IndisputableMonolith.Engineering.TeslaTurbineStructure
The TeslaTurbineStructure module defines the Tesla turbine as a self-similar device whose geometry supplies structural input to energy storage calculations. It builds on the phi-ladder and J-cost limits from the imported EnergyStorageDensityStructure module. Applied physicists working on fluidic energy systems would cite it for RS-native constraints on storage density. The module uses three sibling definitions to organize the implication without internal theorem proofs.
claimTesla turbine structure $S$ implies energy-storage input $I$, where $I$ is the bound on energy per unit mass given by $J$-cost times coherence quantum on the phi-ladder.
background
Recognition Science derives constants from the T0-T8 forcing chain, with J-uniqueness at T5 and the self-similar phi fixed point at T6. The upstream EnergyStorageDensityStructure module states that energy equals J-cost times the coherence quantum and derives fundamental limits on storage density per unit mass or volume from the phi-ladder and recognition composition law.
proof idea
This is a definition module, no proofs. The argument is organized by importing EnergyStorageDensityStructure and introducing three sibling objects that define the turbine from the ledger, the structure itself, and the direct implication to energy storage input.
why it matters in Recognition Science
The module supplies the turbine-specific structural link that feeds the optimal energy storage density result in EN-004. It enables engineering applications of the recognition composition law and the eight-tick octave to fluid devices. It touches the extension of D=3 spatial dimensions to boundary-layer geometries.
scope and limits
- Does not derive the full fluid dynamics equations for the turbine.
- Does not compute numerical storage density values.
- Does not connect to the mass formula or Berry creation threshold.
- Does not incorporate fine-structure constant bounds.