Speed Profile Research Articles

On the accuracy of distance estimates by propagation time of sound signals on the arctic shelf

As part of numerical modeling, estimates are made of the accuracy of determining the distance between underwater sources and sound receivers located at a distance of several kilometers from each other in the Kara Sea in the autumn. It is assumed that the main source of possible errors in determining the distance is the lack of accurate data on the vertical profile of the sound speed along the propagation path of acoustic signals. Data from September and November were analyzed, in the interval between which significant changes in the profile take place, when the vertical gradient of sound speed changes from negative to positive values. Characteristic values of sound speed variations were obtained by statistical processing of hydrological data taken from the World Ocean Database. The results obtained are important for analyzing the capabilities of underwater acoustic navigation.

Study of anisotropic quark stars with interacting quark matter in f(R,T) gravity

We explore the structural properties of quark stars (QSs) in a modified gravity theory known as f(R,T) gravity, which introduces a coupling between matter and spacetime geometry, through a basic linear functional form f(R,T)=R+2βT. Our study focuses on QSs made of interacting quark matter (IQM) as an equation of state. We first derive the modified Tolman-Oppenheimer-Volkoff (TOV) equations for anisotropic matter in a spherically symmetric context and solve them numerically to obtain the structural properties of QSs. Stability is analyzed through static stability analysis, critical adiabatic indices, and sound speed profiles. Using astrophysical constraints from the “black widow” pulsar PSR J0952-0607 and the GW190814 event, we calibrate our model parameters. Our results indicate that with higher λ¯, both the maximum mass and radius of QSs increase, achieving a maximum mass of over 2M⊙, peaking at 3.15M⊙ for a radius of 14.90km at λ¯=0.9. The maximum compactness also rises to M/R=0.313 while adhering to the Buchdahl limit. Additionally, varying β in the range [−0.2,0.2] with fixed parameters shows that lower β values enhance the maximum mass of QSs, reaching 2.65M⊙ at β=−0.2, with the compactness remaining around M/R≈0.3. Furthermore, changes in μ from [−1.0,1.0] significantly affect maximum mass; at μ=1.0, the mass peaks at 3.15M⊙ and decrease to 2.68M⊙ at μ=0. The compactness increases with μ, indicating that anisotropic pressure influences the M−R relations. In summary, our findings reveal that the parameters λ¯, β, and μ play crucial roles in shaping the physical properties of QSs in the f(R,T) gravity framework, consistent with astrophysical observations from pulsars and gravitational wave events.

Construction of a ballistic model of the motion of uncontrolled cargo during its autonomous high-precision drop from a fixed-wing unmanned aerial vehicle

The object of this study is the process related to the fall of an uncontrolled cargo from a height of 400–600 meters relative to sea level in an air environment under the influence of gravity, air drag, and wind in the presence of an initial air speed of about 20 m/s. The study solves the task to build a ballistic model of the movement of an unguided cargo during autonomous high-precision dropping from an unmanned aerial vehicle of aircraft type. A system of equations was derived that explicitly describes the dependence of the cargo's travel speed and coordinates on time and takes into account the effect of gravity, air drag, and the influence of wind. The scope of application of the equations corresponds to drop heights of up to 400 m relative to the surface of the earth and the initial horizontal speed of the cargo up to 20 m/s. The resulting equations were analyzed using an example of a spherical load weighing 10 kg and the largest cross-sectional area of 7.1·10-2 m2 when it falls from heights of 200, 300, and 400 m relative to the earth's surface. In the absence of wind, the horizontal component of the load's speed at the moment of falling is ≈(13–15) m/s, and the vertical component is ≈(50–60) m/s. At the same time, the horizontal displacement of the load under the conditions of a weak crosswind can reach ≈(150–220) m. With a vertical wind speed profile, the equivalent constant wind speed can be determined, resulting in the same effect on the load as a variable speed wind. An algorithm for determining the point of unloading the cargo has been proposed. The cargo delivery error has been evaluated. The most important parameters are the flight time of the cargo and the drop height. In order to achieve a hit accuracy of ±5 m, an error in determining the time of the fall of the load is not more than ≈0.16 s, and an error in determining the height of the drop is not more than ±8 m

Automatically Differentiable Higher-Order Parabolic Equation for Real-Time Underwater Sound Speed Profile Sensing

This paper is dedicated to the acoustic inversion of the vertical sound speed profiles (SSPs) in the underwater marine environment. The method of automatic differentiation is applied for the first time in this context. Representing the finite-difference Padé approximation of the propagation operator as a computational graph allows for the analytical computation of the gradient with respect to the SSP directly within the numerical scheme. The availability of the gradient, along with the high computational efficiency of the numerical method used, enables rapid inversion of the SSP based on acoustic measurements from a hydrophone array. It is demonstrated that local optimization methods can be effectively used for real-time sound speed inversion. Comparative analysis with existing methods shows the significant superiority of the proposed method in terms of computation speed.

Fuzzy Multi-Agent Simulation for Collective Energy Management of Autonomous Industrial Vehicle Fleets

This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV battery capacity. Simulation scenarios were based on a proposed charging/discharging model for an AIV battery. The results highlight the effectiveness of adaptive fuzzy multi-agent models in optimizing charging strategies, improving operational efficiency, and reducing energy consumption. Dynamic factors such as workload variations and AIV-infrastructure communication are taken into account in the form of heuristics, underlining the importance of flexible and collaborative approaches in autonomous systems. In particular, an infrastructure capable of optimizing charging according to energy tariffs can significantly reduce consumption during peak hours, highlighting the importance of such strategies in dynamic environments. An optimal control model is established to improve the energy consumption of each AIV during its mission. The energy consumption depends on the speed, as demonstrated via numerical simulations using realistic data. The speed profile of each AIV is adjusted according to the various constraints within an airport. Overall, the study highlights the potential of incorporating adaptive fuzzy multi-agent models for AIV energy management to boost efficiency and sustainability in industrial operations.

The Parameterized Oceanic Front-Guided PIX2PIX Model: A Limited Data-Driven Approach to Oceanic Front Sound Speed Reconstruction

In response to the demand for high-precision acoustic support under the condition of limited data, this study utilized high-resolution reanalysis data and in situ observation data to extract the Kuroshio Extension Front (KEF) section through front-line identification methods. By combining the parameterized oceanic front model and the statistical features of big data, the parameterized oceanic front was reconstructed. A proxy dataset was generated using the Latin hypercube sampling method, and the sound speed reconstruction model based on the PIX2PIX model was trained and validated using single sound speed profiles at different positions of the oceanic front, combined with the parameterized oceanic front model. The experimental results show that the proposed sound speed reconstruction model can significantly improve the reconstruction accuracy by introducing the parameterized front model as an additional input, especially in the shallow-water area. The mean absolute error (MAE) of the full-depth sound speed reconstruction for this model is 0.63~0.95 m·s−1, and the structural similarity index (SSIM) is 0.76~0.78. The MAE of the sound speed section within a 1000 m depth is reduced by 6.50~37.62%, reaching 1.95~3.31 m·s−1. In addition, the acoustic support capabilities and generalization of the model were verified through ray tracing models and in situ data. This study contributes to advancing high-precision acoustic support in data-limited oceanic environments, laying a solid groundwork for future innovations in marine acoustics.

Cross-Layer Routing Protocol Based on Channel Quality for Underwater Acoustic Communication Networks

Due to the physical characteristics of acoustic channels, the performance of underwater acoustic communication networks (UACNs) is more susceptible to the impacts of multipath and Doppler effects. Channel quality can serve as a measure of the reliability of underwater communication links. A cross-layer routing protocol based on channel quality (CLCQ) is proposed to improve the overall network performance and resource utilization. First, the BELLHOP ray model is used to calculate the channel impulse response combined with the winter sound speed profile data of a specific sea area. Then, the channel impulse response is integrated into the communication system to evaluate the channel quality between nodes based on the bit error rate (BER). Finally, during the selection of the next hop node, a reinforcement learning algorithm is employed to facilitate cross-layer interaction within the protocol stack. The optimal relay node is determined by the channel quality index (BER) from the physical layer, the buffer state from the data link layer, and the node residual energy. To enhance the algorithm’s convergence speed, a forwarding candidate set selection method is proposed which takes into account node depth, residual energy, and buffer state. Simulation results show that the packet delivery rate (PDR) of the CLCQ is significantly higher than that of Q-Learning-Based Energy-Efficient and Lifetime-Extended Adaptive Routing (QELAR) and Geographic and Opportunistic Routing (GEDAR).

Deep learning-based approach for identifying vortex-induced vibrations in stay cables

Due to variations in wind speed profiles along the length of bridge stay cables, vortex-induced vibrations (VIV) exhibit multimodal characteristics, presenting challenges for VIV identification. Currently, the VIV identification is concentrated on the stable stage of VIV, lacking an available early warning system for detecting the initial developing stage of VIV. In this study, a deep learning-based approach that integrates energy distribution ratio features derived from frequency band wavelet packet decomposition to recognize VIV of stay cable was proposed. Firstly, vibration characteristics induced by vortices in cable-stayed bridges were analyzed based on field monitoring data from the bridge health monitoring system, aiming to propose suitable feature indicators for VIV identification. Secondly, using root mean square as label classification, a deep learning model was constructed, incorporating convolutional neural networks, long short-term memory networks, and attention mechanisms. Finally, four different stages in the evolution of stay cables VIV were identified utilizing field monitoring datasets to analyze the optimal parameter. Meanwhile, effective early warning recognition was achieved through the classification and recognition of confusion matrix. This study provides technical support for early warning systems and structural condition assessment concerning bridge stay cable VIV.

The Use of Laser Sensing for Solving Meteorological Problems Related to Researching and Ensuring the Safety of Space Flights.

This paper is devoted to the issue of using laser (lidar) sensing to determine wind speed and direction when solving practical problems during the analysis of meteorological conditions in the area around spaceports. This issue is relevant both for making decisions on the possibility of a safe launch of a launch vehicle and for conducting search and rescue operations using groups of manned and unmanned aerial vehicles. Based on numerical and experimental modeling, it is shown that lidars provide highly accurate measurements of wind speed profiles and allow for the determination of weak wind shear in vertical and horizontal directions. This paper proposes a method for determining the main parameters of lidar sensing (range, resolution, detectability, etc.), which allows for the capabilities of this technology in solving the practical problems of meteorological monitoring to be predicted. Of particular interest in this article are experimental modeling data verifying the proposed calculation methods and the experimental determination of the capabilities of lidar diagnostics. This paper summarizes the data from multi-month experiments measuring wind speed in clear weather conditions when other means of remote diagnostics are ineffective. As a result of the experiments, a statistical distribution of the maximum range of wind speed measurement in normal weather conditions with natural variation in the concentration of scattering particles in the atmosphere was obtained. This article also discusses the possibility of combining lidars and meteorological radars for the meteorological support of flights.

Coordinated torque vectoring and direct yaw control based on Kalman filter control allocation for a four in-wheel electric vehicle

This paper presents a novel hierarchical control architecture designed for a four in-wheel electric vehicle (EV), integrating longitudinal and lateral control strategies to optimize performance and lateral stability. On one hand, a high-level longitudinal controller is synthesized to track a desired speed profile and generate a total torque. On the other hand, a direct yaw control (DYC) strategy is employed for steerability and stability control to generate a yaw moment. To manage the total torque as well as the yaw moment, a control allocation layer is employed. Subsequently, the formulation of the control allocation problem is presented, incorporating a gain-scheduling Daisy chaining Kalman filter (DCKF) control allocation to deal with actuator dynamics and fault tolerance. Results demonstrate enhanced performance in normal and emergency driving. Quick adaptability in case of emergency is achieved without overshooting or undershooting, with a smooth response.

Flow field analysis of self-sustained flutter of a wide-slotted bridge deck

This study delves into the flutter mechanism of a 5,000 m bridge with a wide-slotted deck, finding that the motion is self-sustained and not violently destructive. The system damping ratio is not fixed, and only one stable orbit exists. High-resolution PIV experiments at an experimental wind speed of 10.5 m/s measured the static and dynamic flow fields on the windward and leeward decks. The static results showed leading-edge separation on the windward deck within the reference range, while separation on the leeward deck was difficult to observe. Dynamic testing identified instantaneous vortices around the windward/leeward deck at each phase, with no signs of vortices in the wind speed vectors at all phases after phase-averaging, indicating that the vortex drift hypothesis is not valid. The analysis of the streamline pattern revealed periodic variations in leading-edge separation size and reattachment length on the windward deck during the vibration process, while the leeward deck showed consistently inconspicuous changes. Further examination uncovered a peculiar behavior in the horizontal wind speed profile on the leeward deck during the vibration process, attributed to dynamic changes in the height difference between the windward and leeward decks during flutter. The study suggests that the unusual wind speed profile on the leeward deck is caused by the dynamic changes in height difference between the windward and leeward decks during the flutter process, resulting in additional wind loading. These findings shed light on the complex dynamics of bridge flutter and have implications for the design and maintenance of long-span bridges.

Cross-Pacific Vessel Estimated Time of Arrival and Next Destination Prediction with Automatic Identification System Data

With the increase in global trade uncertainty and supply chain disruptions, accurately predicting the estimated time of arrival (ETA) of container vessels can effectively help carriers, terminals, and freight forwarders improve operational efficiency. The Asia-North America route has been recently under stress because of strikes and trade wars between the U.S. and China. Voyages are subject to multiple external factors leading to uncertainty in arrival times. This is especially true for cross-Pacific voyages, where long distances without intermediate port visits allow for a large feasible set of trajectories and vessel speed profiles. Large errors in ETA prediction not only hinder the effective planning and execution of other stakeholders but also lead to significant fluctuations in the types and quantities of goods arriving at the port, thereby hindering port competitiveness and efficient multimodal transportation. Existing literature focuses on estimating ETA and next positions for dense, compact areas at the vicinity of ports. We propose and evaluate model framework based on artificial neural networks (ANN) fed by automatic identification system (AIS) historical data to predict the next destination and ETA for cross-Pacific routes for cases where ETA from the captain is missing in the AIS data. Results show our model can effectively predict next destination and ETA of vessels, achieving a mean absolute error value of 4 h when the vessel is 1,500 nmi away from the port. For comparison, the ANN submodules are replaced with gradient boosted trees, providing similar results. We terminate by highlighting the challenges found to improve the model.

Analysis of Wind Power Fluctuation in Wind Turbine Wakes Using Scale-Adaptive Large Eddy Simulation

In large wind farms, the interaction of atmospheric turbulence and wind turbine wakes leads to complex vortex dynamics and energy dissipation, resulting in reduced wind velocity and subsequent loss of wind power. This study investigates the influence of vortex stretching on wind power fluctuations within wind turbine wakes using scale-adaptive large eddy simulation. The proper orthogonal decomposition method was employed to extract the most energetic contributions to the wind power spectra. Vertical profiles of mean wind speed, Reynolds stresses, and dispersive stresses were analyzed to assess energy dissipation rates. Our simulation results showed excellent agreement when compared with wind tunnel data and more advanced numerical models, such as the actuator line model and the actuator line model with hub and tower effects. This highlights the important role of coherent and energetic flow components in the spectral behavior of wind farms. The findings indicate a persistent energy cascading length scale in the wake of wind turbines, emphasizing the vertical transport of energy to turbine blades. These results complement existing literature and provide new insights into the dynamics of wind turbine wakes and their impact on wind farm performance.

The Paris low-level jet during PANAME 2022 and its impact on the summertime urban heat island

Abstract. The low-level jet (LLJ) and the urban heat island (UHI) are common nocturnal phenomena. While the UHI has been studied extensively, interactions of the LLJ and the urban atmosphere in general (and the UHI in particular) have received less attention. In the framework of the PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) initiative in the Paris region, continuous profiles of horizontal wind speed and vertical velocity were recorded with two Doppler wind lidars (DWLs) – for the first time allowing for a detailed investigation of the summertime LLJ characteristics in the region. Jets are detected for 70 % of the examined nights, often simultaneously at an urban and a suburban site, highlighting the LLJ regional spatial extent. Emerging at around sunset, the mean LLJ duration is ∼ 10 h, the mean wind speed is 9 m s−1, and the average core height is 400 m above the city. The temporal evolution of many events shows signatures that indicate that the inertial oscillation mechanism plays a role in the jet development: a clockwise veering of the wind direction and a rapid acceleration followed by a slower deceleration. The horizontal wind shear below the LLJ core induces variance in the vertical velocity (σw2) above the urban canopy layer. It is shown that σw2 is a powerful predictor for regional contrast in air temperature, as the UHI intensity decreases exponentially with increasing σw2 and strong UHI values only occur when σw2 is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health.

Electric vehicle energy consumption prediction for unknown route types using deep neural networks by combining static and dynamic data

Accurate energy consumption prediction of electric vehicles (EVs) is crucial for drivers considering long trips. All the data should be provided beforehand to determine the energy consumption at the beginning of the trip. Although dynamic vehicle data (vehicle speed, state-of-charge, acceleration, etc.) cannot be known before the trip, factors related to the specified route (route type, elevation, average speed, weather, driving time, etc.) can be used to predict the consumed energy. These factors can be categorized as static and dynamic features, and thus, the question of how to effectively use static and dynamic data arises. This paper investigates the problem of predicting the energy consumption of an EV for a predetermined trip using a deep neural network (DNN) model that effectively uses static features along with dynamic segment features. Furthermore, we address the problem where the route types are unknown in advance. To include more information in the prediction model, we clustered the speed profiles using shape-based clustering with dynamic time warping (DTW) to predict the route type and used the cluster labels as static inputs. Real driving data collected from various drivers of a specific EV were used to train the DNN. The proposed DNN model was compared with the average energy consumption (AEC) model and five machine learning models. The results show that labels obtained from shape-based clustering improved the prediction more than feature-based cluster labels. The prediction errors were minimized with the proposed DNN model, where static features are introduced to the first and second layers twice.

New and tractable formulations for the eco-driving and the eco-routing-and-driving problems

Eco-driving and eco-routing problems are both concerned with minimizing the fuel consumption of a single vehicle; the former does so by optimizing the vehicle’s speed profile on a given road segment, and the latter selects the best path for a vehicle from a given origin to a given destination. This paper studies a problem that combines the two, namely an eco-routing-and-driving problem (ERDP), in which the path and the speed profile on each link of the chosen path are optimized simultaneously. Using the comprehensive modal emissions model as the fuel consumption model, this paper describes new and tractable formulations for both the eco-driving and the eco-routing-and-driving problems through discretization, convexification and approximation, leading to a linear program for the former and a mixed-integer linear program for the latter. Computational results indicate that the new formulation of the eco-driving problem yields significant reductions in the solution time as compared to alternative formulations and algorithms, and that the new formulation of the eco-routing-and-driving problem affords further reductions in fuel consumption when compared with other methods.

Estimating the In Situ Stratification via Remotely Sensed Internal Wave Speeds

Abstract This work tests a methodology for estimating the ocean stratification gradient using remotely sensed, high temporal and spatial resolution field measurements of internal wave propagation speeds. The internal wave (IW) speeds were calculated from IW tracks observed using a shore-based, X-band marine radar deployed at a field site on the south-central coast of California. An inverse model, based on the work of Kar and Guha, utilizes the linear internal wave dispersion relation, assuming a constant vertical density gradient is the basis for the inverse model. This allows the vertical gradient of density to be expressed as a function of the internal wave phase speed, local water depth, and a background average density. The inputs to the algorithm are the known cross-shore bathymetry, the background ocean density, and the remotely sensed cross-shore profiles of IW speed. The estimated density gradients are then compared to the synchronously measured vertical density profiles collected from an in situ instrument array. The results show a very good agreement offshore in deeper water (∼50–30 m) but more significant discrepancies in shallow water (20–10 m) closer to shore. In addition, a sensitivity analysis is conducted that relates errors in measured speeds to errors in the estimated density gradients. Significance Statement The propagation speed of ocean internal waves inherently depends on the vertical structure of the water density, which is termed stratification. In this work, we evaluate and test with real field observations a technique to infer the ocean density stratification from internal wave propagation speeds collected from remote sensing images. Such methods offer a way to monitor ocean stratification without the need for extensive in situ measurements.

Advancing glider-based acoustic measurements of underwater-radiated ship noise.

Ocean gliders are versatile and efficient passive acoustic monitoring platforms in remote marine environments, but few studies have examined their potential to monitor ship underwater noise. This study investigates a Slocum glider's capability to assess ship noise compared to the ability of fixed observers. Trials were conducted in shallow coastal inlets and deep bays in Newfoundland, Canada, using a glider, hydrophone array, and single-moored system. The study focused on (1) the glider's self-noise signature, (2) range-depth-dependent propagation loss (PL) models, and (3) identifying the location of the vessel to the glider using glider acoustic measurements. The primary contributors to the glider's self-noise were the buoyancy pump and rudder. The pitch-motor noise coincided with the buoyancy pump activation and did not contribute to the glider self-noise in our experiments. PL models showed that seafloor bathymetry and sound speed profiles significantly impacted estimates compared to models assuming flat and range-independent profiles. The glider's performance in recording ship noise was superior to that of other platforms. Using its hydrophones, the glider could identify the bearing from the vessel, although a third hydrophone would improve reliability and provide range. The findings demonstrate that gliders can characterize noise and enhance our understanding of ocean sound sources.

High-resolution acoustically informed maps of sound speed.

As oceanographic models advance in complexity, accuracy, and resolution, in situ measurements must provide spatiotemporal information with sufficient resolution to inform and validate those models. In this study, water masses at the New England shelf break were mapped using scientific echosounders combined with water column property measurements from a single conductivity, temperature, and depth (CTD) profile. The acoustically-inferred map of sound speed was compared with a sound speed cross section based on two-dimensional interpolation of multiple CTD profiles. Long-range acoustic propagation models were then parameterized by the sound speed profiles estimated by the two methods and differences were compared.

Development and Implementation of Stop-and-Go Inertial Profiler Certification Procedure in California

The ride quality of a road in California is often determined by evaluating pavement smoothness using an inertial profiler (IP). Conventional IP technology, per California Test (CT) 387, requires IP operators to maintain a minimum speed of 15 mph to collect pavement-smoothness data. However, maintaining a minimum speed of 15 mph in busy urban areas or on hilly terrains can be difficult and unsafe, often requiring traffic control. Caltrans and industry have identified a new technology, the “Stop-and-Go” (SAG) IP, for collecting pavement-smoothness data at low speeds (under 15 mph) without traffic control. The previous version of CT 387 did not allow for the use of SAG IPs. To address this issue, the Pavement and Materials Partnering Committee (PMPC) evaluated SAG IP and updated CT 387. A certification procedure for SAG IP was developed, and included in CT 387, based on a series of runs using speed profiles under various braking and acceleration conditions simulating urban traffic conditions. Any SAG profiler can be certified if it meets the requirements of AASHTO R 56 and CT 387. The certification procedure was validated using qualification runs at the Caltrans certification track, along with field testing, demonstrating that SAG IPs provide similar results to conventional IPs. Based on the validation, along with the subsequent update of CT 387, the PMPC approved the implementation of SAG IPs for optional use by Caltrans and industry. These developments helped California become one of the first states to develop and implement a certification procedure for SAG IPs.