Adaptive Dynamic Method Research Articles

Unmanned surface vessel heading control of model-free adaptive method with variable integral separated and proportion control

Based on model-free adaptive control theory, the heading control problem of unmanned surface vessels under uncertain influence is explored. Firstly, the problems of compact form dynamic linearization model-free adaptive control method applied to unmanned surface vessel heading control are analyzed. Secondly, by introducing proportional control and variable integral separation factor, an variable integral separation model-free adaptive control algorithm with proportional control is proposed. The introduction of proportional control and variable integral separation factor solves the problems of oscillation, instability, and integral saturation when rudder angle is controlled directly to control the heading of unmanned surface vessel with compact form dynamic linearization model-free adaptive control method. Finally, the effectiveness of the method is verified by the simulation and field experiments results of heading control with model perturbation and system time delay in unmanned surface vessel heading subsystem.

Numerical study on wave phenomena produced by the super high-speed evacuated tube maglev train

With the increasing demand of faster, safer, and more comfortable rail travel, the evacuated tube maglev train (ETMT) has gradually attracted hot concerns in recent years. According to Kantrowitz limit theory, ETMT may encounter chocked flows, leading to more complex generation and development of a series of waves as ETMT moves rapidly in the tube. To study the wave phenomena produced by ETMT running at the super high-speed, the computational domain geometry model was simplified and then 2-D axisymmetric compressible N-S equation was established in the paper. Dynamic mesh method and dynamic adaptive mesh method were used to simulate the real motion of ETMT and improve the capture accuracy of the waves respectively. Results show that the wave structure is mainly composed of expansion waves, reflected shock waves and normal shock waves as ETMT moves for enough time at the speed of 1250 km/h. The intensity of the normal shock wave in front of the head car experiences four states consisting of rapid increase, initial stability, sudden drop and final stability. In the wake, the flow velocity attenuates in a fluctuating way along the opposite motion direction of ETMT due to the complicated interaction between shock waves and expansion waves.

Fetal Heart Rate Monitoring Implemented by Dynamic Adaptation of Transmission Power of a Flexible Ultrasound Transducer Array.

Fetal heart rate (fHR) monitoring using Doppler Ultrasound (US) is a standard method to assess fetal health before and during labor. Typically, an US transducer is positioned on the maternal abdomen and directed towards the fetal heart. Due to fetal movement or displacement of the transducer, the relative fetal heart location (fHL) with respect to the US transducer can change, leading to frequent periods of signal loss. Consequently, frequent repositioning of the US transducer is required, which is a cumbersome task affecting clinical workflow. In this research, a new flexible US transducer array is proposed which allows for measuring the fHR independently of the fHL. In addition, a method for dynamic adaptation of the transmission power of this array is introduced with the aim of reducing the total acoustic dose transmitted to the fetus and the associated power consumption, which is an important requirement for application in an ambulatory setting. The method is evaluated using an in-vitro setup of a beating chicken heart. We demonstrate that the signal quality of the Doppler signal acquired with the proposed method is comparable to that of a standard, clinical US transducer. At the same time, our transducer array is able to measure the fHR for varying fHL while only using 50% of the total transmission power of standard, clinical US transducers.

Critical Events Based Resource Layer Structure Dynamic Adaptive Optimization Method

Critical Events Based Resource Layer Structure Dynamic Adaptive Optimization Method

A Kriging-Based Dynamic Adaptive Sampling Method for Uncertainty Quantification

A Kriging-Based Dynamic Adaptive Sampling Method for Uncertainty Quantification

Supercomputer Simulations of Fluid-Structure Interaction Problems Using an Immersed Boundary Method

The paper describes a supercomputer application in simulations of fluid-structure interaction problems. A compressible flow solver based on a high-accuracy scheme for unstructured hybrid meshes is considered. It combines an immersed boundary method with a dynamic mesh adaptation method in order to represent motion of solid objects in a turbulent flow. The use of immersed boundaries allows you to dynamically adapt the mesh resolution near moving solid surfaces without changing the mesh topology. Multilevel MPI + OpenMP parallelization of these components fits well with the architecture of modern cluster systems. The proposed implementation can engage thousands of CPU cores in one simulation efficiently. An example application is presented in which a high-speed turbulent flow around a cavity with a deflector is simulated.

Dynamic adaptive acceleration of chemical kinetics with consistent error control

The incorporation of detailed chemistry in combustion simulations is challenging due to the large number of chemical species and the wide range of chemical timescales. The performance of acceleration methods such as tabulation/retrieval strategies may deteriorate dramatically when large variation in the accessed composition space is present. In this study, a dynamic adaptive acceleration method (DAAM) is proposed, in which in situ adaptive tabulation (ISAT) or dynamic adaptive chemistry (DAC) is dynamically selected for chemistry integration based on the encountered composition inhomogeneity. The principle component analysis (PCA) of instantaneous representative compositions is employed to identify a low-dimensional subspace, in which the composition inhomogeneity of the computational cells is quantified through reconstructing the histogram of composition. ISAT is invoked for cells being in composition regions with high cell/particle numbers to avoid unnecessary tabulations and DAC is employed for the remaining ones by invoking on-the-fly reduction to generate small skeletal mechanisms for local thermo-chemical conditions and therefore accelerates the chemistry integration. A heuristic approach that dynamically adjusts the DAC reduction threshold based on the user-defined ISAT error tolerance has been proposed for DAAM, which enables a single, intuitive error control parameter for the combined use of these two methods and more importantly enables rigorous local error control. DAAM have been demonstrated in internal combustion engine (ICE) model simulations and premixed charge compression ignition (PCCI) engine simulations of n-heptane/air mixture, respectively. DAAM can improve the acceleration performance up to 50% compared to standalone ISAT while maintaining the same level of accuracy in temperature and species. It also shows advantage in speedup performance over the fixed ISAT-DAC method at the same level of accuracy.

Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

This paper presents a method for dynamic parameter adaptation in the harmony search algorithm (HS) based on fuzzy logic. The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. Experiments are presented with the HS, FHS, IT2FHS, and GT2FHS with noise (uniform random number) and without noise for the controller, and the following error metrics are obtained: ITAE, ITSE, IAE, ISE, and RMSE, to validate the efficacy of the proposed methods.

Computational acceleration of multi-dimensional reactive flow modelling using diesel/biodiesel/jet-fuel surrogate mechanisms via a clustered dynamic adaptive chemistry method

This study proposes a clustered dynamic adaptive chemistry (CDAC) method, which uses an iterative K-means algorithm to partition the computational cells into different groups according to their similarity in terms of the temperature and significant species compositions. Taking advantage of the clustered cells, the averaged thermo-chemical properties of the cells in the respective clusters are then used to identify the adaptive dynamic reduced chemistry through the method of direct relation graph with error propagation (DRGEP). Moreover, the integration of the chemical source term, which commonly dominates the computational effort in reactive flow simulations, is now performed using the dynamic adaptive reduced chemistry at the cluster level instead of the cell level. With this CDAC method, the on-the-fly DRGEP process as well as the chemistry integration only needs to be conducted at the cluster level, dramatically reducing the unnecessary repeated computation for similar computational cells. In addition, the adaptive dynamic reduced chemistry further accelerates the chemistry integration process due to less ordinary differential equations (ODEs) to be solved for each cluster. This newly proposed CDAC method was tested in multi-dimensional homogeneous charged compression ignition engine (HCCI), direct injection compression ignition (DICI) engine and constant volume chamber combustion (CVCC) fueled with diesel, biodiesel and kerosene through the use of their respective surrogate fuel mechanisms under different operating conditions. The performance of CDAC in terms of accuracy and efficiency were extensively analyzed and discussed using different user-defined parameters, mechanisms with different surrogate components and number of species as well as different meshes with different number of grid cells. Based on this analysis, the error tolerances in DRGEP and the error tolerances of the temperature and significant species’ mass fraction are recommended as: εd ≤ 0.001, εT ≤ 20 K and εY ≤ 0.01 to achieve less than 0.1% integral error. With these recommended user-defined tolerances, it can be observed that the current CDAC method is able to accurately predict the in-cylinder pressure as well as the species profile in HCCI, DICI and the flame lift-off length in CVCC when compared with the full chemistry calculations as well as the experimental results. Moreover, with the recommended user defined error tolerances and a chemical kinetic model of 112 species, this CDAC method is capable of achieving a computational time speed-up factor of more than 3 compared to the conventional DAC and of almost 5 compared to the full chemistry. Finally, the CDAC method coupled CFD was applied to simulate a diesel DICI engine under three different engine speeds with a detailed primary reference fuel (PRF) mechanism of more than 1000 species. The 3-D simulation could be finished in acceptable CPU time while being able to well capture the experimental in-cylinder pressure and heat release rate for all the three engine speeds.

Policy formulation for highly automated vehicles: Emerging importance, research frontiers and insights

Highly automated vehicles (HAVs) generate the optimistic prospect of future smart mobility together with the disruptive influence of traditional policies. Formulating appropriate policies based on applicable methods are necessary to cope with the potential uncertainties of HAVs. By reviewing the literature in a structural manner, this paper analyzes the emerging importance and research frontiers in formulating HAV policies and presents insights gained from three major methods of dealing with uncertain, dynamic, and evolving transport problems. First, the formulation of HAV policy is important for at least three reasons: it may accelerate the development and control of potential uncertainties of HAV, balance technology innovations with traffic security, and provide a steady and efficient migration from human drivers to automated driving systems. Second, current research focuses mainly on the role of government, licensing and testing standards, certification, reliability, policy interventions, public health, legal challenges, and restrictive or supportive policies. A common research framework and methodology of HAV systems has not yet been established to deal with the uncertainties of technology security. Finally, three potential methods of formulating HAV policy are identified herein, namely (1) the backcasting method, which could determine the future of HAV objectives and pathways; (2) the dynamic adaptive method, which makes the policy transition to HAV systems more organic and dynamic; and (3) the policy transfer and migration method, which provides a clear vision of the adaptation procedure. These methods are used in different circumstances to formulate HAV policies. Each method has its pros and cons. The present review provides insights into formulating future HAV policies using these methods.

A numerical investigation on the injection timing of boot injection rate-shapes in a kerosene-diesel engine with a clustered dynamic adaptive chemistry method

In this study, we conducted a numerical investigation on the effect of injection timing of boot injection rate shape on the combustion and emission characteristics in a direct injection compression ignition (DICI) engine fueled with kerosene/diesel blending. Considering the complex surrogate in kerosene chemical mechanisms and the huge computational workload in multi-dimensional engine simulations, we employed a clustered dynamic adaptive chemistry method (CDAC) to accelerate the chemistry integration process. This study firstly specified the user-defined parameters in this CDAC method by sensitivity analysis in a HCCI and DICI engine with different user-defined parameter combinations. With these specified parameters, CDAC is then validated by comparing its predicted in-cylinder pressure with the full chemistry ones. It is found that the current CDAC method could reduce the computational time by more than 60% compared with the full chemistry CPU time. CDAC, subsequently, is used to conduct the numerical investigation on the injection timing of boot injection rate shapes. Four different boot injection rate shapes are simulated and compared with the normal rectangular injection. The effect injection timing of the boot injection rate on the engine performance and combustion/emission characteristic is then analyzed in detail. It is found that the change of start of injection (SOI) in boot injection has little influence of the ignition delay in the DICI engine fuelled with diesel and kerosene blending due to the high cetane number of diesel and better volatility of kerosene. In addition, with kerosene addition into the diesel combustion, it is observed that the CO emission could be reduced at all the varied SOI.

Cooperative and priority based on dynamic resource adaptation method in wireless network

In recent years, network traffic has been increasing and when large events or natural disasters occur more network resources are requested at end points of network. Also, with the spread of smart devices can communicate with high-speed such as LTE, anyone are becoming to be able to communicate with high-speed. In order to efficiently handle traffic that locally and temporarily increases, it is effective to utilise smart devices owned by users. However, because there is a limit to the amount of the traffic that a smart device can handle, it is necessary to cooperate smart devices, nevertheless a system that cooperates smart devices and aggregates network resources has not been established. In this paper, we proposed a dynamic resource adaptation method that aggregates the network resources of smart devices and increases the available bandwidth. In evaluation experiments, a relationship between the amount of smart devices and network throughput was evaluated.

Dynamic Surface Adaptive Robust Control of Unmanned Marine Vehicles with Disturbance Observer

This paper presents a dynamic surface adaptive robust control method with disturbance observer for unmanned marine vehicles (UMV). It uses adaptive law to estimate and compensate the disturbance observer error. Dynamic surface is introduced to solve the “differential explosion” caused by the virtual control derivation in traditional backstepping method. The final controlled system is proved to be globally uniformly bounded based on Lyapunov stability theory. Simulation results illustrate the effectiveness of the proposed controller, which can realize the three-dimensional trajectory tracking for UMV with the systematic uncertainty and time-varying disturbances.

Cooperative and priority based on dynamic resource adaptation method in wireless network

Cooperative and priority based on dynamic resource adaptation method in wireless network

Multivariate Symbolic Transfer Entropy Analysis of Different Age Groups

The life activities of the human body are closely related to age, and the life process is constantly associated with the entropy change. In this paper, multivariate symbolic transfer entropy algorithm was proposed based on traditional symbolic transfer entropy, then we also adopted the dynamic adaptive method to symbolize the time series. This algorithm was used to study the relationship between multivariate symbolic transfer entropy and age. Experiments showed that the value of multivariate symbolic transfer entropy of middle-aged man is greater than that of the young. The hypothesis test can prove that the multivariate symbolic transfer entropy shows a significant difference in age. Thus, multivariate symbolic transfer entropy algorithm can provide a new method for the study of other physiological signals.

Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits.

A critical concern of autonomous vehicles is safety. Different approaches have tried to enhance driving safety to reduce the number of fatal crashes and severe injuries. As an example, Intelligent Speed Adaptation (ISA) systems warn the driver when the vehicle exceeds the recommended speed limit. However, these systems only take into account fixed speed limits without considering factors like road geometry. In this paper, we consider road curvature with speed limits to automatically adjust vehicle’s speed with the ideal one through our proposed Dynamic Speed Adaptation (DSA) method. Furthermore, ‘curve analysis extraction’ and ‘speed limits database creation’ are also part of our contribution. An algorithm that analyzes GPS information off-line identifies high curvature segments and estimates the speed for each curve. The speed limit database contains information about the different speed limit zones for each traveled path. Our DSA senses speed limits and curves of the road using GPS information and ensures smooth speed transitions between current and ideal speeds. Through experimental simulations with different control algorithms on real and simulated datasets, we prove that our method is able to significantly reduce lateral errors on sharp curves, to respect speed limits and consequently increase safety and comfort for the passenger.

Effects of the turbulence model and the spray model on predictions of the n-heptane jet fuel–air mixing and the ignition characteristics with a reduced chemistry mechanism

Reynolds-averaged Navier–Stokes simulations with an improved spray model and a realistic chemistry mechanism are performed for turbulent spray flames under diesel-like conditions in a constant-volume chamber. Comprehensive numerical analyses including two turbulence models (the renormalisation group k– ε model and the standard two-equation k– ε model) with different model coefficients are made. The distribution of the fuel mixture fractions is a very important factor affecting the combustion process. In this study, we also use the entrainment gas-jet model, modifications of the the spray model coefficient and two turbulence models to investigate extensively the influence of the gas-jet theory model on the fuel–air mixture process. First, a non-reacting case is validated by comparing the liquid-phase penetration and the vapour-phase penetration and also the mixture fractions at different axis positions. Second, approriate methods are confirmed according to accurate mixture fraction distributions to validate the combustion process. Because of the large number of species and reactions, the calculation of chemically reacting flows is unaffordable, particularly for three-dimensional simulations. Hence, the dynamic adaptive chemistry method for efficient chemistry calculations is extended in this work to reduce the computational cost of the spray combustion process when a reduced chemistry mechanism is used. The results show that, in the evaporation case, the gas-jet theory model can be used to obtain a relatively accurate fuel vapour penetration length with different influential factors and that improved numerical methods can effectively reduce the mesh dependence for the spray evaporation process. It is demonstrated that the Schmidt number Sc and the turbulence models significantly influence the mixture fraction distribution. Very good agreement with available experimental data is found concerning the ignition delay time and the flame lift-off length for different oxygen concentrations owing to the accurate fuel mixture fraction.

Interval type-2 fuzzy logic for dynamic parameter adaptation in the bat algorithm

We describe in this paper a proposed enhancement of the bat algorithm (BA) using interval type-2 fuzzy logic for dynamically adapting the BA parameters. The BA is a metaheuristic algorithm inspired by the behavior of micro bats that use the echolocation feature for hunting their prey, and this algorithm has been recently applied to different optimization problems obtaining good results. We propose a new method for dynamic parameter adaptation in the BA using interval type-2 fuzzy logic, where an especially design fuzzy system is responsible for determining the optimal values for the parameters of the algorithm. Simulations results on a set of benchmark mathematical functions with the interval type-2 fuzzy bat algorithm outperform the traditional bat algorithm and a type-1 fuzzy variant of BA. The proposed integration of the type-2 fuzzy system into the BA has the goal of improving the performance of BA for the future applicability of the algorithm in more complex optimization problems where higher levels of uncertainty need to be handled, like in the optimization of fuzzy controllers.

Adaptive Memory Controller for High-performance Multi-channel Memory

As the number of CPU/GPU cores and IPs in SOC increases and applications require explosive memory bandwidth, simultaneously achieving good throughput and fairness in the memory system among interfering applications is very challenging. Recent works proposed priority-based thread scheduling and channel partitioning to improve throughput and fairness. However, combining these different approaches leads to performance and fairness degradation. In this paper, we analyze the problems incurred when combining priority-based scheduling and channel partitioning and propose dynamic priority thread scheduling and adaptive channel partitioning method. In addition, we propose dynamic address mapping to further optimize the proposed scheme. Combining proposed methods could enhance weighted speedup and fairness for memory intensive applications by 4.2% and 10.2% over TCM or by 19.7% and 19.9% over FR-FCFS on average whereas the proposed scheme requires space less than TCM by 8%.

A dynamic adaptation method based on unstructured mesh for solving sloshing problems

A dynamic mesh-adaptation algorithm based on an unstructured mesh was used to solve sloshing problems by a finite element method. The free surface evolution of sloshing with a low filling ratio is so violent that a local mesh coarsening/refinement (LCR) technique can be effectively used to resolve the flow field near the interface of two immiscible fluids. Since an implicit discretization method was employed to solve the incompressible Navier-Stokes equations, an assembled global matrix was generated using a dynamic compressed sparse row (CSR) method. The proposed algorithm was validated by comparing the simulation results with those of a non-adaptive method with respect to wall impact pressures and mass conservation. Numerical results showed that relative mass error strongly depends on mesh resolution near the interface. Results of adaptive simulations were found to be comparable with those of non-adaptive simulations only if a similar mesh resolution was used near the interface. Moreover, adaptive simulations were about two times faster than the non-adaptive ones. The effect of the adaptive zone and smoothing zone on the impact pressure was also examined for the proposed algorithm.