Multidisciplinary Optimization Engineer

The SIMULIA 3DEXPERIENCE Multidisciplinary Optimization Engineer (MDO) Role provides a framework for engineers to understand and improve the performance and robustness of their designs through design alternatives and exploration.

Users can leverage parameter and sensitivity studies to understand and identify unexpected behavior or the effects on their design by varying inputs. Design robustness can be assessed through uncertainty quantification to evaluate the influence of variability. Optimization of detailed or surrogate models can be leveraged to predict meet performance targets from multiple performance objectives and constraints.

Engineers often lack comprehensive information about how to meet the vast number of conflicting and competing design requirements that are frequently managed across different teams with a multitude of tools. Whether improving the performance of a part or a complex multidisciplinary system, making the right decisions early in the process has a direct impact on the success of a design and its cost.

SIMULIA Multidisciplinary Optimization Engineer

To further leverage simulation and physical test data, a user can abstract data into surrogate models using advanced analytic methods. Surrogate models on the 3DEXPERIENCE platform allow for traceability and interchangeability with other Dassault Systèmes applications, enabling faster design exploration, accelerated innovation, and better decisions.

Dedicated functionality for data post-processing enables users to transform and extract value from large amounts of data. Teams of decision-makers can unlock their data to collaboratively answer questions, such as, “What if?”, “What is my best option?” to explicitly and transparently evaluate scenarios, verify requirements, and assess feasibility while retaining decision history and reference material.

Rather than validating a single design, users can evaluate the full range of design alternatives and collaboratively resolve conflicts between performance targets through trade-off analytics to identify the best design.

Multidisciplinary Optimization Engineer Benefits

• Investigate the relationships between design parameters and their influence on performance objectives to improve the efficiency of design exploration.
• Systematically explore variations of design parameters through optimization to identify the best design that satisfies requirements.
• Assess the influence of variability to reduce the probability of design failures.
• Gain comprehensive insight into the entire design space with visualization tools that enable trade-off across conflicting objectives.
• Enable intelligent decision-making collaboratively across the organization.

SIMULIA Optimization Results

Multidisciplinary Optimization Engineer Highlights

• Design Exploration Algorithms
  • Industry standard algorithms for Design of Experiments, Parametric Optimization, Surrogate Model Generation, and Uncertainty Quantification.
  • Algorithms tuned for high-performance environments to leverage available compute resources.
• Visual Analytics
  • Rapidly merge, post-process, and visualize large data using interactive visualizations.
  • Further examine design exploration results via images and 3D lightweight visualization.
• Decision Traceability
  • Collaboratively evaluate options using explicit, customizable ranking engine, preference elicitation, and customizable analysis workflow.
  • Utilize, modify, and cascade requirements.
• Platform Integration
  • Analyze any object, validate against requirements, and share decisions on 3DEXPERIENCE Platform.
  • Manage traceable approximation objects for further and faster design exploration.