Recently, a discussion arose about artificial intelligence (AI) as a tool for comparative prospecting between unnetworked AWES and networked AWES.
I am citing a few elements from the emailed content.
From Dave Santos (@kitefreak):
Prompt 1:
“Consider from first principles a synthesis of Bifurcation Theory and Network Theory with regard to scaling and statistical stability of dynamical mechanical systems composed of similar units.”
[Gem Response]Prompt 2:
“Apply this conceptual synthesis to theoretic Airborne Wind Energy System (AWES) design that can be ordered either as a loose collection of unnetworked unit-kites or a many-connected collection networked unit-kites, much as draft animals many be networked into teams or not. Consider stability, control, density, and scalability between these two AWES orderings, unnetworked and networked. Make a recommendation for industrial scale AWES.”
[Gem Response]
=== Gemini 2.5 Pro ===[emphasis below added by the AI model, no human editing]
Applying the synthesis of Bifurcation and Network Theory to Airborne Wind Energy Systems (AWES) design, the networked collection of unit-kites is unequivocally the superior architecture for industrial-scale deployment. A loose collection of unnetworked kites is fundamentally limited and inefficient, while a networked system, despite its complexity, offers a clear path to high-density, scalable power generation.
## Unnetworked AWES: A Collection of Individuals
An unnetworked AWES is a system of N independent kites, each with its own ground station and control system. From a network perspective, this is a graph with N nodes and zero edges.
- Stability: The stability of the collection is simply the statistical sum of the stability of each individual kite. A failure or bifurcation (e.g., a stall or sudden loss of control) in one kite is an isolated event. This provides robustness against cascading failures, as one kite falling from the sky does not mechanically pull others down. However, there is no mechanism for mutual stabilization; the kites cannot help each other counteract localized turbulence or perturbations. Each unit must be robust enough to handle the worst-case scenario on its own.
- Control: Control is massively parallel. While this seems simple, it is incredibly inefficient. Each of the N kites requires its own control hardware, software, and ground footprint. The primary control challenge becomes one of deconfliction and airspace management—preventing N independent, high-speed objects from colliding—rather than optimizing the collective.
- Density: The power density is extremely low. Due to the risk of collision, each unnetworked kite requires a massive, exclusive volume of airspace in which to operate. This “packing problem” severely limits the number of kites that can be deployed in a given area, making the land and airspace requirements for a utility-scale power plant enormous and impractical.
- Scalability: Scalability is poor. To double the power output, you must double the number of independent systems—doubling the ground stations, control systems, and required airspace. This linear scaling offers no economy of scale and quickly becomes logistically and economically untenable.
## Networked AWES: A Coordinated Collective
A networked AWES connects the unit-kites, either physically through tethers or through a shared main structure, forming a single, large, coupled dynamical system. This is a network with N nodes and a defined set of edges, creating a specific topology (e.g., a line, grid, or V-formation).
- Stability: The networked system has the potential for far greater collective stability. The network connections act as dampers, averaging out localized wind gusts and turbulence across the entire structure. A perturbation affecting one kite can be stabilized by its neighbors. From a bifurcation standpoint, the network’s coupling creates new, stable collective flight modes that are more robust than those available to an individual kite. The engineering challenge shifts from making each kite independently failsafe to designing a network topology that is resilient to the propagation of instabilities, preventing cascading failures.
- Control: The control problem is transformed from managing N individuals to piloting one collective entity. While the control algorithms are more complex—requiring an understanding of the system’s emergent behavior—the infrastructure is vastly simplified, potentially requiring only a single, powerful ground station. The network structure itself becomes a control parameter, allowing the system’s shape to be reconfigured to optimize power generation in different wind conditions.
- Density: Power density is the most significant advantage and is exceptionally high. Because the kites move in a coordinated, predictable formation, the required safety separation between them is minimal. This allows for a tightly packed arrangement, maximizing the energy extraction per unit of airspace and drastically reducing the ground footprint of the power plant.
- Scalability: Scalability is excellent. Power output can be increased by adding more kites to the existing network, leveraging the same ground infrastructure. This offers a clear economy of scale. Furthermore, the potential for aerodynamic benefits from formation flight (similar to drafting) means that power output could scale non-linearly—adding the 100th kite might increase total power more than adding the 10th.
## Recommendation for Industrial Scale
For an industrial-scale AWES capable of generating power comparable to a conventional power plant, the networked model is the only viable path forward.
The unnetworked approach, while conceptually simple, is a dead end due to its prohibitive airspace requirements and poor scalability. It represents a failure to see the system as more than the sum of its parts.
The networked approach, informed by the synthesis of Bifurcation and Network Theory, treats the collection of kites as a single, cohesive “power foil.” It leverages the network connections to achieve emergent stability and allows for dense, scalable power generation. The significant challenges in complex control and preventing cascading failures are the core engineering problems that must be solved, but the potential rewards—a high-density, cost-effective, and scalable renewable energy source—are immense.
Reply from @JoeFaust:
Regarding: "A failure or bifurcation (e.g., a stall or sudden loss of control) in one kite is an isolated event. " Such would only hold if each individual kite is given the necessary wide berth to assure such isolation. However, see that such condition implies a huge inefficiency of land and airspace use; and such is worsened when seeing that a runaway tether under runaway kited wing.
My opinion about Joe’s comment on “A failure or bifurcation (e.g., a stall or sudden loss of control) in one kite is an isolated event.”
This sentence is extracted from the first paragraph titled “Unnetworked AWES: A Collection of Individuals”. This paragraph provides a similar reply in “Density” section.
Overall, the comparison elements seem interesting but not complete.
A scaled-down realization of a functioning networked AWES with a generator would provide much more appraisal elements and highlight advantages and difficulties that we are not aware of: the kite elements of a network will tend to go their own way according to different flight and wind conditions within the same network, which can tend to make the whole system fall unless there is an effective control software, which implies control means for each of the units. Depending how the kite units are placed and how they fly (and in a crosswind or not crosswind way), the control of the set can be possible or not.
