Highest-Score Simplification Hybrid — A Monte Carlo-Validated Water-Free Data Center Architecture Targeting 9.4565/10 Engineering Performance, 1.1039 PUE, 99.8442% Reliability, and Zero Evaporative Water Use

This publication presents the Water-Free Data Center and its highest-performing architecture, the Highest-Score Simplification Hybrid: a digitally modeled, computationally evaluated, and Monte Carlo-validated engineering concept designed to demonstrate a path toward high-performance data-center cooling without evaporative water consumption. The work addresses a physical infrastructure challenge of increasing importance to modern society. Data centers support artificial intelligence, scientific computing, communications, healthcare, finance, government, education, transportation, manufacturing, and the digital services on which billions of people increasingly depend. Yet computing infrastructure produces substantial heat, consumes energy, and, in many conventional cooling configurations, may depend on significant quantities of water. As global computational demand expands, the physical infrastructure supporting the digital world must become more efficient, reliable, manufacturable, resilient, and responsible in its use of finite natural resources. The Water-Free Data Center study explores that challenge through a structured digital engineering methodology. Successive computational sweeps examined thermal performance, facility efficiency, reliability, hydraulic pressure loss, pump energy, component count, unique-part count, assembly complexity, standardization, manufacturability, relative cost, practical performance, and zero-evaporative-water operation. The final design-simplification and cost-convergence master sweep searched 1,920 combinatorial candidate architectures. Selected finalists and a reference control were subsequently subjected to 100,000-case Monte Carlo evaluations per architecture. The resulting winning architecture, named the Highest-Score Simplification Hybrid, achieved the following modeled median results: • Engineering score: 9.4565/10• Median GPU temperature: 47.92°C• PUE: 1.1039• Modeled reliability: 99.8442%• Hydraulic pressure drop: 52.44 kPa• Component count: 64• Unique-part count: 10• Practical pass rate: 86.82%• Median performance-retention compliance: Yes• Zero evaporative water use within the modeled system boundary: Yes A central result of this research is that design simplification did not necessarily require sacrificing modeled engineering performance. Within the stated computational model and assumptions, reducing components, consolidating unique parts, standardizing modular connections, simplifying controls, and improving hydraulic routing produced a higher overall engineering score. This is physically meaningful as an engineering hypothesis because fewer components can potentially reduce leak paths, connection points, hydraulic restrictions, assembly operations, maintenance burden, manufacturing complexity, and certain categories of failure exposure. These digitally validated results should be understood as computationally established physical engineering targets. They define quantitative targets that a physical prototype, laboratory test article, pilot installation, or full-scale implementation can attempt to reproduce and experimentally validate. The digital work therefore has direct physical-world value: it narrows a large engineering design space, identifies promising configurations, quantifies expected performance envelopes, exposes tradeoffs, establishes reproducible benchmarks, and provides a structured basis for deciding what should be built and tested. Digital validation and physical validation are connected stages of the engineering process, but they are not interchangeable. This publication does not claim that the reported performance has already been experimentally demonstrated by a physical prototype unless separate physical test evidence is provided. Instead, it establishes digitally validated targets for physical validation. That distinction is essential to scientific integrity and to the practical usefulness of the work. The physical value of this digital research lies in its ability to reduce uncertainty before expensive fabrication and deployment. Every physical system begins as information: requirements, dimensions, architectures, simulations, calculations, material selections, control strategies, failure analyses, manufacturing plans, and performance targets. High-quality digital engineering allows researchers and builders to investigate possibilities before committing material, capital, labor, energy, and physical infrastructure. For data centers in particular, that connection between digital information and physical reality is profound. Digital society depends on physical servers, semiconductor devices, electrical distribution systems, cooling equipment, pumps, heat exchangers, buildings, manufacturing supply chains, and energy infrastructure. The digital world does not exist independently of the physical world; it is sustained by it. Improvements to the physical efficiency and resource requirements of computing infrastructure can therefore have consequences extending far beyond a single facility. The Water-Free Data Center research is offered as a reproducible engineering foundation and a set of physical validation targets for further analysis, independent reproduction, detailed computational fluid dynamics, component-level engineering, supplier evaluation, failure-mode testing, prototype construction, laboratory validation, site-specific analysis, and eventual real-world deployment. The broader objective is straightforward but consequential: to explore whether society's rapidly growing computational infrastructure can become simpler, more efficient, highly reliable, less resource-intensive, and independent of evaporative cooling water within the defined system boundary. The reported results represent a computational milestone, not the end of the engineering process. Their greatest value is that they transform a broad physical challenge into explicit, measurable targets that can now be tested against reality.