agility, which allows to set its instant working frequency, a change in pattern shape, or an alteration of the electric field polarization. The reconfiguration is obtained by adjusting the path of currents on the antenna or even by altering the geometry of the radiating device. The three aforementioned degrees of reconfigurability can be realized by recurring to different technologies among which electrical RF switches such as PIN diodes and varactors, photoconductive elements or MEMS. Different kinds of antennas have been proposed for the enhancement of the SDR radio frontend including PIFAs, monopoles and patches. Within this framework, we have recently developed a reconfigurable microstrip patch antenna by using PIN diodes as RF switches whose biasing network is softwarecontrolled via a PIC microcontroller. The microstrip patch antenna has been chosen for its low profile, robustness and easy manufacturing. The aim is to obtain an antenna with a reconfigurable frequency response between 850 MHz and 3.5 GHz by simply changing the state of the RF switches. After a preliminary optimization study based on the cavity model of the patch antenna, we have considered the configuration shown in Fig. 1 in which five PIN diodes are able to guarantee a proper sweep of the working frequency. The positions of Case Histories
15 Newsletter EnginSoft Year 9 n 415 the RF switches have been chosen by inspecting the path of the currents on the patch surface to individuate the most suitable placement of the diodes to guarantee the current flow. The overall size of the patch antenna is 84 mm 70 mm. It is worthwhile to mention that the size of each element in the antenna and the position of the PIN diodes were chosen under two imposed constraints. First of all, in order to reduce the complexity of the design, we aimed at a configuration where all the RFswitch biasing lines had to be placed on the top layer of the antenna substrate, avoiding any cut in the ground plane. Next, we also searched for a solution without any matching network thus requiring in each RFswitch state an impedance close to the 50 Ohm of the feeding line. Two shorting pins with a 1.0mmdiameter were inserted as illustrated in Fig. 1, the former in one of the outer sections and the latter in the inner element. To obtain a correct evaluation of the antenna behavior, the diodes have been considered by using their circuit model in the Ansoft HFSS simulations (Fig. 2) instead of substituting them as an open circuit in the "Off" case and as a short circuit when in "On" state. The employed PIN diode is an Avago HSMP4890 which presents Rs = 2.5 Ohm, L = 1 nh, CT = 0.3 pf and RP in the order of KOhm. The PIN diodes were placed by using silver conductive epoxy to avoid overheating of the device. In our design the five PIN diodes have been divided into two groups (continuous and dashed circles as shown in Fig.1) thus providing four possible configurations. Each group allows current to flow when the diodes are in "On" state whereas the propagation of the RF signal is interrupted when they are set to "Off" state. The biasing network comprises two lines on the top of the dielectric substrate which connect each of the outer sections of the patch to the DC source. A RF block is necessary to isolate the RF and DC source on these biasing lines. Moreover, the inner element of the antenna and the other one inside the similloop element are connected to the ground plane by using the 1.0mmdiameter via. This configuration allows modifying the state of the two RFswitch groups by simply changing the voltage between the ground plane and the two lines connected to each antenna side. In order to change on demand the instantaneous frequency, we have programmed a PIC16F688 flash microcontroller to switch among the four possible configurations described above and we have directly connected the prototype board to a Fig. 2  PIN diode circuit model for the "On" state (a) and "Off" state (b). Fig. 3  The antenna configuration is completely softwarecontrolled by using the PC which operates on the PIC microcontroller to change the state of PIN diodes. Fig. 4  Frequency response of the antenna when PIN diodes belonging to group#2 are in "On" state and others are in "Off" state. laptop through an USB interface (Fig. 3). Therefore the activation and deactivation of the RF switches is performed by a microcontroller which can change the working frequency on the basis of the information collected by another antenna (sensing antenna) which is scouting the available frequency slots, as proposed in the CR paradigm. A comparison between the simulated and measured S11 parameters for the configuration with group#1 in OFF state and group#2 in ON state is reported in Fig.4 and the agreement is satisfactory except for some frequency shifts which could be attributed to discrete component tolerances and soldering effects. Fig. 5  Comparison between the simulated (continuous line) and measured (dashed line with triangles) radiation patterns at 850 MHz: φ = 0 deg., φ = 90 deg. The simulated and measured patterns on the xz (φ = 0) and yz (φ = 90) planes are reported in Fig. 5. From the inspection of the results it is apparent that there is no significant distortion of the antenna pattern caused by the PIC microcontroller and the biasing lines. Ing. Simone Genovesi, Prof. Agostino Monorchio Microwave and Radiation Lab., Dip. Ingegneria dell'informazione (www.mrlab.it) Università di Pisa For more information, please contact: Case Histories
16 16  Newsletter EnginSoft Year 9 n 4 ECS System Simulation  Architecture and Performance Optimization from the Early Phases of the System Design In today s aircraft thermal design, we can observe a trend towards electronics systems integration characterized by higher heat densities and a more frequent use of composite primary structures. All these factors require robust thermal management and thermal architecture design already at the preliminary design stages. The thermal architecture will have to be developed in order to mitigate thermal risks for temperaturesensitive equipment as well as to limit the aircraft systems overdesign. The improvement and optimization of the thermal architecture is regarded as one of the key success factors for future aircraft developments. It requires a complete pyramid of simulation tasks to be set up, from the individual equipment to aircraft section simulation, to the global aircraft thermal analysis. Many difficulties arise from this simulation framework due to the variety of physical models, partners, techniques and tools used at each level of the pyramid. In this context, the aim of this paper is to describe an Environmental Control System design approach as applied in Alenia Aermacchi. The main technical challenges addressed in this paper are: Air conditioning pack architecture design Air distribution line design and tradeoff study, Multidisciplinary optimization of the air distribution system components A/C cabin thermal environment evaluation and occupants thermal comfort. Background The air conditioning system is designed in such a way that it maintains the air within the pressurized fuselage Fig. 2  Thermal aircraft schematic Fig. 1  A/C air conditioning pack and air distribution system Table 1  Electrical equipment dissipated power Case Histories
17 Newsletter EnginSoft Year 9 n 417 compartment at the required level of pressure, temperature, flow rate and purity. The air is supplied to the system from the engine compressor, the hot compressed air is cooled and conditioned in the air conditioning pack before being distributed to the various compartments through the air conditioning system (see Figure 1). As shown in Figure 4, the standard air condition pack architecture has been considered. Figure 4 illustrates also the monodimensional model built in LMS Amesim. The heat exchanger monodimensional model (low fidelity model) has been validated by comparing its results with CFD model results (high fidelity model). In Figure 5, we can see the validation analysis results. Accordingly, in order to guarantee a comfortable A/C cabin environment, it is necessary to design and optimize the air conditioning pack and air distribution system. Air conditioning pack architecture design Requirements This study focuses on the following requirements: A/C schematic configuration (see Figure). Thermoacoustic insulation U factor. Electrical equipment dissipated power (see Table 1). Temperature requirements for cabin/ cockpit. Environmental envelope (see Figure 3). The certification and performance requirements of ECS are reported below: o Minimum fresh flow per passenger: 0.55 lb/min. o Minimum fresh flow per crew member: 10 cfm. o Minimum fresh flow per galley 15 cfm. o Maximum ratio recirculation / total flow 0.4. o Maximum fresh flow per passenger/crew member for single pack operations 0.4. o Cabin stabilized temperature between 21 C 27 C. o Cockpit stabilized temperature between 21 C27 C. Fig. 3  Environmental envelope Fig. 4  ECS pack 1D model Methodology The design of the air conditioning pack architecture has been reached through the following steps: Definition of air conditioning pack monodimensional model. Definition and validation of heat exchanger monodimensional model. Definition of A/C cabin thermal monodimensional model. Optimization of heat exchanger design, in order to meet certification and performance requirements. Fig. 5  Heat exchanger size Case Histories
18 18  Newsletter EnginSoft Year 9 n 4 Steady state, cruise cold day (40 kft, 70 C, Mach 0.85, 20% passengers) The heat exchanger has been defined in terms of its geometrical characteristics and number of plates. As shown in Figure 7, the analysis results confirm the compliance of the air conditioning pack architecture with the certification and performance requirements. Fig. 6  A/C monodimensional thermal model Air distribution line design and tradeoff study In order to determine the air conditioning pack architecture, the second step focused on the definition of the air distribution system. The latter depends on the following aspects: Performance in terms of pressure losses. Integration in aircraft. Reliability and maintainability. Two different architectures have been analyzed. The first one (Architecture A) shown in Figure 8 is a parallel architecture composed of an underfloor line and a low pressure air distribution line that allow to distribute the airflow coming from the mixing chamber in parallel through the risers. Fig. 7  Performance of air conditioning pack Fig. 8  Air distribution system CAD model Architecture A Fig Monodimensional model Architecture A. Fig. 9  Air distribution system CAD model Architecture B In order to design the air cycle machine and heat exchanger, the cabin aircraft monodimensional thermal model shown in Figure 6 has been built. It allowed to evaluate the cabin thermal environment and hence the compliance with varying pack performance depending on the heat exchanger design. The operating conditions analysed have been: Steady state, ground hot day (ISA+25, 100% passengers) Steady state, ground cold day (ISA55, 20% passengers) Steady state, cruise hot day (40 kft, 35 C, Mach 0.85, 100% passengers) Fig Monodimensional modelarchitecture B. Case Histories
19 Newsletter EnginSoft Year 9 n 419 As boundary conditions we assumed the data reported below in various flight conditions, then the steady state analysis has been carried out: Temperature, air flow, pressure and humidity coming from the air conditioning pack. External conditions in terms of temperature. Equipment and light heat load. Passengers heat load. Figure 12 shows the analysis results in terms of pressure drop vs mass flow curve. In particular, the results highlight that the air distribution system pressure losses of Architecture A are higher than those of Architecture B. Fig Pressure loss vs massflow curves The second one (Architecture B) shown in Figure 9 is a sequential architecture where the under floor is much limited, and the cabin air distribution system is developed above the floor. Starting from the CAD model shown above, a monodimensional model for each architecture has been built in LMS Amesim (see Figures 10 and 11). The monodimensional models are composed of the following parts: Connection with air conditioning pack monodimensional model. Cockpit line Cabin line Simplified A/C thermal model as thermal node. Internal macro that allows to simulate the physiology of the passengers in terms of heat load and humidity released. A comparison analysis has been performed by means of a Technical Performance Measure (TPM) methodology. First, all of the key requirements (performance, system integration in the aircraft, RMT) have been defined, categorized and weighted according to their degree of importance. Key factors and preferences have been established on the basis of Alenia s experiences. Normalized weights of 01 range have been assigned as per the above to each key requirement. Then, each requirement has been split into subrequirements, as follows: Performance: o Pressure loss. System Integration in the aircraft: o Influence on cabin noise; o Weight; o Ease of installation. RMT o Reliability; o Maintainability. Each subrequirement has been weighted according to its degree of importance compared to the others. Then, each weight has been normalized in absolute terms, in accordance with the key requirements. Also a score has been assigned to each subrequirement, as follows: 1 = VERY POOR: the proposed solution does not meet the system s requirements; 2 = POOR: the proposed solution does not meet the system s requirements but the requirement deviation is acceptable; Fig Technical performance measure Fig CAD model for Outlet Case Histories
20 20  Newsletter EnginSoft Year 9 n 4 Fig Parametric model 3 = ACCEPTABLE: the proposed solution meets the system s requirements, but with some risks. 4= GOOD: the proposed solution meets the system s requirements. Subsequently, attributes, weights and scores have been allocated, the quantitative frame which builds a rational evaluation has been defined, calculating the relevant weighted score for each subrequirement. Figure 13 shows details of the TPM comparison analysis results. Following the outcome of the TPM approach, the results of architecture A of the air distribution system are preferred. Multidisciplinary optimization of air distribution system components The shape optimization of the air vent outlet has been carried out through the following phases: 1. Mesh accuracy study 2. Input sensitivity study 3. Design of Experiment (DOE) Analysis 4. Optimization 5. Automatic updating of the party in the product Design specifications Based on the flow of incoming air assigned to the maximum operative mass flow rate, the shape of the air vent outlet has been optimized (Figure 14) with the objective of minimizing pressure losses and noise levels. To achieve these goals, the geometry for the surface connection between the inlet and outlet of the nozzle has been parameterized. Among the geometric parameters that were evaluated for the optimization is the angle Alpha; it is important to mention that this angle is formed by the axis coming from the centre of the inlet and the centre of the outlet, it changes the direction in which the air flow enters the cabin. Parameterization For the parameterization 6 points have been identified; these 6 points are located on the intersection of a virtual plane perpendicular to the line joining the centres of the inlet and the exit outlet. The 6 splines in Figure 15 have been initially identified as points A, B, C, D, E, F. As these point change their locations, the area of the opening will be adapted for the purpose of the optimization. Based on this configuration and by changing the location of the points, it becomes possible to update the area of the opening and in this way to modify the purpose of the optimization. modefrontier Model In the modefrontier model the geometric inputs are held constant while varying only the parametric data for the CAE model. The process flow consists of three blocks: 1. CATIA process: it opens the file CatiaV5 CATPart geometry of the nozzle, then it converts files into IGS and sends them to the next process. 2. STAR Process: StarCCM+ runs a mesh with Base Size Length which is assigned to the CAE_Input, then it automatically performs the calculations. It estimates the time taken (CPU_Time), and sends the simulation file (SIMfile) for the next process. 3. Process PostPRO: StarCCM+ checks for the simulation file, and if there are further calculations to determine the pressure and noise levels, in particular, according to the PostPRO_Input, a visual representation is saved in a jpeg file containing the pressure, the speed or noise level, as well as images of the mesh and the graph of the residue. The variables monitored are CPU processing time in seconds, CPU_Time, and total pressures in Pascal in and out of the nozzle: p_in p_out respectively. Fig modefrontier model for shape and noise optimization of the outlets Design Of Experiment (DOE) Analysis In modefrontier (whose workflow is shown in Figure 16) a DOE analysis has been performed taking into account the 3 free parameters dx_cf, dy_ab, and dy_de, while, dx_ab, dx_de, and dy_cf remain constant or the abscissas of points A, B, D and E. The ordinates of tpoints C and F remain stationary as assigned by the geometry. Case Histories
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