Iterative Classification of Graph-Set-Based Designs (IC-GSBD) for the Down-Selection of Aircraft Thermal Management Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909305341476864 |
|---|---|
| author | Sirico Jr., Anthony Herber, Daniel R |
| author_facet | Sirico Jr., Anthony Herber, Daniel R |
| contents | In this paper, we present Iterative Classification of Graph-Set-Based Design (IC-GSBD), a framework utilizing graph-based techniques with geometric deep learning (GDL) integrated within a set-based design (SBD) approach for the classification and down-selection complex engineering systems represented by graphs. We demonstrate this approach on aircraft thermal management systems (TMSs) utilizing previous datasets created using an enumeration or brute-force graph generation procedure to represent novel aircraft TMSs as graphs. However, as with many enumerative approaches, combinatorial explosion limits its efficacy in many real-world problems, particularly when simulations and optimization must be performed on the many (automatically-generated) physics models. Therefore, the approach uses the directed graphs representing aircraft TMSs and GDL to predict on a subset of the graph-based dataset through graph classification. This paper's findings demonstrate that incorporating additional graph-based features using principle component analysis (PCA) enhances GDL model performance, achieving an accuracy of 98% for determining a graph's compilability and simulatability while using only 5% of the data for training. By applying iterative classification methods, we also successfully segmented the total set of graphs into more specific groups with an average inclusion of 75.5 of the top 100 highest-performing graphs, achieved by training on 40% of the data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03270 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Iterative Classification of Graph-Set-Based Designs (IC-GSBD) for the Down-Selection of Aircraft Thermal Management Systems Sirico Jr., Anthony Herber, Daniel R Computational Engineering, Finance, and Science In this paper, we present Iterative Classification of Graph-Set-Based Design (IC-GSBD), a framework utilizing graph-based techniques with geometric deep learning (GDL) integrated within a set-based design (SBD) approach for the classification and down-selection complex engineering systems represented by graphs. We demonstrate this approach on aircraft thermal management systems (TMSs) utilizing previous datasets created using an enumeration or brute-force graph generation procedure to represent novel aircraft TMSs as graphs. However, as with many enumerative approaches, combinatorial explosion limits its efficacy in many real-world problems, particularly when simulations and optimization must be performed on the many (automatically-generated) physics models. Therefore, the approach uses the directed graphs representing aircraft TMSs and GDL to predict on a subset of the graph-based dataset through graph classification. This paper's findings demonstrate that incorporating additional graph-based features using principle component analysis (PCA) enhances GDL model performance, achieving an accuracy of 98% for determining a graph's compilability and simulatability while using only 5% of the data for training. By applying iterative classification methods, we also successfully segmented the total set of graphs into more specific groups with an average inclusion of 75.5 of the top 100 highest-performing graphs, achieved by training on 40% of the data. |
| title | Iterative Classification of Graph-Set-Based Designs (IC-GSBD) for the Down-Selection of Aircraft Thermal Management Systems |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2312.03270 |