Can Coastal Management Programs Protect and Promote Water-Dependent Uses?

Anecdotal evidence indicates that commercial and recreational water-dependent uses have been under development pressure in recent years, and in some cases have been converted to other uses. The conversion of water-dependent uses, which range from commercial shipyards to recreational marinas, may have many public costs, including the loss of access to public trust waters, the loss of jobs and associated economic activity, and the loss of traditional working waterfronts. This two-part study investigated the role of five coastal management programs in the northeastern United States in managing, monitoring, and protecting water-dependent uses. First, coastal managers in Massachusetts, Rhode Island, Connecticut, New York and New Jersey were interviewed to assess the conversion problem. Second, in collaboration with New York City-based Regional Plan Association, follow-up interviews were conducted with coastal managers and local planners in Connecticut, New York, and New Jersey to gain greater insight into the role of coastal management programs and local governments in managing and promoting water-dependent uses. This article presents select findings from this study and discusses recommendations for improving the capacity of coastal management programs and local governments to manage water-dependent uses for the benefit of the public.     

Damage detection in offshore structures using neural networks

This paper discusses the damage detection in offshore jacket platforms subjected to random loads using a combined method of random decrement signature and neural networks. The random decrement technique is used to extract the free decay of the structure from its online response while the structure is in service. The free decay and its time derivative are used as input for a neural network. The output of the neural network is used as an index for damage detection. It has been shown that function N is effective in damage detection in the members of an offshore structure. Experimental studies conducted on a reduced model for a real jacket structure with geometrical scale of 1:30 are used. The applied loads were random loads. Two different load spectra were used: White noise, and Pierson-Moskowitz.     

Non-trading,lexicographic and inconsistent behaviour in stated choice data

This paper discusses a number of issues relating to the pre-analysis and cleaning of stated choice data, where we look specifically at the problems caused by non-trading, lexicographic and inconsistent response patterns. We argue that this process is considerably more complex and challenging than many in the field have hitherto acknowledged, with the standard practice being the use of rather ad hoc procedures for the identification of these phenomena. A detailed analysis on four different stated choice datasets highlights the potential impacts of these methods on model estimation results.     

Experimental study on a model azimuthing podded propulsor in ice

The objective of this study was to investigate the performance of a model azimuthing podded propulsor in ice-covered water. Model tests were carried out with two different depths of cut into the ice (15 and 35 mm), two different ice conditions (presawn and pack ice conditions), and four different azimuthing angles. The depth of cut is the maximum penetration depth of the propeller blade into the ice block. The 0.3-m-diameter model propeller was operated in a continuous ice milling condition. Ice loads were measured by several sensors which were installed in various positions on the model. Six one-axis pancake-style load cells on the top of the model measured the global loads and two six-component dynamometers were installed on the shaft to measure the shaft loads. One six-component dynamometer was attached to the one of the propeller blades inside the hub to measure the blade loads. The pod unit and propeller performance in ice are presented. Ice-related loads, which were obtained when the blade was inside the ice block, are introduced and discussed. During the propeller–ice interaction, a blade can experience the path generated by the previous blade, which is called the shadowing effect. The effects of shadowing, depth of cut, azimuthing angle, and advance coefficient on propulsor performance are presented and discussed.     

Improving pollutant emissions in diesel engines for heavy-duty transportation using retarded intake valve closing strategies

A combined theoretical and experimental study has been carried out to determine the real potential of reducing pollutant emissions in a HD diesel engine by means of retarding the intake valve closing. The effects produced by this alteration of the basic operation cycle have been examined by a preliminary modelling study, and from the obtained results, a modified camshaft was manufactured with a delayed intake valve closing of 60 crank angle degrees. Single-cylinder engine tests were carried out with this modified camshaft, and the emissions and fuel consumption were recorded. The results showed that the retarded intake valve closing can enhance the premixed combustion phase, and thus simultaneously reduce soot and NOx emissions. Moreover, the combustion process attained is extremely tolerant to exhaust gas recirculation, and by adoption of this measure, Euro-5 emissions limits can be achieved at the tested conditions without after-treatment.     

Seasonality of picophytoplankton chlorophyll a and biomass in the central Cantabrian Sea, southern Bay of Biscay

Seasonal changes in the abundance and biomass of cyanobacteria (Synechococcus and Prochlorococcus) and picoeukaryotes were studied by flow cytometry in the upper layers of the central Cantabrian Sea continental shelf, from April 2002 to April 2006. The study area displayed the typical hydrographic conditions of temperate coastal zones. A marked seasonality of the relative contribution of prokaryotes and eukaryotes was found. While cyanobacteria were generally more abundant for most of the year (up to 2.4 10⁵ cells mL^− 1), picoeukaryotes dominated the community (up to 10⁴ cells mL^− 1) from February to May. The disappearance of Prochlorococcus from spring through summer is likely related to shifts in the prevailing current regime. The maximum total abundance of picophytoplankton was consistently found in late summer–early autumn. Mean photic-layer picoplanktonic chlorophyll a ranged from 0.06 to 0.53 µg L^− 1 with a relatively high mean contribution to total values (33 ± 2% SE), showing maxima around autumn and minima in spring. Biomass (range 0.58–40.16 mg C m^− 3) was generally dominated by picoeukaryotes (mean ± SE, 4.28 ± 0.27 mg C m^− 3) with an average contribution of cyanobacteria of 30 ± 2%. Different seasonality of pigment and biomass values resulted in a clear temporal pattern of picophytoplanktonic carbon to chlorophyll a ratio, which ranged from 10 (winter) to 140 (summer). This study highlights the important contribution of picoplanktonic chlorophyll a and carbon biomass in this coastal ecosystem.     

Full vehicle simulation using thermomechanically coupled hybrid neural network shock absorber model

In the case of full vehicle models, the technique of multi-body simulation (MBS) is frequently used to study their highly non-linear dynamic behaviour. Many non-linearities in vehicle models are induced by force elements like springs, shock absorbers, bushings and tires. Commonly, spline functions are used to represent the force responses of these components. If the non-linear relationships are more complicated, the spline approximations are no more accurate. An alternative approach is based on empirical neural networks which are based on the mathematical approximation of measured data. It is well known that neural networks are able to represent and predict complex component responses accurately. The aim of this paper is to perform a dynamic full vehicle simulation using a thermomechanically coupled hybrid neural network shock absorber model. In this shock absorber model, the spline approach is combined with a temperature-dependent neural network. Based on a displacement-controlled excitation on a four post test rig in the ADAMS/Car MBS software, a rugged test track is simulated. In this way, the front and rear shock absorbers are dynamically loaded with comfort-relevant frequencies in the range of 0.75-30 Hz and velocity amplitudes up to 2 m/s. By the simulation, stability of the hybrid neural network model is demonstrated. Furthermore, the damping force, the vertical acceleration of the chassis and the required simulation times are compared. The standard spline approach is used as a reference.     

A hybrid model for better predicting capabilities under parallel wind conditions for NOx concentrations from highways in Ireland

Atmospheric dispersion models based on the Gaussian line source formulation tend to give poor results for low wind speed and parallel wind directions. Certain dispersion models like HIWAY-2 have an inbuilt dispersion algorithm to adjust in such situations but no correction exists for mathematical based Gaussian dispersion models. We evaluate the performance of a hybrid model a mathematical model, to assess its suitability as an alternative to the Gaussian based models.     

Assessment of diesel combustion noise overall level in transient operation

Combustion noise in passenger cars powered with direct injection (DI) diesel engines is frequently the main reason why end-users are reluctant to drive this type of vehicle. Thus, the great potential of diesel engines for environment preservation — due to their lower CO₂ emissions — could be missed. This situation worsens with the current design trends (engine downsizing) and the emerging new diesel combustion concepts (Homogeneous Charge Compression Ignition-HCCI, Premixed Charge Compression Ignition-PCCI, etc.), which are intrinsically noisy. This negative feature can be even more critical in transient operation due to the contribution of the temporal changes of both source and transmission path on engine noise. Therefore, combustion noise must be considered as an additional essential factor in engine development, together with performance, emissions and driveability. Thus, suitable evaluation procedures that can be integrated into the global engine development process in a timely and cost-effective manner are imperative. Regarding the evaluation procedures, most of the work available in the literature addressed combustion noise at steady operation. To surpass this limitation, two possible approaches — adapted from the classical and multiple regression methods — for the overall level assessment of combustion noise in transient conditions are evaluated in this paper.     

Condition monitoring of wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are to large extent based on visual analysis. In this paper a machine vision based approach has been considered to emulate the visual abilities of the human operator to enable automation of the process. Digital images from either ends (left and right) of the sleepers have been acquired. A pattern recognition approach has been adopted to classify the condition of the sleeper into classes (good or bad) and thereby achieve automation. Appropriate image analysis techniques were applied and relevant features such as the number of cracks on a sleeper, average length and width of the crack and the condition of the metal plate were determined. Feature fusion has been proposed in order to integrate the features obtained from each end for the classification task which follows. The effect of using classifiers like multi-layer perceptron and support vector machines has been tested and compared. Results obtained from the experiments show that multi-layer perceptron and support vector machines have achieved encouraging results, with a classification accuracy of 90%; thereby exhibiting a competitive performance when compared to a human operator.