Design Of Experiment Algorithm Research Articles

Comprehensive evaluation of parameter importance and optimization based on the integrated sensitivity analysis system: A case study of the BTOP model in the upper Min River Basin, China

Parameter optimization is mandatory in many numerical modeling studies of hydrology sciences. However, optimizing all parameters (APs) is highly inefficient, including the equifinality phenomenon. This paper proposes an integrated sensitivity analysis system consisting of, multiple Global sensitivity analysis methods, Design of experiment algorithms, Indicators, and Sample size design Combinations (GDISCs), for screening the sensitive parameters (SPs) robustly and reducing parameter optimization burden efficaciously. In this study, four design of experiment (DoE) algorithms are applied to collect samples with varying sizes, and the corresponding three indicator values. We evaluate the effectiveness and efficiency of one qualitative and two quantitative global sensitivity analysis (GSA) methods by identifying the appropriate DoE algorithm and the sufficient sample size. To apply the GDISCs, the Block-wise use of the TOPMODEL (BTOP) model is set up for the hydrological simulation in the upper Min River Basin, China. The results show that the GDISCs system robustly screens out three SPs of the BTOP model, and the DoE algorithms significantly impact the effectiveness and efficiency of the GSA methods. The SA performance with applying the modified Kling–Gupta efficiency (KGE') as the indicator is more robust than the root mean squared error (RMSE) and the Nash-Sutcliffe efficiency (NSE). Compared to the traditional APs optimization, the optimal values are identified quickly through almost 1/2 or even fewer model runs and duration for the SPs optimization, with a very close accuracy (the maximum NSE, KGE' and relative bias (RBias) differences are 0.137, 0.037 and 2.70%, and the minimum are 0.002, 0.001 and 0.20%). Optimizing SPs also has a noticeable improvement for the default parameter values, and the maximum differences of NSE, KGE' and RBias reach 0.764, 0.321, and −20.80%. In summary, the GDISCs system is especially applicable for obtaining the robust SA results to considerably improve model calibration efficiency and reduce model computational burden while ensuring satisfactory model simulation performance.

A novel design of experiment algorithm using improved evolutionary multi-objective optimization strategy

The paper aims to propose an intelligent design of experiment (DOE) algorithm using an improved evolutionary multi-objective optimization approach. Adaptive evolutionary strategies are embedded in the algorithm to support the design of simulation test schemes with multiple factors whose levels are same or different. Comparative results with several existing DOE algorithms show better sampling capacity and fine sampling efficiency of the proposed algorithm. Application effects of a complex flight simulator indicate the algorithm a wide technological prospect of serving well certain complex systems.

Model-Based Design of Experiments for High-Dimensional Inputs Supported by Machine-Learning Methods

Algorithms that compute locally optimal continuous designs often rely on a finite design space or on the repeated solution of difficult non-linear programs. Both approaches require extensive evaluations of the Jacobian Df of the underlying model. These evaluations are a heavy computational burden. Based on the Kiefer-Wolfowitz Equivalence Theorem, we present a novel design of experiments algorithm that computes optimal designs in a continuous design space. For this iterative algorithm, we combine an adaptive Bayes-like sampling scheme with Gaussian process regression to approximate the directional derivative of the design criterion. The approximation allows us to adaptively select new design points on which to evaluate the model. The adaptive selection of the algorithm requires significantly less evaluations of Df and reduces the runtime of the computations. We show the viability of the new algorithm on two examples from chemical engineering.

Numerical simulations and optimization of solar air heaters

Different surface modification techniques have been used for enhancement of heat transfer from absorber plates of solar air heaters. It is necessary to have accurate values of design parameters within the operational window while designing the heat transfer equipments. The present work is focused on the development of empirical correlations and evaluation of maximum thermohydraulic performance of a rectangular section ribs installed in solar air heaters considering all the design combinations within the given range of input parameters. It is prescribed by design of experiment algorithms particularly with response surface methodology considering four input parameters viz., Reynolds number ranging from 4000 to 20000, relative rib pitch ranging from 5 to 60, relative rib height ranging from 0.065 to 0.252 and relative rib width ranging from 0.5 to 10. The maximum THP obtained is 2.77 at Reynolds number 20000, relative rib pitch 17.22, relative rib height 0.044 and relative rib height 0.5. The optimal values of design parameters have been verified with CFD simulations and experiments. The errors are within acceptable limit proving the accuracy of empirical correlations used for the design of solar artificial air heater and proper selection of model equations.

Inversion Algorithm for Civil Flood Defense Optimization: Application to Two-Dimensional Numerical Model of the Garonne River in France

The objective of this study is to investigate inversion-approach for optimization of flood defense in a inundated area. This is a new methodology in this engineering field which consists in definition of a criterion (as for instance the water level in a given location must be lower than a given value) and analysis of all uncertain controlled parameters (i.e. flood defense geometry, location, and so on) combinations allowing to not exceed safety objective for all possible uncontrolled parameters combination (i.e. flow hydrograph parameters) representing natural phenomenon. In order to estimate this safety set, we will use a transient meta-modelling approach, which reduces widely number of model evaluations required. This algorithm relies on a kriging surrogate built from few model evaluations, sequentially fulfilled with new numerical model evaluations as long as remaining uncertainty of whole safety set remains too high. Also known as Stepwise Uncertainty Reduction, this algorithm is embedded in Funz engine in charge of bridging numerical model and any design of experiments algorithm. We applied this algorithm to a real bi-dimensional numerical model of Garonne River. Especially, we focused our attention on water depth at a given area when considering influence of a simplified flood defense during a flooding event. In order to simplify problem, we only analyze two parameters describing slab and dyke elevations of flood defense system. The results will be analyzed in terms of safety control inside operating range of river. It appears that once properly evaluated, this constrained zone gives highly valuable data for a fully risk informed management of area to protect.

Design of Experiment Algorithms for Assembled Products

Designing experiments to identify improvement in products that are assembled from manufactured components does not readily fit into conventional design of experiments methods and can be costly. Efficient methods are explored for determining designs for engineering problems where some, or all, of the factors of interest are (a) not easily set to prescribed values and (b) are dependent on a combination of properties of several components. The methods involve taking a sample of each type of component, measuring the relevant features and then finding a design that specifies an optimal set of assembled products for experiment. Three examples from manufacturing industry are presented to illustrate the approach. Two different algorithms for finding designs are described, an exchange algorithm and a genetic algorithm, and a comparison of their performances is made on the three examples.