Introduction to LS-TaSC...2019/03/26  · LS-DYNA ENVIRONMENT Introduction to LS-TaSC The Arup...

LS-DYNA ENVIRONMENT

Introduction to LS-TaSC

The Arup Campus, Blythe Gate, Blythe Valley Park, Solihull, West Midlands, B90 8AE

tel: +44 (0) 121 213 3399email: dyna.support@arup.com

March 2019

LS-DYNA ENVIRONMENTSlide 1

Introduction to LS-TaSCOUTLINE

• OPTIMISATION

• LS-TaSC

• LS-TaSC Optimisation Types

• LS-TaSC Application

• Interface

• Current Version

• Supported element types

• Supported material models

• Unsupported keywords andlimitations

• Methodology

• Basic Algorithm

• PROCESS

• Inputs

• Submitting the Analysis

• Outputs

LS-DYNA ENVIRONMENTSlide 2

Introduction to LS-TaSCOPTIMISATION

Oxford Dictionary: Optimisation is the action of making the best or most effective use of a situation or resource.

In structural optimisation, typically, the material of the structure is redistributed to achieve the highest stiffness.

Resource = Structural MaterialBest/Most Effective use = Stiffest

LS-DYNA ENVIRONMENTSlide 3

Introduction to LS-TaSCLS-TaSC

LS-TaSC Optimisation Types

• Topology optimization: finds the layout with the best use ofthe material by redistribution of material within a give domain(placing holes in the original domain).

• Topometry optimization: shell thickness is designed perelement basis.

• Free shape Optimisation: a free shape of the outer surfacecontour is chosen and the objective is an uniform stress onthis surface. Moves nodes in mesh.

LS-TaSC ApplicationLS-TaSC is a tool for generating a concept design for structures analyzed usingLS-DYNA (implicit or explicit). LS-TaSC is used for topology and shape design oflinear and nonlinear problems (e.g. dynamic loads, contact conditions).

LS-DYNA ENVIRONMENTSlide 4

Introduction to LS-TaSCLS-TaSC

Interface

LS-DYNA ENVIRONMENTSlide 5

Introduction to LS-TaSCLS-TaSC

Current VersionV4.0: available for both Linux and Windows.V4.0 includes MDO (Multidisciplinary Design Optimization) capabilities.

Supported element typesSolids:

– Hexahedral– Pentahedral– Tetrahedral

Only linear elements are supported.

Supported material models*MAT_ELASTIC *MAT_PIECEWISE_LINEAR_PLASTICITY including the LCSS option.*MAT_ORTHOTROPIC_ELASTIC for the topology design of solids .

Shells:– Quadrilateral– Triangular

LS-DYNA ENVIRONMENTSlide 6

Introduction to LS-TaSCLimitations

Unsupported keywords and limitations• *PART_OPTION is not supported for the design parts.• *PARAMETER is not supported.• Do not use *INCLUDE_OPTION, *INCLUDE is fine. Contents of the included file

will be moved to lst_master.k.

• Tetrahedral and triangular elements cannot be used in an extrusion geometrydefinition.

• In design of solid structures, define contacts involving the design parts by one ofthese alternatives1:

• Use part sets and any *CONTACT definition.• Use *CONTACT_AUTOMATIC_SINGLE_SURFACE with SSID=0.

• Options generate, general, list_generate and column should not be used whencreating part or element sets involving the design parts.

• Some load cases are not suited for topology optimization using LS-TaSC, e.g.progressive buckling modes.

LS-DYNA ENVIRONMENTSlide 7

Introduction to LS-TaSCLS-TaSC

Methodology

LS-TaSC finds the layout with the best use of the material by redesigning for anuniform internal energy density over the part [essentially removing materialwhere it is useless (not doing work) according to the FE analysis].

The outcome is typically the stiffest structure for the given weight (minimumcompliance design).

Optimality Criteria for Dynamic Problems. Objective: Homogenization of internal energy density (𝐼𝐼𝐼𝐼𝐼𝐼) = uniform loading ofmaterial for given mass.

internal energy/volume

LS-DYNA ENVIRONMENTSlide 8

Introduction to LS-TaSCLS-TaSC

Basic algorithmIn each iteration, the structure is evaluated to compute the work done at thematerial locations (internal energy density); these values are then used to updatethe design variables (the amount of material).Sample update during an iteration:

The algorithm therefore adds material where it is used (has a higher energydensity): higher energy density results in more material being added thus loweringthe energy density.

Colour of the circles is the energy density for an element

Size of the circles is the amount of material for an element

LS-DYNA ENVIRONMENTSlide 9

Introduction to LS-TaSCLS-TaSC

Basic algorithmAs a staring point, the user defines the base structure with loads and boundaryconditions. The final optimized topology will be inside this base structure.

?Load

Boundary condition

Base structure

Final Topology

LS-DYNA ENVIRONMENTSlide 10

Introduction to LS-TaSCProcess

Inputs LS-TaSC Outputs

LS-DYNA ENVIRONMENTSlide 11

Introduction to LS-TaSCProcess: Inputs

Inputs LS-TaSC Outputs

LS-DYNA ENVIRONMENTSlide 12

Introduction to LS-TaSCProcess: Inputs

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• Final design “granularity”

LS-DYNA ENVIRONMENTSlide 13

Introduction to LS-TaSCProcess: Inputs

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• Final design “granularity”

The amount of material removed is specifiedby the user through the mass fractionparameter (target mass fraction). E.g., massfraction of 0.5=M/Mo=mass reduction of 50%.

Effect of target mass fraction:

LS-DYNA ENVIRONMENTSlide 14

Introduction to LS-TaSCProcess: Inputs

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• Final design “granularity”

Select “granularity” of final design (filter radius,i.e. neighbourhood size)

Effect of neighbourhood radius:

LS-DYNA ENVIRONMENTSlide 15

Introduction to LS-TaSCProcess: Inputs

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• Final design “granularity”

- Load cases

LS-DYNA ENVIRONMENTSlide 16

Introduction to LS-TaSCProcess: Inputs

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• Final design “granularity”

- Load cases- Constraints

Defined as displacement, forces, absorbedenergy, etc. (upper and/or lower limits can beapplied).

Geometry definitions can also be defined:symmetry, extrusion, casting and forging.

Forging: Two-sided casting preserving a minimum thickness (no holes)

LS-DYNA ENVIRONMENTSlide 17

Introduction to LS-TaSCProcess: Inputs

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• Final design “granularity”

- Load cases- Constraints- Objective

LS-DYNA ENVIRONMENTSlide 18

Introduction to LS-TaSCProcess: Inputs

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• Final design “granularity”

- Load cases- Constraints- Objective - Optimization algorithm- Multi-point method with global variables for constrained optimization.

LS-DYNA ENVIRONMENTSlide 19

Introduction to LS-TaSCProcess: Inputs

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• Final design “granularity”

- Load cases- Constraints- Objective - Optimization algorithm- Multi-point method with global variables for constrained optimization.

• The design domain is specified by selectingparts.

• The optimum parts will be inside theboundaries of these parts.

• The amount of material removed isspecified by the user through the massfraction parameter (target mass fraction).

• The final mass fraction and distributiondepends on the optimization method andglobal constraints.

LS-DYNA ENVIRONMENTSlide 20

Introduction to LS-TaSCProcess: Inputs – Set Up

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• final design “granularity”

- Load cases- Constraints- Objective - Optimization algorithm- Multi-point method with global variables for constrained optimization.

LS-DYNA ENVIRONMENTSlide 21

Introduction to LS-TaSCProcess: Inputs – Set Up

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• final design “granularity”

- Load cases- Constraints- Objective - Optimization algorithm- Multi-point method with global variables for constrained optimization.

LS-DYNA ENVIRONMENTSlide 22

Introduction to LS-TaSCProcess: Inputs – Set Up

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• final design “granularity”

- Load cases- Constraints- Objective - Optimization algorithm- Multi-point method with global variables for constrained optimization.

LS-DYNA ENVIRONMENTSlide 23

Introduction to LS-TaSCProcess: Inputs – Set Up

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• final design “granularity”

- Load cases- Constraints- Objective - Optimization algorithm- Multi-point method with global variables for constrained optimization.

LS-DYNA ENVIRONMENTSlide 24

Introduction to LS-TaSCProcess: Submitting the Analysis

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• final design “granularity”

- Load cases- Constraints- Objective - Optimization algorithm- Multi-point method with global variables for constrained optimization.

Submit optimization

run.

LS-DYNA ENVIRONMENTSlide 25

Introduction to LS-TaSCProcess: Submitting the Analysis

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• final design “granularity”

- Load cases- Constraints- Objective - Optimization algorithm- Multi-point method with global variables for constrained optimization.

Submit optimization

run.

LS-DYNA ENVIRONMENTSlide 26

Introduction to LS-TaSCProcess: Outputs

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• final design “granularity”

- Load cases- Constraints- Objective - Optimization algorithm- Multi-point method with global variables for constrained optimization.

Submit optimization run.

- XY histories:- Scalar quantities vs.iteration

- FEA model histories:- Optimal design superimposed on initial- History of designs overthe iterations

LS-DYNA ENVIRONMENTSlide 27

Introduction to LS-TaSCProcess: Outputs

Inputs LS-TaSC Outputs

- What to optimize• Part• Mass fraction• final design “granularity”

- Load cases- Constraints- Objective - Optimization algorithm- Multi-point method with global variables for constrained optimization.

Submit optimization run.

- XY histories:- Scalar quantities vs.iteration

- FEA model histories:- Optimal design superimposed on initial- History of designs overthe iterations

LS-DYNA ENVIRONMENTSlide 28

Introduction to LS-TaSCOUTLINE

• OPTIMISATION

• LS-TaSC

• LS-TaSC Optimisation Types

• LS-TaSC Application

• Interface

• Current Version

• Supported element types

• Supported material models

• Unsupported keywords andlimitations

• Methodology

• Basic Algorithm

• PROCESS

• Inputs

• Submitting the Analysis

• Outputs

LS-DYNA ENVIRONMENTSlide 29

Introduction to LS-TaSCContact Information

For more information please contact the following:

www.arup.com/dyna

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T +44 (0)121 213 3399dyna.support@arup.com

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