Soheyl Massoudi

Last Name

Massoudi

First Name

Soheyl

ORCID

0000-0002-6763-3625

Organisational unit

09828 - Fuge, Mark / Fuge, Mark

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Publications 1 - 8 of 8

DARTS-NETGAB: Design Automation and Real-Time Simulation Using Neural Networks Ensembles for Turbocompressors on Gas-Bearings
Massoudi, Soheyl; Bejjani, Joseph; Horvath, Timothy; et al. (2024)
Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2024). Volume 3B: 50th Design Automation Conference (DAC)
In the domain of engineering design, where efficiency in simulation and precision in modeling are paramount, this study introduces DARTS-NETGAB, a pioneering platform uniquely designed for real-time simulation and automated design. Specifically tailored for gas-bearing supported turbocompressors, DARTS-NETGAB integrates neural network ensembles with a parametric CAD construction library to deliver unprecedented prediction speeds and modeling precision across various engineering systems. This integration allows for seamless, real-time performance evaluations of complex, multidisciplinary systems and automated CAD model generation. This framework streamlines the design process, reduces cycles times and enhances adaptability to manufacturing imperfection. DARTS-NETGAB features a user-centric interface developed using the advanced Panel-Bokeh Python libraries, facilitating dynamic and interactive design modifications directly within a web browser. This capability enables immediate visualization and adjustment of a comprehensive turbocompressor model, thereby streamlining the transition from theoretical design to practical application. The paper details how the combination of DARTS-NETGAB’s rapid, accurate predictive capabilities with its robust design tools not only advances micro-turbocompressor design but also revolutionizes engineering processes across diverse systems. By merging cutting-edge computational techniques with practical, user-friendly tools, DARTS-NETGAB offers a significant improvement over traditional methods, fostering more efficient and innovative engineering solutions.
Robust Design of Herringbone Grooved Journal Bearings Using Multi-Objective Optimization With Artificial Neural Networks
Massoudi, Soheyl; Schiffmann, Jürg (2023)
Proceedings of the ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. Volume 13D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery
Herringbone grooved journal bearings (HGJBs) are widely used in micro-turbocompressor applications due to their high load-carrying capacity, low friction, and oil-free solution. However, the performance of these bearings is sensitive to manufacturing deviations, which can lead to significant variations in their performance and stability. In this study, design guidelines for robust design against manufacturing deviations of HGJB supported micro-turbocompressors are proposed. These guidelines are based on surrogate model assisted multi-objective optimization using ensembles of artificial neural networks trained on a large dataset of rotor and bearing designs as well as operating conditions. The developed framework is then applied to a series of case studies representative of heat-pump and fuel cell micro-turbomachines. To highlight the importance of rotor geometry and bearing aspect ratio in the robustness of HGJBs, two types of optimizations are performed: one focusing on optimizing the bearing geometry, and the other focusing on both the bearing and rotor geometries. The analysis of the Pareto fronts and Pareto optima of each type of optimization and case study allows for the derivation of design guidelines for the robust design of HGJB supported rotors. Results suggest that by following these guidelines, it is possible to significantly improve the robustness of herringbone grooved journal bearings against manufacturing deviations, resulting in stable operation. The best design achieved ±8 μm tolerance on the bearing clearance, and designs optimized for both rotor and bearing geometry outperformed those optimized for bearing geometry alone. This work successfully identifies guidelines for the robust design of herringbone grooved journal bearings in micro-turbocompressor applications, demonstrating the strength of surrogate model assisted multi-objective optimization. It provides a valuable tool for engineers seeking to optimize the performance and reliability of these bearings.
An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization
Massoudi, Soheyl; Picard, Cyril; Schiffmann, Jürg (2023)
Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3B: 49th Design Automation Conference (DAC)
Abstract. Designing turbocompressors is a complex and challenging task, as it involves balancing conflicting objectives such as efficiency, stability, and robustness against manufacturing deviations. This paper proposes an integrated design methodology for turbocompressors supported on gas bearings, which utilizes surrogate models and ensemble learning with artificial neural networks. The proposed approach addresses the limitations of nominal and separate optimizations by integrating all relevant design aspects into a single optimization problem. A multi-objective optimization is carried out, considering four objectives and over twenty constraints, including robustness against manufacturing deviations of the radial and axial bearings in terms of stability, load capacity, and efficiency, as well as robustness against performance metric gradients. The proposed methodology maximizes the compressors range in speeds and mass flow, while also maximizing the signal-to-noise ratio of the isentropic efficiency over the compressor map. Additionally, the approach maximizes system efficiency, taking into account component losses and isentropic efficiency of the compressor. To enable rapid and automated integrated design, the methodology reduces the compressor representation to a fully cylindrical representation. The study finds that the proposed methodology has the potential to significantly enhance the overall performance of turbocompressors in terms of efficiency, stability, and robustness. The methodology eliminates the need for sequential and iterative design steps, providing an optimal starting point for higher representation of the system with CFD and finite elements study. Furthermore, the proposed methodology has broad applications, including the optimization of other complex and interdependent systems in various fields. This study highlights the crucial role of a comprehensive and integrated approach to turbocompressor design and provides a valuable framework for future research in this area.
ParaturboCAD: Automated Parametric Geometry Construction for Gas-Bearing Supported Turbocompressor Design With Python
Massoudi, Soheyl; Bejjani, Joseph; Horvath, Timothy; et al. (2024)
Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE2024). Volume 3B: 50th Design Automation Conference (DAC)
This research introduces an automated methodology for visualizing gas-bearing supported turbocompressor designs utilizing CadQuery 2, a scripting Computer-Aided Design (CAD) platform. This innovative approach simplifies the traditional design process, which often involves the use of multiple software tools and extensive manual data manipulation, by consolidating it into a singular, efficient, and robust workflow. Moreover, CadQuery 2 enables the rapid visualization of abstract optimization results, enhancing the design phase’s efficiency. A pivotal element of this study is the adept transformation of design variables related to key components such as the rotor, compressor, and bearings into precise three-dimensional turbocompressor models. This intricate procedure is expedited by employing a structured Python dictionary, which serves to encapsulate the geometric parameters of each component comprehensively. The utility of this framework is demonstrated through the creation of turbocompressors featuring diverse geometries, highlighting the methodology’s capacity to produce models with high accuracy and within a reasonable generation timeframe of approximately seven minutes per turbocompressor. While there are minor constraints, notably in parametrization choices and the time efficiency of modeling with CadQuery 2, these do not significantly detract from the method’s overall value. Indeed, this approach represents a substantial advancement in the field of design and manufacturing, promising to refine and expedite the development process of these complex systems.
Robust design using multiobjective optimisation and artificial neural networks with application to a heat pump radial compressor
Massoudi, Soheyl; Picard, Cyril; Schiffmann, Jürg (2022)
Design Science
Although robustness is an important consideration to guarantee the performance of designs under deviation, systems are often engineered by evaluating their performance exclusively at nominal conditions. Robustness is sometimes evaluated a posteriori through a sensitivity analysis, which does not guarantee optimality in terms of robustness. This article introduces an automated design framework based on multiobjective optimisation to evaluate robustness as an additional competing objective. Robustness is computed as a sampled hypervolume of imposed geometrical and operational deviations from the nominal point. In order to address the high number of additional evaluations needed to compute robustness, artificial neutral networks are used to generate fast and accurate surrogates of high-fidelity models. The identification of their hyperparameters is formulated as an optimisation problem. In the frame of a case study, the developed methodology was applied to the design of a small-scale turbocompressor. Robustness was included as an objective to be maximised alongside nominal efficiency and mass-flow range between surge and choke. An experimentally validated 1D radial turbocompressor meanline model was used to generate the training data. The optimisation results suggest a clear competition between efficiency, range and robustness, while the use of neural networks led to a speed-up by four orders of magnitude compared to the 1D code.
An Integrated Approach to Designing Robust Gas-Bearing Supported Turbocompressors Through Surrogate Modeling and Constrained All-At-Once Multi-Objective Optimization
Massoudi, Soheyl; Picard, Cyril; Schiffmann, Jürg (2024)
Journal of Mechanical Design
This research introduces an innovative framework to engineering design to tackle the challenges of robustness against manufacturing deviations and holistic optimization simultaneously in a multi-disciplinary, multi-subsystems context. The methodology is based on an application of ensemble artificial neural networks, which significantly accelerates computational processes. Coupled with the non-dominated sorting genetic algorithm III, this approach facilitates efficient multi-objective optimization, yielding a comprehensive Pareto front and high-quality design solutions. Here, the framework is applied to the design of gas-bearing-supported turbocompressors. These systems are challenging due to their sensitivity to manufacturing variations, particularly in the gas-bearing geometry, which can lead to rotordynamic instability. Additionally, the interdependencies between the subsystems, such as axial and journal bearings, rotor, compressor impellers, and magnets, necessitate a multidisciplinary approach that spans aerodynamics, structural dynamics, rotordynamics, mechanics, loss analyses, and more. A clear tradeoff between system efficiency, mass-flow range, and robustness has been identified for the compressor design. Higher nominal compressor mass-flows, i.e., increased nominal power, is suggested to decrease the hypervolume of feasible manufacturing deviations. Hence, there is a sweet power spot for gas-bearing supported turbomachinery. Further, the frameworks computational efficiency is on par with that of a university cluster, while only employing a desktop computer equipped with a consumer-grade graphics card. This work demonstrates a significant advancement in the design of complex engineering systems and sets a new standard for speed and efficiency in computational engineering design.
ARRID: ANN-based Rotordynamics for Robust and Integrated Design
Massoudi, Soheyl; Schiffmann, Jürg (2022)
arXiv
The purpose of this study is to introduce ANN-based software for the fast evaluation of rotordynamics in the context of robust and integrated design. It is based on a surrogate model made of ensembles of artificial neural networks running in a Bokeh web application. The use of a surrogate model has sped up the computation by three orders of magnitude compared to the current models. ARRID offers fast performance information, including the effect of manufacturing deviations. As such, it helps the designer to make optimal design choices early in the design process. The designer can manipulate the parameters of the design and the operating conditions to obtain performance information in a matter of seconds.
Robust Design of Herringbone Grooved Journal Bearings Using Multi-Objective Optimization With Artificial Neural Networks
Massoudi, Soheyl; Schiffmann, Jürg (2023)
Journal of Turbomachinery
Herringbone grooved journal bearings (HGJBs) are widely used in micro-turbocompressor applications due to their high load-carrying capacity, low friction, and oil-free solution. However, the performance of these bearings is sensitive to manufacturing deviations, which can lead to significant variations in their performance and stability. In this study, design guidelines for robust design against manufacturing deviations of HGJB supported micro-turbocompressors are proposed. These guidelines are based on surrogate model assisted multi-objective optimization using ensembles of artificial neural networks trained on a large dataset of rotor and bearing designs as well as operating conditions. The developed framework is then applied to a series of case studies representative of heat-pump and fuel cell micro-turbomachines. To highlight the importance of rotor geometry and bearing aspect ratio in the robustness of HGJBs, two types of optimizations are performed: one focusing on optimizing the bearing geometry, and the other focusing on both the bearing and rotor geometries. The analysis of the Pareto fronts and Pareto optima of each type of optimization and case study allows for the derivation of design guidelines for the robust design of HGJB supported rotors. Results suggest that by following these guidelines, it is possible to significantly improve the robustness of herringbone grooved journal bearings against manufacturing deviations, resulting in stable operation. The best design achieved 8 μm tolerance on the bearing clearance, and designs optimized for both rotor and bearing geometry outperformed those optimized for bearing geometry alone.

Publications 1 - 8 of 8

Soheyl Massoudi

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