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Introduction to


本課程涵蓋了使用 LS-PrePost 進行預處理和後處理的 LS-DYNA 的基本功能。
提供了數據準備的詳細說明和要求,並附有示例。 還涵蓋了使用 LS-PrePost 的預處理和後處理。 本課程推薦給執行非線性靜態和瞬態仿真的人員。 在幾乎所有工程領域工作的與會者都將受益。 本課程是涵蓋多物理場能力的高級課程的先決條件,包括可壓縮和不可壓縮流體、氣囊粒子法、任意拉格朗日-歐拉 (ALE) 流體、熱學、聲學、振動聲學、金屬成型、電磁學、離散元素、 故障、頻率響應方法和等幾何分析。


  • 有限元分析和工程概念的基礎知識


學生、設計發布工程師、CAE 工程師



Learning Path

Currently, no Learning Path available



  • 了解LS-DYNA的關鍵字結構.
  • 能夠理解懲罰和運動學接觸的關鍵概念.
  • 在為材料、元素長度和時間設置單位時注意單位的一致性.
  • 區分可接受的“正常終止”和不正確的結果.

Available Dates

Currently, no training dates available

Learning Options

Training materials for this course are available with a Ansys Learning Hub Subscription. If there is no active public schedule available, private training can be arranged. Please contact us.


This is a 1-day classroom course covering both lectures and workshops. 

  • Course Outline
  • History
  • Finite Element Simulation
  • Sample LS-DYNA Conference Presentations
  • Sample Simulations
  • FE Analysis (pre-processors, solvers, post-processors)
  • Detailed Example
  • LS-DYNA Input Deck
  • Using LS-PrePost
  • Details of Post-Processing
  • Detailed Capabilities - Keyword Format
  • Material Nonlinearity
  • Running LS-DYNA
  • Execution and Output Files
  • Binary
  • Output Control
  • FE Modeling Techniques
  • Engineering a FEA Model
  • Element Selection
  • Discrete (formulation of elastic and nonlinear elastic spring)
  • Beam
  • Shell (description of the various shell formulations)
  • Solid (description of the various solid formulations)
  • Thick Shells
  • Boundary and Initial Conditions, Symmetry
  • Modeling for Physical Phenomenon
  • Ad-Hoc Guidelines
  • How to tell if your results are correct
  • Er­ror, De­bug­ging, and Oth­er Use­ful In­for­ma­tion (d3hsp)
  • Time Integration
  • The Equations of Motion
  • Implicit
  • Explicit
  • Explicit Time Integration
  • Time Step Calculation
  • Selectively-Reduced Integration
  • Hourglass Phenomenon
  • Contact and Slide Surfaces
  • Friction
  • Damping
  • Restart
  • Quasi-Static Simulations
  • Why static analysis with explicit code
  • Mass Scaling