We use numerical methods to analyze complex structural problems involving interactions such as thermo-mechanical, FSI (fluid structure interaction), or nonlinearities (material, geometric, contact)
Leverage FEA simulations to evaluate structural integrity under various conditions, ensuring reliability and safety of components.
Enhance manufacturing processes by predicting material behavior and optimizing design for durability and efficiency.
Assess thermal performance and manage heat transfer in assemblies to prevent overheating and ensure long-term reliability.
Evaluating stress, strain, and deformation in structures to ensure they can withstand operational loads and environmental conditions.
Predicting heat transfer and temperature distribution to manage thermal loads and prevent overheating.
Assessing vibrational behavior, dynamic loading, and impact forces to ensure structural stability and performance under dynamic conditions.
Refining designs to improve performance, reduce material usage, and ensure manufacturability.
Accurate prediction of material behavior and structural response ensures long-term performance and durability.
Thorough analysis and verification of designs minimize the risk of failure, ensuring safety and compliance with standards.
Optimized designs and accurate simulations reduce the need for physical prototypes, lowering development costs and time.
Detailed simulations enable the refinement of designs for superior performance in real-world applications.
Simulate complex material behaviors, including nonlinear, hyperelastic, and composite materials, to ensure accurate representation of material properties.
Perform simulations that involve multiple physical interactions, such as thermal-stress and fluid-structure interactions, to capture comprehensive behavior of components.
Address nonlinearities in material properties, large deformations, and contact interactions to ensure realistic simulation results.
Predict fatigue life and fracture behavior to assess durability and identify potential failure points in components.
Evaluate vibrational characteristics and acoustic performance to ensure stability and noise reduction in mechanical systems.
Combine structural analysis with other physics, such as thermal and fluid dynamics for a holistic understanding of system behavior.
Utilize HPC resources to run large-scale simulations efficiently, enabling detailed analysis of complex models.
Use parametric, shape, and topology optimization to refine designs, improve performance, and reduce material usage.
Preparing geometric models and assembly drawings, performing meshing for numerical analysis, and establishing initial and boundary conditions to mirror real-world scenarios.
- Geometry Import: Importing the geometric model of the system or structure to be analyzed into the simulation software. This can involve CAD (Computer-Aided Design) models or other geometric representations.
- Model Simplification: Simplifying complex geometric models to improve computational efficiency without compromising accuracy. This may include removing unnecessary details or refining the mesh for better resolution in critical areas.
- Mesh Generation: Creating a mesh, or grid of discrete elements, over the geometric model to represent the physical domain. The quality and density of the mesh play a crucial role in the accuracy and efficiency of the simulation.
- Boundary Conditions: Specifying the boundary conditions that define how the system interacts with its environment. This includes constraints, loads, and other external influences that affect the behavior of the system.
- Material Properties: Assigning material properties to different parts of the model, such as stiffness, density, and thermal conductivity. These properties govern how the materials respond to applied loads and environmental conditions.
- Solver Setup: Configuring the simulation solver with appropriate settings and parameters to solve the mathematical equations governing the behavior of the system. This includes selecting the numerical methods, time steps, and convergence criteria.
Conducting the simulation based on predefined setups, closely supervising its progress to ensure it aligns with expectations, and adjusting parameters as necessary with minimal intervention.
In numerical simulations, the simulation process, also known as the analysis run, is the stage where the mathematical models representing the physical system are solved to obtain the desired results. This phase involves several key steps:
- Solver Execution: The solver, or simulation software, executes the numerical algorithms to solve the mathematical equations governing the behavior of the system. These equations may include differential equations representing physical laws such as conservation of mass, momentum, and energy.
- Iterative Solution: The solver iterates through the simulation time steps, updating the solution at each step based on the current state of the system and the applied boundary conditions. This process continues until the desired simulation time is reached or until convergence criteria are met.
- Time Stepping: For transient simulations, the solver advances the simulation in time, computing the system’s response at discrete time intervals. The size of these time steps may vary depending on the dynamics of the system and the desired level of accuracy.
- Numerical Methods: The solver employs numerical methods, such as finite difference, finite volume, or finite element methods, to discretize the continuous mathematical equations into a set of algebraic equations that can be solved computationally. These methods ensure accurate approximation of the system’s behavior.
- Boundary Conditions Enforcement: Throughout the simulation, the solver enforces the specified boundary conditions, ensuring that the system interacts with its environment as defined in the preprocessing phase. These boundary conditions may include applied loads, constraints, and external influences.
- Convergence Monitoring: The solver monitors the convergence of the solution throughout the simulation run, ensuring that the numerical solution stabilizes and achieves the desired accuracy.
Extracting and analyzing the numerical results from the simulation, identifying and addressing discrepancies, and providing actionable recommendations for potential improvements or corrective actions.
In numerical simulations, the post-processing stage is a critical phase where the results obtained from the simulation process are analyzed, interpreted, and presented in a meaningful way. This phase involves several key activities:
- Result Visualization: The post-processing phase begins with the visualization of simulation results, which often include scalar fields (such as temperature, pressure, or velocity), vector fields (such as flow velocity), and other relevant quantities. Visualization techniques range from simple plots and graphs to more complex 2D and 3D renderings, contour plots, and animations.
- Data Analysis: Once the results are visualized, they are subjected to detailed data analysis to extract meaningful insights about the behavior of the simulated system. This analysis may involve computing statistical quantities, such as averages, maxima, and minima, identifying critical regions or features, and comparing results against design criteria or experimental data.
- Performance Evaluation: In many cases, numerical simulations are used to evaluate the performance of a system or design under specific operating conditions. The post-processing phase involves quantifying key performance metrics, such as efficiency, reliability, durability, or safety, based on the simulation results.
- Validation and Verification: It is essential to assess the accuracy and reliability of the simulation results through validation and verification exercises. Validation involves comparing simulation results against experimental data or analytical solutions to ensure that the numerical model accurately represents the real-world system. Verification, on the other hand, assesses the numerical accuracy of the simulation algorithms and discretization schemes.
- Decision Support: The post-processing phase provides valuable insights that can inform engineering decisions, such as design modifications, optimization strategies, or performance improvements. Engineers use the analyzed results to identify areas for improvement, validate design choices, and optimize system performance.
- Reporting and Documentation: Finally, the findings from the post-processing phase are typically documented in reports, presentations, or technical documents. Clear and concise reporting of simulation results, along with appropriate visualizations and analysis, helps stakeholders understand the implications of the findings and make informed decisions.
Repeating the simulation process as needed to refine outcomes, address dysfunctions, or adjust to varying input data, ensuring continuous optimization and accuracy for meeting project requirements:
- Refinement: Continuously refining simulation results to improve accuracy and performance.
- Issue Resolution: Identifying and rectifying issues or dysfunctions through iterative adjustments.
- Adaptation: Adjusting for variations in input data to maintain relevance and accuracy.
