How do you deal with common challenges or objections when presenting CAE results?

Simplify complex concepts involving technical terms, methods, and assumptions. Explain complex concepts and acronyms in simple terms, using analogies or visual aids to enhance understanding. Highlight key takeaways: Focus on the most important findings and present the results in a clear and concise manner, emphasizing the product design, performance, or optimization. Address limitations and uncertainties: Be transparent about the assumptions made, the accuracy of the results, and any potential uncertainties. Provide context and relevance: Relate the CAE results to the broader context of the project or business goals. Use visual aids: Visual aids such as charts, graphs, or 3D models can greatly enhance the understanding of CAE results.

To present CAE results effectively, utilize visuals like graphs, charts, images, animations, and videos to illustrate your points. These visuals make the data clear and attractive. Additionally, incorporate storytelling through examples, analogies, or anecdotes to demonstrate the relevance and value of your CAE results. This combination engages your audience intellectually and emotionally, helping them relate to the information and understand its significance. Visuals and stories make your presentation more engaging, impactful, and memorable, fostering a deeper understanding and appreciation of the CAE analysis.

A common challenge when presenting CAE results is addressing objections about the validity and accuracy of assumptions and limitations. CAE analysis involves simplifications, assumptions, and uncertainties. To address this, it is crucial to transparently explain assumptions and limitations, and demonstrate their impact on the results. Justify choices and provide supporting evidence or references. For example, explain material models, boundary conditions, or mesh sizes, and how they influence the results. This fosters trust and credibility by showing that decisions were made based on sound reasoning and available evidence.

To overcome this challenge, it is important to discuss the alternatives considered and explain the selection process for the best solution. Acknowledge the advantages and disadvantages of each alternative and trade-off, and demonstrate how they align with project goals, constraints, or preferences. For instance, compare CAE models based on accuracy, complexity, or computational cost, and justify the selection of the most suitable one. This approach highlights a thoughtful decision-making process, considering various factors to arrive at the optimal outcome.

In addition to providing recommendations and next steps based on CAE analysis, it is crucial to emphasize the collaborative nature of the process. Engaging stakeholders, clients, and managers in the decision-making and problem-solving journey can significantly enhance the acceptance and implementation of the CAE results. By involving them early on, seeking their input, and valuing their expertise, you create a sense of ownership and shared responsibility. This collaborative approach fosters a deeper understanding of the CAE analysis, encourages buy-in from key stakeholders, and ensures that the recommended actions align with the broader objectives and constraints of the project as a collaborative effort.

In the early days of CAE at Perkins Engines I had analysed a cylinder head using pretty primitive finite elements using a custom made computer program. Despite this, they clearly showed that the mechanism for an internal failure was due to heeling about the gasket and bolt loads. After an entertaining presentation to senior engineers they were convinced and put in hand corrective measures.

To create high-quality visualisations of your results, consider using software like Blender or ParaView, which offer advanced rendering features and can handle large and complex datasets. It's crucial to note that thorough validation is essential for robust results. Cross-referencing simulations with literature or experiments ensures a comprehensive understanding of the simulated phenomena. Additionally, my expertise extends to leveraging these tools' capabilities for creating compelling visualisations and animations from 3D data exported from post-processors. While this process may require additional time for refinement, the graphical outputs are of superior quality.