湍流气泡水中的折射光相互作用（RLITBW）：一种宏观流体光学熵源
Refracted Light Interaction in Turbulent Bubbling Water (RLITBW): A Macroscopic Fluid-Optic Entropy Source
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中文总结 AI 辅助
本文提出一种基于湍流气泡水折射光相互作用的低成本宏观真随机数生成器，利用多相流体混沌放大微观不确定性，经Toeplitz哈希调节后通过NIST测试，可替代传统PRNG用于优化算法。
中文摘要 AI 辅助
在密码学、随机模拟和优化中，对高质量、不可预测随机数的需求是一项基本要求。虽然伪随机数生成器（PRNGs）计算效率高，但其确定性本质限制了其在安全关键应用中的适用性。真随机数生成器（TRNGs）虽然基于物理过程，但往往依赖于昂贵的量子或严格控制的电子现象。本文提出了一种基于湍流气泡水中折射光相互作用（RLITBW）的低成本、宏观TRNG。所提出的系统利用了由多相流体动力学和时变光学折射产生的复合经典混沌。开发了一个物理数学模型来描述从随机气泡成核和湍流上升到混沌光路扰动的非线性过程级联，这些过程共同将微观不确定性放大为可测量的光学信号。原始光学信号被数字化，并通过基于Toeplitz通用哈希的可证明安全的熵调节管道进行处理，随后进行确定性密码扩展。通过相空间重构、Lyapunov指数估计、自相关分析和熵度量，对物理源的混沌特性进行了实证验证。调节后的输出成功通过了完整的NIST SP 800-22统计测试套件以及非线性动力学度量，包括Lyapunov指数和样本熵。除了统计验证外，生成的随机性还应用于基于群体的优化算法，展示了其作为传统PRNG替代品的实际可用性。最后，讨论了部署架构和可扩展性考虑，将RLITBW定位为面向实际系统的可访问、可复现且经济可行的熵源。
英文摘要
The demand for high quality, unpredictable random numbers is a fundamental requirement in cryptography, stochastic simulation, and optimization. While pseudo-random number generators (PRNGs) are computationally efficient, their deterministic nature limits their suitability for security-critical applications. True random number generators (TRNGs), although physically grounded, often rely on expensive quantum or tightly controlled electronic phenomena. This paper introduces a low-cost, macroscopic TRNG based on Refracted Light Interaction in Turbulent Bubbling Water (RLITBW). The proposed system exploits compound classical chaos arising from multiphase fluid dynamics and time-varying optical refraction. A physical-mathematical model is developed to describe the cascade of non-linear processes from stochastic bubble nucleation and turbulent ascent to chaotic optical path scrambling that collectively amplify microscopic uncertainties into measurable entropy.The raw optical signal is digitized and processed using a provably secure entropy-conditioning pipeline based on Toeplitz universal hashing, followed by deterministic cryptographic expansion. The chaotic nature of the physical source is empirically validated using phase-space reconstruction, Lyapunov exponent estimation, autocorrelation analysis, and entropy metrics. The conditioned output successfully passes the full NIST SP 800-22 statistical test suite and nonlinear dynamical measures including Lyapunov exponents and sample entropy. Beyond statistical validation, the generated randomness is applied to population-based optimization algorithms, demonstrating practical usability as a replacement for conventional PRNGs. Finally, deployment architectures and scalability considerations are discussed, positioning RLITBW as an accessible, reproducible, and economically viable entropy source for real-world systems.
发表机构
- Kalyani Government Engineering College（卡拉尼政府工程学院）
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