Accelerated product development is straining your CAE capabilities (and analysts)

Manufacturers are under pressure to develop and deliver innovative products. That’s no secret, it’s been this way for centuries. But what is new in recent years are expectations.  Advanced tools are drastically shortening the time to design, validate, and manufacture products. This not only raises the innovation bar but is raising expectations among stakeholders. And who is feeling the pressure from this heavier (and accelerated) workload? Your CAE Analysis.

From increased workload, tighter integration with other development phases, and higher expectations for accuracy and efficiency, this accelerated product development pace is putting a strain on your resources.

Increased Workload and Complexity

The maturation of digital simulation technologies is slowly making physical prototype testing obsolete. The validation accuracy of these tools allows companies to slash, or even eliminate physical testing. This has a couple of big advantages:

Increased innovation as more design alternatives can be evaluated in the same timeframe.

Products get to the market faster.

Less costs associated with product development.

One of the primary challenges CAE analysts face is the surge in workload and complexity. Shorter development cycles require more frequent simulations and analyses, often under tighter deadlines. CAE analysts are tasked with performing detailed simulations such as finite element analysis (FEA), computational fluid dynamics (CFD), and multi-body dynamics (MBD) more rapidly.

Furthermore, the complexity of modern products often necessitates sophisticated multi-physics simulations, combining thermal, structural, and fluid dynamics analyses. This multi-disciplinary approach requires CAE analysts to possess a broad and deep understanding of various domains and the ability to integrate different simulation results coherently. The increasing complexity and volume of simulations can lead to significant stress, as analysts must balance speed with precision.

Integration with Agile Development Practices

The shift towards agile and iterative development practices has also reshaped the role of CAE analysts. In traditional development models, CAE was often a distinct, sequential phase following design. However, in agile environments, CAE must be integrated continuously throughout the development process. This integration often requires CAE analysts to work in close collaboration with design engineers, manufacturing teams, and project managers.

Such a dynamic environment necessitates frequent communication and iterative feedback, often requiring rapid adjustments and re-analyses based on evolving design specifications. CAE analysts must adapt to a more collaborative and flexible working style, often participating in daily stand-ups and sprint reviews to align their efforts with the broader development team.

This continuous involvement can be both time-consuming and challenging, as it demands a balance between analytical accuracy and responsiveness to changing (and accelerated) project needs.

Heightened Expectations for Accuracy and Efficiency

The accelerated development process also brings heightened expectations for the accuracy and efficiency of CAE simulations. Companies are relying more heavily on CAE to predict performance and identify potential issues early in the design process, thereby reducing the need for costly physical prototypes and extensive testing. This reliance places significant pressure on CAE analysts to deliver precise and reliable results within shorter time frames.

Moreover, advancements in CAE tools and technologies, such as artificial intelligence (AI) and machine learning (ML) for predictive analytics, add another layer of expectation. Analysts are expected to leverage these technologies to enhance simulation accuracy and reduce computation times, often requiring them to acquire new skills and adapt to emerging methodologies. The integration of AI and ML into CAE workflows introduces additional complexity, as analysts must validate and interpret results from these advanced tools, ensuring that they complement rather than complicate traditional simulation approaches.

Tools and Best Practices

To cope with these challenges, organizations and CAE analysts must adopt new practices and leverage supporting technologies.

High Performance Computing: Investing in high-performance computing (HPC) infrastructure can alleviate some of the computational burdens, enabling faster simulations. Additionally, embracing cloud-based CAE solutions can provide scalable resources that adjust to fluctuating workload demands. Continuous training and professional development are crucial for CAE analysts to stay abreast of new tools and methodologies.

Project Management: Implementing effective project management practices can help streamline workflows and reduce inefficiencies. Establishing clear communication channels and setting realistic expectations across teams can foster a more collaborative environment, allowing CAE analysts to manage their tasks more effectively.

Visual Collaboration: VCollab’s Rapid Results Review capability vastly accelerates the process of going from CAE results to design decision making. This delivers significant time savings to the design process by automating the traditional manual, time consuming post-processing step.

Once computation is completed, analysis results can be automatically post-processed with areas of failure identified and visually called out. This can be done by interactively querying results data with VCollab’s automatic data mining and annotation or automatically through customized templates that evaluate your standard set of design metrics against requirement targets. This process creates interactive, 3D digital slides that can be utilized for quickly creating presentations and sharing results with design teams and other stakeholders.

With VCollab, companies like Amsted Automotive are slashing the CAE reporting process from days to hours.