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This work defines Momentum Engineering as a new discipline that focuses on optimizing dynamic momentum flow in mechanical systems.

May 13, 2026Open Access

MOMENTUM ENGINEERING A New Engineering Grammar for Dynamic Systems.

Key Points

This work defines Momentum Engineering as a new discipline that focuses on optimizing dynamic momentum flow in mechanical systems.
Introduced six foundational axioms and the Momentum Processing Chain (E → L → p → T).
Classified six propulsion architectures and formulated quantitative metrics including Dynamic Recovery Index and others.
Mapped applications of Momentum Engineering across various industrial domains and proposed AI-native optimization.
Demonstrated the ABÏON Drive achieves a Dynamic Recovery Index (DRI) > 0.85, with performance improvements in various applications.
Identified potential DRI improvements of 2× to 10× across eight industrial domains including robotics and aerospace.
Established a new engineering grammar that highlights efficiencies in dynamic systems and introduced research questions to guide future investigation.

Abstract

This foundational white paper introduces Momentum Engineering as a formal engineering discipline — the first to explicitly identify, define, and optimize the recoverable dynamic momentum flow within mechanical systems as a primary design variable. Classical engineering has optimized machines for three centuries around force generation, power delivery, thermal efficiency, and structural rigidity. One dimension has been systematically ignored: the dynamic recoverability of the momentum flow itself. Momentum Engineering proposes that this constitutes a vast unexplored design space of immediate engineering and commercial consequence. The paper establishes six foundational axioms, the Momentum Processing Chain (E → L → p → T), a five-event temporal architecture framework, and a complete suite of quantitative metrics — including the Dynamic Recovery Index (DRI = pᵣecovered / pgenerated), Momentum Purity Index (MPI), Dynamic Crest Factor (DCF), Temporal Coupling Efficiency (TCE), and Parasitic Momentum Loss (PML). The theoretical foundations are traced through Newton (1687), Lagrange (1788), Hamilton (1833), and Noether (1915), culminating in the MIND Theory and TIME Theorem (Bricio, 21st century) as the formal link between classical mechanics and the new discipline. A formal classification of the six propulsion architectures in human history is presented: Animal/Muscular, Thermal/Steam, Internal Combustion/Jet, Reaction/Rocket, Electromagnetic, and Momentum-Domain. The first five operate in the force domain and achieve DRI ≈ 0. 00. The ABÏON Drive is presented as the sixth and first momentum-domain architecture — achieving DRI > 0. 85, E ∝ v linear energy scaling, constant input power at constant thrust, and silent operation without expelled mass. The paper maps Momentum Engineering across eight industrial domains — electric vehicles, robotics, aerospace, maritime, industrial machinery, exoskeletons, rail, and space exploration — demonstrating DRI improvement potential of 2× to 10× in each. It introduces the Saturation Inertial Regime as a potentially non-linear efficiency improvement regime. It proposes AI-native optimization as the natural computational methodology of the discipline. And it closes with ten open, falsifiable research questions that define the initial agenda for the field. This document does not propose new physics and does not violate conservation laws. It establishes a new engineering grammar capable of revealing untapped efficiencies in any dynamic system — and presents the ABÏON Drive as its first complete physical proof of concept. Palabras clave: Momentum Engineering, Dynamic Recovery Index, DRI, momentum-flow architecture, temporal momentum architecture, ABÏON Drive, MIND Theory, TIME Theorem, Momentum Processing Chain, Dynamic Recovery Architecture, sixth propulsion architecture, Noether's theorem, Galilean invariance, fixed-cost momentum transfer, regenerative capture, AI-native mechanical design, Non-Inertial Momentum Dynamics, Saturation Inertial Regime, propulsion classification, Álvaro F. Bricio, PENTTSYS

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