arXiv (All)
open access
A self-learning framework for adaptive free-form pipe routing in aeroengines
Pipe routing is a highly complex, time-consuming, and no-deterministic polynomial-time hard (NP-hard) problem in aeroengine design. Despite extensive research efforts in optimizing constant-curvature pipe routing, the growing demand for free-form pipes poses new challenges. Dynamic design environmen
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Derivative-free optimization is competitive for aerodynamic design optimization in moderate dimensions#147086
- Automated Design of Microwave Planar Circuits Using Genetic Algorithms and its Acceleration Techniques with Machine Learning Methods#285376
- 6Bone Routing Practice#132590
- Universal Interconnected Node Propulsion Engine (UINPE)} : A Lawful Network-Based Framework for Reactionless Propulsion#85806
- Neural Network-Based Optimization of Hybrid Rocket Design for Modular Multistage Launch Vehicle#185600
Record · ID 1355890
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive