Aircraft Control Parameter Estimation Using Self-Adaptive Teaching-Learning-Based Optimization with an Acceptance Probability

This work presents a metaheuristic (MH) termed, self-adaptive teaching-learning-based optimization, with an acceptance probability for aircraft parameter estimation. An inverse optimization problem is presented for aircraft longitudinal parameter estimation. The problem is posed to find longitudinal aerodynamic parameters by minimising errors between real flight data and those calculated from the dynamic equations. The HANSA-3 aircraft is used for numerical validation. Several established MHs along with the proposed algorithm are used to solve the proposed optimization problem, while their search performance is investigated compared to a conventional output error method (OEM). The results show that the proposed algorithm is the best performer in terms of search convergence and consistency. This work is said to be the baseline for purely applying MHs for aircraft parameter estimation.

AN INVERSE OPTIMIZATION PROBLEM OF HEAT TRANSFER IN THE MACHINING PROCESS – A REVIEW

This paper reports on the results of research on thermal aspects in the process of material removal by inverse heat transfer problem. The research focuses on the identification, modeling and optimization of machining process based on the measured temperature at a particular point of the workpiece. The inverse approach determines the overall temperature distribution of the workpiece and the unknown heat flux at the tool/workpiece interface in machining. By introducing and minimizing an objective function based on the heat flux function, relationship of the heating power and duration on the surface layer of the workpiece is optimized. In this way, the most favourable machining conditions are determined in order to achieve high productivity and quality levels. The inverse optimization problem is solved by using the analytical, numerical and regularization methods. Formulation, application and analysis of the inverse optimization problem of heat transfer are shown on the example of creep-feed grinding. The creep-feed grinding process is a widely used abrasive machining process that is characterized by high thermal load of the workpiece. The results of the inverse optimization problem were verified by a series of experiments under different machining conditions.

Damage Detection Using a Graph-based Adaptive Threshold for Modal Strain Energy and Improved Water Strider Algorithm

Damage detection through an inverse optimization problem has been investigated by many researchers. Recently, Modal Strain Energy (MSE) has been utilized as an index (MSEBI) for damage localization that serves to guide the optimization. This guided approach considerably reduces the computational cost and increases the accuracy of optimization. Although this index mostly exhibits an acceptable performance, it fails to find some damaged elements' locations in some cases. The aim of this paper is twofold. Firstly, a Graph-based Adaptive Threshold (GAT) is proposed to identify some of those elements that are not detected by basic MSEBI. GAT relies on the concepts from graph theory and MSE working as a simple anomaly detection technique. Secondly, an Improved version of the Water Strider Algorithm (IWSA) is introduced, applied to the damage detection problems with incomplete modal data and noise-contaminated inputs. Several optimization algorithms, including the newly-established Water Strider Algorithm (WSA), are utilized to test the proposed method. The investigations on several damage detection problems demonstrate the GAT and IWSA's satisfactory performance compared to the previous methods.

Bayesian Approach to Estimating Fireball Parameters From Remote Sensing Data

Remote sensors in the infrared region can be used to study the progression of fireballs generated from experiments involving high explosives (HE). Developing an improved understanding of HE fireballs can be used to validate and improve computational physics codes that simulate such events. In this paper, Bayesian approaches are studied to estimate time-dependent optimal fireball parameters and their uncertainties using Fourier transform infrared (FTIR) spectroscopy. The optical signal measured by an FTIR sensor provides information on the fireball due to thermal emission, particulate emission/absorption, and HE gas product emission/absorption from the fireball. FTIR sensors have the advantage of being able to capture and measure the radiance in a large part of the infrared spectrum. The parameters to be estimated from the fireball include temperature and size, soot quantity, gas species concentrations (e.g., H2O, CO2, CO), and information on the presence of metals. In general, this inverse optimization problem is difficult due to the estimated quantities being correlated, the low spectral resolution of the FTIR sensor, and the intervening atmosphere absorbing the radiation emitted from the fireball. Bayesian calibration and Bayesian model averaging are applied to address these difficulties and to quantify the uncertainty in the estimated optimal parameter values. The fireball parameter settings are evaluated by the fit of a simplified spectral model to FTIR data. The overall problem will be presented together with a description of the Bayesian approaches. In this paper, the Bayesian approaches are applied to artificially generated FTIR data to illustrate the approach.

Development of Discrete Adjoint Approach Based on the Lattice Boltzmann Method

The purpose of this research is to present a general procedure with low implementation cost to develop the discrete adjoint approach for solving optimization problems based on the LB method. Initially, the macroscopic and microscopic discrete adjoint equations and the cost function gradient vector are derived mathematically, in detail, using the discrete LB equation. Meanwhile, for an elementary case, the analytical evaluation of the macroscopic and microscopic adjoint variables and the cost function gradients are presented. The investigation of the derivation procedure shows that the simplicity of the Boltzmann equation, as an alternative for the Navier-Stokes (NS) equations, can facilitate the process of extracting the discrete adjoint equation. Therefore, the implementation of the discrete adjoint equation based on the LB method needs fewer attempts than that of the NS equations. Finally, this approach is validated for the sample test case, and the results gained from the macroscopic and microscopic discrete adjoint equations are compared in an inverse optimization problem. The results show that the convergence rate of the optimization algorithm using both equations is identical and the evaluated gradients have a very good agreement with each other.

Examining the LEED Rating System Using Inverse Optimization

The Leadership in Energy and Environmental Design (LEED) rating system is the most recognized green building certification program in North America. In order to be LEED certified, a building must earn a sufficient number of points, which are obtained through achieving certain credits or design elements. In LEED versions 1 and 2, each credit was worth one point. In version 3, the LEED system changed so that certain credits were worth more than one point. In this study, we develop an inverse optimization approach to examine how building designers intrinsically valued design elements in LEED version 2. Because of the change in the point system between version 2 and version 3, we aim to determine whether building designers actually valued each credit equally, and if not, whether their valuations matched the values in version 3. Due to the large dimensionality of the inverse optimization problem, we develop an approximation to improve tractability. We apply our method to 306 different LEED-certified buildings in the continental United States. We find that building designers did not value all credits equally and that other factors such as cost, building type, and size, and certification level play a role in how the credits are valued. Overall, inverse optimization may provide a new method to assess historical data and support the design of future versions of LEED.

Examining the LEED Rating System Using Approximate Inverse Optimization

The Leadership in Energy and Environmental Design (LEED) rating system is the most recognized green building certification program in North America. In order to be LEED certified, a building must earn a certain number of points, which are obtained through achieving certain credits or design elements. Prior to LEED version 3, each credit was worth one point. In this study, we develop an inverse optimization approach to examine how building designers intrinsically valued design elements in LEED version 2. Due to the large dimensionality of the inverse optimization problem, we develop an approximation to improve tractability. We apply our method to 18 different LEED-certified buildings in the United States. We find that building designers did not value all credits equally and that other factors such as cost and certification level play a role in how the credits are valued. Overall, inverse optimization may provide a new method to assess historical data and support the design of future versions of LEED.

A WEIGHTED INVERSE MINIMUM CUT PROBLEM UNDER THE BOTTLENECK TYPE HAMMING DISTANCE

An inverse optimization problem is defined as follows. Let S denote the set of feasible solutions of an optimization problem P, let c be a specified cost (capacity) vector, and x0 ∈ S. We want to perturb the cost (capacity) vector c to d so that x0 is an optimal solution of P with respect to the cost (capacity) vector d, and to minimize some objective function. In this paper, we consider the weighted inverse minimum cut problem under the bottleneck type Hamming distance. For the general case, we present a combinatorial algorithm that runs in strongly polynomial time.