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How Automating Engineering Data Management Transforms R&D Productivity

Generate metadata with valuable context on every simulation for enhanced collaboration, discovery, and analytics

Engineers and scientists today are generating massive amounts of simulation data which becomes difficult to manage. Metadata-enhanced solutions enable better scalability of simulation efforts, allowing companies to handle a 30% increase in simulation volume without a proportional increase in resources​ (Digital Engineering247)​. Automated metadata-driven solutions further accelerate the scalability of simulation efforts, acting as a force multiplier allowing companies to transform their simulation data operations.

Engineering Data Management Breaks Silos Across Disciplines

Complex, multidisciplinary simulations play a crucial role in designing and testing new products. The data generated from these simulations is massive and multifaceted, making manual metadata management increasingly impractical. For instance, in the aerospace industry, running simulations for aircraft design generates terabytes of data that include structural analysis, fluid dynamics, thermal simulations and more. Each analyst explores the design space for optimized performance creating massive datasets of both input and output data and complex computational pipelines. Further, each analysis has its own specialized numerical methods requiring specialized file formats. Manual processes are not only time-consuming but also prone to errors and inconsistencies, leading to potential data loss and increased costs. 

Manual Data Capture Limits Engineering Efficiency

Despite its importance, managing simulation metadata manually poses significant challenges. The sheer volume and complexity of data can lead to errors, inconsistencies, and inefficiencies. Traditional methods of metadata management often result in:

  • Inefficiency and Errors: Manual processes are time-consuming and prone to human error, leading to inaccurate data and delays in project timelines. For example, typical hand-offs between managers and engineers occur via emails, and static charts/plots shared in documents. If a different chart is requested, the engineer re-runs the simulation and sends it back to the manager as a different static image of a chart. Sometimes taking days to accomplish, and weeks to finalize the chart. Sometimes requests in emails to update a report are missed entirely. 
  • Collaboration Issues: Disparate data management systems hinder seamless collaboration among interdisciplinary teams, impacting project outcomes negatively. Simulation users have to search for data in different systems, export it to their local machines before they can work on it. Finally when results are generated they may have to update different systems.For example, it’s common for companies with large engineering divisions to implement their own data management systems or when  companies grow by acquisition, data exists in a variety of PLM systems which can take years to harmonize. Because of the complexity of the deployment, risks of major data loss occurring or disruptions in operations, organizations opt to continue using multiple disparate systems. Simulation engineers work in silos unable to access multi-disciplinary data to inform overall systems behavior.
  • Compliance and Traceability Problems: Ensuring regulatory compliance and traceability of data changes is difficult without automated systems, increasing the risk of non-compliance and associated penalties. For example, in several industries, standards for safety are different in different countries and can change over time. Further, sustainable packaging requirements are constantly evolving. Shipping the wrong formulation of food or drug, in the wrong package to the wrong country can invite severe penalties.

Automate Data Management Drives Engineering Outcomes

Automation in metadata management offers numerous benefits, addressing common engineering challenges :

  • Context: Automated metadata management ensures rich contextual information about data. For example, their origin, usage, and relationships with other data are consistently captured with automated metadata management. This context is critical for understanding the data’s relevance and applicability in various scenarios, providing insights essential for product decisions.
  • Organization: By automatically tagging and categorizing data, organizations can improve the discoverability of their data sets. For example, once tagged users can choose to save frequently used search criteria making it easier to find their data.
  • R&D Productivity: Automated systems streamline the capture and organization of metadata, increasing productivity and reducing the time required to manage data. For example, programmatic extraction and synthesis of results KPI from simulation results avoids the cumbersome manual searching for data in simulation files, and data entry into custom fields.
  • Accuracy: programmatic tagging ensures data is always categorized correctly, minimizing the risk of human error such as forgetting to tag information or tagging it incorrectly. For example, human error in data entry of KPI derived from simulation results can have severe consequences on product decisions.
  • Visualization: Intuitive dashboards and visual tools enhance data accessibility, making it easier for stakeholders to interpret and utilize the information effectively. For example, decision makers can utilize specialized dashboards intended for the specific decisions they need to make. The simulation engineer needs to generate only the raw data but preparing the data for the right insight can be automated.
  • Governance: Automated policies for data retention to avoid bloat and cost overruns, automated access control based on the team member’s role in the organization, and compliance by providing data capture templates ensure data integrity and security. For example, in healthcare, there are specific regulations related to handling of patient specific data that can be deployed as automated governance policies.

How to Automate Simulation Data Management on Rescale

Rescale automates metadata management regardless of simulation type or format. Scientists and engineers are able to extract metadata for multidisciplinary model based collaboration, simulation data search, and results extraction for informed and timely product decisions. Rescale Metadata is valuable for capturing key engineering variables in addition to job telemetry details such as cost and performance of each job.

1. Job Execution and Data Extraction:

As engineers and scientists perform their simulations, Rescale automatically extracts simulation metadata without manual intervention, ensuring that critical parameters are captured, such as thermal efficiency, structural vibration modes, fluid pressure and velocity and costs per job.

Automated data capture using Python post-processing scripts

2. Standardization and Categorization:

As simulation engineers update product designs, using custom fields, metadata is captured and categorized based on predefined criteria. This process not only organizes the data efficiently but also makes it easily searchable, enhancing retrieval times. For example, with custom fields simulation engineers can capture what system and subsystem of the product is being updated, allowing downstream updates to affected systems.

Custom fields for data capture and categorization

3. Data Visualization and Results Review:

Visualization tools offer intuitive dashboards to easily understand the significance of the data. These dashboards highlight trends, identify anomalies, and present other critical insights derived from the simulation data, facilitating informed decision-making. 

For example, in order for an engineering lead to make a decision about the material used in an automotive heat exchanger, they need to understand the Pressure Drop vs Peak Temperature trend, they need to see the peak stress values associated with structural tests, and they need to understand the design constraints, peak allowable temperatures and pressure drop. These can be automatically derived from multidisciplinary simulation data and requirements data, generating the insights needed for an informed decision.

Shows metadata visualized to support effective trade-off decisions

Future Applications and Recommendations for Engineering Data Management 

The future of engineering data management strategically lies in the integration of simulation results and metadata with AI and machine learning. These technologies can enhance predictive analytics capabilities, providing deeper insights and more accurate predictions. For example, AI and ML can predict future simulation outcomes based on historical data, allowing for more accurate and efficient decision-making. For organizations looking to stay ahead in the data-driven landscape, embracing automation in metadata management is crucial.

Automating metadata management is not just about improving efficiency; it’s about transforming how organizations leverage their data to drive innovation by capturing more insights early in the design process and by drastically reducing time to market by avoiding late stage design changes. By embracing automation, companies can enhance their data handling processes, reduce costs, and gain valuable insights that would be difficult to achieve manually.

Get Started Automating Simulation Metadata Management

Learn how to streamline the simulation processes and accelerate time-to-insight with automated data management. Automatically extract key performance indicators from simulation output files, enrich jobs with relevant context in tags, custom fields, and comments and then review an organized summary of results in the Rescale Jobs view for fast decision-making.

Author

  • Sandeep Urankar

    Sandeep Urankar is a product marketing manager at Rescale. He focuses on Rescale Metadata Management and Rescale Computational Pipelines with the goal of helping engineers achieve deeper insights faster. Prior to joining Rescale, Sandeep held several product management positions at leading simulation software companies, including Dassault Systems and Hexagon Manufacturing Intelligence.

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