An end-to-end differentiable transient vapor-compression framework for automated machine sizing and unified optimal control

[2608.19552] An end-to-end differentiable transient vapor-compression framework for automated machine sizing and unified optimal control Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Systems and Control arXiv:2608.19552 (eess) This paper has been withdrawn by Sam Yang [Submitted on 20 Aug 2026 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: An end-to-end differentiable transient vapor-compression framework for automated machine sizing and unified optimal control Authors: Sam Yang No PDF available, click to view other formats Abstract: Accelerating the electrification of thermal energy requires vapor-compression heat pumps capable of dynamic, grid-responsive operation. However, equipment engineering remains fragmented across static rating-point selection, stiff multi-phase transient simulation, and gradient-based optimal control. Here, we present an end-to-end differentiable, finite-volume vapor-compression framework implemented natively in JAX that automates machine sizing directly from stated thermal duties and unifies dynamic simulation with predictive control under a single compiled residual $\dot{y}={f}(t,{y},{u})$. Thermodynamic evaluations bypass runtime root-finding via bilinear $(p,h)$ manifolds pre-flashed from Helmholtz equations of state, enabling analytical forward-mode automatic differentiation. Mass conservation across multi-phase coils is strictly preserved by incorporating both $(\partial\rho/\partial p)_h$ and $(\partial\rho/\partial h)_p$ partial derivatives into the dynamic pressure differential equation. The sizer directly inverts compressor displacement, electronic expansion valve area, and heat-exchanger tube counts via four-point cycle synthesis and $\varepsilon$-NTU matching using the identical polytropic compressor map. Crucially, the compiled physics kernel is shared symmetrically between $L$-stable TR-BDF2 stiff integration and implicit-Euler Model Predictive Control (MPC), eliminating plant-controller surrogate mismatch. Validated against open-access experimental benchmarks without parameter fitting, the framework predicts cooling capacity with $7.37\%$ MAPE across 16 mini-split operational runs and bounds on-period cooling error within $1.19\%$--$1.62\%$ on utility-scale Hardware-in-the-Loop traces. This work provides an open-source, differentiable foundation for automated machine synthesis, dynamic grid orchestration, and gradient-based hardware-control co-design. Comments: the paper is not novel enough; need further investigation Subjects: Systems and Control (eess.SY) ; Computational Engineering, Finance, and Science (cs.CE); Applied Physics (physics.app-ph) Cite as: arXiv:2608.19552 [eess.SY] (or arXiv:2608.19552v2 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2608.19552 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Sam Yang [ view email ] [v1] Thu, 20 Aug 2026 01:35:50 UTC (759 KB) [v2] Thu, 27 Aug 2026 01:47:17 UTC (1 KB) (withdrawn) Full-text links: Access Paper: Withdrawn Current browse context: eess.SY < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CE cs.SY eess physics physics.app-ph References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• An end-to-end differentiable transient vapor-compression framework for automated machine sizing and unified optimal control#677752
• Order elevation of directly self-starting sub-step implicit integrators for transient dynamics#997139
• AutoRAGTuner: A Declarative Framework for Automatic Optimization of RAG Pipelines#155251
• Data-Driven Successive Linearization for Optimal Voltage Control#999746
• Diameter Overload Rate Control#502270