Optimization-based transient control of turbofan engines: a sequential quadratic programming approach

Engine transient control has been challenging due to its stringent requirements from both performance and safety. Many methodologies have been proposed such as conventional schedule-based methods, linear parameter varying, multiobjective optimization and evolutionary computations etc. These approaches have been well-established and led to a series of significant results. However, they are either not providing limit protection or requiring exhaustive computational resources, particularly when generating results into full flight envelope applications. Consequently a compromise between limit protection and computational complexity is necessitated. This note considers a sequential quadratic programming (SQP)-based method for full flight envelope investigations. The proposed method can provide important design guidance and the corresponding claims are validated through detailed analysis and simulations.

Toward a Cloud based Disease Diagnosis System Using Sequential Quadratic Programming Approach

The Internet of Things (IoT) relates to the process of utilizing computer networks to plan and model Internet-connected things. The Internet of Things (IoT)-based m-healthcare technologies have provided multi-dimensional functionality and real-time resources over the last few years. These apps provide millions of individuals with a forum to get wellness alerts for a healthy lifestyle constantly. Several aspects of these systems have been revitalized with the introduction of IoT devices in the healthcare sector. This work proposed a data-driven disease signal analytics by inventing a novel combination learning approach. The proposed Combination learning integrates different machine learning models to price disease signal for different options by leveraging the availability of a large amount of data through solving a sequential quadratic programming problem. The proposed approach demonstrates its superiority in prediction accuracy and strong model independence by overcoming traditional model-driven approaches' generalization issue. The findings illustrate the efficacy of the task for an effective disease signal diagnosis. It could be a modern and useful health approach to adopt the proposed procedure with potential changes and incorporate it into a low-cost unit.

A Hybrid Metaheuristic Based on Neurocomputing for Analysis of Unipolar Electrohydrodynamic Pump Flow

A unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number (Es), the electrical Reynolds number (ReE), and electrical slip number (Esl), are considered with wide ranges of variation to analyze the UP-EHD pump flow. To interpret the pump flow of the UP-EHD model, a hybrid metaheuristic solver is designed, consisting of the recently developed technique sine–cosine algorithm (SCA) and sequential quadratic programming (SQP) under the influence of an artificial neural network. The method is abbreviated as ANN-SCA-SQP. The superiority of the technique is shown by comparing the solution with reference solutions. For a large data set, the technique is executed for one hundred independent experiments. The performance is evaluated through performance operators and convergence plots.

Falkner–Skan Flow with Stream-Wise Pressure Gradient and Transfer of Mass over a Dynamic Wall

In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocomputing algorithm. We have considered the Falkner–Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heuristic, the Sine Cosine Algorithm (SCA), and the local search characteristic of Sequential Quadratic Programming (SQP). Artificial neural network (ANN) architecture is utilized to construct a series solution of the mathematical model. We have called our technique the ANN-SCA-SQP algorithm. The dynamic of the FS system is observed by varying stream-wise pressure gradient mass transfer and dynamic wall. To validate the effectiveness of ANN-SCA-SQP algorithm, our solutions are compared with state-of-the-art reference solutions. We have repeated a hundred experiments to establish the robustness of our approach. Our experimental outcome validates the superiority of the ANN-SCA-SQP algorithm.

Design of evolutionary cubic spline intelligent solver for nonlinear Painlevé-I transcendent

In this research paper, an innovative bio-inspired algorithm based on evolutionary cubic splines method (CSM) has been utilized to estimate the numerical results of nonlinear ordinary differential equation Painlevé-I. The computational mechanism is used to support the proposed technique CSM and optimize the obtained results with global search technique genetic algorithms (GAs) hybridized with sequential quadratic programming (SQP) for quick refinement. Painlevé-I is solved by the proposed technique CSM-GASQP. In this process, variation of splines is implemented for various scenarios. The CSM-GASQP produces an interpolated function that is continuous upto its second derivative. Also, splines proved to be stable than a single polynomial fitted to all points, and reduce wiggles between the tabulated points. This method provides a reliable and excellent procedure for adaptation of unknown coefficients of splines by searching globally exploiting the performance of GA-SQP algorithms. The convergence, exactness and accuracy of the proposed scheme are examined through the statistical analysis for the several independent runs.

Customized Sequential Quadratic Programming for Solving Large-Scale AC Optimal Power Flow

After decades of research, efficient computation of AC Optimal Power Flow (ACOPF) still remains a challenge. ACOPF is a nonlinear nonconvex problem, and operators would need to solve ACOPF for large networks in almost real-time. Sequential Quadratic Programming (SQP) is one of the powerful second-order methods for solving large-scale nonlinear optimization problems and is a suitable approach for solving ACOPF with large-scale real-world transmission networks. However, SQP, in its general form, is still unable to solve large-scale problems within industry time limits. This paper presents a customized Sequential Quadratic Programming (CSQP) algorithm, taking advantage of physical properties of the ACOPF problem and the choice of the best performing ACOPF formulation. The numerical experiments suggest that CSQP outperforms commercial and noncommercial nonlinear solvers and solves test cases within the industry time limits. A wide range of test cases, ranging from 500-bus systems to 30,000-bus systems, are used to verify the test results.

Customized Sequential Quadratic Programming for Solving Large-Scale AC Optimal Power Flow

After decades of research, efficient computation of AC Optimal Power Flow (ACOPF) still remains a challenge. ACOPF is a nonlinear nonconvex problem, and operators would need to solve ACOPF for large networks in almost real-time. Sequential Quadratic Programming (SQP) is one of the powerful second-order methods for solving large-scale nonlinear optimization problems and is a suitable approach for solving ACOPF with large-scale real-world transmission networks. However, SQP, in its general form, is still unable to solve large-scale problems within industry time limits. This paper presents a customized Sequential Quadratic Programming (CSQP) algorithm, taking advantage of physical properties of the ACOPF problem and the choice of the best performing ACOPF formulation. The numerical experiments suggest that CSQP outperforms commercial and noncommercial nonlinear solvers and solves test cases within the industry time limits. A wide range of test cases, ranging from 500-bus systems to 30,000-bus systems, are used to verify the test results.