Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1606.03855

Mathematical Modeling of Dynamics for Partially Filled Shells of Revolution

Iryna Kononenko·Oleksiy Kononenko
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this work we study the dynamic behaviour of compound shells of revolution partially filled with an ideal incompressible fluid based on boundary-value problems. New analytical mathematical model with corresponding discrete scheme for the elastic displacements and the dynamic liquid pressure is developed. The discrete scheme is based on the method of discrete singularities. A code to perform the numerical analysis is developed. Comprehensive benchmarking of the obtained results against other methods is done and good agreement is observed. The convergence of the proposed numerical method is demonstrated. One of the advantages of this new model is that the initial 3D problem is analytically reduced to a 1D integral equation. Moreover, it can handle the behaviour of the pressure in the vicinity of the nodes explicitly and the computational technique used has a quick convergence requiring a negligible amount of CPU time.

Explore related subjects

cs.CE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Iryna Kononenko, Oleksiy Kononenko. 2016-06-14. Mathematical Modeling of Dynamics for Partially Filled Shells of Revolution. https://arxiv.org/abs/1606.03855

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Cosm: Collective switched motion for sparse Ising optimization

We introduce Collective Switched Motion (Cosm), a heuristic optimization framework based on switched collective dynamics. Cosm compiles the objective into subobjectives that are activated sequentially, temporally separating competing local influences. The interplay of switching and local interactions among variables gives rise to collective search behavior, while a new correlated perturbation mechanism encourages coordinated cluster motion. Tests on tuned-hardness spin-glass benchmarks suggest more favorable algorithmic scaling than that reported for leading dynamical solvers. Cosm heuristically attains the certified optima of three of the largest Gset instances (G72, G77, G81), exceeding the previously reported heuristic solutions. On the large random-graph instances G61 and G70, a CPU implementation reliably attains the best-known solutions, reaching cuts of 5799 and 9595 with 99%-confidence times-to-target of 15 s and 2.4 s, respectively. On the heterogeneous-degree G64 instance, Cosm establishes a new best-known cut of 8753. Broadly, the results suggest an alternative approach to heuristic design in which local dynamics and orchestration mechanisms are carefully designed so that effective search emerges.

cs.CE↗
arXiv2026

Decomposing Firm-Level Crisis Responses from Incomplete Market Signals: Evidence from China's IT Sector During COVID-19

Exogenous shocks generate heterogeneous behavioral responses across firms, yet event studies typically report only sector-level averages. This paper develops a multi-method approach combining causal identification (difference-in-differences with cluster-robust inference), unsupervised behavioral discovery (K-means trajectory clustering, Gaussian hidden Markov models), and cross-sectional resilience prediction (logistic regression with nested cross-validation) to decompose firm-level response heterogeneity from noisy market signals. We demonstrate the approach on 246 Chinese A-share IT firms (216 with complete data for all analyses) during the COVID-19 shock (January 2020), using 252 non-IT CSI 300 firms as controls. The return decline was market-wide, not IT-specific (DID p = 0.59); the IT-specific effect was elevated volatility (DID beta = 0.043, cluster-robust p < 0.001), with the effect surviving Benjamini-Hochberg correction in 13 of 30 alternative specifications. Unsupervised clustering produced three trajectory groups: fast recovery (36 companies, +29.7%), resilient/moderate (67 companies), and persistent drag (113 companies, -6.9%). Pre-crisis financial fundamentals showed only modest predictive power for resilience (nested CV AUC = 0.635, 95% CI: 0.558-0.711; permutation p = 0.016), consistent with the limited informativeness of publicly available signals for anticipating crisis outcomes. The combination of causal analysis, unsupervised learning, and prediction represents a reproducible framework which can be applied to crises in other market periods.

cs.CE↗
arXiv2026

An Insurance Broker for Every Small Business: The Economics of Exceptional Care at Scale

Small-business owners need expert guidance on their own terms, across schedules, languages, and channels, but low premiums make exceptional, continuous human service uneconomic for much of the market. Combining public evidence, Kinro operational data, and an illustrative five-year service model, we show why traditional brokerage economics leave 35 million U.S. small businesses underserved. An AI-native brokerage can change those economics by performing and coordinating routine work continuously, while licensed professionals govern consequential exceptions and the brokerage remains accountable.

cs.CE↗