Search

Despite a much later inception of somatic embryogenesis (SE) propagation protocols for gymnosperms than for angiosperm species, SE is becoming increasingly important due to its applications for commercial forestry. For many conifers, there are however still major bottlenecks in the SE plant production process limiting the use of SE for forestry operations, Christmas tree production and research projects. In the present case study, the effects on plant growth from different cultural factors applied during the SE developmental process were studied in two conifer species of high value for Christmas tree production. Seven clones of Abies nordmanniana and two clones of Abies bornmuelleriana were included in the study. Accumulated effects from cultural treatments were recorded from the start of germination of mature embryos of different quality scores through development into plants in the third growing period. Experimental factors of the cultural treatments included were: germination temperature, germination time, light conditions, survival ex vitro and traits for plant growth and vitality. The results reveal that most of the studied experimental factors influenced plant growth during the first three years however their relative importance was different. Plant survival rate at end of the nursery stage was strongly impacted by germination temperature (p<0.001), initial embryo score (p=0.007), clone (p<0.001) and to a lesser extend week of germination (p=0.017). This case-study highlights and quantifies the strong interrelation between the developmental steps of somatic embryogenesis and show the importance of considering all cultural steps when optimizing SE plant production protocols.

This study assesses the relationship between speed and power of ships, and is based on operational data derived from more than 50,000 noon reports retrieved from 88 tankers. The analysis is made with a data-driven model based on principles from naval architecture and an econometric framework. The model facilitates the determination of a draught- and speed-dependent exponent. It is shown that the exponent is significantly lower than 3 at speed intervals below the design speed. The finding of a (much) smaller exponent, relative to 3, at low speed intervals is important in the slow steaming debate, since slow steaming will not be as good as often stated when the cubic speed-power relationship does not hold. On a practical level, the developed method can be used to set a more reliable benchmark in the performance monitoring of ships.

In the present study, model drift – mostly driven by biofouling – is mitigated by applying continual learning and the hydrodynamic performance of a large LNG tanker trading in worldwide service is assessed in parallel. The available auto-logged sensor data spans five years including two dry docking intervals. An adaptive training methodology of an artificial neural network is established, and the drift score is derived from simulated data under sea trial conditions. In addition, hindcast metocean data from ERA5 is included in the model’s feature space for capturing environmental conditions. Crucially, it was found that the obtained drift estimate is in general accordance to ISO 19030 results, proving the method’s validity. Still, the methodology itself is subject to considerable uncertainty. Finally, limitations and possible extensions of the proposed methodology are presented.

The reliable and accurate determination of prevalent sea state conditions during a vessel’s passage is of high scientific and practical importance not only due to crew and cargo safety, but also due to increasing energy efficiency demands. The present paper deals with the prediction of integral sea state parameters using 5-minute time series samples of wave-induced ship motions and builds upon deep neural networks. The study is based on synthetic motion histories of heave, pitch and roll of a container vessel generated from transfer functions and a parametric wave spectrum. The multi-dimensional search space of the four free variables: significant wave height, zero-upcrossing period, relative wave direction and advance speed, is discretized by Latin Hypercube Sampling. In order to comply with the assumption of moderate wave steepness, an empirical wave breaking constraint is incorporated into the spectral simulation procedure. Both a residual network and a multichannel convolutional LSTM (Long Short Term Memory) network are employed and compared to a state of the art architecture. In the frequency domain, a multilayer perceptron is used for comparison and trained on features obtained from cross-spectral analysis. The results of the simulation-based study show sufficient accuracy and generalization for all models in both time and frequency domain. Lastly, limitations of the present study are pointed out and it stressed that the applicability of the presented methodology to real in-situ measurement data is an important part of future work.

The analysis of wave-ship interactions is highly relevant for the safety – as well as the energy efficiency – of the maritime operations. One application is the onboard estimation of the ship’s responses in incoming seaways, which requires regular and accurate updates of the vessel’s seakeeping model, accounting for possible changes in the operational conditions. This paper presents a simple approach for fast estimation of the wave-to-motion transfer functions of vessels. Prior information of the wave spectrum, characterizing the sea state, and ship motion measurements, i.e. time series sequences from onboard sensors, are supposed to be available. Semi-empirical closed-form expressions derived for a box-shaped vessel define Parameterized Response Amplitude Operators (P-RAOs) for the heave and pitch motions. The five input parameters, namely the ship speed, length, breadth, draught and block coefficient, are regarded as optimization variables. An optimization problem is established to minimize the spectral discrepancy between, on one hand, the measured responses, and, on the other hand, the theoretical responses computed with the wave spectrum and P-RAOs. Numerical simulations of measured motions in a predefined long-crested sea state are carried out in the frequency domain for two different ships, a small research vessel and a container ship, using a set of RAOs obtained by a commercial potential flow code. The simulated measurements are considered as the ground truth. Tuning of the P-RAOs is carried out, and the results show a fairly good agreement between the tuned P-RAOs and the true RAOs over a wide portion of the frequency range. Moreover, the normalized error between the true and estimated response spectra is significantly decreased after tuning the P-RAOs.

The article presents a study investigating the level of variation in sea state parameters encountered by sailing ships crossing the oceans. The sea state parameters have been obtained from a reanalysis, in this case the ERA5. The study is based on the use of different interpolation schemes to compute parameters in geographical positions off the fixed grid. It is shown that the variation in sea state parameters can be significant. Consequently, in case of sailing ships, covering relatively long distances in a short time (30-60 minutes), it is recommended to rely on bilinear interpolation rather than nearest neighbour. The variation in the sea state parameters is, in fact, at a level which means that the normal assumption of a stationary seaway in periods up to 3 hours likely is violated for ships sailing the typical service speed (15 – 20+ knots).

The ever-changing sea state constitutes an integral part of situational awareness for vessels at sea. In this paper, a computationally efficient sea state estimation algorithm is extended by (1) automatic gain and tolerance calculation, and (2) trust measure for wave filtering condition identification. The resulting algorithm can run online in a control system without the need of user input, producing estimates of sea state parameters (significant wave height, peak wave frequency and main propagation direction), while simultaneously monitoring the quality of the estimates produced by identifying when wave filtering occurs. The algorithm is developed for application in dynamic positioning (DP) control systems, where the vessel has zero (or low) forward speed. The estimation method is tested with realistic values of measurement noise and sampling in terms of comprehensive time series simulations based on both long- and short-crested wave conditions. The sea state estimates correspond well with a state-of-the-art Bayesian estimation approach. This indicates that the simplifications made in order to enhance the computational speed, has not affected the quality of the estimates to any significant extent. The trust measu

In the present study, a Panamax container vessel sailing in the Northern Atlantic is taken as a case study. Deep neural networks are trained on results of cross-spectral analysis of 6-DOF accelerations to predict significant wave height, peak period, and relative wave direction, which were initially derived from measurements of an X-band wave radar. Both an Inception network and a residual network are compared. Overall, it is found that the vector decomposition of the relative wave heading shows superior prediction accuracy in contrast to other methods from state-of-the-art literature. Several possible extensions of the presented methodology are pointed out.

This paper is concerned with a machine learning-based approach for sea state estimation using the wave buoy analogy. In-situ sensor data of an advancing medium-size container vessel has been utilized for the prediction of integral sea state parameters. The main novelty of this contribution is the rigorous comparison of time and frequency domain models in terms of accuracy, robustness and computational cost. The frequency domain model is trained on sequences of spectral ordinates derived from cross response spectra, while the time domain model is applied to 5-minute time series of ship responses. Multiple deep neural networks were trained and the sensitivity of individual sensor recordings, sample length, and frequency discretization on estimation accuracy was analysed. An Inception Architecture adapted for sequential data yields the highest out of sample performance in both considered domains. Additionally, multi-task learning was employed, as it is known for increased generalization capability and diminished uncertainty. Overall, it was found that the frequency domain method provides both superior performance and significantly less computational effort for training.

The present paper examines a semi-empirical framework for the estimation of added resistance in arbitrary wave heading under consideration of uncertainty quantification. In this respect, the calibration of the formula’s parameter vector is conducted based on particle swarm optimization as well as a database of model test results comprising 25 different ships and around 1100 samples. In the first iteration, the minimization of reducible systematic uncertainty is of interest and the effect of four objective functions on prediction accuracy is evaluated. Moreover, two different parameter combinations were obtained for blunt (CB≥0.70) and slender-type ships. Conversely, the irreducible statistical uncertainty, i.e. the inherent noise of the experimental data, is taken into account by a quantile regression procedure. Applying this approach, a 90% prediction interval for the formula’s estimates is implemented using the skewed version of the superior loss function in the previous iteration. The practical relevance of an uncertainty estimate for the prediction of the added resistance is emphasized in the final part, in which the proposed approach is validated in regular waves against model test data and other well-established prediction methods. In general, the validation studies suggest satisfactory performance and reliability of the adapted semi-empirical formulation.

This paper studies real-time deterministic prediction of wave-induced ship motions using the autocorrelation functions (ACFs) from short-time measurements, namely the instantaneous ACFs. The Prolate Spheroidal Wave Functions (PSWF) are introduced to correct the large lag time errors in the instantaneous sample ACF, together with a modification of the autocorrelation (AC) matrix for ensuring its positive definiteness. The validity of the PSWF-based ACFs is first examined by using the ship motion measurements from model experiment under stationary wave excitations. It is shown that the use of PSWFbased ACFs leads to better prediction accuracy than direct use of sample ACFs. The validation is then extended to ship motion prediction using in-service data from a container ship, and an improvement of the prediction accuracy by PSWF-based ACFs is again found. Finally, the effectiveness of use of the instantaneous ACFs for non-stationary wave-induced responses is highlighted by comparing with the prediction results based on the ACFs from long-time measurements.

This paper addresses the connection between added wave resistance and required propulsion power of ships, having focus on the early stage of new ship designs, notably tankers and bulk carriers. The paper investigates how mean added wave resistance affects the required torque of a fixed pitch propeller and thus also the operational conditions of a directly coupled main engine. The interest of the study has its background in the assessment of minimum propulsion power, and the study considers the prescriptive guidelines of the IMO as basis. Specifically, the study focuses on an assessment of the minimum forward speed attainable under consideration of the propeller light running margin and static load limits of engines in the early phase of new ship designs, where details of hull geometry are not available. The study considers three semi-empirical methods for predicting mean added wave resistance. All methods are known to be applied in the industry, emphasising that only methods relying solely on main particulars, together with information about sea state and advance speed, are of interest. The paper contains a case study used to illustrate the importance of the added wave resistance prediction with respect to the loading of the main engine. It is shown that, despite small absolute differences, the consequence in relation to the loading of the propeller and hereby the directly coupled main engine can be relatively large. Furthermore, the study illustrates that the propeller light running margin of a fixed pitch propeller directly coupled to the main engine has crucial influence on the attainable speed during adverse weather conditions.

This paper investigates a semi-empirical model used to estimate added-wave resistance on a ship sailing in waves. The model relies on measurements from a continuous monitoring system, and produces an estimate - the indirect measurement - of added-wave resistance, based on the difference between, on the one side, the measured power and, on the other side, a summation of theoretically calculated resistance contributions but neglecting the component because of seaway. Hence, the added-wave power is obtained as the surplus. The model has been applied to more than three months of full-scale data recorded on an in-service operating container ship. The results indicate reasonable levels of the added-wave power, emphasising that both positive and negative values are obtained, since the model is not restricted to relative wave directions forward of beam. It is found that following waves often lead to 'negative resistance', in fact creating a thrust, but, importantly, following waves can also increase the total resistance significantly.

Accurate position measurements are extremely valuable in the shipping industry for various reasons such as safety (collision avoidance), fuel saving (weather identification), punctuality (route prediction), etc. Although GNSS (Global Navigation Satellite System) receivers installed on-board the ships are proven to be highly accurate, the data collection process may occasionally be problematic, mainly due to the complexity of the measurements and the decimal precision that is required. Data was collected from 3 years of operations of 228 Maersk Line container vessels and an analysis reveals that there is a substantial amount (≈20%) of historical position measurements sent to shore that does not reflect reality. With this study, the authors initially identify the source of the faulty logged position measurements, then they categorize them into segments and finally they apply an interpolation methodology in order to validate and correct them by using AIS (Automatic Identification System) data.

Extreme value statistics can often be based on the assumption that exceedance events of a high threshold level are statistically independent and identically distributed (i.i.d. process), which further implies the Poisson assumption to be valid. This makes it possible to express the extreme response statistics through the mean up-crossing rate. For non-linear processes, analytic expressions of the mean up-crossing rate do not in general exist. Reliable statistics of mean up-crossing rate based on the brute-force approach, e.g. Monte Carlo simulation (MCS) require long time domain simulations considering a number of different ensemble input. The associated computations can be very time consuming especially when a detailed physical (e.g. hydrodynamic) model is applied. The First Order Reliability Method (FORM) has previously been found efficient for estimation of extreme value prediction of stationary stochastic time domain processes, However, if the non-linearity in a response is significant, the accuracy of the FORM linearized mean up-crossing rate can be limited. The present work attempts to improve the extreme value prediction for nonlinear parametric roll motions of ships based on applications of the FORM approach and suggests a model for the mean up-crossing rate for strong non-linear response, validated by comparing with MCS results.

The present paper implements machine learning methods for the prediction of the added-wave resistance of ships in head to beam wave conditions. The study is focused on non-linear regression algorithms namely Random Forests, Extreme Gradient Boosting Machines and Multilayer Perceptrons. The employed dataset is derived from results of three different potential flow methods covering a wide range of operational conditions and 18 hull forms in total. The rational data preprocessing makes up the core part of the paper having its focal point on practical application. Moreover, a rigorous hyperparameter study based on Bayesian optimization is conducted, and the validation of the final models for three case studies against numerical and experimental data as well as two established prediction techniques shows satisfactory generalization in case of the neural network. The tree-based ensemble methods, on the other hand, are not able to generalize sufficiently from the given parameter discretization of the underlying dataset.

This paper examines the statistical properties and the quality of the speed through water (STW) measurement based on data extracted from almost 200 container ships of Maersk Line’s fleet for 3 years of operation. The analysis uses high-frequency sensor data along with additional data sources derived from external providers. The interest of the study has its background in the accuracy of STW measurement as the most important parameter in the assessment of a ship’s performance analysis. The paper contains a thorough analysis of the measurements assumed to be related with the STW error, along with a descriptive decomposition of the main variables by sea region including sea state, vessel class, vessel IMO number and manufacturer of the speed-log installed in each ship. The paper suggests a semi-empirical method using a threshold to identify potential error in a ship’s STW measurement. The study revealed that the sea region is the most influential factor for the STW accuracy and that 26% of the ships of the dataset’s fleet warrant further investigation.

Conifer tree species belonging to Abies are notoriously difficult to propagate vegetatively due to aging of material, slow rooting, and persistent plagiotropism in rooted cuttings. We produced orthotropic shoots by reiteration from strongly pruned trees (5–6 years and 12–14 years, respectively), and harvested cuttings in summer. By HPLC–MS we analyzed the stem base of each cutting for initial hormonal status, and used state-of-the art techniques for a lengthy rooting procedure. Rooting efficiency and orthotropism of the resulting ramets were recorded. Hormonal content varied significantly among cuttings types with differing positional origins on the mother tree. High rooting percentages were obtained in all types; a low original position on the main stem gave slightly better rooting but orthotropism in the ramets was slightly less frequent. Striking differences were found in cytokinin profiles between cuttings from young and mature trees. This might explain a generally lower rooting success in the latter; neither auxin levels nor the auxin: active cytokinin ratio showed any correlation with rooting capacity. Cuttings from mature trees were also less likely to remain orthotropic after rooting. Cuttings that eventually developed into orthotropic ramets were characterized by low ABA, and high levels in a range of cytokinins. The study suggests that active: conjugated cytokinins may be related to the maturation process in trees and that cytokinin content may also relate to shoot dimorphism. These results contribute to a protocol for propagation of orthotropic ramets in a Massart model tree species with strong plagiotropism.

This article is about the use of measured wave-induced vessel motions for estimation of ocean wave spectra by application of the wave buoy analogy. In the study, data from a larger, in-service container ship is considered. The estimation of wave spectra, equivalently sea state parameters, is based on measurements from, respectively, a gyro and two accelerometers leading to the simultaneous use of the pitching motion together with the horizontal and vertical accelerations in a position close to the forward perpendicular. The study of in-service data leads to contemplations about the vessel’s advance speed, as the possible existence of sea current means that speed-over-ground (SOG) and speed-through-water (STW) will be dfferent. The article discusses aspects related to advance speed in the context of the wave buoy analogy, and a smaller sensitivity study is conducted. Preceding to the sensitivity study, a comparison is made between sea state estimates by the wave buoy analogy and estimates obtained from a hindcast study. The article shows an acceptable agreement between the two sets of estimates. Following, the main conclusion from the sensitivity study on advance speed is that errors and uncertainties in the speed log have an effect on the estimates of the wave buoy analogy. In fact, the effect can be severe if reliable STW measurements are not available. In the final part, the article includes a few discussions about (non)stationary conditions in the context of the wave buoy analogy, and, although the effect on results is not necessarily detrimental, care must be shown when the wave buoy analogy is applied during in-service conditions.

[Abstract]: Fishing is a very hazardous activity. Stability failures, caused many times by the crew lacking information related to the level of stability of the vessel, are among the most dangerous accidents affecting this fleet. A possible solution is offered by stability assessment systems that can help the skipper to identify potential risks and support his decision making process. The metacentric height is a key parameter for vessel stability and its real-time monitoring may be beneficial for alerting the crew about changes in stability. The paper proposes the design of a novel stability monitoring system that automatically detects changes in metacentric height based on estimates of the roll natural frequency solely using the measured roll angle. The core of the monitoring system is a combined estimation-detection system that exploits methods in advanced signal processing and statistical change detection to properly address issues of robustness. To analyze the monitoring performance, a nonlinear mathematical model of a stern trawler is used to generate roll motion time series in beam irregular waves of different peak period and significant wave height. Estimation and detection results obtained on the simulated data show a convincing ability of the monitoring system in discerning between safe and unsafe sailing conditions for most of the investigated cases.