February 1, 2014
By Sanjay Angadi
Because simulation is now a standard business practice, the volume, velocity and variety of engineering simulation data continues to grow at an alarming rate, making its storage, traceability and management strategically important.
As products become more complex, simulation tools and design methodologies are evolving in response. ANSYS has been working with engineering organizations of all sizes to deploy collaborative simulation approaches to address these simulation data management (SDM) challenges. Often times, simulation best practices are a combination of people, process, data and technology.
Simulation Data Management Challenges
With traditional data management approaches being complex, cumbersome and often inadequate, many companies struggle with SDM challenges. These obstacles are dependent on a customers simulation maturity level, as it will dictate their ability to deal with advanced topics such as product lifecycle management (PLM) integration.
Here are the three most notable SDM challenges that we see our customers facing today:
- Growing volume, velocity and variety of simulation data. Unlike design data, simulation data is unstructured with very large file sizes (tera/petabytes) and represents a wide variety of interconnected domain-specific tools, processes and formats (e.g., noise, vibration and harshness, crash, computational fluid dynamics). Simulation data is also sometimes linked to other closely related engineering datasets. Effectively managing the increasing variety of data and understanding its relationships provide better engineering insights that are often hidden inside multiple files or native data formats. This is crucial for informed decision making.
- Governance and managing isolated distributed data. It is also important to protect simulation data and maintain its integrity and traceability for audits, compliance and organizational policies. Ad-hoc practices of administration over multiple, unsecured platforms add to costs and also expose the organization to intellectual property (IP), security and data loss risks. Since simulation data stored in this way is not readily available or accessible, it leads to engineers spending time on non-value added tasks like manually searching for, verifying accuracy of, formatting and delivering simulation data. This is not a sustainable practice and further contributes to loss of engineering time and productivity.
- The high-performance computing (HPC) resources, connectivity and collaboration. To solve complex multi-scale, multiphysics and multi-model engineering problems, distributed simulation teams with diverse backgrounds must efficiently collaborate. However, HPC use presents challenges like moving files to and from HPC resources, remote access to graphics resources, and more. Monitoring and executing these large, complex simulation workflows efficiently, across geographically dispersed locations, requires focus on computer aided engineering user needs.
Collaborative Simulation Practices
Collaborative simulation encourages organizations to progressively implement practices that address the challenges above, based on their maturity level.
Here are the first key steps toward overall effectiveness:
- Collaboration via access to data and resources. The key is closing the information gap by making existing information available in real time to all stakeholders when they need it regardless of location. At each stage of the engineering analysis process, the ability to extract contextual information, store and retrieve the insights and assumptions made is essential to the value of simulation data. Additionally, many aspects of HPC such as job management, visualization (remote and lightweight visualization) are important for quick sharing and interpretation of information without spending time moving files across the network or between sites.
- Enhancing knowledge capture. It is vital to rigorously and efficiently document simulation best practices throughout the enterprise to inspire continuous growth by building on experience, and collaboration between experts, analysts and design engineers. Enhanced knowledge capture better supports business decisions, leverages simulation IP more effectively and drives innovation. A flexible, easy-to-use system addressing the tools, process, data and simulation execution needs is a must. Managing engineering knowledge in a structured manner requires higher organizational maturity with supporting best practices technologies for data management.
Sanjay Angadi is a senior project manager at ANSYS Inc. Email your thoughts about this article to [email protected]
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