The beauty of gas turbines
Some say that beauty is in the eye of the beholder, but others believe that beauty can be universal. It sounds crazy to some, but often the intricate and complex machinery of a gas turbine, along with the resulting simulation, is considered incredibly beautiful. There is something mesmerizing about all the blades, valves, rotors, as well as the fuel lines and wiring. The shape and structure of the blades, vanes, channels and cavities exhibit an intuitive beauty, which design experts say is essential to the success of a part and assembly. Otherwise, there is a high risk of failure. The theory is that the physics of fluids and structures should align and appear natural, almost as if it came from nature, even if it is counterintuitive to the complexity of the parts, components, wiring, alloys and compounds — the pinnacle of human engineering.
Like gas turbines, computational fluid dynamics (CFD) simulations are also known for their ability to mesmerize people with their colorful results. And with more computing power, increasingly unstable simulations with higher fidelity and more complex physics are being made. And even further and faster with GPUs. And with advances in machine learning, one can go even further and faster.
Advances in gas turbine simulations and machine learning
Siemens Energy has implemented an industry-leading multidisciplinary analysis and optimization (MDAO) workflow supported by Simcenter simulation technologies. This environment incorporates advanced capabilities such as expanded enterprise knowledge capture, artificial intelligence (AI)-powered design wizards, and reduced-order modeling that can operate in near real time. These advancements, including data science methods such as machine learning, have significantly improved the quality and efficiency of the design process.
Benefits of gas turbine simulations
A look at the current state of the art for gas turbine design workflow
The “classical” approach of a CAD image from a jet engine assembly using NX. Designing a gas turbine in the past would take several years and would not always be a success. Thanks to digital tools, we can improve on today’s design quite easily with a multidisciplinary approach of design an optimization.
Jet engine assembly (Generated with NX).
Even though it is very advanced physics and complex geometries, one can today combine several of these steps in an automated way. Keeping the CAD alive, boundary conditions and various versions stay totally in your control. The design process of a component is shown in the schematic below. This is done by joining the CAD from NX to various CAE simulation tools like Simcenter STAR-CCM+ and Simcenter 3D. The automation and optimization are handled by HEEDS and all data is managed by Teamcenter.
It really does not matter if it is higher efficiency through aerodynamics, improved mechanical integrity and durability, reducing cooling air usage or new combustion fuels; they all affect each other and there is no way to be competitive and innovative unless correctly using modern multidisciplinary design space exploration methods.
Current state-of-the-art design process for a turbomachinery component.
In order to effectively do product development, we want to evaluate as many designs as early on in the process as possible. Taking the next steps into the future means combining this with machine learning, since the design space can become large quickly and with many disciplines involved. What if we could have a machine learning algorithm train itself in real time on the design space that is currently being evaluated with computational fluid dynamics (CFD) or finite element method (FEM)?
An improvement on multidisciplinary design optimization for future product engineering
For that, we have two proofs of concept that are related to turbomachinery. One is to optimize a water pump efficiency at a flow rate of 110 kg/s and 1200 rpm. We worked on a parametrized model with 12 geometric variables and the number of blades.
HEEDS, a comprehensive multi-disciplinary design analysis and optimization (MDAO) software, uses its default search method, SHERPA, to conduct multiple search strategies simultaneously, and it dynamically adapts to the problem as it learns about the design space. With SHERPA, HEEDS can discover 300 design variations in 40 hours. With the introduction of HEEDS AI Simulation Predictor, an add-on extension in HEEDS, SHERPA’s search technology is significantly enhanced. Some CFD simulations are replaced by AI evaluations conducted through an automatically trained AI model, leveraging insights gained from early simulations – revolutionizing this process. In this case, it counted 151 CFD runs while 149 were done with AI evaluation (for a total of 300). This took roughly 20 hours reaching the same results and saving 49% in time. The pump’s efficiency increased by 3% and head by 10%.
Water pump – design space exploration with HEEDS AI Simulation Predictor – CAD and CFD results
Water pump efficiency for various designs – design space exploration with HEEDS AI Simulation Predictor.
The second case is a gas turbine blade for cooling optimization. Here, the objective is to minimize blade temperature and minimize cooling air mass flow. A parametrized CAD from NX is used to simulate in Simcenter STAR-CCM+. The CAD has 34 parametrized characteristics on the serpentine channel with changes of cooling ribs and shower head holes. The 500 design evaluations done for this case experienced an approximate 38% time save, skipping CFD simulations with AI and still reaching the same best solution. This might mean 20 days of time saved if 160 cores are used for each simulation. This way, you could easily save weeks and months on projects and get a better product faster to market.
External and internal temperature for conjugate heat transfer turbine blade design space exploration with HEEDS AI Simulation Predictor, NX and Simcenter STAR-CCM+.
Pareto front of design space exploration for minimizing blade temperature and reducing cooling inlet mass flow results using HEEDS AI Simulation Predictor.
From these first examples of adding AI and machine learning to an already impressive CAD-CAE workflow, one can already see the potential and how easy it is to get started without being a machine learning or optimization expert. How big the revolution of AI and ML will be and the impact it will have on the fate of the mechanical industry is too early to say. But we already know that it will be the key to staying in front of the competition.
Digital twin technology for turbomachinery
Schedule a meeting with CAEXPERTS and discover how the latest advances in gas turbine simulations and machine learning can transform your projects. Take advantage of this opportunity to explore innovative solutions that drive accuracy, efficiency and technical excellence in your industry.
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