Marvelocity Pdf Free Here

\newpage \section{Introduction} \label{sec:intro} The global shipping industry transports over \SI{80}{\percent} of world trade by volume \cite{UNCTAD2022}. Despite advances in hull design and propulsion, a substantial fraction of fuel burn is attributable to sub‑optimal speed choices driven by inaccurate speed forecasts \cite{Mitsui2019}. Conventional approaches—e.g., the Holtrop–Mennen method \cite{Holtrop1972} or the ITTC‑1998 friction line \cite{ITTC1998}—rely on static ship parameters and simplified sea‑state corrections. Such models neglect the complex, nonlinear interaction among wind, waves, currents, and ship trim.

\begin{document} \maketitle \thispagestyle{empty} \begin{abstract} Accurate estimation of a vessel’s speed under varying environmental and operational conditions remains a cornerstone of maritime safety, fuel‑efficiency optimisation, and autonomous navigation. We introduce **MarVelocity**, a novel composite metric that fuses physical‑based hydrodynamic modelling with machine‑learning‑derived correction terms. Using a curated dataset of \num{2.3} million AIS (Automatic Identification System) records combined with high‑resolution oceanographic reanalysis, we train Gradient‑Boosted Regression Trees (GBRT) to predict the \emph{effective speed over ground} (SOG) from a low‑dimensional set of inputs: vessel design parameters, draft, wind, wave, and current vectors. MarVelocity achieves a mean absolute error of \SI{0.12}{\knot} (≈ 3 \% relative) on held‑out test ships, outperforming traditional empirical resistance formulas by a factor of 2.3. We further demonstrate real‑time deployment on a fleet of 150 container ships, reporting a 4.8 \% reduction in fuel consumption over a six‑month trial. The metric is released as an open‑source Python package \texttt{marvelocity} (v1.2) together with reproducible notebooks. \end{abstract} marvelocity pdf

\subsection{Baseline Physical Model} We compute the **theoretical speed over ground** $V_{\text{HM}}$ by solving for the equilibrium of propulsive thrust $T$ and total resistance $R_{\text{HM}}$: \begin{equation} R_{\text{HM}}(V) = R_f(V) + R_r(V) + R_a(V) + R_w(V) \,, \end{equation} where $R_f$, $R_r$, $R_a$, and $R_w$ denote frictional, residual, air, and wave resistance respectively (see Holtrop–Mennen \cite{Holtrop1972} for detailed expressions). The thrust is estimated from the ship’s installed power $P$ and propeller efficiency $\eta_p$: \begin{equation} T(V) = \frac{\eta_p P}{V}. \end{equation} The root of $T(V)-R_{\text{HM}}(V)=0$ yields $V_{\text{HM}}$. Such models neglect the complex, nonlinear interaction among

\section{Related Work} \label{sec:related} \subsection{Physical Models} The Holtrop–Mennen (HM) and KVLCC2 families remain industry standards for estimating ship resistance \cite{Holtrop1972, KVLCC1992}. Their primary limitation is the assumption of steady, uniform sea conditions and neglect of wind‑induced drag. Using a curated dataset of \num{2

\subsection{Future Work} \begin{enumerate} \item Extension to **fuel‑consumption** prediction via a joint multi‑task network. \item Incorporation of **ship‑maneuvering** dynamics for autonomous docking. \item Open‑source **benchmark suite** for maritime speed prediction (datasets, evaluation scripts). \end{enumerate}

\subsection{Learning the Residual} Define the residual speed: \begin{equation} \Delta V = V_{\text{SOG}} - V_{\text{HM}}, \end{equation} where $V_{\text{SOG}}$ is the measured speed over ground from AIS. We train a Gradient‑Boosted Regression Tree (XGBoost \cite{Chen2016}) to predict $\Delta V$ from the feature vector $\mathbf{x}$: \[ \mathbf{x} = \bigl[\,\underbrace{L, B, D, C_B}_{\text{design}};\, \underbrace{V_{\text{HM}}}_{\text{baseline}};\, \underbrace{U_{10}, \theta_{\text{wind}}}_{\text{wind}};\, \underbrace{H_s, \theta_{\text{wave}}}_{\text{wave}};\, \underbrace{U_c, \theta_{\text{current}}}_{\text{current}}\,\bigr]. \]

\title{MarVelocity:\\A Data‑Driven Metric for Predicting Maritime Vessel Speed} \author{ \textbf{Alexandra T. Liu}$^{1}$, \textbf{Rahul K. Menon}$^{2}$, \textbf{Elena G. Petrova}$^{3}$\\[2mm] $^{1}$Department of Naval Architecture, Massachusetts Institute of Technology, Cambridge, MA, USA\\ $^{2}$Marine Systems Research Group, Indian Institute of Technology, Bombay, India\\ $^{3}$Institute of Ocean Engineering, Technical University of Munich, Munich, Germany\\[2mm] \texttt{atl@mit.edu, rkm@iitb.ac.in, elena.petrova@tum.de} } \date{\today}