Speed Profile Research Articles

Thermal conductivity variation effects on Marangoni radiative hybridizedAg-ZrO2 and MoS2-ZrO2 nanofluid based-blood with oblique magnetic field

The objective of this examination is to explore the mathematical modeling of a hybridized nanofluid flowing, which involves the transference of biologically fluid via a stretchable sheet. The nanofluid contains of pure blood as the conventional fluid, and it is combined with two nanomolecules. The flow occurs through a porosity stretchable surface. This simulation has potential applications in drugs delivery. The present study relies on the Marangoni condition and the injection/suction properties of two hybrid nanofluids (Ag-ZrO2/blood and MoS2-ZrO2/blood) to explain the time-independent and incompressible flow and transport of energy. Adding magnetic and radiation terms helps develop the issue. Similarity variables are used to define the mathematical phenomena. By using the Fehlberg approach, the reorganized nonlinear model may be carried out. Displaying and elaborating on the roles performed by restrictions in determining engineering physical quantities. The key characteristics of the current analysis are the bigger speed profiles and lower temperatures that emerge in response to the tightening stretching limit. Declining trends in the solid volume percentage have been attributed to the skin friction factor, whereas rising temperatures have had the opposite effect. Nusselt numbers for volume fraction and radiation are the polar opposite of one another. Nusselt number values are also lower for blowing than they are for suction.

Experimental characterization of littoral atmospheric acoustics: Concurrent meteorological and acoustic observations.

The aim of this work is to describe a rich set of acoustic transmission loss observations that were completed in a coastal environment. The data library, enumerated in detail and publicly posted, is comprised of pitch-catch acoustic transmission loss measurements along with concurrent high spatial resolution meteorological observations. The meteorological parameters include near-surface temperature profiles, vertical wind speed profiles in the acoustic propagation direction, and significant wave height estimates. The acoustic source is positioned on an anchored vessel such that the first several hundred meters of the acoustic range is over open water with one microphone array positioned at the shore. A second microphone array is placed several hundred meters inland along the same source-to-receiver heading. The path between the two acoustic arrays is uniform salt marsh vegetation. Observations were made during seven sessions, which represent a variety of atmospheric conditions. That variety of conditions allows for some experimental generalizations about transmission loss as a function of meteorological observations. These include (1) the relationship between vertical effective sound speed profile and transmission loss, and (2) the variability of acoustic pressure with turbulence over time and elevation.

Optimization of the cruising speed for high-speed trains to reduce energy consumed by motion resistances

As one of the effective measures to reduce energy consumption of high-speed railway, train operation control has gained much attention because of its cost-effectiveness. Conventional approaches to eco-driving generally rely on single-mass train models and lack a thorough investigation into the vehicle cruising process, which can overlook real-world operation conditions and hinder further improvements in energy efficiency. To address these issues, this paper proposes a comprehensive framework for optimizing the cruising speed of high-speed trains (HSTs) based on track conditions to reduce energy consumed by motion resistances (ECMR). Firstly, a multibody dynamics simulation model of the HST is developed and validated to accurately evaluate ECMR under a range of operation scenarios. Simulation results show that the impact of speed on ECMR varies considerably according to track characteristics. Subsequently, a neural network is constructed to derive the regressive relationship between operation conditions and ECMR. On this basis, an optimization model for the cruising speed under different track conditions is formulated to minimize the ECMR of a railway segment with a given operation time. Finally, the effectiveness of the model is validated through case studies, which demonstrate that ECMR can be reduced by up to 27.85% compared to traditional control strategies. The proposed optimization method can be integrated with current train speed profiles to further enhance energy efficiency. This framework has broad applicability for addressing engineering problems related to train operation optimization.

Wind-Induced Responses in Tall Buildings Using International Standards: A Review

Abstract: This study investigates the impact of international wind loading regulations on tall buildings by analyzing major codes from the United States (ASCE 7), Australia (AS/NZS 1170.2), Canada (NBC), and India (IS 875). The research reveals significant differences in the estimation of wind loads, attributed to variations in exposure categories, wind speed profiles, and calculation methodologies. Notably, the gust loading factor is commonly used across these standards. The parameters used to estimate wind loads by the international standards are also discussed. The findings underscore the necessity for global wind load limitations and emphasize the importance of considering local factors to ensure the structural safety and integrity of tall buildings under varying wind conditions.

Data-driven neural network model for maximum speed profile based on driver risk

In the field of vehicle control systems, the primary objective is to ensure the safety of road users. The intricate dynamics of road vehicles necessitate a high level of precision. Vehicle safety encompasses a multitude of considerations, including vehicle trajectory, prevailing traffic conditions, road structure, and meteorological factors. This study employs an Artificial Neural Network (ANN) trained with human driver data using SCANeR Studio software to evaluate the risk for the driver. The risk has been defined as a five-level parameter, which depends on the potential danger of a situation, where speed and direction play a crucial role. The system incorporates a simulator, an ANN, and a display interface to present the surroundings and communicate important information to the driver. This research employs a simulated driving scenario comprising a multi-lane roundabout with vehicles travelling in different directions to simulate real-world challenges. Risk estimation is achieved through a Time Delay Neural Network (TDNN) trained with various information about the environment in relation to the driven vehicle. The research employs a Jackknife technique for overall evaluation and introduces an adaptive algorithm for speed limit setting. The findings demonstrate the stability, generality, and practical applicability of the ANN in enhancing road safety.

Extending gravitational potentials from the surface boundaries of compact objects

The solutions of the Einstein field equations of general relativity (GR) are prone to rendering systems which are physically non-viable as shown by Delgaty and Lake (1998). This does not detract from the success of general relativity but rather suggests the possibility for implementing new solution methods while being cautious that the resulting systems are indeed physically viable. This emphasizes the importance of applying appropriate criteria in closing the system of non-linear, coupled differential equations of GR. In the case of static systems describing massive compact bodies, an intrinsic property is the vanishing of the radial pressure at the surface boundary of the object which is embedded in an empty Schwarzschild exterior spacetime. In attempting to complete the gravitational description, we first assume a standard, physically plausible metric potential such as that of Finch and Skea. We then attempt to solve for the other metric component by imposing an ansatz which assists in extrapolating from the vanishing pressure boundary condition. Our model is guided by the physicality of the sound speed profiles and associated stability conditions.

HybridPy: The Simulation Suite for Mesoscopic and Microscopic Traffic Simulations

Mesoscopic, agent-based simulations efficiently model and assess entire regions’ daily activities and travel patterns, exemplified by smaller countries like Switzerland. The queue-based simulation represents a compromise between computational speed on the one hand and the necessity of detailed modeling infrastructure on the other hand. Thus, mesoscopic simulations enable an efficient and reasonably detailed analysis of the complex interplay between supply and demand in mobility research. Conversely, microsimulations excel at reproducing individual speed profiles and behavior by modeling the interactions between traffic participants, including pedestrians, bicycles, and scooters. Although allowing for more detailed system analysis, the downside is the high computational burden, which often prevents large-scale microscopic simulations from running in optimization or calibration loops. hybridPY, an extension of SUMOPy, aims to close the gap and benefit from both environments. The simulation suite allows the running of mesoscopic as well as microscopic traffic simulations based on the core idea: running a microscopic simulation in a smaller dedicated area, using the routes or mobility plans generated from a larger mesoscopic model. The main features of this software are: (i) import, editing and visualization of MATSim and BEAM CORE networks; (ii) conversion of MATSim plans to SUMO routes or plans within the SUMO area; (iii) configuring and running of MATSim simulations. The capability of hybridPY is demonstrated by two applications: the simulation of Schwabing, Germany, based on the MITO MATSim model, and the San Francisco municipality, USA, based on the mesoscopic BEAM CORE model of the entire San Francisco Bay area. Both scenarios demonstrate that the hybrid approach results in significant computational gains with respect to a pure microscopic approach.

Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy

ABSTRACT Establishing the links between running technique and economy remains elusive due to high inter-individual variability. Clustering runners by technique may enable tailored training recommendations, yet it is unclear if different techniques are equally economical and whether clusters are speed-dependent. This study aimed to identify clusters of runners based on technique and to compare cluster kinematics and running economy. Additionally, we examined the agreement of clustering partitions of the same runners at different speeds. Trunk and lower-body kinematics were captured from 84 trained runners at different speeds on a treadmill. We used Principal Component Analysis for dimensionality reduction and agglomerative hierarchical clustering to identify groups of runners with a similar technique, and we evaluated cluster agreement across speeds. Clustering runners at different speeds independently produced different partitions, suggesting single speed clustering can fail to capture the full speed profile of a runner. The two clusters identified using data from the whole range of speeds showed differences in pelvis tilt and duty factor. In agreement with self-optimisation theories, there were no differences in running economy, and no differences in participants’ characteristics between clusters. Considering inter-individual technique variability may enhance the efficacy of training designs as opposed to ‘one size fits all’ approaches.

Evaluation of the Coupled Arctic Forecast System’s representation of the Arctic atmospheric boundary layer vertical structure during MOSAiC

Observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) were used to evaluate the Coupled Arctic Forecast System (CAFS) model’s ability to simulate the atmospheric boundary layer (ABL) structure in the central Arctic. MOSAiC observations of the lower atmosphere from radiosondes, downwelling longwave radiation (LWD) from a pyranometer, and near-surface wind conditions from a meteorological tower were compared to 6-hourly CAFS output. A self-organizing map (SOM) analysis reveals that CAFS reproduces the range of stability structures identified by the SOM trained with MOSAiC observations of virtual potential temperature (θv) profiles, but not necessarily with the correct frequency or at the correct time. Additionally, the wind speed profiles corresponding to a particular θv profile are not consistent between CAFS and the observations. When categorizing profiles by static stability, it was revealed that CAFS simulates all observed stability regimes, but overrepresents the frequency of near-surface strong stability, and underrepresents the frequency of strong stability between the top of the ABL and 1 km. The 10 m wind speeds corresponding to each stability regime consistently have larger values in CAFS versus observed, and this offset increases with decreasing stability. Whether LWD is over or underestimated in CAFS is dependent on stability regime. Both variables are most greatly overestimated in spring, leading to the largest near-surface θv bias, and the greatest underrepresentation of strong stability in spring. The results of this article serve to highlight the positive aspects of CAFS for representing the ABL and reveal impacts of misrepresentations of physical processes dictating energy, moisture, and momentum transfer in the lower troposphere on the simulation of central Arctic ABL structure and stability. This highlights potential areas for improvement in CAFS and other numerical weather prediction models. The SOM-based analysis especially provides a unique opportunity for process-based model evaluation.

Full Envelope Flight Control System Design and Optimization for a Tilt-Wing Aircraft

Novel vertical take-off and landing advanced air mobility aircraft which are overactuated and transition between vertical and forward flight modes pose unique challenges for the design of safe and robust full‐envelope fly‐by‐wire flight control systems. This paper presents a methodology for designing and optimizing a control system architecture for a tilt-wing urban air mobility concept. It features the total energy control system algorithm, which is extended to be applicable to hover and transitioning flight and explicit model following inner loops. Control system parameters are optimized using a genetic algorithm optimization scheme, subject to constraints on closed-loop dynamic stability and control response characteristics. Control system performance is demonstrated by simulation of lateral, longitudinal, and directional maneuvering for hover, transition, and forward flight conditions, followed by simulation of departure and arrival transitions while tracking a prescribed flightpath and speed profile in calm and turbulent air. The results demonstrate the effectiveness of the proposed control system architecture and the demonstrated optimization methodology.

Solar Models and Astrophysical S-factors Constrained by Helioseismic Results and Updated Neutrino Fluxes

The ratio of metal abundance to hydrogen abundance of the solar photosphere, (Z/X) s , has been revised several times. Standard solar models, based on these revised solar abundances, are in disagreement with seismically inferred results. Recently, Magg et al. introduced a new value for (Z/X) s , which is still under debate in the community. The solar abundance problem or solar modeling problem remains a topic of ongoing debate. We constructed rotating solar models in accordance with various abundance scales where the effects of convection overshoot and enhanced diffusion were included. Among these models, those utilizing Magg’s abundance scale exhibit superior sound speed and density profiles compared to models using other abundance scales. Additionally, they reproduce the observed frequency separation ratios r 02 and r 13. These models also match the seismically inferred surface helium abundance and convection zone depth within the 1σ level. Furthermore, the calculated neutrino fluxes from these models agree with detected ones at the level of 1σ. We found that neutrino fluxes and density profile are influenced by nuclear reactions, allowing us to use the combination of detected neutrino fluxes and seismically inferred density for diagnosing astrophysical S-factors. This diagnostic approach shows that S 11 may be underestimated by 2%, while S 33 may be overestimated by about 3% in previous determinations. The S-factors favored by updated neutrino fluxes and helioseismic results can lead to significant improvements in solar models.

Vibration-Based Railway Suspension Monitoring Under Varying Operating Conditions via Robust Statistical Time Series Type Methods

The on-board condition monitoring of railway suspension components is important for ensuring passenger comfort, maintaining proper operation, enhancing safety, and reducing operational costs. In this context statistical time series type random-vibration-based methods constitute a potentially efficient and cost-effective framework. Yet a significant challenge emerges from the competing requirements for high sensitivity to suspension component degradation and robustness to varying Operating Conditions (OCs) and uncertainty. Varying Operating Conditions and uncertainty are related to factors such as varying payload, travelling speed, and rail profile. The problem is further aggravated by the complexity of the vehicle dynamics, including the wheelbase filtering effect. Despite the significant progress made so far and the development of various methods, the challenges related to varying OCs and uncertainty largely remain as a technical barrier. The aim of the present study is the introduction of a data-based methodology aspiring to overcome them. The methodology is based on Multiple-Input Single-Output Transmittance Function (MISO-TF) time series models of a special form allowing for eliminating the effects of varying rail profile while properly treating the wheelbase filtering effect. Monitoring is based on a MISO-TF feature space, using either a Random Coefficient Gaussian Mixture or a Multiple Model based scheme. The former, although more elaborate, allows for the introduction of a complete probabilistic framework. An interesting advantage of both versions is that operation can be based on a very limited number of sensors and only a partial data-driven model of the dynamics. The methodology is comprehensively assessed via hundreds of SIMPACK based Monte Carlo runs under variable payload and rail profiles. The achievable performance is shown to be very good for both versions and for 10%, 15%, and 20% of condition degradation in either primary or secondary suspension components.

Eigenvalues of the noise covariance matrix in ocean waveguides.

The eigenvalue (EV) spectra of the theoretical noise covariance matrix (CM) and observed sample CM provide information about the environment, source, and noise generation. This paper investigates these spectra for vertical line arrays (VLAs) and horizontal line arrays (HLAs) in deep and shallow water numerically. Empirically, the spectra are related to the width of the conventional beamforming output in angle space. In deep water, the HLA noise CM tends to be isotropic regardless of the sound speed profile. Thus, the EV spectrum approaches a step function. In contrast, the VLA noise CM is non-isotropic, and the EVs of the CM jump in two steps. The EVs before the first jump are due to sea surface noise, while those between the first and second jump are due to bottom-reflected noise. In shallow water, the VLA noise CM is affected by the environment (sound speed profile and seabed density, sound speed, attenuation, and layers) and array depth, the EVs have a more complicated structure. For Noise09 VLA experimental data, the noise sample CM EVs match the waveguide noise model better than the three-dimensional isotropic noise model.

Joint Concern over Battery Health and Thermal Degradation in the Cruise Control of Intelligently Connected Electric Vehicles Using a Model-Assisted DRL Approach

Eco-driving aims to enhance vehicle efficiency by optimizing speed profiles and driving patterns. However, ensuring safe following distances during eco-driving can lead to excessive use of lithium-ion batteries (LIBs), causing accelerated battery wear and potential safety concerns. This study addresses this issue by proposing a novel, multi-physics-constrained cruise control strategy for intelligently connected electric vehicles (EVs) using deep reinforcement learning (DRL). Integrating a DRL framework with an electrothermal model to estimate unmeasurable states, this strategy simultaneously manages battery degradation and thermal safety while maintaining safe following distances. Results from hardware-in-the-loop simulation testing demonstrated that this approach reduced overall driving costs by 18.72%, decreased battery temperatures by 4 °C to 8 °C in high-temperature environments, and reduced state-of-health (SOH) degradation by up to 46.43%. These findings highlight the strategy’s superiority in convergence efficiency, battery thermal safety, and cost reduction compared to existing methods. This research contributes to the advancement of eco-driving practices, ensuring both vehicle efficiency and battery longevity.

Evaluation of the Planetary Boundary Layer Height From ERA5 Reanalysis With MOSAiC Observations Over the Arctic Ocean

AbstractThe planetary boundary layer height (PBLH) is a crucial indicator reflecting the region of the atmosphere characterized by continuous turbulence. Here, we use radiosonde and surface meteorological observations (4–7 times per day, year‐round measurements) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to derive the PBLH (PBLHMOSAiC), and further evaluate the PBLH from the ERA5 reanalysis (PBLHERA5). Comparisons between PBLHMOSAiC and PBLHERA5 from different perspectives reveal that: (a) The overestimation of PBLHERA5 when the sea ice concentration is >90% is significant with the centered root mean squared error reaching up to 201 m; (b) The difference between the two products is notably pronounced in cold seasons, while it is comparatively diminished in warm seasons; (c) In neutral boundary layers, differences in PBLHERA5 are larger compared with stable and convective boundary layers. In addition, the analysis of error sources indicates that the bias of PBLHERA5 is sensitive to the bias of vertical thermal structure and wind speed profiles in ERA5 data sets in all conditions. Finally, we find a Random Forest model effectively reduces the bias of PBLHERA5 with the index of agreement reaching up to 0.71 in the test data set, while a multiple linear regression demonstrates comparable performance to the Random Forest model.

Spatially distributed atmospheric boundary layer properties in Houston – A value-added observational dataset

In 2022, Houston, TX became a nexus for field campaigns aiming to further our understanding of the feedbacks between convective clouds, aerosols and atmospheric boundary layer (ABL) properties. Houston’s proximity to the Gulf of Mexico and Galveston Bay motivated the collection of spatially distributed observations to disentangle coastal and urban processes. This paper presents a value-added ABL dataset derived from observations collected by eight research teams over 46 days between 2 June - 18 September 2022. The dataset spans 14 sites distributed within a ~80-km radius around Houston. Measurements from three types of instruments are analyzed to objectively provide estimates of nine ABL parameters, both thermodynamic (potential temperature, and relative humidity profiles and thermodynamic ABL depth) and dynamic (horizontal wind speed and direction, mean vertical velocity, updraft and downdraft speed profiles, and dynamical ABL depth). Contextual information about cloud occurrence is also provided. The dataset is prepared on a uniform time-height grid of 1 h and 30 m resolution to facilitate its use as a benchmark for forthcoming numerical simulations and the fundamental study of atmospheric processes.

Research on reconstruction of the global sound speed profile combining partial underwater prior information

The sound speed profile (SSP) is an important factor affecting the acoustic propagation characteristics of the ocean, making the accurate acquisition of SSP a crucial step in the interdisciplinary research of oceanography and underwater acoustics. Limited by the cost of in-situ measurement and the performance of the instrument itself, direct measurement of SSP inevitably leads to insufficient depth or even missing information. In this paper, we propose using partial underwater prior information (UWPI) only including underwater sound speed to obtain preliminary reconstruction results of global SSP for the first time. The empirical orthogonal function (EOF) reconstruction algorithm is optimized by employing assimilated SSP as the background SSP to further reduce reconstruction errors. The maximum global average reconstruction error and root mean square error (RMSE) after optimization decrease by >51% and 71%, respectively, which indicates that the performance of the optimized algorithm combined with partial UWPI is further improved. Finally, the performance of the optimized algorithm is discussed from the perspective of acoustic propagation. This research provides a reliable technical approach for SSP reconstruction under incomplete depth conditions, which can be applied in underwater sound field prediction and acoustic detection in the future.

E-Heater Performance for Aftertreatment Warm-Up in a 48V Mild-Hybrid Heavy-Duty Truck over Real Driving Cycles

High-efficiency and low-emissions heavy-duty (HD) internal combustion engines (ICEs) offer significant GHG reduction potential. Mild hybridization via regenerative braking and enabling the use of an electric heater component (EHC) for the aftertreatment system (ATS) warm-up extends these benefits, which can mitigate tailpipe GHG and NOx emissions simultaneously. Understanding such integrated hybrid powertrains is essential for the system optimization of real-world driving conditions. In the present work, the potential of a low engine-out NOx (1.5–2.5 g/kWh range) ‘Low-NOx’ HD diesel engine and EHCs were analyzed in a 48V P1 mild-hybrid system for a class 8 commercial vehicle concept and compared with those in an EPA-2010-certified HD diesel truck as a baseline under real-world driving cycles, including those from the US, Europe, India, China, as well as the world harmonized vehicle cycle (WHVC). For analysis, an integrated 1-D vehicle model was utilized that consisted of models of the ‘Low-NOx’ HD engine, the stock ATS, and a production EHC. For the real driving cycles, ‘GT-RealDrive’-based vehicle speed profiles were generated for busy trucking routes for different markets. For each cycle, the effects of the Low-NOx and EHC performances were quantified in terms of the ATS warm-up time, engine-out NOx emissions, and net fuel consumption. Depending on the driving route, the regenerative braking fully or partly neutralized the EHC power penalty without a significant impact on the ATS thermal performance. For a two-EHC system, the fueling penalty associated with every second reduction in the warm-up time FCEHC (g/s) was several-fold higher for the real driving routes compared with the WHVC. Overall, while a multi-EHC setup accelerated the ATS warm-up, a single EHC integrated at the SCR inlet showed minimized EHC heating power, leading to a minimized fueling penalty. Finally, for the India and China routes, being highly transient, the P1 hybridization proved inadequate for GHG reduction due to the limited energy recuperation. A stronger hybridization was desirable for such driving cycles.

Real-time estimation of underwater sound speed profiles with a data fusion convolutional neural network model

Sound speed profiles (SSPs) characterize the spatial distribution of sound velocity, significantly impacting the trajectory of acoustic signals. Consequently, they play a crucial role in determining the efficiency and precision of communication, localization, and other practical applications. Recently, SSP inversion methods significantly improve real-time performance compared to the direct measurement method, but it is highly dependent on sonar observation data so that the application scope is limited. To obtain real-time sound speed distribution in larger scale areas, we proposes a data-fusion-driven multi-input multi-output convolutional regression neural network (DF-MIMO-RCNN) , which combines satellite-based real-time remote sensing sea surface temperature (SST), historical SSP feature vector, historical mean temperature below the sea surface with corresponding spatial coordinate information. The model gets rid of the dependence on sonar observation data and can be applied to a wider spatial region. Then two sets of experiments were conducted based on the Argo database and marine experimental data respectively. Experimental results based on Argo data show that the proposed method is not only suitable for deep-sea environments at the same depth, but even for shallow water of different depths. At the same time, compared with other algorithms, the root mean square error (RMSE) and RMSE distributions are smaller and more robust. Finally, we conducted deep-sea experiments in the South China Sea. The results show that even in non-strictly gridded sea areas, our proposed model still has lower RMSE and higher robustness, which demonstrates the superiority of our proposed algorithm.

Productivity enhancement in top-down VPP via concurrent grayscaling and platform speed profile optimization for symmetrical parts having micro scale features

Productivity enhancement in top-down VPP via concurrent grayscaling and platform speed profile optimization for symmetrical parts having micro scale features