Show newer

Analyzing Dynamical Systems Inspired by Montgomery's Conjecture: Insights into Zeta Function Zeros and Chaos in Number Theory arxiv.org/abs/2406.12852

Analyzing Dynamical Systems Inspired by Montgomery's Conjecture: Insights into Zeta Function Zeros and Chaos in Number Theory

In this study, we delve into a novel dynamic system inspired by Montgomery's pair correlation conjecture in number theory. The dynamic system is intricately designed to emulate the behavior of the nontrivial zeros of the Riemann zeta function. Our exploration encompasses bifurcation analysis and Lyapunov exponents to scrutinize the system's behavior and stability, offering insights into both small and large initial conditions. Our efforts extend to unveiling the probability distribution characterizing the dynamics for varying initial conditions. The dynamic system unfolds intricate behaviors, displaying sensitivity to initial conditions and revealing complex bifurcation patterns. Small deviations in the initial conditions unveil significantly different trajectories, reminiscent of chaotic systems. Lyapunov exponents become our lens into understanding stability and chaos within the system. A comparative analysis between the dynamic system's approximate solutions and the actual nontrivial zeros of the Riemann zeta function enhances our comprehension of model accuracy and its potential implications for number theory. This research illuminates the versatility of dynamic systems as analogs for studying complex mathematical phenomena. It provides fresh perspectives on the pair correlation conjecture, establishing connections with nonlinear dynamics and chaos theory. Notably, we delve into the boundedness of solutions for both small and large initial conditions, unraveling the distinctive probability distribution governing the dynamics in each scenario. Furthermore, we introduce an in-depth analysis of the entropy of our dynamic system for both small and large initial conditions. The entropy study enhances our understanding of the predictability and stability of the system, shedding light on its behavior in different parameter regimes.

arxiv.org

Review of the analytical prediction method of surf-riding threshold in following sea, and its relation to IMO second-generation intact stability criteria arxiv.org/abs/2406.12853

Review of the analytical prediction method of surf-riding threshold in following sea, and its relation to IMO second-generation intact stability criteria

In high-speed maritime operations, the broaching phenomenon can pose a significant risk when navigating in following/quartering seas. The occurrence of this phenomenon can result in a violent yaw motion, regardless of the steering effort, which, in turn, cause the resulting centrifugal force to capsize a vessel. A necessary condition for the occurrence of broaching is the surf-riding phenomenon. Therefore, the International Maritime Organization (IMO) has set up criteria to include theoretical formulas for estimating the occurrence of surf-riding phenomena. The theoretical equation used in the IMO's second-generation intact stability criteria (SGISC) to estimate the surf-riding threshold is based on Melnikov's method. This paper presents nonlinear equations describing the forward and backward motions of a ship. However, such equations cannot be directly solved; therefore, we proposed the use of and explain various approximate solution methods, including Meknikov's method. Subsequently, the relationship between the theoretical prediction method of the surf-riding threshold rooted in Melnikov's method and the IMO's SGISC is determined.

arxiv.org

A Global Solution Algorithm for AC Optimal Power Flow through Linear Constrained Quadratic Programming arxiv.org/abs/2406.11899

A Global Solution Algorithm for AC Optimal Power Flow through Linear Constrained Quadratic Programming

We formulate the Alternating Current Optimal Power Flow Problem (ACOPF) as a Linear Constrained Quadratic Program (LCQP) with many negative eigenvalues ($r$) and linear constraints, making it NP-hard. We propose two algorithms, Feasible Successive Linear Programming (FSLP) and Feasible Branch-and-Bound (FBB), for a global optimal solution. These use optimization strategies like bounded successive linear programming, convex relaxation, initialization, and branch-and-bound to find a globally optimal solution within a predefined $ε$-tolerance. The complexity of FSLP and FBB is $\mathcal{O}\left(N \prod_{i=1}^r\left\lceil\frac{\sqrt{r}(t_u^i-t_l^i)}{2 \sqrtε}\right\rceil\right)$, where $N$ is the complexity of solving subproblems at each FBB node. Variables $t_l$ and $t_u$ are the lower and upper bounds of $t$, respectively, and $-|t|^2$ is the negative quadratic component in the ACOPF objective function. We use penalized semidefinite modeling, convex relaxation, and line search to design a globally feasible branch-and-bound algorithm for the LCQP form of ACOPF, finding an optimal solution within $ε$-tolerance. Initial results show FSLP and FBB can find global optimal solutions for large-scale ACOPF instances, even with large $r$, and outperform other methods in most PG-lib tests.

arxiv.org
Show older
Qoto Mastodon

QOTO: Question Others to Teach Ourselves
An inclusive, Academic Freedom, instance
All cultures welcome.
Hate speech and harassment strictly forbidden.