Fatigue Analysis of Kizomba 'A' FPSO using Direct Calculation based on FMS

'How To' of Fiscal Sustainability in Oil-Rich Countries: The Case of Azerbaijan

The performance of a railway turnout (switch and crossing) is influenced by a large number of input parameters of the complex train–turnout system. To reach a robust design that performs well for different traffic situations, random distributions (scatter) of these inputs need to be accounted for in the design process. Stochastic analysis methods are integrated with a simulation model of the dynamic interaction between train and turnout. For a given nominal layout of the turnout, using design of experiments methodology and a two-level fractional factorial screening design, four parameters (axle load, wheel–rail friction coefficient, and wheel and rail profiles) are identified to be the most significant. These parameters are further investigated using a three-level full factorial design and stochastic analysis. The random distributions of transverse wheel profile and set of transverse rail profiles along the switch panel are accounted for by the Karhunen–Loève expansion technique. The influence of the random distributions of the input parameters on the statistical outputs of wheel–rail contact forces, wear and rolling contact fatigue is assessed using Latin hypercube sampling to generate a number of stochastic load realizations.

This paper presents a basic introduction to some popular stochastic analysis methods from an unbiased disciplinary perspective. Examples ranging from fields as diverse as defense analysis, cognitive science, and instruction are illustrated throughout to demonstrate the variety of applications that benefit from such stochastic analysis methods and models. Two applications of longitudinal stochastic analysis methods to collaborative and cognitive training environments are discussed in detail. The first application applies a combination of latent mixed Markov modeling and multidimensional scaling for modeling, analyzing, and supporting the process of online knowledge sharing. In the second application, a combination of iterative nonlinear machine learning algorithms is applied to identify latent classes of problem-solving strategies. The examples illustrated in this paper are instances of an increasing global trend toward interdisciplinary research. As this trend continues to grow, research that takes advantage of the gaps and overlaps in analytical methodologies between disciplines will save time, effort, and research funds.

Multi-scale stochastic dynamic response analysis of offshore risers with lognormal uncertainties

The aim of the study is to systematize current methods for assessing the economic efficiency of the best available food and agri-biotechnologies in domestic and international science and practice. The research applies methods of comparative analysis, systematization, and critical evaluation of scientific publications focused on the economic and environmental effectiveness of best available technologies (BAT). A classification-based approach was also employed to identify and group methods according to their orientation and application area. The empirical base includes regulatory documents, in particular, methodological guidelines for identifying technologies as BAT, national standards (GOST), and scientific publications on biotechnology, precision agriculture, organic waste processing, alternative protein sources, and more. The study examines methods such as investment feasibility analysis, life cycle environmental impact assessment, life cycle costing, multi-criteria decision analysis (MCDA), stochastic analysis, cluster analysis, among others. The findings demonstrate that integration of several methods enables assessment of both the financial and environmental viability of technology adoption. For example, in the case of cultivated meat, key indicators include both unit production cost and carbon footprint, which necessitates a shift from narrow economic assessment to eco-efficiency evaluation. The use of stochastic frontier analysis and clustering methods reveals that differences in farm efficiency are influenced not only by technology type, but also by the degree of its adoption. Overall, the systematization of methods for evaluating the efficiency of BAT indicates a clear trend toward transitioning from isolated economic indicators to comprehensive, data-driven approaches that combine economic and environmental metrics and validate results through empirical evidence.

Dynamic reliability analysis of slopes based on the probability density evolution method

Fatigue crack growth (FCG) in welded joints of orthotropic steel decks (OSDs) is highly stochastic, which complicates the accurate assessment of fatigue reliability. To consider the stochastic factors affecting fatigue performance in welded joints, this study proposed a stochastic FCG analysis method that incorporates machine learning to evaluate the fatigue reliability. Firstly, a probabilistic model for fatigue stress spectrum in welded joints was developed using site-specific traffic data. Subsequently, stochastic FCG analysis method assisted by a random forest model was developed to replace the time-consuming finite element solution. Finally, a fracture limit state function was established to evaluate fatigue reliability with consideration of the stochastic FCG process. Results indicate that a lognormal distribution accurately fits fatigue characteristic of the equivalent stress intensity factor range, with mean values consistently lower at the crack tip compared to the crack edge. Additionally, the deterioration rate of welded joints accelerates significantly at a crack depth of 5 mm, resulting in a shift of the remaining fatigue life distribution from lognormal to exponential. The reliability indices decrease significantly under overloading conditions. A 20% increase in vehicle weight reduces the fatigue life from the expected 100 years to 82 years.

Power system stability has become an increasing concern in renewable power integration because the active power generated by renewable energy varies randomly. Thus, we focus on random outside excitations in this study. Since the power obeys Non‐Gaussian distribution in most case, a novel small disturbance stochastic stability analysis method based on entropy is presented. First, the dynamic model of power system with random outside excitation is established by stochastic differential equation. Then, the values of mean and mean square stability are efficiently derived by transforming the stochastic differential equation into a deterministic mean or variance equation. For non‐Gaussian disturbance, entropy stability is presented by this transformation. At last, the numerical simulations are presented on a WSCC three‐generator nine‐bus power system with a wind farm © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A new, efficient algorithm is developed for the sensitivity analysis of a class of continuous-time recurrent neural networks with additive noise signals. The algorithm is based on the stochastic sensitivity analysis method using the variational approach, and formal expressions are obtained for the functional derivative sensitivity coefficients. The present algorithm uses only the internal states and noise signals to compute the gradient information needed in the gradient descent method, where the evaluation of derivatives is not necessary. In particular, it does not require the solution of adjoint equations of the back-propagation type. Thus, the algorithm has the potential for efficiently learning the network weights with significantly fewer computations. The effectiveness of the algorithm in a statistical sense is shown, and the method is applied to the familiar layered network.

Fatigue assessment of FPSO hull side shell longitudinals using component stochastic and full spectral method

Determination of sensitivity gradient is a major prerequisite for structural optimization, reliability assessment, and parameter identification. As the conventional deterministic sensitivity analysis cannot provide complete information, stochastic analysis is needed to tackle the uncertainties in structural parameters. This study focuses on the utility of the stochastic finite-element method for random response sensitivity analysis. The stochastic modeling of a random parameter is based on a commonly used 2D local averaging method generalized for a 3D case. The Choleski decomposition technique is then employed for digital simulation. The Neumann expansion based finite-element simulation method is extended for stochastic sensitivity analysis. This technique leads to a considerable saving of computational time. Example problems are used to compare the accuracy of this method to the direct Monte Carlo simulation and perturbation method in terms of varying stochasticity and efficiency in CPU time.

This article aims at proposing a method to stochastically analyze values streams taking into consideration the effect of critical uncertainty sources on lead time. The proposed method combines value stream mapping (VSM) and Monte Carlo simulation to identify improvement opportunities. To illustrate this approach, we carried out a case study in the special nutrition value stream of a Brazilian public hospital. Results show that the proposed method allows the identification of improvement opportunities that would not be considered in the classical deterministic VSM approach. Further, the integration of the stochastic analysis enables the determination of a more realistic lead time, which supports a more assertive planning and scheduling of the value stream. The proposed method addresses a fundamental gap in traditional VSM without adding much complexity to the analysis procedure, which is a common practical issue in previous works that integrated other stochastic methods into VSM.

Probabilistic fatigue damage assessment of coastal slender bridges under coupled dynamic loads

Natural gas should be distributed and consumed optimally in a given country since it is an important intermediate good for producers and a necessary final good for households with a low-income elasticity. Thus, this study aims to measure the efficiency of natural gas distribution companies responsible for delivering natural gas to economic units. The efficiency of 63 natural gas distribution companies operating in Turkey is estimated by the Stochastic Frontier Analysis method for 2013-2018. According to the findings, it is found that no firm operates within full efficiency. It is also concluded that while Bursa Natural Gas Distribution firm has the highest efficiency, Aksa Çanakkale Natural Gas Distribution firm has the lowest efficiency for the period studied. The findings show that inefficiency mainly stems from technical inefficiency rather than measurement errors and that natural gas distribution firms experience increasing returns to scale.

This paper presents a stochastic fracture analysis method for random non-homogenous cracked structures using the scaled boundary finite element method (SBFEM). The analyzed cracked domain is divided into polygon-based sub-domains such that the material property of each sub-domain can be modeled differently in the SBFEM. The spatial variability of the material property is represented as random field which is then incorporated directly into the coefficient matrices of the SBFEM. The stochastic global stiffness matrix and the stochastic equilibrium equation of the SBFEM through which the stochastic structural responses can be successfully calculated are further formulated. The stochastic stress intensity factors (SIFs) considering the spatial variability of the material property are then directly extracted from the stress solution of the cracked polygon. Numerical examples are given to demonstrate the validity of the proposed method and investigate the effect of the spatial distribution of the material property on the SIFs.