J. Holtrop

Bio: J. Holtrop is an academic researcher from Marin Software. The author has contributed to research in topics: Propulsion & Ship model basin. The author has an hindex of 5, co-authored 6 publications receiving 741 citations.

An approximate power prediction method

Abstract: In an earlier publication, a statistical method was presented for the determination of the required propulsive power at the initial design stage of a ship. This method was developed through a regression analysis of random model experiments and full-scale data, available at the Netherlands Ship Model Basin. Because the accuracy of the method was reported to he insufficient when unconventional combinations of main parameters were used, an attempt is made in the present article to extend the method by adjusting the original numerical prediction model to test data obtained in some specific cases. This adaptation of the method has resulted in a set of prediction formulae with a wider range of application. Nevertheless, it is pointed out that the given modifications have a tentative character only, because the adjustments are based on a small number of experiments. In any case, the application is limited to hull forms resembling the average ship described by the main dimensions and form coefficients used in the method. The extension of the method was focused on improving the power prediction for high-block ships with low L/B ratios, and for slender naval ships with complex appendage arrangements and immersed transom sterns.

A statistical re-analysis of resistance and propulsion data

Abstract: Une methode de prevision de la puissance basee sur une analyse par regression d'un modele aleatoire et sur des donnees d'un essai vraie grandeur a ete presentee. Les analyses par regression sont maintenant basees sur les resultats d'essais sur 334 modeles. Outre ces analyses des proprietes de resistance et de propulsion une methode a ete concue dans laquelle on a tenu compte de l'influence de la cavitation de l'helice. Ces formules ont ete derivees dans une etude effectuee dans un programme de recherche cooperatif MARIN

A statistical power prediction method

Abstract: This paper presents revised formulae for statistical power prediction based on a greater number of results than the original equations reported in a previous paper which comprised a numerical representation of resistance properties and propulsion factors that could be used for statistical ship performance prediction. Following over a year of experience, several fields for improvement of the derived prediction methods given before are indicated, namely: The formula for wavemaking resistance does not include the influence of the bulbous bow. The resistance of ships with large bulbous bows is overestimated by the original formula. The wavemaking resistance of ships with a large waterplane-area coefficient is overestimated by the previous formula. The accuracy of the formula for the thrust-deduction fraction for a slender single-screw ship appeared to be insufficient. The wake fraction and the model/ship correlation were not properly represented by the formula for full ships at ballast draught. Order from BSRA as No. 49,917.

Extrapolation of Propulsion Tests for Ships with Appendages and Complex Propulsors

Abstract: Prediction of ship powering is traditionally based on the results of model experiments. This paper covers the extrapolation of model test results for ships that may have a multitude of appendages and one or more complex propulsors. The major differences from conventional extrapolation methods are the application of the scale effect corrections to the model propulsion test, the treatment of the appendages and acknowledging the effects of the propeller load on the propulsion parameters. The last feature is considered essential to successfully handle complex propulsors with both rotating and passive components.

Statistical data for the extrapolation of model performance tests

Abstract: In view of an adequate extrapolation of model performance test results and statistical analysis of results of towing tank experiments and full-size speed trials has been made. This analysis covers not only the derivation of the correlation factors that account for the scale effects that are present in the model-test results, but also the separation of the resistance into components of different origin with emphasis on the determination of the form factor from either low-speed resistance measurements or from a statistical formula. Some of these results have been published in a previous paper. In that article, however, the extrapolation method has not been described in detail.

Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide

Abstract: . A method is presented for the evaluation of the exhaust emissions of marine traffic, based on the messages provided by the Automatic Identification System (AIS), which enable the positioning of ship emissions with a high spatial resolution (typically a few tens of metres). The model also takes into account the detailed technical data of each individual vessel. The previously developed model was applicable for evaluating the emissions of NOx, SOx and CO2. This paper addresses a substantial extension of the modelling system, to allow also for the mass-based emissions of particulate matter (PM) and carbon monoxide (CO). The presented Ship Traffic Emissions Assessment Model (STEAM2) allows for the influences of accurate travel routes and ship speed, engine load, fuel sulphur content, multiengine setups, abatement methods and waves. We address in particular the modeling of the influence on the emissions of both engine load and the sulphur content of the fuel. The presented methodology can be used to evaluate the total PM emissions, and those of organic carbon, elemental carbon, ash and hydrated sulphate. We have evaluated the performance of the extended model against available experimental data on engine power, fuel consumption and the composition-resolved emissions of PM. We have also compared the annually averaged emission values with those of the corresponding EMEP inventory, As example results, the geographical distributions of the emissions of PM and CO are presented for the marine regions of the Baltic Sea surrounding the Danish Straits.

Predicting ship fuel consumption based on LASSO regression

Abstract: During the voyage, predicting fuel consumption of ships under different sea-states and weather conditions has been a challenging and far-reaching topic, because there are a great number of feature variables affecting the fuel consumption, including main-engine status, cargo weight, ship draft, sea-states and weather conditions, etc. Data driven statistical models have been employed to model the relationship between fuel consumption rate and these voyage parameters. However, some of the feature variables are highly correlated, e.g. wind speed and wave height, air pressure and wind force, cargo weight and draft etc., thus a typical multiple collinearity problem arises so that the fuel consumption cannot be accurately calculated by using the traditional multiple linear regression. In this study, the LASSO (Least Absolute Shrinkage and Selection Operator) regression algorithm is employed to implement the variable selection for these feature variables, additionally, it guides the trained predictor towards a generalizable solution, thereby improving the interpretability and accuracy of the model. On the basis of the LASSO, a novel ship fuel consumption prediction model is proposed. Experimentally, the superiority of the proposed method was confirmed by comparing it with some existing methods on predicting the fuel consumption. The proposed method is a promising development that improves the calculation of the fuel consumption.