Virtual manufacturing in the energy industry using predictive data analytics

Energy companies are using digital twins to navigate economic volatility while improving sustainability. Defined as a virtual model or representation of a physical item, digital twins can help optimize product designs and provide continuous insights into equipment performance throughout its operational life. In this white paper, you’ll learn how digital twins can use predictive data analytics to enable confident decision-making for asset maintenance or system improvement when data is unavailable.

Monitor subsea flow assurance using advanced thermal management

Many subsea production systems have a hydrate risk for compositions and produced fluid mixes, operating temperatures, pressures, system configuration and design. Thermal management is a key part of subsea production flow assurance, whether from hydrate risk management or maintaining the required operating conditions between the reservoir and production facility. In the whitepaper, you’ll learn how high-fidelity CFD can be used to design and validate subsea system thermal performance while reducing testing time and costs.

Predictive data and simulation tools for FEA analysis

High-fidelity simulation uses geometrically accurate representations of a system or components to provide the most detailed insight into system behavior. This raises the capacity for predicting complex behaviors using the fundamental governing equations of physics or newly-learned science. Read the white paper to learn why your business should adopt cutting-edge tools like FEA analysis to solve complex engineering challenges!

Improve system efficiency with predictive maintenance

Using asset data captured by sensors, digital twins can curate and analyze the data to gain engineering insights. After completing this process, your business can use the data to inform operating deci­sions and enable predictive maintenance that prevents unexpected outages and downtime. Download the whitepaper to discover how to improve energy asset and system efficiency using data-driven decisions.