On energy efficiency of inland waterway self-propelled cargo vessels

Da bi se u fazi projektovanja broda analizirao uticaj forme i primenjenih tehničkih rešenja na energetsku efikasnost, potkomitet IMO-a zadužen za zaštitu životne sredine (MERS) predložio je u uvođenje tzv. indeksa energetske efikasnosti pri projektovanju (EEDI). Predloženi pokazatelj predstavlja odnos mase ugljen-dioksida emitovanog u atmosferu, i količine tereta koja je pri tome prevezena po kilometru. Za neke tipove morskih brodova već su razvijene jednačine koje određuju referentne vrednosti za EEDI. U ovom radu je predstavljen jedan od prvih pokušaja procene vrednosti ovog indeksa za rečne samohodne teretne brodove. U toku istraživanja su sprovedena ispitivanja rečnih samohotki na plovnom putu u realnim okolnostima. Proširena je baza podataka i na osnovu nje je razvijen novi matematički model za procenu potrebne snage motora u zavisnosti od osnovnih dimenzija broda i ograničenja plovnog puta. Analizirane su i značajne razlike između rezultata modelskih ispitivanja i ispitivanja brodova u prirodnoj veličini, a na osnovu čega je procenjena vrednost tzv. dodatka za službu. U radu je predložen približan postupak za određivanje koeficijenata propulzije na osnovu ispitivanja apsorpcije snage broda. Konačno, nagovešten je način na koji se mogu odrediti referentne vrednosti indeksa EEDI koje bi mogle da se koriste pri projektovanju brodova ovog tipa.The Energy Efficiency Design Index (EEDI) was introduced by IMO - Marine Environment Protection Committee in order to stimulate innovation and technical development of all elements that influence energy efficiency of a ship from its design phase. According to definition, it represents weight of ship’s CO2 emissions per transport work. Baseline equations for EEDI were developed for several most common types of seagoing ships. This paper presents one of the first attempts to evaluate EEDI of inland-waterway, dry-cargo, self-propelled vessels. Within research that is explained in the paper, full-scale measurements were performed with the purpose to enrich the database according to which new mathematical model for power evaluation was developed. Large differences between the model- and full-scale measurements were also analysed. Finally, application of relatively large power margins for inland-waterway ships was suggested. EEDI baseline can be used as a benchmark of future ship designs

Resistance Prediction for Hard Chine Hulls in the Pre-Planing Regime

A mathematical representation of calm-water resistance for contemporary planing hull forms based on the USCG and TUNS Series is presented. Regression analysis and artificial neural network (ANN) techniques are used to establish, respectively, Simple and Complex mathematical models. For the Simple model, resistance is the dependent variable (actually R/Delta for standard displacement of Delta = 100000 lb), while the Froude number based on volume (F-nV) and slenderness ration (L/V-1/3) are the independent variables. In addition to these, Complex model's independent variables are the length beam ratio (L/B), the position of longitudinal centre of gravity (LCG/L) and the deadrise angle (beta). The speed range corresponding to F-nV values between 0.6 and 3.5 is analyzed. The Simple model can be used in the concept design phases, while the Complex one might be used for various numerical towing tank performance predictions during all design phases, as appropriate

Resistance Prediction for Hard Chine Hulls in the Pre-Planing Regime

A mathematical representation of calm-water resistance for contemporary planing hull forms based on the USCG and TUNS Series is presented. Regression analysis and artificial neural network (ANN) techniques are used to establish, respectively, Simple and Complex mathematical models. For the Simple model, resistance is the dependent variable (actually R/Delta for standard displacement of Delta = 100000 lb), while the Froude number based on volume (F-nV) and slenderness ration (L/V-1/3) are the independent variables. In addition to these, Complex model's independent variables are the length beam ratio (L/B), the position of longitudinal centre of gravity (LCG/L) and the deadrise angle (beta). The speed range corresponding to F-nV values between 0.6 and 3.5 is analyzed. The Simple model can be used in the concept design phases, while the Complex one might be used for various numerical towing tank performance predictions during all design phases, as appropriate

Extending the life of a ship by extending her length: Technical and economic assessment of lengthening of inland vessels

The objective of MoVe IT! (Modernisation of vessels for inland waterway freight transport) project is to investigate cost-effective options for modernisation of the European inland fleet. One of the project tasks was to examine the feasibility of lengthening of existing small vessels (LOA < 86m) from both the technical and the economic point of view. With respect to that, the gradual lengthening (in several predefined steps) of two typical inland vessels of CEMT class II and III was examined. For each step, the ship structure scantlings were verified against the rules of classification societies, the manoeuvring features were simulated and the power necessary for attaining certain speed was calculated. Finally, the economic and environmental impacts of lengthening were assessed. The results of the analysis confirmed that lengthening can be viable, in particular for larger vessels (in this case, class III) where the payback periods were found to be relatively short. In addition, the lengthening proved to have a positive effect from the environmental point of view. The analysis also demonstrated that there are conditions related to waterway characteristics and economic environment under which the lengthening would not pay off, even though it would be technically feasible

RESISTANCE AND TRIM MODELING OF THE NAPLES HARD CHINE SYSTEMATIC SERIES

An Artificial Neural Network (ANN) method with multiple-outputs is used to develop the mathematical models for the Naples Systematic Series (NSS) of resistance, dynamic trim, wetted surface area and length of wetted surface area, as functions of length beam ratio, slenderness ratio, longitudinal centre of gravity and volumetric Froude number. Multiple ANN output enables simultaneous use of all the available resistance and trim data, producing both an output for resistance and for trim. Similar results are obtained for the wetted surface area and length of wetted surface area datasets. Note that the multiple-output models share a common ANN structure, with only slight differences in equations for resistance and trim, and for wetted surface area and length of wetted surface area. *This paper is upgraded and corrected version of a paper published under the same title at the 11th High Speed Marine Vehicles Conference (HSMV 2017) in Naples, 25th -26th October 2017

Resistance and Trim Modeling of Naples Hard Chine Systematic Series

An Artificial Neural Network (ANN) method with multiple outputs is used to develop the mathematical models for the Naples Systematic Series (NSS) of resistance (actually (RT/Δ)100000), dynamic trim (τ), wetted area (S/V2/3) and length of wetted area (LWL/LP), as functions of length beam ratio (LP/BPX), slenderness ratio (LP/V1/3), longitudinal centre of gravity (LCG/LP) and volumetric Froude number (FnV). Multiple ANN output feature enables simultaneous use of all the available (RT/Δ)100000 and τ data, producing both, an output for R/Δ and for τ. Similar results are obtained for the S/V2/3 and LWL/LP datasets. Note that the multiple output models share a common ANN structure, with only slight differences in equations for R/Δ & τ, and S/V2/3 & LWL/LP

Resistance and Trim Modeling of Naples Hard Chine Systematic Series

An Artificial Neural Network (ANN) method with multiple outputs is used to develop the mathematical models for the Naples Systematic Series (NSS) of resistance (actually (RT/Δ)100000), dynamic trim (τ), wetted area (S/V2/3) and length of wetted area (LWL/LP), as functions of length beam ratio (LP/BPX), slenderness ratio (LP/V1/3), longitudinal centre of gravity (LCG/LP) and volumetric Froude number (FnV). Multiple ANN output feature enables simultaneous use of all the available (RT/Δ)100000 and τ data, producing both, an output for R/Δ and for τ. Similar results are obtained for the S/V2/3 and LWL/LP datasets. Note that the multiple output models share a common ANN structure, with only slight differences in equations for R/Δ & τ, and S/V2/3 & LWL/LP

Power prediction modeling of conventional high-speed craft

The proposed book addresses various power prediction methods, a principal design objective for high-speed craft of displacement, semi-displacement, and planing type. At the core of the power prediction methods are mathematical models based on experimental data derived from various high-speed hull and propeller series. Regression analysis and Artificial Neural Network (ANN) methods are used as extraction tools for this kind of models. The most significant factors for in-service power prediction are bare hull resistance, dynamic trim, and the propeller’s open-water efficiency. Therefore, mathematical modeling of these factors is a specific focus of the book. Furthermore, the book includes a summary of most of the power-prediction-relevant literature published in the last 50 years, and as such is intended as a reference overview of the best high-speed craft modeling practices. Once these mathematical models have been developed and validated, they can be readily programmed into software tools, thereby enabling the parametric analyses required for the optimization of a high-speed craft design. The proposed book is intended primarily for naval architects who design and develop various types of high-speed vessels (yachts, boats etc.), as well as for students who are interested in the design of fast vessels. The book includes useful Excel Macro Codes for the outlined mathematical models. Moreover, software for all considered models is provided

Power Prediction Modeling of Conventional High-Speed Craft

The proposed book addresses various power prediction methods, a principal design objective for high-speed craft of displacement, semi-displacement, and planing type. At the core of the power prediction methods are mathematical models based on experimental data derived from various high-speed hull and propeller series. Regression analysis and Artificial Neural Network (ANN) methods are used as extraction tools for this kind of models. The most significant factors for in-service power prediction are bare hull resistance, dynamic trim, and the propeller’s open-water efficiency. Therefore, mathematical modeling of these factors is a specific focus of the book. Furthermore, the book includes a summary of most of the power-prediction-relevant literature published in the last 50 years, and as such is intended as a reference overview of the best high-speed craft modeling practices. Once these mathematical models have been developed and validated, they can be readily programmed into software tools, thereby enabling the parametric analyses required for the optimization of a high-speed craft design. The proposed book is intended primarily for naval architects who design and develop various types of high-speed vessels (yachts, boats etc.), as well as for students who are interested in the design of fast vessels. The book includes useful Excel Macro Codes for the outlined mathematical models. Moreover, software for all considered models is provided