Methods for Fitting the Limit State Function of the Residual Strength of Damaged Ships

The limit state function is important for the assessment of the longitudinal strength of damaged ships under combined bending moments in severe waves. As the limit state function cannot be obtained directly, the common approach is to calculate the results for the residual strength and approximate the limit state function by fitting, for which various methods have been proposed. In this study, four commonly used fitting methods are investigated: namely, the least-squares method, the moving least-squares method, the radial basis function neural network method, and the weighted piecewise fitting method. These fitting methods are adopted to fit the limit state functions of four typically sample distribution models as well as a damaged tanker and damaged bulk carrier. The residual strength of a damaged ship is obtained by an improved Smith method that accounts for the rotation of the neutral axis. Analysis of the results shows the accuracy of the linear least-squares method and nonlinear least-squares method, which are most commonly used by researchers, is relatively poor, while the weighted piecewise fitting method is the better choice for all investigated combined-bending conditions.

Electronic thermodynamics part 1: Thermo-electromechanical system modeling, instrumentation, and simulation

This research proposes to obtain a mathematical model that describes the dynamic operation of a brushed DC motor, to obtain a state function considering the electrical, mechanical, and thermal effects of the DC motor. The dynamic evolution of the proposed function is evaluated by simulation using Matlab software, and by applying different values of the step type inputs for the brushed motor excitation employing pulse width modulation (PWM) to obtain a wide range of operations. Experimental results show that the developed state function, provides a reliable approximation to estimate the voltage, armature current, mechanical torque, and temperature of the brushed DC motor, showing an error percentage of 0.2%.

Tackling Issues of Global Concern by Revisiting the Justifications for State Approaches to Governance

Issues of global concern typically arise where there is a limited commitment to accountable governance. This paper argues that there has been an evolution in the state function. This evolution has made it possible to envisage a progression to accountable governance across all states. If attained, this would establish accountable governance as the threshold for state participation in international relations. Failure to meet the threshold would justify intrusion in the governance affairs of states by the international community of states to ensure accountability. Thus, the paper argues that the key to addressing issues of global concern lies in getting states to embrace accountable governance. This would be the first step towards empowering the international community of states to hold accountable those states that adopt governance decisions that perpetuate issues of global concern.

Effect of randomly dispersed short fibers on the flexural resistance factor of concrete beams reinforced with GFRP bars

Purpose The purpose of this paper is to investigate the potential design advantage in terms of resistance factors for normal weight concrete beams containing moderate-dose randomly dispersed short fibers and reinforced with glass fiber reinforced polymer (GFRP) bars.Design/methodology/approach An analytical model based on the current code specifications is used to calculate the moment capacity of over-reinforced sections. The vast majority of the considered beams are over-reinforced, compression-controlled. The data of the fiber-reinforced concrete (FRC) reinforced with GFRP bars are collected from three published research studies which are based on experimentally tested results. Three different types of short fibers with four volume fractions are considered. Probabilistic model is established to conduct reliability-based calibration using Monte-Carlo Simulation. Limit state function, relevant load and resistance random variables are identified, and adequate statistical parameters are selected. Target reliability index consistent with the one used to develop current design code specifications is used.Findings Reliability analysis and calibration process are carried out with the intention of estimating the flexural resistance factors for FRC beams reinforced with GFRP bars.Originality/value The predicted flexural resistance factors ranged from 0.72 to 0.95, giving the resistance factors the potential to be increased above the currently specified value of 0.65 for compression-controlled members reinforced with FRP bars.

New Interpretations of The State Function of An Elementary Particle Based On The Origin of Quantum Probability

The theoretical results of quantum mechanics (QM) have been verified by experiments, but the probabilistic Copenhagen interpretation is still controversial, and many counterintuitive phenomena are still difficult to understand. To trace the origin of probability in QM, we construct the state function of a multiparticle quantum objective system and find that the probability in QM originates from the particle number distribution rate in a unit volume near position r at time t in the multiparticle quantum objective system. Based on the origin of probability, We find that the state function of the particle has precise physical meaning; that is, the particle periodically and alternately exhibits the particle state and wave state in time and space, obtain the localized and nonlocalized spatiotemporal range of the particle, the apparent trajectory of the particle motion. Based on this, through rigorous mathematical derivation and analysis, we propose new physical interpretations of the quantum superposition state, wave-particle duality, the double-slit experiment, the Heisenberg uncertainty principle, and the quantum tunnelling effect, and these interpretations are physically logical and not counterintuitive.

Origin of Probability in Quantum Mechanics and the Physical Interpretation of the Wave Function

The theoretical results of quantum mechanics (QM) have been verified by experiments, but the probabilistic Copenhagen interpretation is still controversial, and many counterintuitive phenomena are still difficult to understand. To trace the origin of probability in QM, we construct the state function of a multiparticle quantum objective system and find that the probability in QM originates from the particle number distribution rate in a unit volume near position r at time t in the multiparticle quantum objective system. Based on the origin of probability, We find that the state function of the particle has precise physical meaning; that is, the particle periodically and alternately exhibits the particle state and wave state in time and space, obtain the localized and nonlocalized spatiotemporal range of the particle, the apparent trajectory of the particle motion. Based on this, through rigorous mathematical derivation and analysis, we propose new physical interpretations of the quantum superposition state, wave-particle duality, the double-slit experiment, the Heisenberg uncertainty principle, and the quantum tunnelling effect, and these interpretations are physically logical and not counterintuitive.

F-neuron in the clustering problems

We propose a new type of neuron based on the use of Fourier transform properties. This new type of neuron, called Fourier neuron (F-neuron), simplifies solving of a range of problems belonging to the class of problems of creating self-organizing networks using teacherless learning. The application of such F-neuron improves the quality and efficiency of automatic clustering of objects. We described the basic principles and approaches that allow to consider the properties vector as a parametric piecewise linear function, which provides the possibility to switch to Fourier-images operation both for input objects and for learning weights. The reasons for transferring information processing to Fourier space are justified, automatic orthogonalization and ranking of the Fourier image of the feature vector is explained. The advantages of the statistical approach to neuron training and construction of the refined neuron state function based on the parameters of the normal distribution are analyzed. We describe the procedure of training and pre-training the F-neuron that uses a statistical model based on the use of parameters of a normal distribution to calculate the confidence interval. We described an algorithm for recalculating normal distribution parameters when a new sample is added to the cluster. We reviewed some results of F-neuron technology and compared it with a traditional perceptron. A list of references and citations to the author’s previous works are given below.

An adaptive wavelet collocation method for the optimal heat source problem

Purpose This paper aims to propose a new adaptive numerical method to find more accurate numerical solution for the heat source optimal control problem (OCP). Design/methodology/approach The main aim of this paper is to present an adaptive collocation approach based on the interpolating wavelets to solve an OCP for finding optimal heat source, in a two-dimensional domain. This problem arises when the domain is heated by microwaves or by electromagnetic induction. Findings This paper shows that combination of interpolating wavelet basis and finite difference method makes an accurate structure to design adaptive algorithm for such problems which usually have non-smooth solution. Originality/value The proposed numerical technique is flexible for different OCP governed by a partial differential equation with box constraint over the control or the state function.

Dependency of active pressure and equation of state on stiffness of wall

Autonomous motion and motility are hallmarks of active matter. Active agents, such as biological cells and synthetic colloidal particles, consume internal energy or extract energy from the environment to generate self-propulsion and locomotion. These systems are persistently out of equilibrium due to continuous energy consumption. It is known that pressure is not always a state function for generic active matter. Torque interaction between active constituents and confinement renders the pressure of the system a boundary-dependent property. The mechanical pressure of anisotropic active particles depends on their microscopic interactions with a solid wall. Using self-propelled dumbbells confined by solid walls as a model system, we perform numerical simulations to explore how variations in the wall stiffness influence the mechanical pressure of dry active matter. In contrast to previous findings, we find that mechanical pressure can be independent of the interaction of anisotropic active particles with walls, even in the presence of intrinsic torque interaction. Particularly, the dependency of pressure on the wall stiffness vanishes when the stiffness is above a critical level. In such a limit, the dynamics of dumbbells near the walls are randomized due to the large torque experienced by the dumbbells, leading to the recovery of pressure as a state variable of density.