Simulation of the mean zero-up-crossing wave period using artificial neural networks trained with a simulated annealing algorithm

The aim of this work was to develop a predictive model to forecast the mean zero-up-crossing wave periods (T _z ) for 3-hourly sea states at a location in the Pacific using artificial neural networks (ANNs). Seven multilayer ANNs were trained with a simulated annealing algorithm. The output of each trained ANN was used to estimate each of the seven parameters of a new distribution called the hepta-parameter spline proposed for the conditional distribution of T _z , given some mean zero-up-crossing wave periods and significant wave heights. After estimating the parameters of the distribution, the model was used to simulate and predict future values of T _z . Forecasting a sea state and developing the joint distribution of sea state characteristics with the help of the simulated characteristics are also discussed in this article.     

Performance Design of an Exhaust Superheater for Waste Heat Recovery of Construction Equipment

Although fuel cost has been the largest portion of annual operating costs of construction equipment, it is possible to save the energy and reduce cost using fuel economy enhancement technology. In this study, an organic Rankine cycle is applied to an excavator in order to recover waste heat, reproduce it into electrical energy, and consequently reduce the fuel consumption by 10 %. A design process was carried out to develop an exhaust gas superheater that recovers the waste heat from exhaust gas through a composite-dimensional thermal flow analysis. A one-dimensional code was developed to perform a size design for the exhaust gas superheater. The ranges for the major design parameters were determined to satisfy the target of the heat recovery, as well as the pressure drop at both fluid sides. Performance analysis was done through onedimensional design code results, which were compared with three-dimensional CFD analysis. By utilizing a 3D commercial code, the arrangement of the tubes was selected and the working fluid pressure drop was reduced through a detailed layout design. The design procedure was verified by a performance evaluation of the prototype, which yielded only a 7 % tolerance in heat recovery.     

Research on the coefficient of sound absorption in turbid water

China’s coastal waters are turbid and the properties of the seabed are complex. This negatively impacts the performance of underwater detection equipment. The properties of sound absorption in turbid water are not well understood. In this paper, the coefficient of sound absorption in turbid water was measured by the reverberation technique. All work was done in a reverberation barrel made of seamless aluminum. First, pure water was poured into the reverberation barrel and its reverberation time measured. Next, various concentrations of turbid water were poured into the barrel and their reverberation time measured. After all data had been gathered, the coefficient of sound absorption in turbid water of different concentrations was calculated. From this we determined a law of sound absorption in turbid water as summarized in the paper.     

How remote is remote pilotage?

“Today, the idea of allowing aircraft pilots to operate as they see fit, without any control from the land, is unthinkable. Will the same things apply to ships in twenty to thirty years’ time?”Current maritime pilotage regimes are clearly seen in some quarters as too restrictive and old fashioned. Change is “in the air” and VTS, pilotage, and the relationship between them cannot be immune to it. Subject to the attainment of the appropriate standards by both ships and their crews there is no practical reason why more flexible Vessel Traffic Management cannot be introduced. The required technology is largely available and, with the introduction of AIS, the required dynamic data can be available. Technical problems foreseen are believed capable of solution. The more significant obstacles to implementation are, therefore, largely political, commercial, organisational, and cultural. These issues are discussed in this paper taking into account the viewpoints of major stakeholders.     

结构动力学基于摄动法的高阶非线性随机有限元方法研究(英文)

Several algorithms were proposed relating to the development of a framework of the perturbation-based stochastic finite element method (PSFEM) for large variation nonlinear dynamic problems. For this purpose, algorithms and a framework related to SFEM based on the stochastic virtual work principle were studied. To prove the validity and practicality of the algorithms and framework, numerical examples for nonlinear dynamic problems with large variations were calculated and compared with the Monte-Carlo Simulation method. This comparison shows that the proposed approaches are accurate and effective for the nonlinear dynamic analysis of structures with random parameters.     

An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.     

Improving the coexistence of offshore wind farms and shipping: an international comparison of navigational risk assessment processes

The continued growth and evolution of the offshore wind industry, and the emergence of other novel marine uses such as wave and tidal generators, have upped the ante for spatial planners, as well as consenting and approval authorities in various coastal states. These stakeholders rely on processes such as navigational risk assessments (NRAs) to balance safety and efficiency requirements and to make optimal decisions over use of space. Given the increasingly complex and crowded seascape, however, there are some apprehensions about potential shortcomings in these NRA processes. There is also some concern that these inadequacies may lead to unsafe or inefficient marine spatial use. To understand how NRA processes can be improved further, a literature review is conducted, followed by a survey of respondents who are involved in the planning, consenting and/or approval of offshore wind farms across seven different countries. A summary of the NRA processes in these seven countries is presented, and several shortcomings are identified. Based on the findings of the survey, a list of recommendations is presented to enhance existing NRA processes—and to improve the coexistence of shipping and offshore wind farms (OWFs).     

Five-axis interpolation of continuous short linear trajectories for 3[PP]S-<Emphasis Type="Italic">XY</Emphasis> hybrid mechanism by dual Bezier blending

A novel five-axis real-time interpolation algorithm for 3[PP]S-XY hybrid mechanism is proposed in this paper. In the algorithm, the five-axis tool path for controlling this hybrid mechanism is separated into two sub-paths. One sub-path describes the movement of 3[PP]S parallel kinematic mechanism module, and the other one describes the movement of XY platform. A pair of cubic Bezier curves is employed to smooth the corners in those two sub-paths. Based on the homogenous Jacobian matrix of 3[PP]S mechanism, a relationship between the position errors of every driving joint in hybrid mechanism and the position deviation of the tool tip center point at the moving platform is established. This relationship is used to estimate the approximation error for the corners smoothing according to the accuracy requirement of tool tip center in interpolation. Due to the high computational efficiency of this corner smoothing method, it is integrated into the look-ahead module of computer numerical control (CNC) system to perform online tool path smoothing. By performing the speed planning based on a floating window scheme, a jerk limited S-shape speed profile can be generated efficiently. On this basis, a realtime look-ahead scheme, which is comprised of path-smoothing and feedrate scheduling, is developed to acquire a speed profile with smooth acceleration. A monotonic cubic spline is employed for synchronization between those two smoothed sub-paths in tool path interpolation. This interpolation algorithm has been integrated into our own developed CNC system to control a 3PRS-XY experimental instrument (P, R and S standing for prismatic, revolute and spherical, respectively). A club shaped trajectory is adopted to verify the smoothness and efficiency of the five-axis interpolator for hybrid mechanism control.     

A relaxed-PPA contraction method for sparse signal recovery

Sparse signal recovery is a topic of considerable interest, and the literature in this field is already quite immense. Many problems that arise in sparse signal recovery can be generalized as a convex programming with linear conic constraints. In this paper, we present a new proximal point algorithm (PPA) termed as relaxed-PPA (RPPA) contraction method, for solving this common convex programming. More precisely, we first reformulate the convex programming into an equivalent variational inequality (VI), and then efficiently explore its inner structure. In each step, our method relaxes the VI-subproblem to a tractable one, which can be solved much more efficiently than the original VI. Under mild conditions, the convergence of the proposed method is proved. Experiments with l ₁ analysis show that RPPA is a computationally efficient algorithm and compares favorably with the recently proposed state-of-the-art algorithms.