On-board Measurements Research Articles

Gaidai multimodal risk evaluation methodology based on cargo vessel onboard measurements, given structural damage accumulation

Intercontinental transportation relies heavily on medium-to-large cargo vessels, constituting an essential component of the global trade. Thus, it is of paramount importance for trading companies, engineers, designers to advance innovative, economically viable logistical and structural schemes, that are safe and reliable. Full-scale onboard recorded data, when available, serves as valuable diagnostic tool that is hard to overestimate. An operating medium-size cargo ship TEU2800 had been selected for the current investigation. TEU2800 hull dynamics is by excessive deck panel strains, occurring during ship's intercontinental sailings through potentially adverse weather conditions. Inherent risks of damaging vessel hull and losing containers, caused by excessive motions/loads, being one of the primary concerns for the cargo transportation industry. Primary novelty of this investigation is twofold: for the first, a unique onboard measured representative dataset had been analyzed; for the second, novel multimodal reliability methodology had been employed to analyzed raw measured onboard data.Presented study advocates the state-of-the-art Gaidai multimodal spatiotemporal risks assessment methodology, enabling conservative structural hazard/damage/failure risk forecasting for nonstationary, nonlinear, multimodal cargo vessel hull dynamics, under accumulated fatigue damage. Note that the advocated novel multimodal risks evaluation methodology being of generic nature and may be straightforwardly applied to a wide range of contemporary complex naval, offshore, marine systems, hence not being limited to cargo vessels only.

Magnetospheric Line Radiation: Temporal Modulation Corresponding to a Bouncing Wave

AbstractMagnetospheric line radiation (MLR) is a peculiar type of whistler‐mode emissions formed by several similarly spaced spectral lines, whose frequencies may vary with time. These emissions typically occur at frequencies between about 1 and 8 kHz and are observed in both ground‐based and satellite measurements. However, their origin and source locations are not yet understood, as their detection by spacecraft instruments is challenging due to the limited frequency resolution of onboard wave measurements. We use high‐resolution multicomponent wave data obtained by the Van Allen Probes to demonstrate that, in addition to the frequency modulation, MLR events possess a temporal modulation with periods on the order of seconds, which may be related to the wave bouncing between hemispheres. Observed modulation periods are used to estimate a tentative source L‐shell. The modulation periods are shorter for events with larger frequency spacing, and the estimated source L‐shells correlate with model plasmapause locations.

Onboard multivariate hazard assessment for UIKKU chemical tanker by Gaidai reliability method

The current study outlines a novel multimodal structural reliability methodology, suitable for naval dynamic systems, that are either numerically simulated, or directly physically measured. Cross-correlations between the system's principal/key dimensions/components, along with the high dimensionality of complex naval dynamic systems, are not easily addressed by existing reliability methods, that are mostly univariate or bivariate. The objective of this study had been to apply a novel reliability/hazard assessment methodology to UIKKU chemical tanker onboard dynamic measurements to demonstrate the efficiency of the proposed multimodal structural reliability methodology. Current investigation utilized onboard measured ice loadings, exerted on the UIKKU tanker hull and propulsion devices during its Arctic voyage. Onboard dynamic data recordings then were interpreted to gain knowledge about dynamic load levels. The hull of UIKKU had been instrumented so that ice-induced loads/stresses on vessel shell plating had been monitored at various key locations, both in longitudinal as well as in vertical directions, along the vessel’s hull. Longitudinal bending stresses at several locations on the vessel hull and vessel vertical accelerations had been measured to analyze global vessel behaviour when colliding e.g., moderately heavy ice ridges. In addition to UIKKU onboard measurements onboard, Russian partners had instrumented icebreaker vessel Capitan Dranitsyn to real-time measure strains/stresses within ice belt grillage, located at the vessel’s bow. Icebreaker vessel Capitan Dranitsyn acted as escort icebreaker during the whole Arctic voyage. Obtained results may be utilized to harmonize IACS Polar ship rules. In the current study, the onboard measuring system along with measured results, during the whole voyage have been briefly represented.

Experimental analysis of wheel and bogie frame transfer functions for on-board measurements of rail roughness

Acceleration measurements on in-service rail vehicles give many valuable insights into track condition. These measurements are favourable in terms of measurement frequency, no need for additional track possession for measurements, operator safety, etc. The collected acceleration data however needs to be further processed to extract more detailed information on rail surface defects. To derive rail roughness from bogie frame acceleration, a complex signal processing approach is required, as well as determining the dynamic parameters of the track, vehicle, and operational conditions. In this paper, a method to evaluate the influence of resilient wheel and bogie frame on the acceleration data received from bogie frame-mounted accelerometers is described. The method is based on a standstill measurement of frequency response functions on wheel and bogie frame positions using an impact hammer and accelerometers. Following the preliminary results of bogie acceleration measurements, in-situ wheel receptance measurements were carried out on the same tramway vehicle in a standstill position, to determine the transfer function between the wheel-rail contact point and the position of the accelerometer mounted on the wheel frame. After the experimental study, a discussion of their potential application in the method for deriving rail roughness from vibration data is presented.

Multi-task Gaussian Processes based transient aerodynamic load reconstruction for maglev vehicle using acceleration response

The accurate estimation of aerodynamic loads is crucial for developing high-speed maglev trains, which can reach speeds of up to 600 km/h. Traditionally, this estimation has been achieved through computational aerodynamic simulation or direct pressure measurement. In this paper, we propose a novel framework for reconstructing transient aerodynamic loads on maglev vehicles using on-board acceleration measurements. In the framework, an inverse mathematical model that correlates the measured acceleration and external aerodynamic loads is derived from a well-calibrated maglev vehicle model. To avoid the ill-posed problem when solving the inverse mathematical model, a multi-task Gaussian Processes method is proposed, in which all reconstructed transient aerodynamic loads are treated as Gaussian Processes and the closed-form posterior distributions of these aerodynamic loads could be calculated. To validate the proposed framework, a set of transient vibration data collected from an operational maglev train passing through a double-track tunnel is utilized for load reconstruction. The results demonstrate that the framework offers a cost-effective and efficient means to obtain aerodynamic loads, highlighting its practical relevance for aerodynamic field testing in the context of evolving high-speed maglev technologies.

Fish-inspired tracking of underwater turbulent plumes

Autonomous ocean-exploring vehicles have begun to take advantage of onboard sensor measurements of water properties such as salinity and temperature to locate oceanic features in real time. Such targeted sampling strategies enable more rapid study of ocean environments by actively steering towards areas of high scientific value. Inspired by the ability of aquatic animals to navigate via flow sensing, this work investigates hydrodynamic cues for accomplishing targeted sampling using a palm-sized robotic swimmer. As proof-of-concept analogy for tracking hydrothermal vent plumes in the ocean, the robot is tasked with locating the center of turbulent jet flows in a 13,000-liter water tank using data from onboard pressure sensors. To learn a navigation strategy, we first implemented RL on a simulated version of the robot navigating in proximity to turbulent jets. After training, the RL algorithm discovered an effective strategy for locating the jets by following transverse velocity gradients sensed by pressure sensors located on opposite sides of the robot. When implemented on the physical robot, this gradient following strategy enabled the robot to successfully locate the turbulent plumes at more than twice the rate of random searching. Additionally, we found that navigation performance improved as the distance between the pressure sensors increased, which can inform the design of distributed flow sensors in ocean robots. Our results demonstrate the effectiveness and limits of flow-based navigation for autonomously locating hydrodynamic features of interest.

Angles-Only Cooperative Local Catalog Maintenance of Close-Proximity Satellite Systems

In space mission sets like on-orbit servicing and manufacturing, agents in close proximity may operate too close to yield resolved localization solutions to operators. This requires that the system maintain a catalog of its local neighborhood; however, this imposes a large burden on each agent to update its catalog. This paper considers the case of multiple satellites each maintaining their catalog. Specifically, this paper considers the case of numerous noncooperative objects and a collection of agents operating in the same local environment. The goal of each agent is to maintain their catalog of all bodies (objects and agents) within this neighborhood through onboard angles-only measurements and cooperative communication with the other agents. A central supervisor selects the target body for each agent, a local controller tracks the selected target body for each agent, and a local estimator coalesces both an agent’s measurements and state estimates provided by neighboring agents within the communication graph. Numerical results are provided to demonstrate the supervisor’s ability to select an appropriate target subject to an uncertainty threshold, the controller’s ability to track the selected target, and the estimator’s ability to maintain an accurate and precise estimate of each of the bodies in the local environment.

Hierarchical predictive optimal control for range extension of EV with ANN based torque control for IPMSM drives

This study presents a novel methodology to enhance the energy efficiency of Electric Vehicles (EVs) while maintaining dynamic stability and driving comfort, even on uneven roads, using onboard vehicle measurements. The approach involves a hierarchical control scheme that integrates a model predictive controller with a torque vectoring algorithm to optimize torque demand and accurately anticipate road-specific torque requirements. The proposed control is designed and implemented on an EV with Interior Permanent Magnet Synchronous Motors (IPMSM) on four wheel drives. The optimal torque control is realised through an Artificial Neural Network (ANN) - based motor torque control scheme. The design is validated through tests in real-world driving scenarios. In comparison with conventional methods, the proposed method shows a 37% increase in energy efficiency across different test conditions, thereby resulting in an increase in EV driving range. These advancements are realised without substantial modifications to the EV’s drivetrain, representing a significant step forward in sustainable and efficient electric mobility.

Calibration and validation of a multi-beam LiDAR onboard China Chang’e lunar probe for landing obstacle avoidance

For automatic exploration of specific areas with high scientific value on the moon and other planets, high-precision obstacle detection and hazard avoidance capabilities are required. Three-dimensional (3D) LiDAR can obtain a high-resolution 3D point cloud, offering significant advantages in landing obstacle avoidance. During the landing of the Chang’e lander, a laser 3D imaging sensor (L3DIS) is used to measure and generate accurate topographic maps of the candidate landing area in real-time to help find a safe landing zone. The L3DIS employs 16 beamlets split from a Gaussian laser beam and 16 channels of linear-array avalanche photodiodes within the same optical path and scans the object with a two-axis galvanometer in a field of view of 29°×33°. The calibration of the systematic errors and the performance of obstacle detection on this sensor are conducted in this paper. First, the instrument components and main error sources of the sensor are introduced, and the systematic errors are calibrated based on planar targets, indicating that the sensor’s accuracy is greater than 4 cm, and the corresponding obstacle detection accuracy is better than 12 cm. Second, the ground validation of obstacle detection is demonstrated, and the obtained point cloud is compared with that obtained by a terrestrial laser scanner, indicating that the sensor has good performance. Finally, the performance of onboard measurement and obstacle detection is analyzed, showing that the sensor can identify obvious craters and rocks, i.e., nine major craters with a maximum and minimum depth of 1.36 m and 0.16 m, respectively, and major rocks with a maximum and minimum height of 0.1 m and 0.05 m, respectively, and main landing area with slop less than 10° except for the edges of craters and rocks.

Application Of Onboard PIV To Front Wheelhouse Wake Behavior Under Stationary Crosswind Conditions

When driving a car, the driver and passengers may feel larger drag from a headwind, the steering wheel would be taken away by a crosswind or pushed by a tailwind. Anytime, anywhere, we will encounter various scenes with natural wind under real world conditions such as urban, suburb and highway. We will develop the appropriate vehicle by simulating these conditions to satisfy with various customer requirements. For vehicle aerodynamic research and development, we are simply running simulations in physical- and cyber-space, that is, wind tunnel facility and computational fluid dynamics to reproduce these real worlds. However, it is not easy to simulate these wind inlet conditions, such as the temporal change in crosswind, especially, in wind tunnel facility. The stationary yaw angle by rotating turntable would reproduce a crosswind, if there is no turbulent generator system like in Pininfarina and Swing in FKFS. Therefore, we focus on the unsteady and asymmetrical flow from the front wheelhouse of the vehicle with tire rotation. There is three-dimensional complicated flow that are interacted by the flow from various directions, such as the flow along the front bumper, the flow along the rotating tire and wheel, the blowing flow from the rotating wheel opening area and the flow from the tire house. These complicated flow produces the front wheelhouse wake along the body side that affects the vehicle aerodynamic performance. Furthermore, there are asymmetrical flows around the vehicle by different front wheelhouse wake in windward and leeward sides. The larger side wake increases the aerodynamic drag, and the fluctuating side wake may induce the instability of the vehicle handling stability. Here, the proposed onboard 2D-3C PIV measurement system in the previous study enables us to measure same measurement area along the vehicle body side without change in optical configuration effectively, that indicates to reduce the time by resetting the optical configuration. We will investigate what kind of flow field should be focused on and controlled to prevent the vehicle instability by investigating these asymmetrical flow field around the vehicle under crosswinds.

Drive-by modal identification of railway bridges via on-board measurements from a single railroad vehicle

The global railway network spans over one million kilometers of tracks, and this extensive infrastructure is set to expand even further. The objective is to promote rail transportation as an environmentally sustainable solution to address the growing demands of mobility. A significant portion of these tracks spans across bridge structures, which must also accommodate the rising demands for mobility and increased travel speeds, placing additional demands in terms of imposed loads. At the same time, bridges worldwide suffer from aging, leading to the deterioration of their structural integrity. Such combined effects necessitate monitoring the structural health of bridges to ensure the quality and safety of railway networks by detecting possible alterations in their response characteristics. Traditional Structural Health Monitoring (SHM) techniques use stationary sensors affixed to the bridge structure, enabling the direct assessment of the collected data. While such approaches are reliable, they present limitations in the comprehensive inspection of multiple railway bridges within a network. As an alternative, mobile vibration-based sensing, utilizing sensors on passing trains, presents the potential to gather data from multiple railway bridges using only a few sensors installed on board trains. Furthermore, operating the sensor-equipped trains at frequent intervals allows for collecting continuous data, offering valuable insights into the ongoing deterioration of bridges. In light of this, our work proposes a model-based methodology for extracting the modal frequencies of bridges based on acceleration data collected from traversing trains. Our approach hinges on Kalman filtering for estimating the state and input of the train and employs a subspace identification method to ascertain the frequencies of the bridge. The ultimate goal is to develop a drive-by bridge monitoring scheme that accommodates the assessment of the remaining lifespan of bridges as well as timely repairs in the event of damage, ensuring the safety and reliability of rail transportation.

Microscopic characteristics and influencing factors of ship emissions based on onboard measurements

The sailing environment has a significant impact on the microscopic characteristics of ship emissions. A portable emission measurement system was used to measure and quantify the emission characteristics of two ships using different energy types in the Yangtze River. The effects of the key influencing factors, waterway environment, operating conditions, and fuel type on ship emissions were investigated. The reasonable control of ship speed, acceleration and engine revolution speed and torque can reduce ship exhaust emissions. The use of liquefied natural gas (LNG) can substantially increase CO emissions. The emission levels of the ship during departure and berthing were higher than that during cruising. As an alternative fuel, LNG may increase CO emissions; hence fuel selection requires careful consideration. This study provides key information for the development of effective emission reduction measures and can facilitate the adaptation of inland waterway shipping to low-carbon policies and the mitigation of the associated environmental impacts.

Data-driven forecasting of FOWT dynamics and load time series using lidar inflow measurements

This study focuses on forecasting the fairlead tension and floater dynamics time series of floating offshore wind turbines (FOWTs) using a data-driven approach that incorporates onboard sensor measurements and lidar inflow data. Sensors on FOWTs can provide data on turbine dynamics, such as rotational and translational movements, and load metrics like mooring line loads. However, these sensors are limited to current state measurements and do not provide future signal projections. In this research, we investigate a data-driven forecasting methodology using a simulated dataset. This dataset encompasses FOWT responses to diverse environmental conditions and the associated lidar measurements. Utilizing a Long Short-Term Memory (LSTM) sequence-to-sequence model, this study forecasts the time series of fairlead tension, surge, and pitch for forecasting horizons of 20, 40, and 60 seconds, considering lidar ranges from 100m to 500m. The performance of these forecasting models is benchmarked against a simple persistence model. The results indicate that incorporating lidar inflow measurements significantly improves the forecasts of fairlead tensions and platform motions. The enhancement for pitch motion forecasts is observed across all forecasting horizons. For fairlead tension and surge motion, the enhancement is observed for the longer horizons of 40s and 60s. These findings underscore the value of lidar data in accurate forecasting and emphasize the need to account for the interplay between lidar range, wind speed, and forecasting horizon to achieve optimal forecast accuracy.

Underwater radiated noise from small craft in shallow water: Effects of speed and running attitude

Underwater radiated noise from marine vessels is a growing problem, with a large body of evidence now showing the detrimental impact this is having on marine life. Whilst most of the discussion currently focusses on the impact of large vessels, recent evidence shows that small vessels can dominate the soundscape in shallow coastal waters. In this study, acoustic trials are carried out on two small vessels: a pilot boat and a rigid inflatable boat (RIB) across a wide speed range. The average source levels range from 156–173 dB for the pilot boat and 164–166 dB for the RIB (re1μPam). The engine noise is dominant for the RIB across the speed range, and it is further shown that the heading relative to the prevailing waves is a bigger determining factor for noise levels than speed. In upper sea state 3, the overall sound level is up to 6 dB higher when in head waves compared to following waves. Acoustic measurements are combined with onboard vibration measurements to provide further insights into the relationship between noise and vibration. Analysis of this data shows the vessel running attitude plays a key role in determining how much noise is transmitted into the water.

Contact-point response reconstruction for indirect bridge monitoring via Bayesian expectation-maximization based augmented Kalman filter

The drive-by bridge dynamic measurement using an instrumented vehicle during its passage over the bridge can be an indirect bridge structural health monitoring system. The identification of the contact-point (CP) responses between vehicle wheels and bridge structure from vehicle responses offers an efficient and economical way for the modal identification and condition assessment of short- to mid-span road bridges. This paper proposes a novel method for the contact-point displacement response reconstruction of the vehicle-bridge interaction (VBI) system in a joint input-state estimation manner based on Bayesian Expectation-Maximization (BEM). An analytical model of VBI including the road approach in front of a simply-supported bridge is presented firstly. Then the augmented state-space model of the vehicle with the contact-point responses included as variables along with vehicle states is established. A new method by integrating the augmented Kalman filter (AKF) with a BEM strategy is proposed to solve the state estimation problem without knowing the vehicle axle responses. The vehicle states and CP displacement responses are identified simultaneously. The effects of the measurement noise, road surface roughness and vehicle speed on the identified results are investigated using numerical simulations. The experimental tests are used to further verify the feasibility and accuracy of the proposed method. The results show that the proposed method has potential for drive-by bridge condition assessment using on-board vehicle response measurements for a large population of short- to mid-span bridges.

Measurement Techniques, Calculation Methods, and Reduction Measures for Greenhouse Gas Emissions in Inland Navigation—A Preliminary Study

Emissions originating from inland navigation should be reduced to achieve climate targets. This paper aims to identify (1) onboard GHG emission measurement systems, (2) calculation methods for GHG emissions of inland vessels and (3) reduction measures. A systematic literature review, examining 6 databases, yielded 105 initial outcomes, with 17 relevant references. The review reveals a scarcity of studies, with the majority concentrated in Europe and Asia, while North America, Africa, Australia, and South America remain largely unexplored. Four of the seventeen relevant studies focused on real-world GHG emissions measurement. Future research should explore more efficient and calibrated approaches for real-time CO2 insights in inland vessels. In the section on calculating GHG emissions, most papers attempt to adapt the EEDI or EEXI to inland navigation. Reduction measures for GHG emissions concentrate on alternative fuels, like LNG, methanol, hydrogen, or alternative power sources. As the research in this area is limited, prioritizing it in academic discourse is not only essential for advancing our understanding but also imperative for shaping a resilient and environmentally conscious future for inland navigation.

The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data

Zero-carbon shipping is the prime goal of the seaborne trade industry at this moment. The utilization of ammonia and liquid hydrogen propulsion in a carbon-free propulsion system is a promising option to achieve net-zero emission in the maritime supply chain. Meanwhile, optimal ship voyage planning is a candidate to reduce carbon emissions immediately without new buildings and retrofits of the alternative fuel-based propulsion system. Due to the voyage options, the precise prediction of fuel consumption and carbon emission via voyage operation profile optimization is a prerequisite for carbon emission reduction. This paper proposes a novel fuel consumption and carbon emission quantity prediction method which is based on the onboard measurement data of a smart ship. The prediction performance of the proposed method was investigated and compared to machine learning and LSTM-model-based fuel consumption and gas emission prediction methods. The results had an accuracy of 81.5% in diesel mode and 91.2% in gas mode. The SHAP (Shapley additive explanations) model, an XAI (Explainable Artificial Intelligence), and a CO2 consumption model were employed to identify the major factors used in the predictions. The accuracy of the fuel consumption calculated using flow meter data, as opposed to power load data, improved by approximately 21.0%. The operational and flow meter data collected by smart ships significantly contribute to predicting the fuel consumption and carbon emissions of vessels.

Design and evaluation of a calibration system for portable motor vehicle emission particle number detectors

The supervision of the number concentration of particles emitted from motor vehicles is becoming increasingly strict. The regular calibration of portable particle number (PN) detectors is a prerequisite to ensure the effective supervision of PN. At present, the calibration of PN detectors is mostly completed in the laboratory. However, due to the complexity and poor portability of the laboratory calibration system, it is difficult to use this system for the on-site calibration of PN detectors. To this end, a portable motor vehicle emission PN detector calibration system based on the principles of negative pressure siphon atomization, differential electrical mobility classifier, and fA-level weak current measurement was developed, which could quickly and accurately calibrate the PN detector on-site. The system consists of three parts: an atomizer generator, a plate differential mobility analyzer (PDMA), and an aerosol electrometer. The atomizer generator produces NaCl particles with a median particle size of 70 nm and fluctuation of <10%. The particle size classification deviation of the PDMA is <3.7% and the counting error of the aerosol electrometer is <10%. The On-board Emissions Measurement System was calibrated as a PN detector on a self-developed portable calibration system as well as on a laboratory calibration system. The comparison results showed that the measurement results of the portable calibration system were closer to the instrument factory report certificate results, and the maximum error did not exceed 6.72%.

Derivations of transfer functions for estimating ship underwater radiated noise from onboard vibrations

The adverse impacts of underwater radiated noise (URN) from marine vessels on marine life are increasingly recognized. URN estimation and subsequent monitoring could be used to track URN and take measures to reduce it in sensitive environments (e.g., a marine life protected area). Two common analytical radiated noise power approximations and two empirical methods are assessed for URN estimation. The assessments were carried out by testing the methods on data from acoustic range measurements of an ORCA-class training vessel of the Royal Canadian Navy, named Patrol Craft Training (PCT) Moose and a DAMEN Combi Freighter 3850, named Heinz G. The analytical approximations are called equivalent radiated power and power from volume velocity; both are used to estimate URN directly from the onboard ship vibration measurements. The empirical methods are based on the correlations of onboard vibrations with measured URN, from which the derived transfer functions. The analytical and empirical transfer functions are compared. A statistical energy analysis model of PCT Moose is also used to estimate onboard vibrations from the ship’s engine, generator, and propeller specifications for URN estimation at the design stage.

Synthesis of a measurement procedure for estimating the orientation of a small unmanned aerial vehicle under conditions of changing status of measurement results

The problem of processing measurement information with changing status of measurement results of micromechanical sensors of an intelligent on-board measuring system of a small unmanned aerial vehicle is considered. While vehicle is in the air, the measurement results status changes from confirmed to orienting, for example, due to sensor defects, degradation of the measuring channel, the appearance of false measurement output signals of micromechanical sensors due to vibrations and shocks caused by the movement of air masses. As a result, the probability of stability loss of a small unmanned aerial vehicle increases and it is necessary to raise the accuracy of estimating its orientation in conditions of changing status of measurement results. The measuring procedure of the Kalman structure is considered, the equations of which are determined with accuracy to the parameters of the transition and noise matrices of the state, as well as the perturbation vector, characterizing the measuring process. The parameter values are determined by a mathematical model for converting measuring information based on a dynamic mathematical model, which distinguishes the developed measuring procedure from a measuring procedure with a classical transition matrix. A neural network is used to find unknown parameters. A multilayer perceptron was selected as the basis of a neural network, for which an error back propagation algorithm is used to train. Based on the results of mathematical modeling and measurement experiment, it was found that the accuracy of the synthesized measuring procedure based on a dynamic mathematical model is higher than the accuracy of the measuring procedure of the Kalman structure with a classical transition matrix. The results of the study will be useful in the development of intelligent measurement procedures for on-board measurement systems operating under conditions of changing status of measurement results