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We explore the crystal structure and ionic conductivity of unfilled and cellulose nanowhisker‐filled polymer electrolytes, based on high molecular weight PEO and LiClO4at a concentration EO:Li = 6:1 (EO = ether oxygen). At this concentration, the polymer electrolyte forms crystalline PEO6(PEOy—the crystal structure co‐crystallizes “y” PEO ether oxygens to one Li‐anion pair). While the diffraction patterns for PEO3and PEO6with LiClO4are available, their structures are not. Thus, we optimize these structures using density functional theory and show agreement between simulated and experimental diffraction patterns. The PEO6crystal shows insufficient Li‐ion conductivity due to a lack of percolated conduction paths, and the crystal structure is unstable following 2 months after annealing. Cellulose nanowhiskers, with their patterned surface OH groups, extend the length of PEO6tunnels, stabilize these tunnels for as long as a year, and result in the percolation of crystals. Although the EO:Li = 6:1 stoichiometry should not favor PEO3formation, we observe this crystal phase in some unfilled samples. But, with the addition of nanowhiskers, the patterned surface templates PEO6formation rather than PEO3. Despite these advantages, nanowhiskers do not improve ionic conductivity as hypothesized. The tools developed in this work allow further mechanistic exploration of conduction through crystalline domains.

This article presents solutions to challenges in using a low-density flash lidar for application to a heavy road vehicle Autonomous Emergency Braking (AEB) system. Low-density flash lidars are relatively new to the commercial market and are much less expensive compared to point-cloud lidars. However, due to their low angular resolution, it becomes challenging to use the information provided by them for applications such as the AEB. Specifically, the challenges in identifying relevant targets and determining target width are discussed in this article, and solutions to the same are presented. Later, an AEB algorithm is developed for a heavy commercial road vehicle for demonstrating the usefulness of the proposed solutions. The AEB system is experimentally evaluated using a brake Hardware-in-loop system along with IPG TruckMaker, and the results are presented.

Overtaking on two-lane undivided highways poses complex challenges, with attention traditionally focused on the overtaking vehicle. Yet, slow-moving vehicle (impeder) behaviour has been largely overlooked. This study investigates the yielding process whereby impeders actively support overtaking vehicles. This phenomenon can have implications for safety in autonomous environments and optimising Traffic control strategies. Empirical data, including drone-captured trajectories, were analyzed to develop predictive models for yielding behaviour. Prediction accuracies up to 82% and 85% for speed and space yielding were achieved, respectively. Integrating these models into microsimulation tools can supplement policy decisions, optimize Traffic flow, and mitigate accidents, yielding significant societal benefits.

Flash light detection and ranging (LiDAR) is a cost-effective sensing technology and is seen as a potential complement to RADAR and point-cloud LiDAR sensors. This study explores the performance of a low-density 2-D flash LiDAR in various weather conditions for active road vehicle safety. The influence of different weather conditions, such as fog, rain, and clear sky, on the maximum detection range for different categories of vehicles, such as motorcycles, cars, and heavy vehicles, are studied, and the results are presented. Furthermore, some interesting observations noticed in wet conditions, namely, water splashing effects and false detections, are discussed along with the potential solutions to handle these challenges. Sensor fusion of flash LiDAR and camera along with the object detection technique is proposed to improve the detection distance in adverse weather conditions. Furthermore, the maximum detectable distance of various vehicle categories in different weather conditions is compared for you only look once (YOLO), region-based convolutional neural networks (faster R-CNNs), and single shot detector (SSD) object detection techniques. The results show that implementing sensor fusion using YOLO and R-CNN increase the detection distance of cars and heavy vehicles, and on the other hand, the object detection technique needs to be further improved for motorcycles.

The wheel slip ratio is a function of the longitudinal speed of the wheel center, angular wheel speed, and Effective Tire Radius (ETR). The longitudinal speed of the wheel center and angular wheel speed can be calculated/estimated using an accelerometer and wheel speed sensor. However, ETR has uncertainties owing to variation in vehicle mass, air pressure in the tire, and dynamic load transfer. This article firstly demonstrates the sensitivity of ETR on wheel slip estimation using a Fixed Radius Recursive Least Squares (FR-RLS) estimator and analyzes its effects on Antilock Brake System (ABS) performance. Secondly, it proposes a Radius Searching RLS (RS-RLS) wheel slip estimator that mitigates the drift in estimates, maintains vehicle stability, and improves ABS performance. The proposed method was evaluated on a Hardware-in-Loop system with IPG TruckMaker. The RS-RLS estimator based ABS algorithm was compared against three FR-RLS estimator based ABS algorithms having different values of ETR and was found to have one order of magnitude lower mean absolute error.

The distress of asphalt pavements on horizontal curves can be relatively higher because of the lateral load transfer and the increase in lateral forces on vehicles during their cornering manoeuvre. In this study, the lateral load transfer in horizontal curves and its effect on pavement disstress is evaluated using a commercial vehicle dynamics software, IPG TruckMaker®. Road profiles were generated with a straight section followed by a horizontal curve. A sensitivity analysis was conducted by varying the radii of horizontal curves, road-surface roughnesses, vehicle speed, and its laden weight. The results were compared with results from static analysis and an analytical model. The curved section was found to sustain about two to six times more damage. Further, layered elastic analysis of the curved section showed higher shear stresses at the layer interface. This emphasises the need for a separate design of pavements on a horizontal curve.

Faults in the air brake system used in Heavy Commercial Road Vehicles (HCRVs) would adversely affect the vehicle’s dynamic performance, and hence their prompt detection is critical for vehicle safety. This paper first investigates the effect of air brake system faults through extensive hardware-in-loop experiments. These faults were observed to degrade the braking response, yaw stability, and vehicle braking distance. In many countries, an antilock brake system is mandatory in HCRVs, and wheel speed data are readily available. Inspired by this, the feasibility of using wheel speed data to detect faults is investigated in this study. As an initial step of predictive maintenance, a fault diagnostic scheme based on a supervised learning algorithm, Support Vector Machine (SVM) that uses only wheel speed data has been developed. The SVM algorithm’s efficacy was tested for 1937 test cases that encompassed a wide range of operating conditions. It was found that a Gaussian kernel SVM (G-SVM) provided a good classification accuracy of 96.54%, demonstrating its ability to predict a faulty condition accurately. The standard deviation of G-SVM’s prediction accuracy for five groups of data sets with 100 instances was found to be 1.57%, which shows that the model is more precise to predict the fault/no-fault condition of the air brake system.

ABSTRACTDynamic prediction of queues and delays at signalized intersections utilize data obtained from automated traffic sensors as inputs for Intelligent Transportation Systems (ITS) applications. Errors are inevitable during automated data handling, especially when the traffic involved is heterogeneous and lacking in lane discipline. This paper presents a model-based estimation scheme that can handle both mixed traffic conditions and erroneous detector input data to estimate queue and, in turn, delay. The models were developed for Queue within Advance Detector (QWAD) and Queue beyond Advance Detector (QBAD) scenario. The statistical properties of detector errors were incorporated into the estimation scheme, and the scheme was tested for varying traffic conditions. The estimation scheme’s performance was evaluated using field data from a signalized intersection in Chennai, India, and simulated data. It is found that the incorporation of statistical properties of detector error allows accurate estimation of queues and delays despite erroneous data input.

Knowledge of the tyre-road interface traction limit during braking of a road vehicle can drastically improve safety and ensure stable braking on varied road conditions. This study proposes an optimal reference slip algorithm that determines the road surface while the vehicle is braking, by implicitly tracking the traction limit. It presents wheel slip variance regulation as a potential approach towards reference wheel slip estimation for wheel slip regulation (WSR). The variance regulation approach computes reference wheel slip using past wheel slip estimates and regulates wheel slip variation at a set point. This variance regulation problem was solved using least-squares estimation, yielding reference slip dynamics. A 3-staged nested control architecture was developed with reference slip dynamics to yield an anti-lock braking system (ABS) algorithm consisting of a brake controller, WSR algorithm and reference slip estimation. The algorithm was experimentally corroborated in a Hardware-in-Loop setup consisting of the pneumatic brake system of a heavy commercial road vehicle, and IPG TruckMaker®, a vehicle dynamics simulation software. The proposed ABS algorithm was tested on straight roads with homogeneous surfaces, split friction surfaces, and transition friction surfaces. It ensured stable braking in all road cases, with a 7%–18% reduction in braking distance on homogeneous road surfaces compared to the same vehicle without ABS. The vehicle directional stability was retained on a split-friction surface, and the ABS algorithm was observed to adapt to sudden transitions in the road surface.

The process of modelling vehicle motion in a road traffic environment requires the integration of trajectory generation with vehicle control. The steps involved here are generating a feasible trajectory based on the existing traffic and tracking the trajectory to control it with a steering angle input. Since the parameters of a physical system vary with changes in operating conditions, it is important to consider robustness when designing controllers. This article aims at developing a trajectory-following model with robust steering control strategies to accurately follow a generated trajectory. In this study, performance-based proportional, robust proportional and sliding mode control strategies are designed for trajectory following. The robustness of the proportional controller is established using Kharitonov’s theorem, which is compared with a proportional controller tuned for performance. Sliding mode control is designed for robustness and chattering elimination using two kinds of reaching laws – a constant reaching law and a novel power rate exponential reaching law. The controllers are designed using a dynamic bicycle model considering the error with respect to the trajectory. The controllers are then evaluated in IPG CarMaker®. The resulting trajectories and control inputs are compared for the considered control methodologies using the ISO double lane change and the Slalom tests. Sliding mode control with power rate exponential reaching law is concluded to be more robust as compared to the other controllers, with lower response times, up to 84% lower heading angle deviations from the trajectory and an overshoot of only 3.2% in lane changing.

Wheel lock in a vehicle during braking is detrimental to its safety, in addition to causing poor braking performance. Wheel slip regulation algorithms could potentially prevent wheel lock and are hence required to be tested and tuned thoroughly prior to in-vehicle deployment. Generally, software-in-the-loop and hardware-in-the-loop tests are explored before on-road vehicle testing. A brake dynamometer can potentially be utilized for wheel slip regulation testing, and this can be placed in between hardware-in-the-loop tests and on-road vehicle testing. Prior to evaluation of wheel slip regulation on a brake dynamometer, it is imperative to realize a wheel lock scenario. This work proposes a methodical framework for emulating wheel lock in a brake dynamometer. In this study, the dynamic effects during braking, particularly load transfer, wheel slip and tyre–road interactions, are subsumed into a single variable termed ‘equivalent inertia’ to replicate a wheel lock event. The variations of this variable were captured through extensive tests on a hardware-in-the-loop platform that consists of a pneumatic brake setup interfaced with IPG TruckMaker®co-simulated with MATLAB/Simulink®, across varying load, road and braking conditions. Equivalent inertia profiles thus generated were then realized in the brake dynamometer, via mechanical discs and electrical inertia. Angular speed profiles from hardware-in-the-loop and dynamometer tests were compared to corroborate the framework. A close correlation between the profiles, highlighted by the root mean square deviation of the order of 100rad/s, established the effectiveness of the proposed scheme.

Additional powertrain components and regenerative braking are two important factors that may affect the performance and stability of electrified vehicle cornering. The location of additional components affects the vehicle’s center of gravity (CG) position and thereby the stability of the vehicle. As regenerative braking is possible only on driven wheels, the brake force distribution between front and rear wheels may not follow the ideal brake force distribution curve. Hence, applying maximum regenerative braking during cornering may affect vehicle stability, and this has motivated the analysis presented in this paper. The scope of this research work includes obtaining a model for the regenerative brake system, which was then used to analyze the heavy commercial road vehicle lateral dynamic response during combined cornering and regenerative braking. A sensitivity study was carried out regarding variations in center of gravity, longitudinal speed, and tire–road traction coefficient (μ). The IPG TruckMaker®vehicle simulation software running in a hardware-in-loop experimental system was used to study the heavy road vehicle cornering performance. The results showed that applying braking on a constant radius path required correction in the steering input to follow the desired path. However, the amount of steering correction required during regenerative braking was higher than that with conventional friction braking. Moreover, applying maximum regenerative braking at higher longitudinal speeds on snowy roads and split-µroads has a higher impact on vehicle cornering performance compared with that on dry roads. Furthermore, a co-operative braking strategy with an optimum brake force sharing between regenerative braking and friction braking was developed to improve the electrified heavy commercial road vehicle’s cornering stability and handling performance during cornering and braking.

An Antilock Braking System (ABS) is an Active Vehicle Safety System (AVSS) employed to prevent wheel lock in road vehicles. Wheel lock is undesirable as it may lead to loss of vehicle steerability and directional stability. These become critical in Heavy Commercial Road Vehicles (HCRVs) as unintentional yaw motion resulting from directional instability may also lead to vehicle roll-over. ABS algorithms are broadly classified as Model-Based Algorithms (MBAs) and Rule-Based Algorithms (RBAs). MBAs are typically physics based and employ vehicle dynamic models. RBAs, which are currently predominantly used in vehicles, are threshold based. RBAs require measurement of wheel speed that is readily available, while MBAs require real time information on vehicle parameters and tire models. Most commercially available RBAs are proprietary and the details are not revealed. This has motivated the present study, and the physical significance of the wheel slip and wheel acceleration thresholds used in RBAs are clearly identified, backed by experiments from a Hardware-in-Loop (HiL) pneumatic brake setup along with IPG TruckMaker®, a software for vehicle dynamic simulation. This study is an important first step in developing an advanced RBA for HCRVs.

This paper proposes a control scheme to use regenerative braking for Wheel Slip Regulation (WSR) in Heavy Commercial Road Vehicles (HCRVs). Typically, in HCRVs, WSR is achieved using pneumatic friction brakes. However, in electrified vehicles regenerative braking is available and this can be beneficial as electrical machines have higher bandwidth compared to a pneumatic brake system. In this study, a sliding mode control based active co-operative braking strategy is proposed for WSR in an electrified HCRV. The proposed control strategy was programmed in MATLAB/Simulink® and implemented in a Hardware-in-loop brake system. The experimental results showed that the proposed active co-operative braking strategy reduced the Mean Absolute Percentage Error in tracking slip ratio in the range of 6.7 % to 1.1 % compared to conventional friction braking for different operating conditions. This reduced the stopping distance in the range of 3.5 % to 0.6 %. Thus, co-operative braking provided better slip tracking leading to reduced stopping distance.

Faults in an air brake system affect the performance of Heavy Commercial Road Vehicles (HCRVs). One of the major faults in the air brake system is the “out-of-adjustment”of pushrod due to excessive brake wear, which may cause a significant yaw angle deviation from the current path and an increase in stopping distance. Hence, this paper aims to design a controller that would maintain the vehicle’s directional stability under brake fault scenario. In order to design such a controller, knowledge of vehicle side slip angle is essential, but this is not a measurable quantity. Hence, an Artificial Neural Network (ANN) based estimation scheme for side slip angle prediction is also proposed. As an output regulator problem, Sliding Mode Control (SMC) was used for correction of yaw angle under brake fault scenario through appropriate steering angle input. This controller provided a percentage correction of yaw angle of 93.4 % and 99.8 % respectively for fully laden and fully unladen vehicle on a high friction tire road interface surface, and the same corresponding to a low friction tire road interface was 99.8 % and 98.9 % respectively.

Given the condition of mixed road traffic, few models exist that can predict the motion of a vehicle in it. Mixed road traffic can be defined as being both, lane indisciplined and heterogeneous. This study aims at developing a model that can analyze a given traffic condition, generate a safe trajectory and provide a control input to the vehicle to follow it. The paper explains the flow of the model, starting with traffic interaction, leader detection, and waypoint derivation. Post this, the trajectory is generated, the tracking errors are discussed and a controller is designed to navigate the trajectory by minimizing the discussed errors. Since the assumption of low speed is made, a kinematic model is used when generating feasibility criteria for the trajectory. Once the trajectory is determined to be feasible, a closed-loop Proportional Integral Derivative (PID) controller provides steering input to the vehicle to follow the trajectory. The controller tuning is performed using a dynamic bicycle model considering the error with respect to the trajectory. The trajectory generation model and the controller for trajectory following are implemented in independent simulator environments. The resulting output is a collision-free trajectory as followed by the subject vehicle (SV) to meet the generated waypoints which are based on the traffic scenario.

Regulation of heterogeneous traffic is a challenging task on urban roads, particularly those where traffic congestion is routinely encountered. In this paper, a model based traffic signal control scheme via state feedback controller is presented. An original contribution of this study is the use of area occupancy as the measurement variable, which is apt for characterising heterogeneous and lane less traffic. An adaptive Kalman filter is used to estimate traffic density. The developed control scheme was implemented on a road stretch simulated in VISSIM, a commercial microscopic traffic simulation software, and interfaced with MATLAB using VISSIM COM interface. The implementation was shown to satisfy the objective of maintaining the desired density in the study stretch which demonstrated the effectiveness of the developed control scheme.

In heavy commercial road vehicles, the air brake system is a critical vehicle safety system whose performance degradation increases the risk of accidents and hence requires periodic inspection and maintenance. The wear of brake pad lining and brake drum during operation leads to increase in the stroke of a component called pushrod whose ‘out-of-adjustment’ creates severe brake performance degradation. The fact that the driver does not receive a corresponding tactile feedback till it is too severe adds to the complexity of manual detection. Motivated by the increase in onboard sensing, electronics, and computation capabilities, this study proposes an artificial neural network–based approach to predict pushrod stroke based on measurement of brake chamber pressure. Here, a back propagation algorithm was used to train the multilayer feed-forward network. The effect of excessive pushrod stroke on vehicle braking response was first studied using a Hardware-in-Loop system that consists of brake system hardware and a commercial vehicle dynamics simulation software (IPG TruckMaker®). Experimental data collected from this system with manual slack adjuster and automatic slack adjuster have then been used to train and test the artificial neural network for pushrod stroke prediction. The performance of the prediction scheme has been tested over the entire range of brake operating conditions. The prediction error corresponding to manual slack adjuster was found to be within ±15% in 322 out of the entire test set of 328 instances (98.17%) and automatic slack adjuster within ±8% in all 57 test sets (100%). Statistical analysis based on confidence interval revealed a prediction error between −1.62% and −3.05% for manual slack adjuster and 0.43% and −1.62% for automatic slack adjuster for 99% confidence interval, which demonstrated the efficacy of the proposed prediction scheme.

Hybrid electric vehicles are emerging technologies that are considered as eco-friendly alternative solutions to internal combustion engine–driven vehicles. This paper proposes a modified hybrid electric vehicle powertrain system that addresses the shortcomings of a series hybrid electric vehicle powertrain. The proposed configuration replaces the conventional generator of a series hybrid electric vehicle with an integrated starter generator that supports the traction motor of the vehicle during acceleration and peak torque requirements and maintains the state of charge of the batteries to provide an extended electric range of the vehicle. The work done in this paper can be categorized into two stages. The first stage is the methodical development of the powertrain in terms of initial parameter matching and sizing of the vehicle components by considering the fundamentals of longitudinal vehicle dynamics. The second stage describes the optimization of the proposed configuration to meet the design objective of maximizing fuel economy subjected to a set of vehicle performance constraints. The performance of the proposed powertrain was evaluated and compared with a series hybrid electric vehicle powertrain for an on-road Indian driving cycle using AVL CRUISE, which is a commercially available software for the study and analysis of road vehicle powertrains. Result analysis during initial parameterization showed a reduction in gross vehicle weight of the proposed configuration by 244 kg (1.5%) and an improvement in the average operating efficiency of the traction motor by around 11%, when compared to a series hybrid electric vehicle. Furthermore, the optimization results for the proposed configuration established an improvement in the fuel economy by 21% while meeting vehicle performance requirements.

In electric and hybrid vehicles, regenerative braking is applied only at the driven wheels by the electric drive, whereas the non-driven wheels are not subjected to brake force during the pure regenerative braking mode. The application of pure regenerative brake may affect the vehicle’s lateral stability during a turn. The impact could be more severe when the pure regenerative brake is applied at the turn on the rear wheels (for a rear wheel drive vehicle) over a low friction road surface. As part of a solution to reduce this impact, a brake force sharing (BFS) strategy between regenerative and friction brake has been proposed in this paper, which improves the brake force distribution between front and rear wheels to ensure a stable turn. The vehicle model and the BFS strategy were developed, and the IPG Car Maker® software was used to evaluate the effectiveness of the proposed strategy. The simulation results on BFS strategy have been corroborated using experimental data collected from a test vehicle. Further, a closed loop control structure was developed for implementing the proposed BFS strategy in electric and hybrid vehicles.

An agricultural tractor finds extensive application in agriculture and is used with various types of implements. When it is driven with an implement attached to its rear, on-road or on uneven terrain, it experiences excessive vibrations in the pitch plane, which results in front wheel lift-off as an extreme scenario. The objective of this paper is to minimize these vibrations caused due to road disturbances by automatically controlling the lifting and lowering of the implement with the help of a hydraulic actuator. Using a Linear Time Invariant (LTI) approximation, a model relating the current input and the force variation output on the implement was developed based on the analysis of frequency response and time response. The data used in this process were acquired from an experimental setup consisting of control valves, hydraulic actuator and hitch system. Since the lifting and lowering of the implement is controlled by separate solenoid valves, individual models were obtained for each of them. These models were the used to design a lag compensator in order to reduce the gain of the system in the desired frequency range while maintaining the control variable (current) within its design limits. Results showed that the designed lag compensator exhibited a positive performance by reducing the variation of force on the hitch point by 95.8% during lifting and 87.2% during lowering.

With increasing focus on addressing fossil fuel shortage and harmful automotive emissions, there has been a growing interest in hybrid electric and pure electric vehicles. A hybrid electric vehicle is driven by a combination of an internal combustion engine and an electric motor. The changes in the powertrain components result in changes in vehicle mass and mass distribution that affects the location of the vehicle’s centre of gravity amongst other things. Hence, a hybrid vehicle may not possess the same handling performance as a conventional internal combustion engine driven vehicle. This research work focuses on comparing the handling performance of a conventional engine driven vehicle and a hybrid electric vehicle. The bicycle dynamic model has been used to characterize the cornering dynamics of these two vehicles and the Mimuro plot has been used to analyze their handling characteristics. A sensitivity analysis has been carried out with respect to centre of gravity location and the cornering stiffness of the tires. An approach has been proposed to obtain comparable cornering characteristics in the hybrid vehicle as compared to the engine driven vehicle.

Hybrid electric vehicles are considered as an eco-friendly alternative solution to conventional internal combustion engine driven vehicles. This paper investigates the development of a Series-Parallel hybrid electric vehicle powertrain for a heavy-duty truck based on vehicle performance requirements, and compares its performance with a Series hybrid electric configuration for the same base vehicle. The parameter matching of the vehicle powertrains was followed by optimization in terms of maximizing fuel economy for an Indian highway drive cycle using the software ADVISOR. The simulation results quantified the potential benefits of the optimized series-parallel hybrid electric configuration for a highway drive cycle with respect to the series hybrid electric configuration.

Heavy commercial road vehicles have a longer stopping distance, owing to the inherent time lag associated with the conventional air brake system used in them. In general, electropneumatic brake systems would respond more quickly, resulting in a shorter stopping distance; these are the focus of this study. In an earlier work, the best configuration of the electropneumatic brake system was determined, considering both response and cost perspectives. This study focuses on the development of an experimentally corroborated mathematical model for this brake system. Non-linear model-based deceleration and brake chamber pressure control schemes were subsequently developed and their performance evaluated using a hardware-in-loop system. The model-based analysis and the control schemes presented in this article will assist in the development of advanced and cost-efficient active safety systems for heavy commercial road vehicles.

It has been observed during the operation of the diesel engine fuel injection pump that the fuel and the lubrication oil leak through the working clearance between the piston and the cylinder bore and mix with each other. The leakage of lubrication oil to fuel (OtF) leads to injector nozzle coking and emission, which necessitates the automotive industry to design a robust fuel injection pump to meet stringent emission norms. Similarly, the leakage of fuel to lubrication oil leads to depreciation of lubrication property of the oil, thereby reducing the life of engine oil lubricated components. In this research, the leakage flow through this clearance gap was studied using the numerical simulation tool, Ansys CFX, to estimate the volume flow rate of lubrication oil to fuel and fuel to lubrication oil. Pressure and drag effects are two important mechanisms that drive the leakage process. The simulation was carried out for various design parameters such as clearance, clearance taper and speed, and experiments were performed to determine the lubrication oil to fuel and fuel to lubrication oil flow rates. The diluted samples collected from fuel and lubrication oil tanks were analyzed using ICP-AES (inductively coupled plasma – atomic emission spectrometry) for calcium and barium element tracing. Calibration was performed on the ICP bench to study the accuracy and repeatability of the test sample analysis method. The results of numerical simulations and experiments were compared for various design parameters. The proposed analysis could serve as a valuable aid in the fuel injection pump development process.

Numerical study on MHD natural convection inside enclosure filled with Ag-water with centre heater is conducted. The vertical walls of enclosure are maintained at temperature Tcwhereas its top wall is insulated and bottom wall is considered with constant heat source. The governing equations are solved numerically by finite volume method and SIMPLE algorithm. The effects of heater length, Rayleigh number, Hartmann number and heat generation parameter are investigated. Results are presented with streamlines, isotherms, variation of average Nusselt number and mid height velocity profile. It is found that high heat transfer is obtained for the vertical heater and Hartmann number is a good control parameter for suppressing heat transfer. Moreover, the fluid flow, thermal fields and rate of heat transfer are strongly depending on heater length and heat generation parameter. Also, increasing solid volume fraction of nanoparticles cause better enhancement of heat transfer inside enclosure.

In this paper, a dynamic mathematical model of an autonomous ground vehicle was used to analyse its transient response and to design a heading-angle controller for the vehicle. A suitable ‘control-oriented model’ that could accurately characterize the phenomenon of interest was used to design the controller. The efficacy of this model was evaluated by corroborating its results with experimental data. This model included the cornering stiffness of the tyres as an unknown parameter, and two approaches were attempted to estimate its value. The dynamics of the actuator were included in the analysis since the response time to steer the front wheel is of the same order as that of the heading-angle dynamics of the vehicle. The performance of two controllers (namely a classical transfer-function-based controller and an optimal linear quadratic regulator) were evaluated using the IPG: CarMaker®simulation platform over a range of speeds. The transfer-function-based controller was also implemented on the experimental test vehicle at low speeds (high-speed experimental implementation was not possible because of safety concerns). It was found that control gain scheduling helped to track the desired heading angles of the vehicle at various speeds. Subsequently, a lane-change manoeuvre using the test vehicle was performed to evaluate the controller further. It was found that the transfer-function-based heading-angle controller could provide a comparable performance with that of the linear quadratic regulator, while keeping the sensing requirements to a minimum; thus, it was suitable for real-time implementation in an autonomous ground vehicle.

In this paper, a rear-wheel-driven series hybrid electric vehicle which has a mechanically operated friction brake system is studied. A new cooperative control of regenerative braking and friction braking called ‘combined braking’ is proposed for this vehicle configuration. A mechanism to adjust the proportions of regenerative braking and friction braking was proposed in this paper. Further, the braking force distribution between the front wheels and the rear wheels was analysed to ensure stable braking. The brake system characteristics were considered to ensure that the driver’s feel remains the same in the new proposed combined braking strategy. The simulation results under urban driving and across the Modified Indian Driving Cycle and vehicle road testing results show that the proposed combined braking can regenerate more than twice the braking energy of conventional parallel braking. Also, with combined braking, the braking force distribution between the front wheels and the rear wheels is closer to the ideal braking force distribution curve, which is desirable to ensure stable braking.

Even though new infrastructure is being developed to meet demand, increased urbanization and vehicle ownership have increased the congestion levels in Indian cities. Attracting more travelers to public transport is an option to reduce congestion but still remains a challenge, mainly because of the uncertainty of service. A reliable and accurate system for predicting vehicle arrival can help make public transportation more attractive. An accurate prediction method should be used to provide reliable information to passengers, and accuracy depends on the input data used. Therefore, identifying the optimum inputs and incorporating them in the prediction method become important. The optimum number of inputs required for best prediction performance was identified with an analytical approach. A model-based algorithm motivated by the Kalman filter was used to predict bus travel time with the use of GPS data. A case study was conducted on two selected bus routes in the city of Chennai, India, to evaluate the prediction accuracy of the proposed method. Results obtained from the algorithm were promising and showed the prediction accuracy to be ±5 min for a prediction window of 30 min in 92% of instances. The predicted travel time can be used to provide real-time bus arrival information to the public through various media, including web pages, mobile applications, and display boards.

Even though new infrastructure is being developed to meet demand, increased urbanization and vehicle ownership have increased the congestion levels in Indian cities. Attracting more travelers to public transport is an option to reduce congestion but still remains a challenge, mainly because of the uncertainty of service. A reliable and accurate system for predicting vehicle arrival can help make public transportation more attractive. An accurate prediction method should be used to provide reliable information to passengers, and accuracy depends on the input data used. Therefore, identifying the optimum inputs and incorporating them in the prediction method become important. The optimum number of inputs required for best prediction performance was identified with an analytical approach. A model-based algorithm motivated by the Kalman filter was used to predict bus travel time with the use of GPS data. A case study was conducted on two selected bus routes in the city of Chennai, India, to evaluate the prediction accuracy of the proposed method. Results obtained from the algorithm were promising and showed the prediction accuracy to be 65 min for a prediction window of 30 min in 92% of instances. The predicted travel time can be used to provide realtime bus arrival information to the public through various media, including web pages, mobile applications, and display boards.

With the increasing incidence of multidrug-resistant organisms, there is a need for newer antibiotics. However, due to the lack of new antimicrobial agents, it is necessary to re-evaluate the older agents like minocycline which is a second-line antimicrobial agent. In this study, minocycline susceptibility testing was performed for 693 Escherichia coli, 316 Klebsiellaspp. and 89 Acinetobacterspp. Among extended spectrum beta-lactamase producing E. coliand Klebsiellaspp. percentage susceptibility to minocycline were 76 and 85, respectively. Among the carbapenem resistant E. coli, Klebsiellaspp. and Acinetobacterspp. minocycline susceptibility were 52%, 55% and 42%, respectively. Based on the susceptibility profile, minocycline can be considered for treatment of infections by multidrug-resistant organisms.

Traffic parameters in general follow some patterns and identifying them is important since it will lead to more efficient end applications. With the introduction of various automated sensors, huge amounts of data are being collected, which can be used for identifying traffic patterns. In general, traffic patterns can be classified as yearly, monthly, weekly, daily and hourly. Travel time has been recognized as one of the most important parameters to fully facilitate many of the Intelligent Transportation Systems (ITS) applications. However, to predict the travel time, its weekly, daily and hourly patterns need to be analyzed. The present study analyzes the pattern followed by Global Positioning System (GPS) based bus travel time data. The travel time pattern may be different for different days of the week and hence the analysis was carried out separately for each day of the week. A systematic statistical analysis was carried out to rank these patterns in the order of significance for each day of the week separately. The statistical test namely, the Z-test for the mean of a population of differences for „paired‟ samples data, was used for hypothesis testing at 5% level of significance using one month‟s data (with a total sample size of 5700). It was observed that all days have a similar pattern except Sunday.

Congestion and associated traffic safety are the major challenges for transportation systems all over the world in recent years. Congestion occurs when traffic demand exceeds the operational capacity of the roadway. Better management of infrastructure is required in improving the operational capacity. Traffic signals are one of the most popular traffic control strategies that have been adopted for managing the demand and to reduce congestion; but if not properly designed will lead to excessive delay and increase in travel time. Therefore, signals need to be designed based on theory of traffic flow that requires a good understanding of the traffic system. The present study proposes a macroscopic model based control scheme for Indian traffic conditions. Traffic density and speed are used as the state variables to characterize the system and the developed control scheme regulates the density inside the section.

The characterization of a closed-cell aluminum foam with the trade name Alporas is carried out here under compression loading for a nominal cross-head speed of 1 mm/min. Foam samples in the form of cubes are tested in a UTM and the average stress-strain behavior is obtained which clearly displays a plateau strength of approximately 2 MPa. It is noted that the specific energy absorption capacity of the foam can be high despite its low strength which makes it attractive as a material for certain energy-absorbing countermeasures. The mechanical behavior of the present Alporas foam is simulated using cellular (i.e. so-called microstructure-based) and solid element-based finite element models. The efficacy of the cellular approach is shown, perhaps for the first time in published literature, in terms of prediction of both stress-strain response and inclined fold formation during axial crush under compression loading. Keeping in mind future applications under impact loads, limited results are presented when foam samples are subjected to low velocity impact in a drop-weight test set-up.

Traffic data can be classified into spatial data and location-based data depending on the frame of measurement involved. Location-based traffic data pertain to information at a certain point, and spatial traffic data pertain to a longer section of the roadway. This larger coverage makes spatial traffic data better indicators of the traffic condition and the level of service of the roadway section. Therefore, spatial traffic data are preferred in any congestion mitigation program. Because the greater spatial coverage makes measurement of spatial traffic data more difficult than that of location-based data, the spatial data are usually estimated from other easily measurable location-based parameters. This paper proposes a methodology to estimate two of the most important spatial traffic parameters—density and space mean speed (SMS)—from the location-based parameters—flow and time mean speed (TMS)—under Indian traffic conditions. The estimation scheme was based on the Kalman filter and incorporated a dynamic macroscopic traffic flow model formulated in the state–space representation. This framework also included an empirical model calibrated with field data and a parameter a, which was used to relate SMS to TMS. The parameter estimation was formulated as an optimization problem, and the estimation scheme was corroborated with a comparison of density estimates with field data. Because the collection of SMS from the field was difficult, travel time collected from probe vehicle data was used as a surrogate measure to corroborate the SMS estimates. The results were promising and agreed well with the traffic behavior observed in the field.

Accurate estimation of traffic in intelligent transportation system applications, such as the advanced traveler information system and the advanced traffic management system, requires fixed location-based measurements, vehicle-based measurements, or both. Using both data sources is too expensive for most government agencies, especially in developing countries such as India, and also leads to issues related to installation and maintenance, especially on urban roads. The main drawback of vehicle-based measurements is the potential lack of participation because of privacy concerns; lack of participation would limit data collection to a sample of the population, primarily on public transport vehicles. The study aims to overcome such difficulties by using only location-based flow data for the estimation of spatial parameters, such as density and travel time. These parameters are difficult to measure or estimate on an urban arterial, especially under heterogeneous traffic conditions, because of lack of lane discipline and because of complex interactions among different vehicle types. The Lighthill–Whitham–Richards macroscopic traffic flow model discretized in both space and time was employed in the estimation scheme. The resulting partial differential equations were solved numerically with the finite difference formulation of forward–time backward–space. Both linear and exponential speed–density relationships were considered and incorporated into the macroscopic model. Linear and cubic spline interpolations of input flow values were compared. The estimated density was corroborated with the density obtained from input–output analysis. Estimated travel times were compared with manually observed travel times and travel times obtained from probe vehicles fitted with Global Positioning System devices.

In recent days of competitive world industries are getting upward pressure to keep environment green to go for sustainable manufacturing with green manufacturing. Sustainable methods are expected to provide Triple Bottom Line benefits. To improve the performance of environmental conditions, sustainability of environmental desires to be reviewed. Multi grade fuzzy approach is used to get an effectual calculation. Environmental Sustainability Index (ESI) will be calculated and examined for enhancement. An automobile manufacturing industry is being exemplified for this study. The final calculated value of ESI of the manufacturing industry is 6.1 and is in the accepted level of 6 to 8, which shows the industry is Strongly Environmental Sustainable for improving the image of company and production efficiency.

It may happen that the equations governing the response of dynamical systems have some parameters whose values may not be known a priori and have to be obtained using parameter estimation schemes. In this article, we present a parameter estimation scheme for a class of sequential hybrid systems. By hybrid systems, we refer to those systems whose response is described by different governing equations corresponding to various regimes/modes of operation along with some criteria to switch between the same. In a sequential hybrid system, the different modes are arranged in a specific sequence and the system can switch from a given mode to either the previous mode or the following mode in this sequence. Here, we consider those systems whose governing equations consist of ordinary differential equations and algebraic equations. The conditions for switching between the various modes (referred to as transition conditions) are in the form of linear inequalities involving the system output. We shall first consider the case where the transition conditions are known completely. We present a parameter update scheme along with sufficient conditions that will guarantee bounded parameter estimation errors. Then, we shall consider the case where the transition conditions are not known in the sense that some parameters in these conditions are not known. We present a parameter estimation scheme for this case. We illustrate the performance of the parameter estimation scheme in both cases with some examples.

The effects of COVID-19 are continuing to increase around the world as the pandemic claims thousands of lives. Bangladesh is no exception and has been greatly affected by SARS-CoV-2. Apart from the number of people who are or have been directly getting infected with this disease, millions of people are directly or indirectly facing many challenges to their livelihoods and the security of their food and nutritional supply, along with other societal issues created by the pandemic. In this study, a hybrid approach of online and telephone questionnaire surveys was used to investigate the food security of Dhaka city's inhabitants at household level. Approximately 80% of the respondents reported reduced income, and a quarter of respondents lost their jobs between March and June 2020. The frequency of fish consumption, an essential component of Bangladeshi diets, significantly reduced during the pandemic. This was especially apparent in affluent segments of the community. Out of the respondents, 75% reported an increase in the price of fish in Dhaka city. A range of coping strategies were observed: including decreasing the frequency of grocery shopping, shifting to online shopping, reducing consumption of high price commodities, reducing junk food consumption, cleaning fish and meat with hot water and vinegar, and increasing the consumption of protein and vitamin C rich food items. Prior to COVID-19, 80% of the households surveyed bought fish from wet markets. This number dropped to 45% during the pandemic. Many households substituted fish and meat with poultry, eggs and dried fish. About half of the households stockpiled rice, lentils and potatoes during the peak of the pandemic. However, if the pandemic lasts for a prolonged period, those living on low incomes in urban areas will experience some level of food insecurity from a reduced income or loss of work. Because of this, a large-scale sustainability policy should be undertaken to secure the food and nutritional security of low-income and middle-class household.

A Remote High Dose Rate (HDR) Brachytherapy Iridium-192 System for outpatient brachytherapy procedures necessitates a variety of applicators in order to accomplish the optimal desired treatment for the patient’s tumor involved anatomy. The Nucletron Corporation that manufactures the microselection HDR Iridium-192 treatment unit provided us with their standard applicators; however, a rectal applicator system was not available at the time of purchase. In order to provide this treatment to the patient, the physicians and the physicists designed a custom set of rectal applicators that would meet the current and immediate needs of our patients. A description of the clinical rationale and physical aspects of the rectal applicator system, including design and radiation dosimetry will be presented.

Performance enhancement of typically sluggish pneumatic brake actuators in Heavy Commercial Road Vehicles (HCRVs) is necessary for the efficacious operation of active safety systems. This study develops a robust Proportional Integral Derivative (PID) controller using the Kharitonov theorem for pneumatic brakes. To account for the inherent time delay present in pneumatic brakes, Padé approximation and state prediction methods were used. The efficacy of the brake controller when used for active safety system operation has been investigated by conducting Hardware-in-Loop (HiL) experiments. It was found that state prediction based design turned out to be a better choice for handling time delays in the brake actuator. This controller was then compared with a Sliding Mode Control (SMC) based brake controller and the performance of both controllers was comparable. Further, the state prediction based PID brake controller was found to be robust up to 100% variation in system time constant and 40% variation in time delay for different road and load conditions.