When efficiency demands, tight tolerances and decreasing variability in component performance can yield large returns, uncertainty quantification becomes a competitive necessity for reducing costs, improving durability and uptime, and maximizing efficiency across operating ranges. Understanding the sources of variability, variability’s impacts on performance, and the most cost-effective ways to handle this uncertainty are now an integral part of cutting-edge turbomachinery design, manufacturing, and maintenance.
There are several challenges when deploying uncertainty quantification, probabilistic methods, and analytics for turbomachinery: turbomachinery is complex and operates at extreme conditions, yielding high dimensional input spaces and complex variable responses. Combined with the expense of physical tests on components and systems, and the difficulty of building and running high fidelity simulations, traditional methods of analytics and uncertainty quantification are pushed to their limits.
SmartUQ was created to handle challenges in turbine engine analysis that previous tools could not. SmartUQ’s tools use more efficient designs of experiment (DOEs) coupled to breakthrough statistical modeling and machine learning methods to reduce the cost of simulation and testing while maintaining high levels of accuracy. Novel algorithmic advances allow these models and techniques to be employed on larger and more complex problems than ever before. With these tools, it’s now possible to quickly apply uncertainty quantification and analytics to many different scenarios encountered in design, manufacturing, and analysis of turbine engines and other high-performance machinery.
Geometry, inlet conditions, and operating speed affect compressor performance. High-fidelity flow simulations make dense performance maps costly to generate.
SmartUQ uses efficient sampling and surrogate models to predict performance across the studied operating range, quantify uncertainty, and identify regions requiring additional simulations or tests.
Cooling geometry, material properties, and hot-gas conditions affect blade temperatures and stresses, creating competing efficiency and durability objectives.
SmartUQ accelerates thermal-fluid and structural studies with predictive models, ranks influential inputs, and supports design optimization that accounts for manufacturing and operating variation.
Uncertain component parameters and measurement noise cause system simulations to differ from engine test data across operating conditions.
SmartUQ calibrates model parameters using test measurements while accounting for model discrepancy, then propagates remaining uncertainty to performance predictions for comparison with independent test data.
To learn more about UQ in the turbine engine and turbomachinery industries, check out SmartUQ's white papers and webinars.
With existing tools, the Jet Engine OEM couldn’t scale up their engine performance exploration and characterization efforts without an exponential increase in simulation resources. Particularly challenging was high fidelity CFD simulation of transient thermal events.
With a SmartUQ demo for illustration, this webinar will go through the three steps of decision analytics - descriptive, predictive, prescriptive - and how Uncertainty Quantification plays a role in each.