Skip to content

Universal Flow and Shear: A Model of Cosmic Motion

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)
Noncommutative and Quantum Gravity Theories

Abstract

The universe moves. Not just galaxies drifting apart — everything, at every scale, is in directional flow through a physical substrate that fills space. That substrate is not empty. It has mechanical properties: resistance, shear, compression, and flow gradients. This paper presents a unified model — the Unified Substrate Theory (UST) — that explains galactic rotation curves without dark matter, magnetic field orientation at planetary, galactic, and cosmic scales, the alignment of galaxy clusters into filaments, the bulk flow of superclusters, and the large-scale structure of the cosmic web, all from a single mechanical cause: differential flow through a substrate with shear. The substrate is not the 19th-century luminiferous aether. The 19th-century aether was assumed stationary. The substrate presented here is in large-scale directional flow, coherent across scales from planetary to universal. The Michelson-Morley experiment did not rule out a flowing medium — it ruled out a stationary one. The Cosmic Microwave Background dipole anisotropy confirms that the solar system, the Milky Way, and the Local Group are all moving at approximately 627 km/s in a specific direction. That is not motion through nothing. That is motion through something. The model makes testable predictions about rotation curve shapes, magnetic field alignment with large-scale structure, cluster elongation axes, galaxy spin alignment gradients, and star formation rate distributions relative to substrate shear boundaries. It requires no new particles. It requires no geometric tricks. It requires only that space is not nothing — that it is a medium with mechanical behavior, and that everything swimming through it is shaped by how it moves relative to that medium. One mechanism. Five anomalies resolved. The substrate is real, the flow is real, and the shear is the engine of all large-scale structure in the universe. This work is part of a larger collection of UST documents. The other versions available in the DOI record are not revisions of this document. They are separate papers written for different purposes. Some versions present the full mathematical proofs behind the update rules, others provide a technical physical description of substrate behavior, and others are formal proof papers built around the Universal Balance Laws. Together, these documents form a complete set: a plain‑language booklet, a physical description paper, and full mathematical proof papers, each offering a different perspective on the same underlying theory. If you have questions or want to discuss the work, you can contact me directly at dustin@unifiedsubstratetheory.com Don't be shy. I want to discuss science. It is fun and should be. Reachout and lets get started on new discoveries.

View source

Similar papers

#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Conference Open access Dec 2013

Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment

The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.

P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al. · 110 citations · ⚡7
#computer vision Book Open access Mar 2017

On the Unhappiness of Software Developers

The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.

D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al. · 84 citations · ⚡6

Related blog posts

MIT News · Artificial Intelligence Sep 14, 2026

New method enables AI for safety-critical situations

The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.

GPT-Lab Sep 10, 2026

Responsible AI Must Consider Its Afterlife

AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.