What is Engineering Simulation? The Complete Guide to CAE & FEA

Engineering simulation enables organizations to predict product performance before building physical prototypes. From stress analysis to fluid dynamics to thermal behavior, simulation answers critical questions about how designs will perform in the real world—reducing costs, accelerating development, and improving product quality.

This guide covers everything you need to know about engineering simulation: what it is, key simulation types including FEA and CFD, the simulation workflow, available tools, and how to implement simulation-driven design.

What is Engineering Simulation?

Engineering simulation uses computational methods to model and predict the physical behavior of products and systems. Instead of building and testing physical prototypes, engineers create virtual models and subject them to simulated conditions—loads, temperatures, fluid flows, and more.

Simulation answers questions that CAD alone cannot:

• Will it break? Structural analysis predicts stresses and failure modes
• Will it overheat? Thermal simulation shows temperature distributions
• How will fluids behave? CFD predicts flow patterns and pressures
• Will it vibrate excessively? Dynamic analysis reveals resonance issues
• How will components interact? Multiphysics couples multiple phenomena

The umbrella term CAE (Computer-Aided Engineering) encompasses all simulation software, while specific disciplines have their own terminology—FEA for structures, CFD for fluids, and so on.

Types of Simulation

Engineering simulation spans multiple physics disciplines, each addressing different aspects of product behavior.

Structural Analysis (FEA)

The most common simulation type, structural analysis predicts:

• Stress and strain: Where material is under load
• Deformation: How parts bend or stretch
• Factor of safety: Margin against failure
• Fatigue life: Cycles until crack initiation
• Buckling: Stability under compressive loads

Structural FEA applies to virtually every mechanical product.

Thermal Analysis

Thermal simulation addresses heat-related phenomena:

• Temperature distribution: Hot and cold spots
• Heat transfer modes: Conduction, convection, radiation
• Transient response: How temperatures change over time
• Thermal stress: Expansion-induced loads
• Cooling effectiveness: Heat sink and airflow performance

Electronics cooling, engine components, and HVAC systems rely heavily on thermal analysis.

Fluid Dynamics (CFD)

CFD simulates liquids and gases:

• Flow patterns: Streamlines, velocity fields
• Pressure drops: Losses through systems
• Aerodynamics: Drag, lift, downforce
• Mixing: Combining fluids or gases
• Multiphase flows: Bubbles, droplets, particles

Automotive, aerospace, and process industries use CFD extensively.

Electromagnetic Simulation

EM simulation covers electrical and magnetic phenomena:

• Electric fields: Voltage distributions
• Magnetic fields: Motor and actuator performance
• Electromagnetic compatibility (EMC): Interference between systems
• Antenna performance: Radiation patterns
• Signal integrity: High-speed electronics behavior

Multibody Dynamics

Mechanism simulation predicts motion:

• Kinematic analysis: Range of motion, paths
• Dynamic analysis: Forces during operation
• Contact and collision: Impacts between bodies
• Flexible bodies: Combining mechanism and structural effects

Vehicle suspensions, robots, and packaging machinery benefit from dynamics simulation.

Multiphysics

Real products involve coupled phenomena:

• Thermal-structural: Heat causes expansion causes stress
• Fluid-structure interaction: Flow forces deform structures
• Electro-thermal: Current causes heating

Multiphysics simulation captures these interactions that single-physics analysis misses.

FEA Explained

Finite Element Analysis deserves deeper explanation given its widespread use.

How FEA Works

FEA divides complex geometry into a mesh of simple elements:

1. Preprocessing: Create geometry, apply mesh, define materials, set boundary conditions
2. Solution: Solver calculates behavior of each element, assembles into system response
3. Postprocessing: Visualize and interpret results

The “finite element” name comes from dividing the infinite number of points in a solid into a finite number of elements for numerical solution.

Mesh Quality Matters

The mesh—the division of geometry into elements—critically affects results:

• Element types: Tetrahedra, hexahedra, shells, beams
• Mesh density: More elements = more accuracy but longer solve time
• Element quality: Aspect ratios, angles affect accuracy
• Mesh convergence: Verify results don’t change with finer mesh

Poor mesh quality can produce misleading results regardless of solver sophistication.

Linear vs Nonlinear

FEA falls into categories based on assumptions:

Linear FEA assumes:

• Small deformations (geometry doesn’t change significantly)
• Linear material behavior (stress proportional to strain)
• Constant boundary conditions

Nonlinear FEA handles:

• Large deformations (rubber, sheet metal forming)
• Nonlinear materials (plasticity, hyperelasticity)
• Contact between parts
• Load-dependent boundary conditions

Nonlinear analysis requires more expertise and computational resources but models reality more accurately for many problems.

Static vs Dynamic

Static analysis assumes loads are applied slowly—equilibrium at each instant. Most structural FEA is static.

Dynamic analysis captures time-varying effects:

• Modal analysis: Natural frequencies and mode shapes
• Harmonic response: Behavior under oscillating loads
• Transient dynamics: Response to time-varying forces
• Random vibration: Statistical vibration environments

Products experiencing vibration, impact, or rapidly varying loads require dynamic analysis.

CFD Explained

Computational Fluid Dynamics simulates fluid behavior through numerical methods.

How CFD Works

CFD solves conservation equations for mass, momentum, and energy:

1. Geometry preparation: Create fluid domain (often extracting from CAD solid)
2. Meshing: Divide domain into cells
3. Physics setup: Turbulence model, boundary conditions, material properties
4. Solution: Iterate until convergence
5. Postprocessing: Visualize flow fields, extract quantities

CFD solves the Navier-Stokes equations numerically—equations too complex for analytical solution except in simple cases.

Turbulence Modeling

Most engineering flows are turbulent, requiring models to capture effects:

• RANS models: Average turbulent fluctuations (k-epsilon, k-omega SST)
• LES: Resolve large eddies, model small ones
• DNS: Resolve all scales (computationally prohibitive for most problems)

Turbulence model selection significantly affects accuracy and computational cost.

Internal vs External Flow

Internal flow: Fluid inside boundaries (pipes, ducts, channels)

• Pressure drop calculations
• Flow distribution
• Heat exchanger performance

External flow: Fluid around objects (vehicles, buildings, aircraft)

• Aerodynamic forces
• Wake patterns
• Surface pressures

Different physics emphasis for each type.

Steady-State vs Transient

Steady-state CFD: Time-averaged behavior, faster to solve, appropriate when conditions don’t change.

Transient CFD: Time-varying behavior, captures unsteady phenomena like vortex shedding, pulsating flows, startup/shutdown.

Simulation Workflow

Effective simulation follows a structured process.

1. Define Objectives

Before modeling, clarify what you need to learn:

• What questions must simulation answer?
• What decisions depend on results?
• What accuracy is required?
• What is the acceptable computational budget?

Clear objectives prevent wasted effort on unnecessary detail.

2. Geometry Preparation

CAD geometry rarely translates directly to simulation:

• Defeaturing: Remove small features that don’t affect results
• Idealization: Replace thin solids with shells, beams
• Fluid extraction: Create the fluid domain from solid geometry
• Cleanup: Fix gaps, overlaps, sliver faces

Geometry preparation often consumes significant project time.

3. Meshing

Create appropriate discretization:

• Global mesh size: Based on geometry scale and accuracy needs
• Local refinement: Finer mesh where gradients are steep
• Mesh type selection: Match element type to geometry and physics
• Quality checking: Verify elements meet quality metrics

Invest time in good meshes—they’re foundation of accurate results.

4. Physics Setup

Define the problem:

• Material properties: Density, modulus, conductivity, etc.
• Boundary conditions: Loads, constraints, inlets, outlets
• Initial conditions: Starting state for transient problems
• Solver settings: Convergence criteria, time steps

5. Solution

Run the analysis:

• Monitor convergence: Watch residuals, key quantities
• Check for errors: Divergence, element distortion, warnings
• Manage computational resources: Memory, parallelization

6. Postprocessing

Extract and interpret results:

• Visualization: Contour plots, vectors, streamlines
• Quantitative extraction: Maximum values, averages, integrals
• Validation checks: Do results make physical sense?
• Documentation: Capture key findings and assumptions

7. Validation

Build confidence in predictions:

• Mesh convergence study: Verify results don’t change with finer mesh
• Comparison to tests: Correlate with physical measurements when available
• Sensitivity analysis: Understand how inputs affect outputs

Simulation-Driven Design

Moving simulation earlier in development transforms its value.

Traditional vs Simulation-Driven

Traditional approach: Design in CAD → build prototype → test → find problems → redesign. Simulation used only for final validation.

Simulation-driven: Simulate during concept design → optimize during detail design → validate before prototype → test confirms predictions. Simulation guides decisions throughout.

Benefits of Early Simulation

• Catch problems earlier: Changes cost less early in development
• Explore more alternatives: Virtual testing enables broader design space exploration
• Reduce prototypes: Fewer physical builds when simulation predicts behavior
• Improve performance: Optimization based on analysis rather than intuition

Implementation Challenges

Shifting left requires overcoming barriers:

• Tool accessibility: Analysts often gatekeep simulation tools
• Skill gaps: Designers lack simulation training
• Process changes: Workflows must accommodate simulation loops
• Cultural shifts: “Design then validate” mindset is entrenched

Democratization Trend

The industry moves toward broader simulation access:

• CAD-embedded simulation: Basic analysis in the design environment
• Guided workflows: Templates reduce expertise requirements
• Cloud computing: On-demand capacity removes hardware barriers
• Automated meshing: Reduces preprocessing skill requirements

Simulation Tools & Software

The simulation market offers tools for various needs.

Enterprise CAE

ANSYS Comprehensive multiphysics platform. Market leader in structural, CFD, and electromagnetic simulation. Strong high-fidelity capabilities and extensive physics coupling.

Abaqus (Dassault Systèmes) Premium nonlinear FEA with exceptional accuracy for complex material behavior and contact. Dominant in automotive crashworthiness and rubber/polymer applications.

Siemens Simcenter Broad portfolio spanning FEA (NX Nastran), CFD (STAR-CCM+), system simulation, and test integration. Part of Xcelerator portfolio.

Mainstream Simulation

SolidWorks Simulation CAD-integrated FEA accessible to designers. Good for linear analysis and thermal studies. Part of SolidWorks ecosystem.

Autodesk CFD/Nastran Cloud-connected simulation integrated with Inventor and Fusion 360. Accessible pricing for mid-market.

Altair HyperWorks suite covering optimization, FEA, CFD, and multibody dynamics. Strong in optimization and lightweight design.

Specialized Tools

COMSOL Multiphysics Equation-based multiphysics platform enabling custom physics coupling. Popular in academia and research.

OpenFOAM Open-source CFD with extensive capability. No license cost but requires expertise to use effectively.

Tool Selection Criteria

Criterion	Why It Matters
Physics coverage	Must support required analysis types
Accuracy requirements	Some problems demand high-fidelity tools
CAD integration	Geometry transfer workflow efficiency
User skill level	Enterprise tools require more expertise
Computational resources	License and hardware costs
Support ecosystem	Training, consulting, community

Common Challenges

Simulation adoption faces recurring obstacles.

Geometry Preparation Bottleneck

CAD-to-simulation geometry transfer consumes excessive time:

• Native CAD contains manufacturing detail irrelevant to analysis
• Defeaturing requires judgment calls
• Meshing fails on poor geometry quality

Solutions include better CAD practices, automated defeaturing, and simulation-specific geometry tools.

Skill Gaps

Simulation requires physics knowledge beyond tool operation:

• Understanding what to model and what to ignore
• Selecting appropriate element types and mesh density
• Interpreting results critically
• Recognizing when results are suspect

Training and mentorship address skill gaps over time.

Validation Uncertainty

Without physical test correlation, simulation predictions carry uncertainty:

• Material properties from databases may not match actual material
• Boundary conditions approximate real loading
• Simplifications introduce unknown errors

Systematic validation against tests builds confidence.

Computational Costs

High-fidelity analysis demands resources:

• Hardware investment (workstations, HPC)
• License costs for commercial software
• Engineering time for complex setup
• Calendar time for long solves

Cloud computing and efficient methods help manage computational demands.

Industry Trends

Several trends shape simulation’s evolution.

Cloud HPC

Cloud computing transforms simulation economics:

• Burst capacity: Scale up for large jobs, pay only when running
• Eliminate hardware: No workstation or cluster investment
• Collaboration: Share models and results across locations

Cloud adoption accelerates as security concerns decrease.

AI and Machine Learning

ML augments traditional simulation:

• Surrogate models: Trained on simulation data for instant predictions
• Physics-informed neural networks: Blend data and physics
• Automated meshing: ML-driven mesh generation
• Result interpretation: Pattern recognition for quality assessment

AI won’t replace physics-based simulation but will accelerate and augment it.

Digital Twins

Simulation connects to operational products:

• Model updating: Calibrate simulations with sensor data
• Predictive maintenance: Forecast remaining life
• What-if analysis: Evaluate operational scenarios
• Design feedback: Operational data improves next-generation design

Digital twins extend simulation value beyond development.

Democratization

Simulation expands beyond specialist analysts:

• CAD-embedded tools: Analysis where designers work
• Guided workflows: Templates reduce expertise barriers
• Automated recommendations: AI-assisted setup
• Results interpretation aids: Help non-experts understand outputs

Broader access multiplies simulation’s organizational impact.

Getting Started with Simulation

For organizations building or improving simulation capabilities:

Assess Current State

Understand your starting point:

• What simulation is currently performed?
• Who has simulation skills?
• What are the pain points and gaps?
• How does simulation connect to design processes?

Define Strategy

Clarify objectives:

• Which physics disciplines matter most?
• Should simulation enable design exploration or final validation?
• What accuracy levels are required?
• Build internal capability or outsource?

Start with Pilots

Test before full commitment:

• Select representative projects for pilot
• Measure time, cost, and outcome quality
• Gather user feedback
• Refine approach based on learning

Invest in People

Tools accomplish nothing without skilled users:

• Formal training on software operation
• Physics fundamentals for those lacking background
• Mentorship from experienced analysts
• Ongoing skill development

Integrate with Design Process

Embed simulation in workflows:

• Define when simulation occurs in development phases
• Establish handoff processes for geometry and results
• Create standards for model documentation
• Build feedback loops from simulation to design

Related Resources

Explore our in-depth articles on simulation topics:

• What is Simulation-Driven Design?
• How to Improve Designs by Compressing Simulation Analysis
• The Best Simulation Toolbox: Integrated Suite or Granular Apps?
• Top 4 Factors to Unlock Simulation-Driven Design

Browse all simulation articles for the latest research and analysis.

Frequently Asked Questions

What is engineering simulation?

Engineering simulation uses computational methods to predict how products will behave under real-world conditions before physical prototypes are built. It includes structural analysis (FEA), fluid dynamics (CFD), thermal analysis, and other physics-based assessments that help engineers optimize designs and reduce development costs.

What is FEA?

FEA (Finite Element Analysis) is a computational method that divides complex geometry into small elements to calculate stresses, strains, and deformations under load. FEA predicts how parts will respond to forces, vibration, heat, and other physical effects—enabling engineers to verify designs will meet requirements before manufacturing.

What is CFD?

CFD (Computational Fluid Dynamics) simulates fluid flow, heat transfer, and related phenomena. Engineers use CFD to analyze aerodynamics, cooling systems, HVAC, and any application involving liquids or gases. CFD solves the Navier-Stokes equations numerically to predict flow patterns, pressures, and temperatures.

What is the difference between CAE and FEA?

CAE (Computer-Aided Engineering) is the broad category encompassing all engineering analysis software. FEA is one type of CAE focused on structural analysis. CAE also includes CFD for fluids, multibody dynamics for mechanisms, electromagnetic simulation, and other physics disciplines.

What is simulation-driven design?

Simulation-driven design integrates analysis early and continuously throughout product development rather than using simulation only for final validation. Engineers run simulations during concept and detailed design phases, using results to guide geometry decisions. This approach catches problems earlier when changes cost less.

What are the benefits of engineering simulation?

Simulation reduces physical prototypes, shortens development cycles, improves product performance, lowers testing costs, and enables exploration of more design alternatives. It catches problems before manufacturing when fixes are cheaper. Studies show simulation can reduce prototype iterations by 50% or more.

What skills are needed for engineering simulation?

Effective simulation requires understanding of physics fundamentals (mechanics, thermodynamics, fluid dynamics), numerical methods basics, the specific simulation software, and engineering judgment to interpret results. Domain expertise matters—knowing what questions to ask and whether results make physical sense.

How accurate is engineering simulation?

Simulation accuracy depends on model quality, mesh refinement, material properties, boundary conditions, and appropriate physics selection. Well-constructed simulations typically predict real-world behavior within 5-15%. Validation against physical tests builds confidence in simulation predictions.

What is the difference between linear and nonlinear FEA?

Linear FEA assumes small deformations, linear material behavior, and constant boundary conditions—fast to solve but limited applicability. Nonlinear FEA handles large deformations, plastic materials, contact between parts, and changing conditions—more computationally intensive but necessary for realistic analysis of many designs.

Do I need simulation if I have CAD?

CAD defines geometry—what the product looks like. Simulation predicts performance—how it will behave. While some CAD packages include basic simulation, dedicated CAE tools provide deeper analysis capabilities. Simulation answers questions CAD cannot: Will it break? Will it overheat? How will air flow around it?