Lean blowout detection using topological data analysis.

Modern lean premixed combustors are operated in ultra-lean mode to conform to strict emission norms. However, this causes the combustors to become prone to lean blowout (LBO). Online monitoring of combustion dynamics may help to avoid LBO and help the combustor run more safely and reliably. Previous studies have suggested various techniques to early predict LBO in single-burner combustors. In contrast, early detection of LBO in multi-burner combustors has been little explored to date. Recent studies have discovered significantly different combustion dynamics between multi-burner combustors and single-burner combustors. In the present paper, we show that some well-established early LBO detection techniques suitable for single-burner combustor are less effective in early detecting LBO in multi-burner combustors. To resolve this, we propose a novel tool, topological data analysis (TDA), for real-time LBO prediction in a wide range of combustor configurations. We find that the TDA metrics are computationally cheap and follow monotonic trends during the transition to LBO. This indicates that the TDA metrics can be used to fine-tune the LBO safety margin, which is a desirable feature from practical implementation point of view. Furthermore, we show that the sublevel set TDA metrics show approximately monotonic changes during the transition to LBO even with low sampling-rate signals. Sublevel set TDA is computationally inexpensive and does not require phase-space embedding. Therefore, TDA can potentially be used for real-time monitoring of combustor dynamics with simple, low-cost, and low sampling-rate sensors.

Analyses of entropy generation in micropolar liquid using overlapping multi-domain spectral quasilinearization method.

In this article, entropy generation on Micropolar fluid through a perpendicular hot sheet is studied. The governing equations of the mathematical model of this article will transfer to non-dimensional system of ordinary differential equations by similarity transformations. This system of non-dimensional ODEs will be solved by a newly developed spectral collocation method. The dimensionless mathematical calculations are handled through utilizing the spectral quasilinearization method (SQLM) along with the concept of overlapping grids. Condition numbers, residual error and solution error norms estimations are provided to appraise accuracy, convergence and the stability of this numerical method. Using the overlapping multi-domain (M-D) approach has been found to produce most accurate, stable and convergent solutions when contrasted to the single-domain (S-D) technique. The outcomes are displayed and depicted graphically and through tabular forms. The effects of different non-dimensional parameters on flow, temperatures, concentration and entropy generation are studied. The accuracy of the numerical method has been checked through comparison with previously published articles and error analyses test.

Consensus Tracking of Nonlinear Agents Using Distributed Nonlinear Dynamic Inversion with Switching Leader-Follower Connection.

In this paper, a consensus tracking protocol for nonlinear agents is presented, which is based on the Nonlinear Dynamic Inversion (NDI) technique. Implementation of such a technique is new in the context of the consensus tracking problem. The tracking capability of nonlinear dynamic inversion (NDI) is exploited for a leader-follower multi-agent scenario. We have provided all the mathematical details to establish its theoretical foundation. Additionally, a convergence study is provided to show the efficiency of the proposed controller. The performance of the proposed controller is evaluated in the presence of both (a) random switching topology among the agents and (b) random switching of leader-follower connections, which is realistic and not reported in the literature. The follower agents track various trajectories generated by a dynamic leader, which describes the tracking capability of the proposed controller. The results obtained from the simulation study show how efficiently this controller can handle the switching topology and switching leader-follower connections.

Bipartite Consensus of Nonlinear Agents in the Presence of Communication Noise.

In this paper, a Distributed Nonlinear Dynamic Inversion (DNDI)-based consensus protocol is designed to achieve the bipartite consensus of nonlinear agents over a signed graph. DNDI inherits the advantage of nonlinear dynamic inversion theory, and the application to the bipartite problem is a new idea. Moreover, communication noise is considered to make the scenario more realistic. The convergence study provides a solid theoretical base, and a realistic simulation study shows the effectiveness of the proposed protocol.

Neuro-adaptive augmented distributed nonlinear dynamic inversion for consensus of nonlinear agents with unknown external disturbance.

This paper presents a novel neuro-adaptive augmented distributed nonlinear dynamic inversion (N-DNDI) controller for consensus of nonlinear multi-agent systems in the presence of unknown external disturbance. N-DNDI is a blending of neural network and distributed nonlinear dynamic inversion (DNDI), a new consensus control technique that inherits the features of Nonlinear Dynamic Inversion (NDI) and is capable of handling the unknown external disturbance. The implementation of NDI based consensus control along with neural networks is unique in the context of multi-agent consensus. The mathematical details provided in this paper show the solid theoretical base, and simulation results prove the effectiveness of the proposed scheme.

Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem.

This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.

Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm.

This paper presents an idea of how new agents can be added autonomously to a group of existing agents without changing the existing communication topology among them. Autonomous agent addition to existing Multi-Agent Systems (MASs) can give a strategic advantage during the execution of a critical beyond visual line-of-sight (BVLOS) mission. The addition of the agent essentially means that new connections with existing agents are established. It is obvious that the consensus control energy increases as the number of agent increases considering a specific consensus protocol. The objective of this work is to establish the new connections in a way such that the consensus energy increase due to the new agents is minimal. The updated topology, including new connections, must contain a spanning tree to maintain the stability of the MASs network. The updated optimal topology is obtained by solving minimum additional consensus control energy using the Two-Dimensional Genetic Algorithm. The results obtained are convincing.

The Effects of Thermal Radiation on an Unsteady MHD Axisymmetric Stagnation-Point Flow over a Shrinking Sheet in Presence of Temperature Dependent Thermal Conductivity with Navier Slip.

In this paper, the magnetohydrodynamic (MHD) axisymmetric stagnation-point flow of an unsteady and electrically conducting incompressible viscous fluid in with temperature dependent thermal conductivity, thermal radiation and Navier slip is investigated. The flow is due to a shrinking surface that is shrunk axisymmetrically in its own plane with a linear velocity. The magnetic field is imposed normally to the sheet. The model equations that describe this fluid flow are solved by using the spectral relaxation method. Here, heat transfer processes are discussed for two different types of wall heating; (a) a prescribed surface temperature and (b) a prescribed surface heat flux. We discuss and evaluate how the various parameters affect the fluid flow, heat transfer and the temperature field with the aid of different graphical presentations and tabulated results.