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<a href="javascript:void()">Multi-Disciplinary Optimization of a Radial Compressor using…</a>
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<h1 class="name cadence-blog-title">Multi-Disciplinary Optimization of a Radial Compressor using Cadence CFD and Concepts NREC</h1>
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<span><time datetime="2023-06-08T04:43:00.000Z">7 Jun 2023</time></span> • <span>4 minute read</span>
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<p>Radial compressors, also known as radial fans or blowers, are primarily used for compression purposes. Radial blades attached to a rotating impeller draw air into the unit's center. They are well-suited for high-pressure
applications, where their efficient design can save energy. Isentropic efficiency and blade loading are critical factors in the design of these compressors. A multiphysics approach, including both aerodynamic and
structural objectives will ensure optimal results. Streamlining the design process and minimizing iterations can lead to practical, resource-efficient designs. This reduces time-to-market and enhances the overall
efficiency of the design process. In this short case study, the optimization of a shrouded radial compressor considering both the aerodynamic stage performance and the structural integrity of the impeller, i.e., to
maximize <a href="https://www.sciencedirect.com/topics/engineering/isentropic-efficiency#:~:text=The%20isentropic%20efficiency%20is%20the,an%20isentropic%20efficiency%20of%20100%25.">isentropic efficiency</a> and reduce
<a href="https://www.continuummechanics.org/vonmisesstress.html" rel="noopener noreferrer" target="_blank">von Mises stress</a>, is performed using Cadence Fidelity Turbomachinery suite and
<a href="https://www.conceptsnrec.com/home" rel="noopener noreferrer" target="_blank">Concepts NREC</a>.</p>
<h3 id="mcetoc_1h2cnlh1j0"><strong>Description</strong></h3>
<p>For this study, a single-stage compressor with a shrouded impeller, a vaneless diffuser, and no flow collector has been selected, as illustrated in the figure below. Here, the objective is to attain the following design
conditions.</p>
<ul>
<li>Minimize the von Mises stresses or blade loading due to centrifugal forces.</li>
<li>Increase the total isentropic efficiency.</li>
</ul>
<p><img style="display: block; height: 269px; margin-left: auto; margin-right: auto; max-height: 269px; max-width: 520px; cursor: zoom-in;" alt=" " height="291"
src="https://community.cadence.com/resized-image/__size/1040x538/__key/communityserver-blogs-components-weblogfiles/00-00-00-01-24/570817.png" width="520"></p>
<p style="text-align:center;">3D Stage Geometry (left) and Meridional View of the Stage Geometry (right).</p>
<h3 id="mcetoc_1h2cnlh1j1"><strong>Optimization Methodology</strong></h3>
<p><strong>Optimization Workflow</strong></p>
<p>In this study, a <a href="https://www.sciencedirect.com/science/article/pii/S0376042105000102" rel="noopener noreferrer" target="_blank">surrogate-based optimization methodology</a> is adopted whereby the workflow is
initiated by creating a parametric geometry model, i.e., the geometry is defined by a set of parameters. Subsequently, a subset of free parameters is defined. As a next step in this workflow, the design of experiments
(DOE) is conducted to explore the design space, and the results from these experiments are evaluated and summarized in a database. Further, a surrogate model is created using this database that can predict the efficiency
and total pressure ratio based on the input parameters.</p>
<p><img style="max-height: 480px; max-width: 4px; cursor: zoom-in;" alt=" " src="https://community.cadence.com/resized-image/__size/8x960/__key/communityserver-blogs-components-weblogfiles/00-00-00-01-24/2732425.png"><img
style="display: block; margin-left: auto; margin-right: auto; max-height: 480px; max-width: 400px; cursor: zoom-in;" alt=" "
src="https://community.cadence.com/resized-image/__size/800x960/__key/communityserver-blogs-components-weblogfiles/00-00-00-01-24/2732425.png"></p>
<p style="text-align:center;">Optimization Workflow</p>
<p>Single or multiple candidates are generated by optimizing this surrogate model. These candidates further undergo
<a href="https://www.cadence.com/en_US/home/tools/system-analysis/computational-fluid-dynamics.html" rel="noopener noreferrer" target="_blank">computational fluid dynamics (CFD)</a> and
<a href="https://www.tracc.anl.gov/joomla/index.php/transportation-research/computational-structural-mechanics" rel="noopener noreferrer" target="_blank">computational structural mechanism (CSM)</a> analysis. Results
from these analyses are fed into the database. This loop continues until the objective or convergence criteria are met and a new optimized design is generated.</p>
<p><strong>Combination of Different Optimization Methods</strong></p>
<p><strong><img style="display: block; height: 262px; margin-left: auto; margin-right: auto; max-height: 262px; max-width: 470px; cursor: zoom-in;" alt=" " height="262"
src="https://community.cadence.com/resized-image/__size/940x524/__key/communityserver-blogs-components-weblogfiles/00-00-00-01-24/35373.png" width="470"></strong></p>
<p style="text-align:center;">von Mises Stress vs. Isentropic Efficiency – Database, Multi-Objective Optimisation, Single-Objective-Optimisation.</p>
<p><span>To attain the intended results, this study effectively utilizes a variety of optimization techniques.</span> After the DOE step in the workflow, a database for the surrogate model is created, which is
cross-validated using the <a href="http://www.cebm.brown.edu/openmeta/doc/leave_one_out_analysis.html" rel="noopener noreferrer" target="_blank">leave-one-out analysis</a>. Using this analysis, the accuracy of the
surrogate model is confirmed before optimization is initiated. At first, a
<a href="https://www.sciencedirect.com/topics/engineering/multiobjective-optimization" rel="noopener noreferrer" target="_blank">multi-objective optimization</a> focusing on objectives such as efficiency and von Mises
stresses in feasible ranges is explored. As a second step in the optimization workflow,
<a href="https://www.sciencedirect.com/topics/computer-science/single-objective-optimization-problem#:~:text=The%20goal%20of%20a%20single,consumption%20or%20power%20dissipation%20metrics." rel="noopener noreferrer" target="_blank">single objective optimization</a>
fixated on stage efficiency as the objective and von Mises stress (set to maximum) as the constraint.</p>
<h3 id="mcetoc_1h2cnlh1j2"><strong>Tools Used in the Design Process</strong></h3>
<p><img style="display: block; margin-left: auto; margin-right: auto; max-height: 480px; max-width: 640px; cursor: zoom-in;" alt="Workflow for each new design during optimization"
src="https://community.cadence.com/resized-image/__size/1280x960/__key/communityserver-blogs-components-weblogfiles/00-00-00-01-24/54473.png"></p>
<p style="text-align:center;">Tools Used in the Optimization Workflow</p>
<p><strong>Geometric Design and Meshing</strong></p>
<p>The generation of the parametric model is carried out in <a href="https://www.conceptsnrec.com/hubfs/Data_Sheets/DataSheet_AxCent_Concepts_NREC.pdf" rel="noopener noreferrer" target="_blank">AxCent</a>. It is a powerful
tool for detailed turbomachinery 3D geometric design. This tool enables the user to conduct a preliminary flow and stress analysis. The parametric model for this optimization has 27 free parameters and 50 mathematical
equations that can link different parameters or define complex quantities.</p>
<p><img class="align-left" style="float: left; height: 131px; max-width: 254px; cursor: zoom-in;" alt="Grid for CSM Computation"
src="https://community.cadence.com/resized-image/__size/508x262/__key/communityserver-blogs-components-weblogfiles/00-00-00-01-24/052225.png" width="253"></p>
<p>AxCent is also used for CSM meshing and simulation using a module called push-button FEA, a fully integrated stress analysis tool allowing users to do both structural and aerodynamic analysis simultaneously.</p>
<p>For CFD analysis, a structured mesh was generated using
<a href="https://www.cadence.com/en_US/home/tools/system-analysis/computational-fluid-dynamics/fidelity/automesh.html" rel="noopener noreferrer" target="_blank">Fidelity Autogrid</a>. It works with predefined topologies
for different turbomachinery configurations. It encompasses advanced smoothing algorithms and scripting capabilities.</p>
<p><strong>Solver</strong></p>
<p>As mentioned in the geometric design and meshing section, the CSM simulation is carried out in AxCent, and the CSM setup is as follows:</p>
<ul>
<li>Single passage model – fillets included</li>
<li>Unstructured grid with relative cell size</li>
<li>One operating point <ul>
<li>Material used - Austenitic Stainless Steel (300 series)</li>
<li>Rotation Speed: = 1.15</li>
<li>Material Properties at = 100°</li>
</ul>
</li>
</ul>
<p>For CFD simulation, the mesh obtained from Fidelity Automesh Autogrid is plugged into
<a href="https://www.cadence.com/en_US/home/tools/system-analysis/computational-fluid-dynamics/turbomachinery.html" rel="noopener noreferrer" target="_blank">Fidelity Flow</a> (Fine/Turbo), the fastest structured
solver on the market, offering advanced options for rotor-stator interfaces, convergence acceleration, and with full and batch scripting capabilities. The CFD setup is as follows:</p>
<p><img class="align-right" style="float: right; height: 207px; max-width: 370px; cursor: zoom-in;" alt="Operating Points for CFD Analysis"
src="https://community.cadence.com/resized-image/__size/740x414/__key/communityserver-blogs-components-weblogfiles/00-00-00-01-24/836256.png" width="370"></p>
<ul>
<li>Single passage model</li>
<li>Structured grid</li>
<li>Spalart–Allmaras turbulence model</li>
<li>Wall function</li>
<li>Fluid: 2, ideal gas</li>
<li>Boundary conditions: inlet total pressure, inlet temperature, and outlet mass flow</li>
<li>Three operating points along a one-speed line</li>
</ul>
<h3 id="mcetoc_1h2cnlh1j3"><strong>Results and Conclusion</strong></h3>
<p><strong><img style="display: block; margin-left: auto; margin-right: auto; max-height: 480px; max-width: 640px; cursor: zoom-in;" alt=" "
src="https://community.cadence.com/resized-image/__size/1280x960/__key/communityserver-blogs-components-weblogfiles/00-00-00-01-24/2678.png"></strong></p>
<p style="text-align:center;"><strong></strong>Maximum von Mises Stress and Total Isentropic Efficiency for five designs.</p>
<p>Based on the plotted data, it is clear that the v3 design is the most optimized, striking a good balance between objectives. This design boasts minimal von Mises stresses, high efficiency, and a satisfactory safety limit.
To summarize, the total efficiency is increased by 0.6 %, while the stresses from centrifugal forces are decreased by more than 50%.<br><strong></strong></p>
<p>This case study clearly demonstrates that integrating different disciplines into one optimization process is essential for achieving practical designs. Cadence CFD and Concepts NREC offer effective tools to streamline
this process. When facing conflicting objectives, implementing multi-objective optimization can help identify the most optimal solution.</p>
<hr>
<p>Watch the webinar on <a href="https://www.numeca.de/webinar-multi-disciplinary-optimisation/" rel="noopener noreferrer" target="_blank">Navier & Stokes vs. von Mises: Optimisation of a Radial Compressor</a> to
learn more about optimizing a radial compressor using Cadence CFD and Concepts NREC.</p>
<p>
<a href="https://www.numeca.de/webinar-multi-disciplinary-optimisation/?utm_source=Blog&utm_medium=link&utm_campaign=CFDCommunityWebinar" rel="noopener noreferrer" target="_blank"><img style="max-height:200px;max-width:300px;" alt=" " src="https://community.cadence.com/resized-image/__size/600x400/__key/communityserver-blogs-components-weblogfiles/00-00-00-01-24/2678.WATCH-WEBINAR_5F00_Red_5F00_Button_5F00_200x48.png"></a>
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Robust Design Optimization of a Ford Turbocharger Compressor
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<p> What is robust design optimization (RDO), and is it better than standard optimization? In this customer case, we describe... </p>
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<span class="pubdate"><time datetime="2023-02-16T11:26:00.000Z">16 Feb 2023</time> • 2 min read</span>
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Ford: Multidisciplinary Design Optimization of a Turbocharger Compressor
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<p> There are several reasons why optimization methods can, at this time, not be considered in routine design work: Lack... </p>
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<span class="pubdate"><time datetime="2016-08-25T12:02:00.000Z">25 Aug 2016</time> • 4 min read</span>
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Go Back * Products * Solutions * Support * Company * LOGIN / SIGN UP * Back * LOGIN / SIGN UP * Login * New User * * Products * Solutions * Support * Company * * * Login * New User Community Computational Fluid Dynamics Multi-Disciplinary Optimization of a Radial Compressor using… MULTI-DISCIPLINARY OPTIMIZATION OF A RADIAL COMPRESSOR USING CADENCE CFD AND CONCEPTS NREC 7 Jun 2023 • 4 minute read Radial compressors, also known as radial fans or blowers, are primarily used for compression purposes. Radial blades attached to a rotating impeller draw air into the unit's center. They are well-suited for high-pressure applications, where their efficient design can save energy. Isentropic efficiency and blade loading are critical factors in the design of these compressors. A multiphysics approach, including both aerodynamic and structural objectives will ensure optimal results. Streamlining the design process and minimizing iterations can lead to practical, resource-efficient designs. This reduces time-to-market and enhances the overall efficiency of the design process. In this short case study, the optimization of a shrouded radial compressor considering both the aerodynamic stage performance and the structural integrity of the impeller, i.e., to maximize isentropic efficiency and reduce von Mises stress, is performed using Cadence Fidelity Turbomachinery suite and Concepts NREC. DESCRIPTION For this study, a single-stage compressor with a shrouded impeller, a vaneless diffuser, and no flow collector has been selected, as illustrated in the figure below. Here, the objective is to attain the following design conditions. * Minimize the von Mises stresses or blade loading due to centrifugal forces. * Increase the total isentropic efficiency. 3D Stage Geometry (left) and Meridional View of the Stage Geometry (right). OPTIMIZATION METHODOLOGY Optimization Workflow In this study, a surrogate-based optimization methodology is adopted whereby the workflow is initiated by creating a parametric geometry model, i.e., the geometry is defined by a set of parameters. Subsequently, a subset of free parameters is defined. As a next step in this workflow, the design of experiments (DOE) is conducted to explore the design space, and the results from these experiments are evaluated and summarized in a database. Further, a surrogate model is created using this database that can predict the efficiency and total pressure ratio based on the input parameters. Optimization Workflow Single or multiple candidates are generated by optimizing this surrogate model. These candidates further undergo computational fluid dynamics (CFD) and computational structural mechanism (CSM) analysis. Results from these analyses are fed into the database. This loop continues until the objective or convergence criteria are met and a new optimized design is generated. Combination of Different Optimization Methods von Mises Stress vs. Isentropic Efficiency – Database, Multi-Objective Optimisation, Single-Objective-Optimisation. To attain the intended results, this study effectively utilizes a variety of optimization techniques. After the DOE step in the workflow, a database for the surrogate model is created, which is cross-validated using the leave-one-out analysis. Using this analysis, the accuracy of the surrogate model is confirmed before optimization is initiated. At first, a multi-objective optimization focusing on objectives such as efficiency and von Mises stresses in feasible ranges is explored. As a second step in the optimization workflow, single objective optimization fixated on stage efficiency as the objective and von Mises stress (set to maximum) as the constraint. TOOLS USED IN THE DESIGN PROCESS Tools Used in the Optimization Workflow Geometric Design and Meshing The generation of the parametric model is carried out in AxCent. It is a powerful tool for detailed turbomachinery 3D geometric design. This tool enables the user to conduct a preliminary flow and stress analysis. The parametric model for this optimization has 27 free parameters and 50 mathematical equations that can link different parameters or define complex quantities. AxCent is also used for CSM meshing and simulation using a module called push-button FEA, a fully integrated stress analysis tool allowing users to do both structural and aerodynamic analysis simultaneously. For CFD analysis, a structured mesh was generated using Fidelity Autogrid. It works with predefined topologies for different turbomachinery configurations. It encompasses advanced smoothing algorithms and scripting capabilities. Solver As mentioned in the geometric design and meshing section, the CSM simulation is carried out in AxCent, and the CSM setup is as follows: * Single passage model – fillets included * Unstructured grid with relative cell size * One operating point * Material used - Austenitic Stainless Steel (300 series) * Rotation Speed: = 1.15 * Material Properties at = 100° For CFD simulation, the mesh obtained from Fidelity Automesh Autogrid is plugged into Fidelity Flow (Fine/Turbo), the fastest structured solver on the market, offering advanced options for rotor-stator interfaces, convergence acceleration, and with full and batch scripting capabilities. The CFD setup is as follows: * Single passage model * Structured grid * Spalart–Allmaras turbulence model * Wall function * Fluid: 2, ideal gas * Boundary conditions: inlet total pressure, inlet temperature, and outlet mass flow * Three operating points along a one-speed line RESULTS AND CONCLUSION Maximum von Mises Stress and Total Isentropic Efficiency for five designs. Based on the plotted data, it is clear that the v3 design is the most optimized, striking a good balance between objectives. This design boasts minimal von Mises stresses, high efficiency, and a satisfactory safety limit. To summarize, the total efficiency is increased by 0.6 %, while the stresses from centrifugal forces are decreased by more than 50%. This case study clearly demonstrates that integrating different disciplines into one optimization process is essential for achieving practical designs. Cadence CFD and Concepts NREC offer effective tools to streamline this process. When facing conflicting objectives, implementing multi-objective optimization can help identify the most optimal solution. -------------------------------------------------------------------------------- Watch the webinar on Navier & Stokes vs. von Mises: Optimisation of a Radial Compressor to learn more about optimizing a radial compressor using Cadence CFD and Concepts NREC. PreviousBoost Your CFD Workflow Productivity with Fidelity Python API – Part I NextWhat's New In Fidelity 2023.1 Author Veena Parthan Community Member Blog Activity Options * SUBSCRIPTIONS Never miss a story from Computational Fluid Dynamics. Subscribe for in-depth analysis and articles. Subscribe by email * CFD turbomachinery optimization Fidelity CFD engineering simulation software NUMECA Mesh Generation Fidelity Turbo RELATED POSTS ENTECHMACH: Multidisciplinary Design Optimization of a Multi-Stage Centrifugal Compressor Authors: Vladimir Neverov, Ivan Cheglakov, Specialists on compressor machines, Aleksandr Liubimov, Head of Advanced Development... AnneMarie CFD 15 Feb 2019 • 4 min read Robust Design Optimization of a Ford Turbocharger Compressor What is robust design optimization (RDO), and is it better than standard optimization? In this customer case, we describe... AnneMarie CFD 16 Feb 2023 • 2 min read CFD , fluid simulations , featured , turbomachinery , Computational Fluid Dynamics Ford: Multidisciplinary Design Optimization of a Turbocharger Compressor There are several reasons why optimization methods can, at this time, not be considered in routine design work: Lack... AnneMarie CFD 25 Aug 2016 • 4 min read CFD , Automotive , automotive engineering , turbomachinery , Computational Fluid Dynamics © 2023 Cadence Design Systems, Inc. All Rights Reserved. * Terms of Use * Privacy * Cookie Policy * US Trademarks * Do Not Sell or Share My Personal Information ShareThis Copy and Paste About Cookies On This Site This website uses essential cookies that are necessary for the operation of this website and that are always set. With your consent and by selecting "Accept All Cookies," we may also enable non-essential cookies (such as analytics and behavioral cookies) in order to personalize and enhance your experience on our website. You can modify or further customize your selections at any time. We also share your personal data with our trusted third-party providers to achieve the purposes described above. For a list of our third-party providers and for more information about our cookies and how we use them, please see our Cookie Policy and Privacy Policy. 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