Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization

• URL: http://arxiv.org/abs/2309.02818v1
• Date: Wed, 6 Sep 2023 08:06:15 GMT
• Title: Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization
• Authors: Shadi Ghiasi, Guido Pazzi, Concettina Del Grosso, Giovanni De Magistris, Giacomo Veneri
• Abstract summary: We propose the Active-CompDesign framework in which we combine a thermodynamics-based compressor model and a Gaussian Process-based surrogate model. We show a significant performance improvement in surrogate modeling by leveraging on uncertainty-based query function of samples. Our framework in production has reduced the total computational time of compressor's design optimization to around 46% faster than relying on the internal thermodynamics-based simulator.
• Score: 1.393251976777607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The design process of centrifugal compressors requires applying an optimization process which is computationally expensive due to complex analytical equations underlying the compressor's dynamical equations. Although the regression surrogate models could drastically reduce the computational cost of such a process, the major challenge is the scarcity of data for training the surrogate model. Aiming to strategically exploit the labeled samples, we propose the Active-CompDesign framework in which we combine a thermodynamics-based compressor model (i.e., our internal software for compressor design) and Gaussian Process-based surrogate model within a deployable Active Learning (AL) setting. We first conduct experiments in an offline setting and further, extend it to an online AL framework where a real-time interaction with the thermodynamics-based compressor's model allows the deployment in production. ActiveCompDesign shows a significant performance improvement in surrogate modeling by leveraging on uncertainty-based query function of samples within the AL framework with respect to the random selection of data points. Moreover, our framework in production has reduced the total computational time of compressor's design optimization to around 46% faster than relying on the internal thermodynamics-based simulator, achieving the same performance.

Related papers

• PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing [66.27103948750306]
  PearSAN is a machine learning-assisted optimization algorithm applicable to inverse design problems with large design spaces. It uses a Pearson correlated surrogate model to predict the figure of merit of the true design metric. It achieves a state-of-the-art maximum design efficiency of 97%, and is at least an order of magnitude faster than previous methods.
  arXiv  Detail & Related papers  (2024-12-26T17:02:19Z)
• Modeling Time-Variant Responses of Optical Compressors with Selective State Space Models [0.0]
  This paper presents a method for modeling optical dynamic range compressors using deep neural networks with Selective State Space models. It features a refined technique integrating Feature-wise Linear Modulation and Gated Linear Units to adjust the network dynamically. The proposed architecture is well-suited for low-latency and real-time applications, crucial in live audio processing.
  arXiv  Detail & Related papers  (2024-08-22T17:03:08Z)
• Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios [14.48369551534582]
  A learning-based approach seeks to minimize the compromise between compression rate and reconstructed image quality. A successful technique consists in introducing a deep hyperprior that operates within a 2-level nested latent variable model. This paper extends this concept by designing a generalized L-level nested generative model with a Markov chain structure.
  arXiv  Detail & Related papers  (2024-06-10T11:00:26Z)
• Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics [0.0]
  This paper demonstrates the development and application of a deep learning framework for predictions of the flow field and aerodynamic performance of multi-stage axial compressors. A physics-based dimensionality reduction unlocks the potential for flow-field predictions. The proposed architecture is proven to achieve an accuracy comparable to that of the CFD benchmark, in real-time, for an industrially relevant application.
  arXiv  Detail & Related papers  (2023-10-06T14:11:21Z)
• Dynamic Kernel-Based Adaptive Spatial Aggregation for Learned Image Compression [63.56922682378755]
  We focus on extending spatial aggregation capability and propose a dynamic kernel-based transform coding. The proposed adaptive aggregation generates kernel offsets to capture valid information in the content-conditioned range to help transform. Experimental results demonstrate that our method achieves superior rate-distortion performance on three benchmarks compared to the state-of-the-art learning-based methods.
  arXiv  Detail & Related papers  (2023-08-17T01:34:51Z)
• Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation [2.741266294612776]
  Low-rank model compression is a widely used technique for reducing the computational load when training machine learning models. Existing compression techniques are not directly applicable to efficient over-the-air (OTA) aggregation in federated learning systems. We propose a novel manifold optimization formulation for low-rank model compression in FL that does not relax the low-rank constraint.
  arXiv  Detail & Related papers  (2023-06-04T18:32:50Z)
• Learning Accurate Performance Predictors for Ultrafast Automated Model Compression [86.22294249097203]
  We propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost.
  arXiv  Detail & Related papers  (2023-04-13T10:52:49Z)
• What do Compressed Large Language Models Forget? Robustness Challenges in Model Compression [68.82486784654817]
  We study two popular model compression techniques including knowledge distillation and pruning. We show that compressed models are significantly less robust than their PLM counterparts on adversarial test sets. We develop a regularization strategy for model compression based on sample uncertainty.
  arXiv  Detail & Related papers  (2021-10-16T00:20:04Z)
• Machine Learning Framework for Quantum Sampling of Highly-Constrained, Continuous Optimization Problems [101.18253437732933]
  We develop a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate unconstrained binary optimization problems. We showcase the framework's performance on two inverse design problems by optimizing thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering.
  arXiv  Detail & Related papers  (2021-05-06T02:22:23Z)
• Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
  We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models. We are using such models for real-time diagnosis applications.
  arXiv  Detail & Related papers  (2020-03-04T00:44:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.