Input Velocity Research Articles

Adaptive global prescribed performance control for rigid spacecraft subject to angular velocity constraints and input saturation

Adaptive global prescribed performance control for rigid spacecraft subject to angular velocity constraints and input saturation

Trajectory tracking control of nonholonomic wheeled mobile robots using only measurements for position and velocity

This paper studies the trajectory tracking control of nonholonomic wheeled mobile robots, and only measurements for Cartesian position and velocity are used. A full-order observer is proposed to estimate the unmeasurable attitude and filter the position signal. Its effectiveness relies on the persistent excitation (PE) condition of non-zero linear velocity. Using the observer states, we design a local tracking controller that guarantees: (a) the linear velocity input is positive, and (b) the position tracking error is convergent to zero locally asymptotically. Experiments performed on a robot built in-house illustrate the control algorithm.

Transient mass and heat transport modeling in a multi-tray packed bed solid desiccant dehumidifier: A parametric analysis

Using desiccant dehumidification technology, it is possible to dry the air before it reaches a climate-controlled area. Additionally, the development of theoretical models is crucial to understanding the intrinsic processes of this technology due to the interdependent physics involved in transport, adsorption, and heating dynamics. This study develops a computer model to better explain transient mass and heat transport in a multi-tray packed bed solid desiccant dehumidifier (MPBDD). A two-dimensional (2D) model that considers the conservation of mass, momentum, and heat is built. A Linear Driving Force (LDF) model is used in the study to theoretically characterize the moisture adsorption properties of the solid desiccant (Silica gel). Also, Saha, Boelman and Kashiwagi (S-B-K) equation has been applied to calculate the equilibrium water concentration. The model's validity is evaluated by contrasting theoretical predictions with experimental findings also testing with a different published article. To explore how the dehumidification system behaves under various operating situations and parameter variations, a parametric analysis was conducted. The system inlet temperature, inlet relative humidity, and bed length are among the analyzed variables. It was discovered that the highest performance was achieved with an input velocity of 0.4 m.sec−1, relative humidity of 54 %, and bed length of 0.15 m.

Vascular Age Assessed From an Uncalibrated, Noninvasive Pressure Waveform by Using a Deep Learning Approach: The AI-VascularAge Model.

Aortic stiffness, assessed as carotid-femoral pulse wave velocity, provides a measure of vascular age and risk for adverse cardiovascular disease outcomes, but it is difficult to measure. The shape of arterial pressure waveforms conveys information regarding aortic stiffness; however, the best methods to extract and interpret waveform features remain controversial. We trained a convolutional neural network with fixed-scale (time and amplitude) brachial, radial, and carotid tonometry waveforms as input and negative inverse carotid-femoral pulse wave velocity as label. Models were trained with data from 2 community-based Icelandic samples (N=10 452 participants with 31 126 waveforms) and validated in the community-based Framingham Heart Study (N=7208 participants, 21 624 waveforms). Linear regression rescaled predicted negative inverse carotid-femoral pulse wave velocity to equivalent artificial intelligence vascular age (AI-VA). The AI-VascularAge model predicted negative inverse carotid-femoral pulse wave velocity with R2=0.64 in a randomly reserved Icelandic test group (n=5061, 16%) and R2=0.60 in the Framingham Heart Study. In the Framingham Heart Study (up to 18 years of follow-up; 479 cardiovascular disease, 200 coronary heart disease, and 213 heart failure events), brachial AI-VA was associated with incident cardiovascular disease adjusted for age and sex (model 1; hazard ratio, 1.79 [95% CI, 1.50-2.40] per SD; P<0.0001) or adjusted for age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, prevalent diabetes, hypertension treatment, and current smoking (model 2; hazard ratio, 1.50 [95% CI, 1.24-1.82] per SD; P<0.0001). Similar hazard ratios were demonstrated for incident coronary heart disease and heart failure events and for AI-VA values estimated from carotid or radial waveforms. Our results demonstrate that convolutional neural network-derived AI-VA is a powerful indicator of vascular health and cardiovascular disease risk in a broad community-based sample.

A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams

Abstract. Ice sheet models are the main tool to generate forecasts of ice sheet mass loss, a significant contributor to sea level rise; thus, knowing the likelihood of such projections is of critical societal importance. However, to capture the complete range of possible projections of mass loss, ice sheet models need efficient methods to quantify the forecast uncertainty. Uncertainties originate from the model structure, from the climate and ocean forcing used to run the model, and from model calibration. Here we quantify the latter, applying an error propagation framework to a realistic setting in West Antarctica. As in many other ice sheet modelling studies we use a control method to calibrate grid-scale flow parameters (parameters describing the basal drag and ice stiffness) with remotely sensed observations. Yet our framework augments the control method with a Hessian-based Bayesian approach that estimates the posterior covariance of the inverted parameters. This enables us to quantify the impact of the calibration uncertainty on forecasts of sea level rise contribution or volume above flotation (VAF) due to the choice of different regularization strengths (prior strengths), sliding laws, and velocity inputs. We find that by choosing different satellite ice velocity products our model leads to different estimates of VAF after 40 years. We use this difference in model output to quantify the variance that projections of VAF are expected to have after 40 years and identify prior strengths that can reproduce that variability. We demonstrate that if we use prior strengths suggested by L-curve analysis, as is typically done in ice sheet calibration studies, our uncertainty quantification is not able to reproduce that same variability. The regularization suggested by the L curves is too strong, and thus propagating the observational error through to VAF uncertainties under this choice of prior leads to errors that are smaller than those suggested by our two-member “sample” of observed velocity fields.

Parametric investigation of premixed gas inlet conditions effects on flow and combustion characteristics

The need to examine combustion devices' combustion characteristics and reduce emissions has gained importance with the implementation of the Energy Related Products Directive in Europe. Therefore, this study aims to investigate flow and combustion characteristics, including emissions in the combustion domain of a condensing boiler. Different premixed gas input velocities and temperatures in the premixed gas burner are considered in the numerical study validated with literature. Effects of inlet velocity and temperature to flow and combustion characteristics are examined parametrically. Variations of velocity and temperature along the combustion domain and CO and NO emissions are obtained at the outlet region.Variations in premixed gas inlet velocity directly influence the residence time and average temperature of the flue gases, therefore, CO and NO emissions. The velocity profiles are effective for flame structure and emission production. In terms of emissions, a substantial escalation occurs in outlet CO emissions with the increase in gas velocity. An increase in NO emissions is observed with the rise in average temperatures and a decrease in NO emissions is observed by reducing residence time. It is concluded that crucial flow and combustion characteristics can be estimated with the use of the current approach to contribute to design improvements and pollutant emission reduction.

Dual-supported nanoscale zero-valent iron for As(III) contaminated groundwater remediation

Nanoscale zero-valent irons (nZVIs) are easy to agglomerate and deactivate, which limited the practical applications in the subsurface for in situ remediation of contaminated groundwater due to poor radius of influence. Here, we utilized the dual support effect of coupling surface-modified carbon black with a surfactant (carboxymethyl cellulose (CMC) or sodium dodecyl sulfate (SDS)) to manipulate the charge, electron, and geometric structure of the interface, the obtained Fe/CB/CMC (wt%=1:1:2) and Fe/CB/SDS (wt%=1:2:1) suspensions exhibited superior removal capacity toward As(III) with capacities of 80.7 and 90.7 mg g-1, respectively. In a clay medium, 25.2% and 18.0% of nZVIs in Fe/CB/CMC-1:1:2 and Fe/CB/SDS-1:2:1 can break through the column while approximately 100% are retained in bare nZVIs under the same conditions. In addition, input velocity and ionic strength demonstrate a positive correlation with the retention of nZVIs in the Fe/CB/CMC-112 and a slight impact on the deposition of nZVIs in the Fe/CB/SDS-121 due to the rheology and charge differences between CMC and SDS. This study demonstrates the feasibility of combining anionic surfactants and carbon supports to enhance the reactivity and mobility of nZVIs for in situ remediation.

Heat transfer enhancement and pressure loss analysis of hydrogen-fueled microcombustor with slinky projection shape channel for micro-thermophotovoltaic system

Micro thermophotovoltaic (MTPV) system, as a type of micro-combustion power system, possesses several advantages, including higher energy density, sustained and stable energy output, and significantly extended service life. The energy conversion performance of the MTPV system is known to be closely correlated with the thermal performance of the microcombustor. This study proposes a unique microcombustor for the MTPV system with a slinky projection form channel. The influence of the amplitude of the slinky projection, the slinky projection fins number, and the basic oscillating channel radius on the external wall temperature, temperature uniformity, and pressure loss are investigated by adopting the detailed hydrogen/air reaction mechanism with 3-D numerical models. The results show that the increase of the slinky projection amplitude and the slinky projection fins number can improve the external wall temperature. Compared to the traditional microcombustor, the average external wall temperatures of the microcombustor with a slinky projection amplitude of 0.4 mm are 31.6 K lower at an input velocity of 5 m/s. Additionally, when the input mixture velocity is 5 m/s, the average outer wall temperature improves by 29.4 K as the number of slinky projection fins grows from 10 to 42, and the pressure drop increases by 1.4 times. Furthermore, the basic oscillating channel radius is suggested to be 1.5 mm. In summary, this research provides a valuable method for enhancing the energy production of the MTPV system.

Mapping structures on the core–mantle boundary using Sdiff postcursors: Part I. Method and Validation

SUMMARY Ultra-low velocity zones (ULVZs) are patches of extremely slow seismic velocities on the core–mantle boundary (CMB). Here, we target them using the postcursors to S core-diffracted phases (Sdiff) caused by ULVZs. We use traveltimes of these postcursors to make probabilistic maps using a reversible-jump Markov chain Monte Carlo inversion setup. For the forward model, we extend 2-D wave front tracking (2DWT) software, previously developed for surface wave multipathing studies, to the CMB. The 2DWT is able to model the full multipathing behaviour of Sdiff postcursors and compute arrival times for a given ULVZ input velocity structure on the order of a few CPU seconds, as opposed to 100s of CPU hours required for 3-D full waveform synthetics. We validate the method using synthetic data sets produced by the 2DWT, as well as 3-D full waveform synthetics, using a parametrisation formed from a collection of ellipses. We also test idealistic data coverage versus a case of more realistic coverage. We show ULVZ size and velocity reduction can typically be well recovered, and our maps show the inherent trade-off between these parameters around the edge of the ULVZ. Our method cannot directly constrain ULVZ height; tests show that it underestimates ULVZ velocity reductions and overestimates ellipticity for thinner ULVZs due to neglecting mantle effects.

Robust consensus control of second-order uncertain multiagent systems with velocity and input constraints

In this paper, we investigate the consensus problem of second-order multiagent systems under directed graphs. Simple yet robust consensus algorithms that advance existing achievements in accounting for velocity and input constraints, agent uncertainties, and lack of neighboring velocity measurements are proposed. Furthermore, we show that the proposed method can be applied to the consensus control of uncertain robotic manipulators with velocity and control torque constraints. We rigorously prove that the velocity and control inputs stay within prespecified ranges by tuning design parameters a priori and that asymptotic consensus can be achieved through Lyapunov functions and fixed-time stability. Simulations are performed for symmetric and asymmetric constraints to show the efficiency of the theoretical findings.

Each step time-limited iterative learning control for a cushion robot with motion velocity constraints

An each step time-limited iterative learning control model was developed for a cushion robot with velocity constraints. A predictive modeling method was proposed to solve the velocity mutation problem by employing a kinematic model to constrain the velocity inputs, which can then constrain the robot’s actual velocity. Furthermore, a tracking error system was established that used constrained motion velocity and a dynamic model. A new iterative controller with each step time-limited learning was built to reduce the transient adjustment time. Simulation results and comparative analyses revealed that the proposed control method quickly stabilizes the system and ensures that the human–robot system operates at a safe velocity.

ANALISA PERFORMA HEAT EXCHANGER DENGAN PENAMBAHAN VORTEX GENERATOR TIPE DELTA WING DAN DELTA WINGLET TAPE INSERT GUNA MENGATASI ICING PADA KARBURATOR PISTON ENGINE

The simple simulation tool of the heat exchanger has a limitation, that is, when the heat transfer process cannot be analyzed directly, and the flow motion cannot be observed. Therefore it can be solved by the CFD method. The purpose of this study is to provide observable visual images. In this simulation, it uses a tube given a vortex generator with delta variations adding angles, each of which is given a constant inlet velocity, inlet temperature, and heatflux input values. Each angle is given a value of 200, 400, 600. This study aims to obtain a vortex generator with the best angular variation. From the research that has been carried out, it shows that the addition of delta provides a difference in the results of each specimen tested, the addition of a delta winglet with the addition of an angle of 400 is produced to have the best heat transfer coefficient, it is shown at a low temperature difference (ΔT). Analysis of the results of a low temperature difference (ΔT) means that the specimen performed good heat transfer performance.

Safety-Certified Constrained Control of Maritime Autonomous Surface Ships for Automatic Berthing

The berthing of maritime autonomous surface ships (MASSs) is a challenging operation even for an experienced captain due to the required complicated maneuvering at low speeds in a constrained water space. This paper addresses safety-aware automatic berthing of MASSs moving in a constrained water region with stationary and moving obstacles. A safety-critical constrained anti-disturbance control method is proposed for the automatic berthing of MASSs subject to velocity constraints, input constraints, and collision-avoidance constraints, as well as ocean disturbances. At the kinematic level, desired linear velocities and yaw rate are prescribed by incorporating a line-of-sight guidance scheme to achieve position-heading stabilization. At the kinetic level, an anti-disturbance kinetic control law is designed based on an extended state observer. Through converting the input constraints to velocity constraints on guidance signals, the safety-guidance signals are obtained by solving a quadratic programming problem subject to the state, input, and collision-avoidance constraints. It is proven that an MASS controlled by using the proposed method is input-to-state safe in the presence of ocean disturbances. Simulation results are elaborated to substantiate the efficacy of the proposed safety-certified control law for the automatic berthing of an MASS subject to physical constraints and environmental disturbances.

Implementation of Deep Neural Networks for performance prediction and optimization of a porous volumetric solar receiver considering mechanical safety

The volumetric solar receiver is a major component of concentrating solar thermal systems. Proper selection of design and operating parameters is necessary to obtain the best receiver performance. The current work models and optimizes the hydraulic, thermal, and mechanical performance of a porous volumetric solar receiver using the Deep Neural Network (DNN) technique. The dataset for DNN training is generated by numerical simulations and porosity and pore size of foam structure, inlet fluid velocity and absorber length are selected as the input parameters. The pressure drop, outlet fluid temperature and maximum failure index are selected as the output variables. The trained model demonstrates high accuracy in performance prediction for any combination of input variables in the trained range. Further, the present work proposes the integration of the DNN technique with Genetic Algorithm for the constrained multi-objective optimization of the absorber performance with a novel constraint of maximum failure index to select configurations preventing mechanical failure. Such integration is found to be nearly 524 times faster than the conventional direct approach, demonstrating a significant decrease in the time needed for optimization. Furthermore, the potential of the Technique for Order of Preference by Similarity to Ideal Solution in selecting the desired configuration from the Pareto optimal solutions as per individual requirements is also investigated. The optimization results indicate that the optimum values for porosity, pore size, and absorber length are in the range of 0.9–0.93, 2–4 mm, and 0.01–0.06 m, respectively, when all variables are optimized simultaneously. Furthermore, the optimal input velocity is observed to be close to the lower bound of 0.4 m/s while the corresponding maximum failure index varies from 0.346 to 0.836, indicating mechanically safe configurations. The high accuracy and faster optimization results obtained in the present work demonstrate the ability of the proposed approach in implementing quick and computationally cheap optimization studies for improved and mechanically safe absorber designs.

Adaptive path following control for wave gliders in ocean currents and waves

In this paper, a design method is presented for path following control of wave gliders (WGs) subject to velocity and rudder input constraints, as well as marine currents and wave disturbances. The control method integrates an adaptive integral line-of-sight (AILOS) guidance law, an adaptive target-path-switching control and a modified incremental PID heading control for WGs with the multi-straight-line path following, which can extremely overcome the influence of such marine currents interference and guarantee path following accuracy. For the control loop, adaptive adjustments on the parameters of the lookahead distance and the error-circle radius are built to provide a current-influenced position error compensation suitable for the time-varying velocity of the WG. Additionally, an online wave filter using an extended Kalman filter (EKF) is developed to address the issue of wave-induced disturbance influence worked on the position and heading of the WG. Further, simulations and sea trials demonstrate both tracking accuracy and high robustness under ocean disturbances with rudder constraints.

Transient numerical simulation of heat recovery coke oven based on chemical percolation devolatilization model

ABSTRACT The actual coking engineering challenge is changed into a numerical simulation calculation problem due to the difficulty in monitoring the parameters of the heat recovery coke furnace. The release law of coal volatiles is calculated using the chemical percolation devolatilization (CPD) model in this study. To explore the distribution properties of the temperature field and flow field in the furnace as well as the burning condition of the surface of the coal cake, the user-defined function (UDF) is coupled with the porous medium model, and the transient condition simulation is carried out. The accuracy of the model is verified by comparing the temperature values at different times with the actual parameters. According to the calculations, the fuel burns more fully when the equivalency ratio is close to 1, and that the flame propagation speed increases with combustion temperature. When the bulk density is 1100 kg/m3 and the air input velocity is 13.3 m/s, the reaction lasts for 15 hours and the emission of volatiles is very minimal, resulting in a poor combustion reaction. The coal cake surface’s major temperature range is 1200 K to 1400 K after 30 hours of reaction time, which is beneficial to accelerate the maturation of coke. The coal cake surface’s temperature distribution at 45 and 60 hours is lower than that of the coke. Additionally, an increase in air and flame propagation velocity can strengthen furnace disturbance, enhance the temperature and velocity distribution uniformity, hasten coke ripening, and enhance coke quality.

Trajectory Tracking Control Study of Unmanned Fully Line-Controlled Distributed Drive Electric Vehicles

Unmanned fully line-controlled distributed drive electric vehicles with four-wheel independent drive, dependent braking and dependent steering have significant advantages over conventional vehicles in terms of dynamic control, but at the same time multiple actuators with multiple degrees of freedom also pose the risk of failure in the steering system, which is studied in this paper for trajectory tracking control. Rational control of multiple systems such as drive, braking, steering and fault tolerance of the unmanned fully line-controlled distributed drive electric vehicles are carried out. For longitudinal control, a fuzzy PI algorithm is used to input velocity error and velocity error rate of change, and to solve the required drive torque of the vehicle based on fuzzy rules; for lateral control, according to model prediction control theory, the exact model is predicted and an optimized search is performed to reasonably allocate the forward and backward wheels turning corners ensuring the accuracy and roadholding of trajectory tracking; for fault-tolerant control, differential drive and other methods of control, when a fault is detected, the number and position information of the faulty steering motor is transmitted to the fault-tolerant decision module, which outputs control commands according to the decision. The outcomes demonstrate that the presented trajectory following the control policy enhances the precision, roadholding and safety of trajectory following in an effective way.

Nussbaum-based finite-time containment control for multi-UAVs with input saturation and velocity constraints

This paper devotes to addressing the finite-time containment control for the multi-UAVs with input saturation and velocity constraints. A novel smooth function is presented to estimate the saturation function with adjustable estimate errors. By utilizing the mean-value theorem, the containment control for UAVs with input saturation can be turned into a variable gain control problem. To address this issue, the Nussbaum-based method is used to design the controller in this paper. Besides, based on the backstepping method, the controller can constrain the velocity states of UAVs within a desired time-varying asymmetric region by limiting the values of virtual velocity commands and the Lyapunov function. Finally, the finite-time convergence of the errors and the practicability of the control law are verified by Lyapunov stability analysis and numerical simulations.

Contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with ASD and XYY syndrome.

The M50 electrophysiological auditory evoked response time can be measured at the superior temporal gyrus with magnetoencephalography (MEG) and its latency is related to the conduction velocity of auditory input passing from ear to auditory cortex. In children with autism spectrum disorder (ASD) and certain genetic disorders such as XYY syndrome, the auditory M50 latency has been observed to be elongated (slowed). The goal of this study is to use neuroimaging (diffusion MR and GABA MRS) measures to predict auditory conduction velocity in typically developing (TD) children and children with autism ASD and XYY syndrome. Non-linear TD support vector regression modeling methods accounted for considerably more M50 latency variance than linear models, likely due to the non-linear dependence on neuroimaging factors such as GABA MRS. While SVR models accounted for ~80% of the M50 latency variance in TD and the genetically homogenous XYY syndrome, a similar approach only accounted for ~20% of the M50 latency variance in ASD, implicating the insufficiency of diffusion MR, GABA MRS, and age factors alone. Biologically based stratification of ASD was performed by assessing the conformance of the ASD population to the TD SVR model and identifying a sub-population of children with unexpectedly long M50 latency. Multimodal integration of neuroimaging data can help build a mechanistic understanding of brain connectivity. The unexplained M50 latency variance in ASD motivates future hypothesis generation and testing of other contributing biological factors.

Fate of ejected debris from the explosion of the rear surface of fused silica in the high-power laser facility

This study focuses on the issue of uncontrollable debris generated by laser-matter interactions in high-power laser facilities, which significantly impairs operational efficiency. The maximum motion distance of debris, referred to as the safety distance, is a crucial parameter that this work investigates. Three prediction methods that utilize aerodynamic coefficients and trajectories are proposed, with input parameters derived from velocity curves extracted from high-resolution time-resolved images. The results obtained from these methods show good agreement with experimental data collected under laser fluence conditions ranging from 18 to 79 J/cm2 that damage the rear surface of fused silica. The analytically predicted safety distance, with an input velocity fitted at 0 ns and an upper diameter limit of 10 µm, was closest to the experimental result at 155 mm. Analytically predicted values with input velocities fitted at 13,000 ns and all diameter ranges did not exceed 192.3 mm. Numerically predicted values, with input velocity fitted at 13,000 ns and a median diameter of small debris (5 µm), were 140.4 and 295.2 mm for aspect ratios of 2 and 4, respectively. The numerical method is recommended for predicting the safety distance in high-power laser facilities, but obtaining accurate estimates of transient velocity is crucial for the method’s effectiveness.