Dassault Syst` emes - SIMULIA PowerFLOW
RGUILLERMO D´IAZ V´ AZQUEZ
Master’s Degree Project
Stockholm, Sweden June 2019
for PowerFLOW
Dassault Syst` emes - SIMULIA PowerFLOW
RGuillermo D´ıaz V´ azquez
Master’s Degree Thesis Project MSc in Aerospace Engineering
Academic supervisor and examiner:
Evelyn Otero
KTH Royal Institute of Technology Department of Aerospace Engineering
Stockholm, Sweden, 114 28
URL: https://www.kth.se/en
Abstract
The field of computational fluid dynamics (CFD) is exponentially growing in
terms of performance, robustness, and applications. The expansion of CFD
also means more users and more simulations, which translates into more
human errors and mistakes in the simulation set up. Because the simulation
set up should be the correct in order to accurately reproduce the desired
phenomenon, such errors must be mitigated in order to increase the reliability
and robustness of the simulations. In this project a tool has been developed
to tackle this issue, within the CFD software SIMULIA PowerFLOW. The
tool extracts and analyzes the data of the cases before simulation, reporting
the results to the user for error detection. The present work aims to present
the implementation, the application and the benefits of the designed tool.
Case Data Analysverktyg f¨ or PowerFLOW
Str¨ omningsmekaniska ber¨ akningar (CFD) omr˚ adet v¨ axer exponentiellt med
avseende p˚ a prestanda, robusthet och till¨ ampningar. Expansionen av CFD
bidrar ¨ aven till fler anv¨ andare och simuleringar, vilket i sin tur leder till fler
m¨ anskliga fel och misstag i upps¨ attningen av simuleringar. Eftersom simuler-
ingsupps¨ attningen beh¨ over vara korrekt f¨ or att ˚ aterskapa ¨ onskade fenomen,
beh¨ over s˚ adana fel undvikas f¨ or att kunna ¨ oka simuleringens tillf¨ orlitlighet
och robusthet. Med avseende p˚ a detta utvecklades ett verktyg i CFD mjuk-
varan SIMULIA PowerFLOW. Verktyget extraherar och analyserar inst¨ allnin-
garna f¨ ore simulering och rapporterar resultaten till anv¨ andaren f¨ or feldetek-
tering. Det h¨ ar arbetet redog¨ or f¨ or utvecklingen, till¨ ampningen och f¨ ordelarna
med framst¨ allda verktyget.
Acknowledgements
Firstly, I would like to thank Evelyn Otero for taking the role as my super-
visor and examiner, for the careful follow up of my work, and for the effort
placed in increasing the quality of this project by giving me an outstanding
feedback. I would also like to express my gratitude to the entire SIMULIA
team in Stuttgart for their patience and help. In particular to Monti Indro
for welcoming into his team. To Matthieu Plagnard for his support, guid-
ance and supervision throughout the whole project. Additionally, to Filippo
Boscolo for his technical assistance and continuous cooperation. Finally, I
am thankfull for the unconditional support and motivation of my family and
my girlfriend, Patricia.
1 Introduction 1
2 Background 3
2.1 Dassault Syst` emes . . . . 3
2.1.1 Field Office Stuttgart . . . . 4
2.2 SIMULIA PowerFLOW Suite . . . . 5
2.3 PowerFLOW background . . . . 7
2.3.1 Navier-Stokes Equations and Continuum Theory . . . . 7
2.3.2 Lattice Boltzmann Method . . . . 9
2.3.3 Contrast of PowerFLOW and Traditional CFD . . . . 12
3 Tool Implementation 15 3.1 Background and Motivation . . . . 15
3.2 Project Statement . . . . 16
3.3 Methodology . . . . 16
3.3.1 Tool Workflow . . . . 17
3.3.2 Features and Content Generated . . . . 18
3.4 Project Management . . . . 20
4 Results 22 4.1 Python Routines . . . . 22
4.1.1 Routines Common Sections . . . . 22
4.1.2 Routine: CdiCheck.py . . . . 23
4.1.3 Routine: CdiComp.py . . . . 24
4.2 PowerINSIGHT . . . . 26
4.2.1 File: CaseData AnalysisTool.pins . . . . 26
4.3 Tool Evaluation . . . . 26
4.3.1 CDI Check results . . . . 27
4.3.2 CDI Comparison results . . . . 36
5 Discussion 44
List of Figures
2.1 ”The 3D EXPERIENCE Platform” logo and its brands [12] . 4
2.2 SIMULIA PowerFLOW suite [9] . . . . 5
2.3 D3Q19 Model . . . . 11
2.4 Overview of one cycle of the LB algorithm. The dark grey boxes show sub-steps that are necessary for the evolution of the solution. The lighter grey box indicates the optional out- put step. The lighter grey box has macroscopic fields to be written to the hard disk for visualisation or post-processing. [8] 12 2.5 NSM and LBM overview . . . . 13
3.1 Case Data Analysis Tool workflow . . . . 17
3.2 Case Data Analysis Tool PowerINSIGHT tree . . . . 19
4.1 Case Data Analysis Tool Content Tree in PowerINSIGHT . . . 27
4.2 NAS Overview: EV12 Baseline . . . . 28
4.3 Files in CDI and NOT in storage: EV12 Baseline . . . . 28
4.4 Open Shells: EV12 Baseline . . . . 29
4.5 Case Variables: EV12 Baseline . . . . 29
4.6 Case Method Overview: EV12 Baseline . . . . 30
4.7 VR07 Viewpoint: Ground-ISO EV12 Baseline . . . . 30
4.8 VR08 Viewpoint: Ground-ISO EV12 Baseline . . . . 30
4.9 VR09 Viewpoint: Ground-ISO EV12 Baseline . . . . 30
4.10 Body Viewpoint: Ground-Front EV12 Baseline . . . . 31
4.11 Body Viewpoint: Ground-ISO EV12 Baseline . . . . 31
4.12 Body Viewpoint: Ground-Left EV12 Baseline . . . . 31
4.13 Body Viewpoint: Ground-Top EV12 Baseline . . . . 31
4.14 Engine Viewpoint: Ground-ISO EV12 Baseline . . . . 31
4.15 Suspension Viewpoint: Ground-ISO EV12 Baseline . . . . 31
4.16 Wheels Viewpoint: Ground-ISO EV12 Baseline . . . . 32
4.17 Subsystems Viewpoint: Ground-Top EV12 Baseline . . . . 32
4.18 NAS Overview: EV12 Variant . . . . 32
4.19 Files in CDI and NOT in Storage: EV12 Variant . . . . 33
4.20 Files in Storage and NOT in CDI: EV12 Variant . . . . 33
4.21 Name-Matching Files BUT Different Times: EV12 Variant . . 33
4.22 Open Shells: EV12 Variant . . . . 34
4.23 Case Variables: EV12 Variant . . . . 34
4.24 Case Method Overview: EV12 Variant . . . . 35
4.25 VR07 Viewpoint: Ground-ISO EV12 Variant . . . . 35
4.26 VR08 Viewpoint: Ground-ISO EV12 Variant . . . . 35
4.27 VR09 Viewpoint: Ground-ISO EV12 Variant . . . . 35
4.28 VR10 Viewpoint: Ground-ISO EV12 Variant . . . . 35
4.29 Body Viewpoint: Ground-Front EV12 Variant . . . . 36
4.30 Engine Viewpoint: Ground-ISO EV12 Variant . . . . 36
4.31 Suspension Viewpoint: Ground-ISO EV12 Variant . . . . 36
4.32 Wheels Viewpoint: Ground-ISO EV12 Variant . . . . 36
4.33 Parts Analysis Overview . . . . 37
4.34 Parts in Variant and NOT in Baseline . . . . 37
4.35 Different Position Only . . . . 38
4.36 Different Area and Position . . . . 38
4.37 Faces Analysis Overview . . . . 38
4.38 Faces in Variant and NOT in Baseline . . . . 39
4.39 Faces with Different Area . . . . 39
4.40 Case Analysis Overview . . . . 40
4.41 Different Value or Unit Variables . . . . 40
4.42 Percentage Difference . . . . 41
4.43 VR10 Viewpoint: Ground-ISO EV12 Comparison . . . . 41
4.44 VR10 Viewpoint: Ground-Left EV12 Comparison . . . . 41
4.45 VR09 Viewpoint: Ground-Front EV12 Comparison . . . . 42
4.46 VR09 Viewpoint: Ground-ISO EV12 Comparison . . . . 42
4.47 Body Viewpoint: Ground-ISO EV12 Comparison . . . . 42
4.48 Body Viewpoint: Ground-Front EV12 Comparison . . . . 42
4.49 Engine Viewpoint: Ground-ISO EV12 Comparison . . . . 43
4.50 Engine Viewpoint: Ground-Front EV12 Comparison . . . . 43
Nomenclature
CF D - Computational Fluid Dynamics CAE - Computer Aided Engineering P LM - Product Lifecyle Management CAM - Computer Aided Manufacturing CAD - Computer Aided Design
V R - Refinement Regions N -S - Navier-Stokes
LBM - Lattice Boltzmann Method
P DE - Partial Differential Equation
P F - SIMULIA PowerFLOW
P I - SIMULIA PowerINSIGHT
P V - SIMULIA PowerVIZ
OS - Operating System
LRF - Local Rotating Frame
CDAT - Case Data Analysis Tool
CSV - Comma-Separated Values
P N G - Portable Network Graphics
BB - Bounding Box
∇· - Divergence
∂ - Partial derivative
D
Dt - Material Derivative ρ - Density
p - Pressure q - Heat flux v - Flow velocity τ 0 - Stress tensor e - Internal energy s - Entropy
T - Temperature
R - Specific gas constant
Chapter 1 Introduction
The automotive industry encompasses a wide range of companies and organi- zations which are involved in the design, analysis and manufacturing of motor vehicles [2]. This industry generates one of the world largest revenue mar- kets. Just the top ten companies accumulated revenue of 1.64 trillion U.S.
dollars in 2017 [11], producing more than 97 million vehicles in the same year [5]. The automotive industry is constantly evolving, seeking to fulfill customer needs and requests as well as imposed regulations for fuel efficiency, quality, functionality and cost. Manufacturers must carefully trade-off be- tween those values when designing a product. Time is a very critical factor in the development of a product, and automobiles are not an exception. En- gineers need to understand the design compromises either very early in the process or very quickly in order to reduce costs and time, to deliver the best compelling products. Nowadays, such early feedback can be achieved with computer-aided engineering software due to their current levels of accuracy.
In the field of CAD and PLM software, Dassault Syst` emes is a world leader due to its vast portfolio of products as well as their quality, function- ality and performance. Among its brands, SIMULIA focuses in software for virtual testing and realistic simulations of different fields. Within SIMU- LIA, the area of CFD is covered by the software conglomerated under the name of PowerFLOW. It is a unique CFD software, considering it is based on inherently transient Lattice Boltzmann physics, which differs from the conventional Navier-Stokes equations solvers. The ability to simulate tran- sient complex shapes, true rotating geometry, wind tunnel conditions, couple simulations with thermal and aeroacoustic analysis, along with paralleliza- tion for quick turn-around times makes it the preferred CFD software in the automobile industry. Experienced engineers are based in strategically located field offices to provide fluid flow simulation and consulting services.
This study was performed in one of the aforementioned offices located in
Stuttgart, Germany.
This thesis is determined to provide a deeper knowledge of the physics behind the CFD software PowerFLOW and how to employ it in real-world fluid dynamic problems. It is also intended to materialize the development and application of the performed project. The ambition of the project is to design a tool that will tackle the growing issue of human errors during the simulation set up in PowerFLOW. This is a very important factor to take into account when a phenomenon has to be simulated. The tool should be designed to add a layer of assurance and confidence in the cases ready to be simulated. To develop it, the programming language Python in combination with SIMULIA PowerINSIGHT will be used. The tool will extract the desired data from the simulation case files, will process this data and display it in an organized and user-friendly manner. This way user errors and mistakes can be easily found, and undesired differences between case runs discovered.
With this tool, fail simulations and wrong results will decrease, time and costs will be reduced and more importantly results will be more reliable and robust.
The report will start with a background review. This section will first introduce the company Dassault Syst` emes, their software suite PowerFLOW and the field office in Stuttgart. Then, it will cover the theory behind Pow- erFLOW, the Lattice Boltzmann method. The next section, will present the problem to be tackled, the proposed solution and the methodology used.
Then, the results are exposed with an inside explanation of the designed tool
and actual tool evaluation. The report will finish with a discussion of the
results and future work.
Chapter 2 Background
This section will provide with a brief background of Dassault Syst` emes, its branch SIMULIA, the software suit PowerFLOW and more important the theory behind it, the Lattice-Boltzmann method (LBM).
2.1 Dassault Syst` emes
Dassault Syst` emes, known as ”the 3DSEXPERIENCE Company”, is a Euro- pean software company subsidiary of Dassault Group and with headquarters in France. It was created in 1981 to develop a new CAD software called CA- TIA. The software became well received by the industry, which positioned Dassault as a leader in the field, allowing it to grow and expand. New soft- ware were developed or acquired, and by the end of the 20th century the describing term CAD/CAM had become too restrictive to be identified with its products. The acronym PLM, which stands for Product Lifecycle Manage- ment, covers more accurately the portfolio of products offered. The branches that comprise Dassault Syst` emes’ PLM products are DELMIA for manufac- turing, ENOVIA to support internal and external collaboration, SIMULIA for analysis and simulation, SolidWorks for 3D modeling and 3DVIA for 3D visualization. [12] [1]
Nowadays, Dassault resources are focused on the development of its most important business, a software platform that comprises all its branches, called
”The 3D EXPERIENCE Platform”. It aims to provide software solutions for every possible area of operations, from marketing to engineering, for any company or organization. The logo with its macro-areas and its respective software suits are illustrated in Figure 2.1.
As of 2018, the company has a staff of more than 16000 employees over 100
countries, with annual revenue of 3 billion U.S. dollars. Dassault’s revenue
Figure 2.1: ”The 3D EXPERIENCE Platform” logo and its brands [12]
originates from selling permanent or temporal licensed to specific products or to the whole 3D Experience. This was usually done on the customer site computer, however, the same software is more and more being licensed through the 3DS Cloud instead. In addition, support and training are pro- vided for any customer and offered tool. Such business requires to have experience engineers strategically based in field offices around the world to provide customer consultancy services.
2.1.1 Field Office Stuttgart
The project covered in this report was carried out at one of the aforemen- tioned field offices, in this case, located in Stuttgart, Germany. The city of Stuttgart was selected for its large automotive industry, having the head- quarters of Daimler and Porche. The office hosts three different teams with different functions. There is a small software group in charge of the Pow- erVIZ development, a sub-program of PowerFLOW. There is an aerospace team that focuses on turbine engine aeroacoustics. Finally, the biggest team is the automotive one, whose scope is the automotive industry in Germany, Italy and Sweden.
The automotive, as well as the aerospace team, are composed of appli- cation engineers who are in charge of the service aspect of the company’s software. The goal is to promote and sell licenses if possible but more im- portant to provide technical support and consultancy services to customers.
The technical support aspect covers from training to assistance as well as
advice with issues, discrepancies and best practices. Complete projects that
span from the customer initial geometry to simulation results and comparison
Chapter 2
studies are also carried out often.
2.2 SIMULIA PowerFLOW Suite
The SIMULIA brand was created in 2005 with the acquisition of Abacus Inc. Since then, Dassault has acquired other state-of-the-art simulation tools to create a complete portfolio ranging from electromagnetic to structural analysis as well as CFD. The latest acquisition, in 2017, was Exa Corporation, which brought the CFD software suit PowerFLOW into SIMULIA.
PowerFLOW is a CAE software suit employed in aerodynamics, thermal and aeroacoustic simulations. It is a well-established tool in the automobile industry, but quite new in the aeronautical field. However, in this latter field, its usage is growing exponentially. The suite consists of nine individual computer programs, each one devoted to a specific stage of the simulation, as shown in Figure 2.2.
Figure 2.2: SIMULIA PowerFLOW suite [9]
Once the CAD geometry is acquired, the pre-processing stage is initiated
with PowerDELTA. This program is responsible to prepare the geometry,
removing CAD defects, making it watertight and then meshing it. Pow- erDELTA is also used to modify the model, wrap complex geometries, per- form quality repairs and morphing among others. Afterward, the data is exported as ”.stl” or ”.nas” to the next program, PowerCASE. As its name suggests, it is responsible for the case setup, where the desired simulation conditions are assigned. The main parameters have to be defined such as the boundary conditions and the refinement of the VR regions. The VR regions are predefined volumes around the geometry used to refine the discretization in critical regions. Templates are used to facilitate and speed up the case setting based on best practices. Once the case is finalized and ready, it is saved in a ”.cdi” format. The CDI 1 file contains all the necessary information for the simulation. [9]
In the second stage, which is the core of the simulation process, the com- putational domain is actually discretized and the Lattice Boltzmann equa- tions are numerically solved for a given lattice by PowerFLOW, which is the main solver. PowerFLOW offers solutions for aerodynamic efficiency, driving dynamics, vehicle handling, water management and panel deforma- tion. If the sought application is thermal management and climate control of surfaces temperature and heat fluxes, a conduction and radiation solver, PowerTHERM, is coupled with PowerFLOW. PowerTHERM allows to pre- dict under-body and engine thermal protection, brake cooling, electronics and battery cooling, HVAC system performance, cabin comfort, defrost and demist among some. The third additional solver, PowerCOOL, can also be coupled to predict the heat transferred between the airflow calculated by PowerFLOW and heat exchangers as the car engine, the radiator or a con- denser. [9]
At last, the post-processing stage uses the software available to display the results. The main program, PowerVIZ, is in charge to interpret the re- sults from the solver into flow parameters and 3D visualization solutions. It provides a wide range of capabilities including volume, fluid and surface mea- surement and visualization, quantitative analysis, particle tracking, rendering and animations. The second post-processing program, PowerINSIGHT, aims to automatize the simulation analysis and result generation to efficiently ac- celerate the overall engineering process towards conclusions. Aeroacoustic inquires are processed with PowerACOUSTICS, while photo-realistic engi- neering and design are carried out with PowerREALITY. [9]
1
The CDI is the name used to refer to the compiled case file in ”.cdi” format containing
all the simulation data.
Chapter 2
2.3 PowerFLOW background
In the early 1990s, the testing of the Lattice Boltzmann method reached so- lutions equivalent to the classical approach based on the Navier-Stokes equa- tions. This theoretical proof laid the foundation for the company’s DIGITAL PHYSICS technology to be based on the LBM. The motivation behind the development and the spread of this new method are the unsolved issues and difficulties with the traditional CFD approach as well as the LBM numerous advantages. Some of the advantages are being inherently transient which allows to simulate time-dependent phenomena, which are numerically stable and highly accurate for complex geometries [10].
To reach insightful knowledge of the novelty brought by LBM, as well as to understand the differences with respect to the classic Navier-Stokes method, a theory portrayal for both approaches is exposed in the following sections.
2.3.1 Navier-Stokes Equations and Continuum Theory
Classic fluid dynamics focuses in the macroscopic phenomena of the fluid be- havior where fluids are described as a continuum, which means treating them as continuous blobs of matter. The Navier-Stokes (N-S) equations, named after Claude-Louis Navier and George Gabriel Stokes, describe the motion of viscous fluid substances based on the premise that fluid is a continuum.
They are derived from the conservation laws: Conservation of mass or conti- nuity, conservation of momentum or Newton’s second law and conservation of energy or the first law of thermodynamics. The conservation equations describe the variation with time of the amount of mass, momentum and en- ergy contained in a fluid volume V f (t) delimited by a closed surface Σ f (t) [6]. These equations in the integral form are only useful for simple cases with many simplifications. They are not suitable when one is interested in computing the local properties of the flow. For that purpose, they must be transformed using Gauss formula to express the rate balances locally based on fluid elements.
The continuity equation (2.1), states that the addition of the rate of local density variation and the rate of mass loss by convective outflow equals zero [6]. It is a PDE that reflects the conservation of mass.
∂ρ
∂t + ∇ · (ρv) = 0 (2.1)
An alternative expression of the continuity equation (2.2) can be inter-
preted as the variation of the density following the fluid particle being ex-
clusively due to the rate of volume variation [6]. The material derivative denotes the rate of change as the fluid elements move rather than the rate of change at a fixed point in space.
1 ρ
Dρ
Dt = −∇ · v (2.2)
The incompressible momentum equation (2.3) can be interpreted as representation of Newton’s second law expressed per unit volume of fluid [6]. The right-hand side of the equation has the contribution of forces acting on the fluid, which can be differentiated in body and surface forces. Body forces f m act over the whole volume of the body as gravity or magnetic fields. Surface forces on the other hand act over the surface. Normal surface forces, in this case, are the divergence of the pressure ∇p, while the shear is represented by the stress tensor component ∇ · τ 0 .
ρ Dv
Dt = −∇p + ∇ · τ 0 + ρf m (2.3)
At this point, there are four equations, one from continuity and three from each spatial component in the momentum equation, but there are five unknowns (v, p, ρ) making an unsolvable system. To resolve it an extra equa- tion is necessary.
The usual approach adds the conservation of energy equation, also known as energy equation (2.4). It shows clearly how the rate of variation of inter- nal energy is related to the heat addition and the surface forces deformation work rate. The compression work, represented by −p∇ · v, is reversible and increases the internal energy when the compression work reduces the fluid volume. The term φ v accounts for the deformation work done by viscous forces. It is a positive term that correlates to the rate of mechanical energy dissipation per unit volume and unit time [6]. The heat addition is repre- sented by the rates of heat release due to chemical Q c and radiation Q r , and by heat conduction through the surface ∇ · q [6].
ρ De
Dt = −p∇ · v + φ v − ∇ · q + Q c + Q r (2.4) Introducing the energy equation to solve for the extra unknown variable also adds four more variables to solve, which need additional approximations or relations as the state principle of equilibrium thermodynamics. It states that any state variable (ρ, p, T, e, s) can be related to any other two with the equation of state. The most famous equation is the Ideal gas law (2.5) [8].
p = ρRT (2.5)
Chapter 2
The system of equations is a set of non-linear partial differential equations deeply dependent on the boundary and initial conditions. The non-linearity is due to the convective acceleration, which is associated with the change in velocity with respect to the position. Due to this non-linearity, a general solu- tion can no be obtained, except for very simple cases. The turbulence, which is a time-dependent chaotic behavior of the fluid, is generally considered to be produced by the inertia of the fluid or in other words by the combina- tion of time-dependency and convective acceleration [4]. The turbulence is modeled through the aforementioned non-linearity of the equations.
Even with numerical methods, the N-S equations are very difficult to solve for turbulent flow. To obtain a stable solution, direct numerical simulations are very computational expensive due to the required high mesh resolution, begin prohibited for practical cases. To solve such issue, time-average equa- tions such as Reynolds-averaged Navier–Stokes equations (RANS), combined with turbulence models as Spalart–Allmaras, k–ω, k– or SST are used for efficient CFD. Another option, more costly but more accurate, is the time and spatial averaging given by the large eddy simulation (LES) [3].
The N-S equations with additional equations and well-defined conditions model quite accurately the fluid, even for turbulent cases. Their main issue is not on the physics but on the mathematics to solve their non-linearity.
Moreover, it is important to remind that N-S equations assume that the fluid is continuum, thus infinitely divisible and not composed of particles, which makes them imprecise at very small scales or extreme conditions.
2.3.2 Lattice Boltzmann Method
In the field of fluid analysis, one refers to the molecular description of the fluid as ”microscopic”, while ”macroscopic” is used for a continuum picture of it with tangible qualities. Microscopic systems are governed by Newton’s dynamics, while the previously discussed N-S equations govern the continuum fluid. In between both descriptions, there is the ”mesoscopic” description, which tracks distributions or representative collections of molecules. The mesoscopic fluid description is modelled with Kinetic theory, the cornerstone of the LBM [8].
Kinetic theory describes the fluid behavior using the conservative interac- tions of air molecules. The central variable in kinetic theory is the particle or velocity distribution function f (~ x, ~ ξ, t) with units [kg·s 3 /m 6 ]. It represents the density of particles with velocity ~ ξ = (ξ x , ξ y , ξ z ) at position ~ x and time t.
It can be interpreted as a density that accounts for particles velocity, there-
fore, representing the density of mass in three-dimensional physical space
and three-dimensional velocity space. The distribution function is related
to macroscopic variables, like density or velocity, throughout its moments.
These are integrals of the distribution function weighted with a function of ξ over the velocity space, accounting the density of particles of all velocities at position x and time t. Mass, momentum and internal energy density can be found in equations 2.6, 2.7 and 2.8 respectively.
ρ(~ x, t) =
Z
f (~ x, ~ ξ, t)d 3 ξ (2.6)
ρ(~ x, t)~ u(~ x, t) =
Z
ξf (~ x, ~ ξ, t)d 3 ξ. (2.7)
ρ(~ x, t)e(~ x, t) = 1 2
Z
(~ ξ − ~ u) 2 f (~ x, ~ ξ, t)d 3 ξ (2.8) Having introduced the distribution function f , the next step is to char- acterize its evolution in time with an equation. Such equation is obtained throughout its total derivative with respect to time. Inserting the renown notation for the total differential Ω(f ) = df /dt, the Boltzmann equation (2.9) is obtained [8].
df dt = ∂
∂t f (~ x, ~ ξ, t) + ~ ξ · ∇f (~ x, ~ ξ, t) = Ω(f ) (2.9) The Boltzmann equation (2.9) can be interpreted as an advection 2 equa- tion. Overall it describes the rate of change of the velocity distribution func- tion due to non-equilibrium. In the right-hand side, there is the so-called collision operator Ω, which is a source term that describes the local redistri- bution of the distribution function f , due to collisions. The collision term satisfies the mass, momentum and energy conservation laws [8].
The Boltzmann’s collision operator considers all possible outcomes when two particles collide for any inter-molecular force, leaving a complicated dou- ble integral over the velocity space. LBM uses a simpler collision operator called BGK (2.10) after its inventors Bhatnagar, Gross and Krook.
Ω(f ) = − 1
τ (f − f eq ). (2.10)
This operator modules the relaxation distribution function towards the equilibrium distribution, where f eq is the equilibrium distribution and τ the relaxation time which determined the speed of such equilibrium [8].
In order to discretize Boltzmann equation, first the discrete-velocity dis- tribution function f i (x, t) must me introduced. Analogous to the distribution
2
In the field of physics advection refers to the transport of a substance or quantity by
bulk motion. The properties of that substance are carried with it.
Chapter 2
function f , f i describes the density of particles with velocity ~c i = (c ix , c iy , c iz ) at time t and position ~ x with a major difference, which resides on the argu- ment variables of f i being discrete. ~c i , to which the subscript i in f i refers, is one of the discrete set of velocities {~c i }. This f i is defined at point ~ x which are located as a square lattice in space, with lattice spacing ∆x. Finally, f i is only characterized at time t with time step ∆t [8]. Similar to its continuous sibling the mass density and momentum density can be expressed through f i moments
ρ(~ x, t) = X f i (~ x, t) (2.11) ρ~ u(~ x, t) = X ~c i f i (~ x, t). (2.12) The set of discrete velocities ~c i can have different number of spatial di- mensions d and number of velocities q. These sets are denoted as Dd Qq, and the most common ones are D1Q3, D2Q9, D3Q15, D3Q19 and D3Q27 [8]. PoweFLOW solver uses the set D3Q19 Figure 2.3, which has the best accuracy-efficiency ratio. This means that the continuous distribution func- tion is defined on a lattice of equally shaped regular cubic cells. Therefore, during a time interval ∆t, particles can only hop from one center of a cell ~ x to one of the q positions of the neighboring cells ~ x + ~c i ∆t according to their velocity ~c i .
Figure 2.3: D3Q19 Model
The particle dynamics are now described by the discretized Boltzmann equation, known as Lattice Boltzmann Equation (2.13). The term ”lat- tice”, as previously introduced, refers to the grid used to discretize the com- putational domain [7]. The collision operator which determines if the lattice system produces a physically meaningful fluid behavior, is obtained from the discretized version of the BGK operator (2.10).
f i (~ x + ~c i ∆t, t + ∆t) = f i (~ x, t) + Ω i (~ x, t) (2.13)
Overall, the LBE concept consists of move and collide. The collision, which conserves local mass, momentum and energy is a local algebraic op- eration where the density and macroscopic velocity are calculated to find the equilibrium distribution f i eq and post-collision distribution f i ∗ . After the collision, the resulting distribution f i ∗ is streamed to neighbour nodes com- pleting a time step. Afterward, these operations are repeated. Figure 2.4 illustrates a sketch of one LB algorithm cycle.
Figure 2.4: Overview of one cycle of the LB algorithm. The dark grey boxes show sub-steps that are necessary for the evolution of the solution. The lighter grey box indicates the optional output step. The lighter grey box has macroscopic fields to be written to the hard disk for visualisation or post-processing. [8]
To finalize this section about LBM with a note about PowerFLOW core technology, it is important to state that PowerFLOW is based on the Lattice Boltzmann Method, but combines it with models to robustly and efficiently deal with unsteadiness and turbulence. The turbulence model used is the Very Large Eddy Simulation (VLES) while the boundary layer model is the Advance Boundary Layer Model (ABLM).
2.3.3 Contrast of PowerFLOW and Traditional CFD
PowerFLOW differs from the competing RANS-based CFD technology in
fundamental ways. Recapping, conventional CFD methods assume the fluid
as a continuum and construct the fluid equations as partial differential equa-
tions. For most of the cases, these equations flawlessly describe the real fluid
behavior, but the main issue comes from the mathematical perspective and
not in the equations themselves. Because the equations are complex and
highly non-linear, and characterized by many degrees of freedom, analytical
solutions are only possible for straightforward cases [10]. For more compli-
cated cases, a discrete approximation of the PDE is necessary for numerical
Chapter 2
integration. The equations are numerically resolved for a given mesh and boundary conditions. On the other hand, LBM uses a ”Lattice” discretiza- tion of kinetic theory based on Boltzmann equation. No further discretiza- tion is required to numerically integrate, solving lattices and applying kinetic based boundary conditions. Finally, a simple conversion to fluid variables is required. A graphical representation of the aforementioned information can be observed in Figure 2.5.
Figure 2.5: NSM and LBM overview
Fundamentally, the initial advantage of LBM lies on its approach based on microscopic particle physics instead of the continuum assumption of fluid mechanics equations. The method allows to have a wider physical validity, from complex fluids and from micro to macro scale. Being time-dependent and inherently unsteady it makes it advantageous for unsteady flow simu- lations. It should be noted as an advantage that no artificial dissipation is needed and boundary conditions are physically realized. In general, all of these make the method strongly accurate and robust.
From the software development point of view, it is appreciated for its
intrinsically simpler mathematical background and implementation of the
algorithm. Also, it is naturally suitable for parallelization, since each time
step update is local to each node, and this allows to divide the computational
domain into sub-domains handled by different processors. Other advantages
which are worth mention, are the incorporation of the thermal fluctuations
originated on the microscopic level and sound-flow interaction simulation.
However, LBM is still a very young method that needs further devel-
opment in terms of physical and numerical aspects in order to completely
overtake traditional N-S based CFD. On the negative side, LBM is an inten-
sive memory consumer and its turbulence modeling framework is not com-
pletely understood. Moreover, capturing compressible flow phenomena for
high supersonic and hypersonic aerospace applications has not yet reached
a complete level of maturity. At last, LBM is quite inefficient dealing with
steady flows due to its inherently time-dependency.
Chapter 3
Tool Implementation
After having introduced the SIMULIA PowerFLOW suite, its methodology and its core technology, the following sections will revolve around the engi- neering project performed at the SIMULIA field office in Stuttgart.
3.1 Background and Motivation
The reliability and robustness of CFD analysis are higher than ever. It is usually attributed to new modeling physics, new numerical solver methods as well as a further refinement of the discretization. However, it is sometimes forgotten that this increase in reliability and robustness could not be achieved without the application of best practices, which can only be gained through experience in the field and the software. SIMULIA PowerFLOW software is no exception, and these best practices are materialize through the application engineers in field offices. This business method brings to the table an overall more expensive product and service but with superior support and assistance that channels into higher reliability and robustness.
An issue that best practices aim to fix is human factors. It does not refer to the quality of the work, but to the errors, mistakes and flaws intro- duced in any stage of the simulation process. This is a well known issue in the world of CFD and highlighted in the automotive and aerospace indus- tries where numerous cases are simulated with only some specific elements modified for comparison purposes. Stuttgart’s field office engineers who deal with issues and projects of fewer experience customers, heavily suffer from this user mistakes. Solving or minimizing this problem can be translated into more reliable results and less misused simulation hours in the SIMULIA PowerFLOW cluster, which means huge savings for the customer.
Having a tool capable of extracting, analyzing and efficiently presenting
the desired information of a case to be simulated has been in discussion for years in the Stuttgart. It was not until one of the engineers was asked by a customer to resurface an old project whose original responsible engineer was not available anymore. The lack of information led to problems with the procedure used, the geometry, the naming and the case set up of all the runs. This set the wheels in motion to develop a tool to solve this problem of information lack and to minimize human errors in the simulation set up phase.
3.2 Project Statement
The ambition is to create a fast, intuitive, user-friendly tool that can analyze numerous cases. The idea with this is to extract, process and conveniently expose, very sensitive and specific information from the massive amount of data comprised in the case file before simulation. Such file is known as CDI, due to its file format ”.cdi”. The data to be analyzed is selected based on the two premises of very critical data and error recurring inputs. Having this tool, the user can efficiently revise the runs before sending them to simulation avoiding inaccurate results and costly running hours of the computer cluster.
Moreover, the tool is intended to compare the data extracted between the cases. Large number of runs of the same project with specific discrepancies are a notorious source of error, therefore, the tool should also be capable of comparing the runs and expose their differences. By highlighting the differences, the user will be able to detect the undesired features. From now on the tool will be referred to as the Case Data Analysis Tool (CDAT).
3.3 Methodology
Since the beginning, it was clear that the CDAT will be based on a script that would extract and analyze data, and a user interface that would display the results. Consequently, a programming language and a graphical user interface are needed.
The graphical user interface (GUI) chosen is PowerINSIGHT, which be-
longs to the PowerFLOW suit. Two main reasons explain this decision. The
first one is because the program was conceived for a similar task, offering a
graphical interface to easily configure and generate comparative results, in-
teractively browse results as well as serve intermediary between the script and
its interpreter. The second one, was business related, to encourage customers
interested in the tool to use SIMULIA software.
Chapter 3
The first programming language considered was shell scripting for direct command-line interaction. Even though this method would be very advan- tageous internally, where Unix-like OS is used, it was quickly realized that it would be useless for windows based customers. Therefore, a free high-level cross-platform programming language was required. Python was an obvious choice because it fulfilled all those requirements and has many advantages, such as being easy syntax, object-oriented, widely supported and with over 300 standard library modules that contain modules and classes for a wide variety of programming tasks.
3.3.1 Tool Workflow
The tool works around two main pillars, the Python routines and the GUI PowerINSIGHT. The technical development of the tool resides mostly in the Python routines, where the data extraction and processing is carried out.
The second pillar is PowerINSIGHT, it is the interface where the CDAT is implemented and the results displayed. It also functions as a link to Pow- erVIZ and the CDI files. The workflow of the tool is represented in Figure 3.1.
Figure 3.1: Case Data Analysis Tool workflow
As previously defined, PowerINSIGHT (PI) is a software that aims to
automatize data exposure and comparison, and it works by sourcing a unique
template that has been created in advance for the desired content. In the
Case Data Analysis Tool, the file is named CaseData AnalysisTool.pins. This
file contains the template of the information which will be created when the
tool is run. The results generated by the tool are described in the next Section
3.3.2. Once the pins file is loaded, the CDIs to be studied and compared are selected through PI. Then, the routines are loaded by PI. The scripts access the requested content and the CDI information to extract their data and process it. The images are generated by PowerVIZ when the script asks PI for them. At last, the results are saved and displayed also in PI.
3.3.2 Features and Content Generated
The advantage of this tool lays on the carefully selected information that is produced. Accordingly, an important effort was placed on thoroughly under- standing what content should be included and how to organize it. As men- tioned before, the information displayed was chosen based on the premises of important data of the simulation and common sources of errors. Even though the major features were agreed on during the definition of the project scope, smaller characteristics were included after testing and user feedback.
Initially, only one script was developed to perform the whole analysis, but it was quickly understood that two were necessary to first check the CDI and then compared it. The first routine originally meant to only extract data, but it grew to become a meaningful analysis by itself. Therefore, the tool performs two divided but interrelated assignments. First, the CdiCheck.py script carries out a check of each CDI, afterward, the CdiComp.py executes a comparison between the preceding CDIs. The corresponding content gener- ated by each routine is displayed in Figure 3.2. Both analysis are divided into three separated study categories based on different topics of the simulation:
Model Geometry, Case Analysis and Pictures. The described information is visually represented in Figure 3.2.
Following there is a more detail explanation of each feature of the tool.
CDI Check
The first analysis of the tool is performed by the routine CdiCheck.py and it is divided into three sections.
The first section is the CDI Check - Model Geometry. It reads from the CDI the information about the parts conforming the geometry (e.g., the engine, the body and the wheels among others) and their last modified date.
Then, such information is compared with the parts found in the storage of the
work station. It shows the user if there are parts missing in the CDI or if the
parts are not updated to the latest version. Secondly, the Model Geometry
also shows which parts in the CDI have open shells, which can be critical for
the simulation. An open shell is a surface made of continuous facets where
at least one of the edges belongs to only one facet. Facets are the smallest
Chapter 3
Figure 3.2: Case Data Analysis Tool PowerINSIGHT tree
entities of the geometry, which have a triangular shape delimited by edges and vertex. Having a part containing an open shell is generally undesired.
The CDI Check - Case Analysis is the second study of the CDI Check.
It extracts and shows the case variables and their value. The case variables which are some of the most important inputs define the environment and boundary conditions of the simulation. Inside the Case Analysis, it is also exposed the floor configuration set up, which is a common source of error.
The floor configuration, only used in vehicle simulations, refers to the type of floor that will be simulated to match the desired condition. The options are Moving Road, Moving Center Belt+Wheel Belt, Moving Center Belt, Static Floor With Friction and Friction-less Floor.
Finally, the CDI Check - Pictures is in charge of generating the images of the following:
• The most important VR regions 1 : VR10, VR9, VR8 and VR7.
• The rotating parts, known as LRF, as the fans or wheels of the vehicle.
• The different modules of the geometry. In the case of a complete vehi- cle, it comprises the body, the power train, the suspension, the subsys- tems, the porous media and wheels if they do not rotate.
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