Efficient Global Optimization Method for Multipoint Airfoil Design

Many systems nowadays follow not only physical laws but also manmade rules. These systems are known as discrete-event dynamic systems (DEDSs), where simulation is the only faithful way for performance evaluation. Due to various advantages in practice, designs (or solution candidates) with low descriptive complexity (called simple designs) are usually preferred over complex ones when their performances are close. However, the descriptive complexity (DC) is usually nonlinear and takes discrete value, which makes traditional methods such as linear programming and gradient-based local search not applicable. Existing methods for simulation-based optimization (SBO) do not explore the preference on descriptive complexity and thus cannot solve the problem efficiently. The major contributions of this paper are to point out the importance of considering SBO problems with DC preference, and to develop an adaptive sampling algorithm (ASA) to find the simplest good design. It is shown that ASA terminates within finite iterations and with controllable probability of making mistake. The computational complexity of ASA and its dependence on various parameters are discussed. ASA is then applied to three parameter optimization problems and a node activation policy optimization problem in a wireless sensor network. Numerical results show that ASA is more efficient than blind picking and Levin search in most cases. We hope this work can shed some insight to how to find simple and good designs in general.

Sampling is a core process in IoT systems. It deter-mines the data volume circulating within the network as well as the energy consumption on the IoT devices. Adaptive sampling aims to control the volume of generated data to reduce energy and bandwidth consumption without undermining data quality. Within this context, we propose two new adaptive sampling techniques: a light-weight adaptive sampling algorithm and an optimized uniform sampling method. We tested our methods using various real data-sets and compared their performances against state-of-the-art adaptive sampling algorithms in terms of data quality and data volume. The results show that the proposed methods are consistently among the best with a noticeable reduction in computational load.

Efficient multi-response adaptive sampling algorithm for construction of variable-fidelity aerodynamic tables

In this paper, a simultaneous analysis and design method is derived and applied for a non-linear constrained aerodynamic optimization problem. The method is based on the approach of defining a Lagrange functional based on the objective function and the aerodynamic model’s equations, using two sets of multipliers. A fully-coupled, non-linear system of equations is derived by requiring that the Gateaux variation of the Lagrange functional vanishes for arbitrary variations of the aerodynamic model’s dependent variables and design parameters. The optimization problem is approached using a one-shot technique, by solving the non-linear system in which all sensitivities and problem constraints are included. The computational efficiency of the method is compared against a gradient-based optimization algorithm using adjoint-provided gradient. A conceptual-stage aerodynamic optimization problem is solved, based on a non-linear numerical lifting-line method with viscous corrections.

This paper proposed an efficient and adaptive frequency sampling algorithm for frequency response analysis using dynamic condensation-based reduced-order modeling. For the degree of freedom-based model reduction method, the reduced-order basis becomes a frequency-dependent matrix since the relationship between master and slave degrees of freedom stems from partial equations of a second-order dynamical system. Such frequency-dependency makes the analysis inefficient for investigating the frequency response of the system. Considering that the coverage of a local reduced-order basis at a single frequency varies depending on the frequency, a new frequency sampling algorithm was proposed with a strategy of constructing multiple local reduced-order models (ROMs) at sample frequencies. For adaptive sampling, the frequency range of a local ROM was evaluated, and a new sample was added if there was a gap between two adjacent ROMs. As a result, the accuracy of the local ROM can be estimated, and the efficiency in the online stage was greatly enhanced. The proposed method was verified by performing frequency response analysis with several numerical examples, including a large-scale structural and dynamic system.

PurposeAerodynamic shape optimisation is a complex problem usually governed by transonic non-linear aerodynamics, a high dimensional design space and high computational cost. Consequently, the use of a numerical simulation approach can become prohibitive for some applications. This paper aims to propose a computationally efficient multi-fidelity method for the optimisation of two-dimensional axisymmetric aero-engine nacelles.Design/methodology/approachThe nacelle optimisation approach combines a gradient-free algorithm with a multi-fidelity surrogate model. Machine learning based on artificial neural networks (ANN) is used as the modelling technique because of its ability to handle non-linear behaviour. The multi-fidelity method combines Reynolds-averaged Navier Stokes and Euler CFD calculations as high- and low-fidelity, respectively.FindingsRatios of low- and high-fidelity training samples to degrees of freedom of nLF/nDOFs = 50 and nHF/nDOFs = 12.5 provided a surrogate model with a root mean squared error less than 5% and a similar convergence to the optimal design space when compared with the equivalent CFD-in-the-loop optimisation. Similar nacelle geometries and aerodynamic flow topologies were obtained for down-selected designs with a reduction of 92% in the computational cost. This highlights the potential benefits of this multi-fidelity approach for aerodynamic optimisation within a preliminary design stage.Originality/valueThe application of a multi-fidelity technique based on ANN to the aerodynamic shape optimisation problem of isolated nacelles is the key novelty of this work. The multi-fidelity aspect of the method advances current practices based on single-fidelity surrogate models and offers further reductions in computational cost to meet industrial design timescales. Additionally, guidelines in terms of low- and high-fidelity sample sizes relative to the number of design variables have been established.

We present novel adaptive sampling algorithms for particle-based fluid simulation. We introduce a sampling condition based on geometric local feature size that allows focusing computational resources in geometrically complex regions, while reducing the number of particles deep inside the fluid or near thick flat surfaces. Further performance gains are achieved by varying the sampling density according to visual importance. In addition, we propose a novel fluid surface definition based on approximate particle-to-surface distances that are carried along with the particles and updated appropriately. The resulting surface reconstruction method has several advantages over existing methods, including stability under particle resampling and suitability for representing smooth flat surfaces. We demonstrate how our adaptive sampling and distance-based surface reconstruction algorithms lead to significant improvements in time and memory as compared to single resolution particle simulations, without significantly affecting the fluid flow behavior.

Probabilistic safety margin characterization of an integrated small modular reactor using MFM and adaptive polynomial chaos

Real-time environmental sensor data: An application to water quality using web services

New ranking functions for adaptive sampling improve backtracking and pathfinding in complex landscapes

We investigate the problem of optimizing the sampling frequency of a real-time control system under the constraint of available computational resource. We propose the novel notion of sampling urgency to indicate the time-dependent importance of sampling frequency, and establish that system performance is directly related to sampling urgency. We then synthesize an adaptive sampling algorithm that varies the sampling frequency on-line according to sampling urgency and show that this adaptive sampling algorithm leads to significant reduction in computation (without sacrificing performance) in a real-time control process.

A new method for finite element model updating in structural dynamics

Sampling is one of the most common and repeated tasks in a target tracking sensor network. However, tuning the sampling rate parameter can be a challenging issue considering all the sensor network restrictions. In this paper, we propose two adaptive sampling algorithms in a target tracking sensor network while considering a multi-objective fitness function. The restrictions used as objective functions are energy consumption and prediction error which provide a direct feedback to the sampling rate adaptation algorithms. We support our proposed methods with well structured experimental evaluations.

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for single objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size and cost of the gas turbine engine. A 3-Dimensional steady Reynolds Averaged Navier Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well known test cases. Blade geometry is modeled by 36 design variables plus the number of blades variable in a row. Fine and coarse grid solutions are respected as high and low fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid genetic algorithm successfully accelerates the optimization at the initial generations for both optimization problems, while preventing converging to local optimums.

Frequency domain sweeps of array antennas are well-known to be time-intensive, and different surrogate models have been used to improve the performance. Data-driven model order reduction algorithms, such as the Loewner framework and vector fitting, can be integrated with adaptive frequency sampling algorithms, in an iterative scheme, to reduce the number of full-wave simulations required to accurately capture the requested frequency behavior of multiport array antennas. In this work, we propose two novel adaptive methods exploiting a block matrix function which is a key part of the Loewner framework generating system approach. The first algorithm leverages an inherent matrix parameter freedom in the block matrix function to identify frequency points with large errors, whereas the second utilizes the condition number of the block matrix function. The first method effectively provide a frequency domain error estimator, which is essential for improved performance. Numerical experiments on multiport array antenna S-parameters demonstrate the effectiveness of our proposed algorithms within the Loewner framework, where the proposed algorithms reach the smallest errors for the smallest number of frequency points chosen.