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The high probability of the occurrence of separation bubbles or shocks and early transition to turbulence on surfaces of airfoil makes it very difficult to design high-lift and high-speed Natural-Laminar-Flow (NLF) airfoil for high-altitude long-endurance unmanned air vehicles. To resolve this issue, a framework of uncertainty-based design optimization (UBDO) is developed based on an adjusted polynomial chaos expansion (PCE) method.

The γ ̄Re-θt transition model combined with the shear stress transport k-ω turbulence model is used to predict the laminar-turbulent transition. The particle swarm optimization algorithm and PCE are integrated to search for the optimal NLF airfoil. Using proposed UBDO framework, the aforementioned problem has been regularized to achieve the optimal airfoil with a tradeoff of aerodynamic performances under fully turbulent and free transition conditions. The tradeoff is to make sure its good performance when early transition to turbulence on surfaces of NLF airfoil happens.

The results indicate that UBDO of NLF airfoil considering Mach number and lift coefficient uncertainty under free transition condition shows a significant deterioration when complicated flight conditions lead to early transition to turbulence. Meanwhile, UBDO of NLF airfoil with a tradeoff of performances under both fully turbulent and free transition conditions holds robust and reliable aerodynamic performance under complicated flight conditions.

In this work, the authors build an effective uncertainty-based design framework based on an adjusted PCE method and apply the framework to design two high-performance NLF airfoils. One of the two NLF airfoils considers Mach number and lift coefficient uncertainty under free transition condition, and the other considers uncertainties both under fully turbulent and free transition conditions. The results show that robust design of NLF airfoil should simultaneously consider Mach number, lift coefficient (angle of attack) and transition location uncertainty.

The noise measurement on magnetoresistive (MR) sensors is generally conducted by techniques including single-channel data sampling and fast Fourier transform (FFT) analysis as well as two-channel cross-correlation. The single-channel method is easy to implement and is widely used in the noise measurement on MR sensors, whereas the two-channel method can only eliminate part of the system noise. This study aims to address two key issues affecting measurement accuracy: calibration of the measurement system and the elimination of system noise.

The system is calibrated by using a low-noise metal film resistor in that the system noise is eliminated through power spectrum subtraction. Noise measurement and analysis are conducted for both thermal noise and detectivity of magnetic tunnel junction (MTJ) sensor.

The thermal noise measurement error is less than 2%. The detectivity of the MTJ sensor reaches 27 pT/Hz^1/2 at 2 kHz.

This study provides a more practical solution for noise measurement and system calibration on MR sensors with a bias voltage and magnetic field.

The purpose of this paper is to define a new method (grey relational analysis (GRA)) for extracting pattern samples of dissolved gases in power transformer oil, then a hybrid algorithm of the back‐propagation (BP) network and fuzzy genetic algorithm‐artificial neural network (FGA‐ANN) is used to power transformer fault diagnosis based on extracted pattern samples.

The existing manners (e.g. international electro technical commission triple‐ratio method), in practice, have certain faultiness due to the ambiguity of the inference and insufficient standard for judgment. So GRA method is chosen to solve a problem of optimal pattern samples data, then a hybrid algorithm of the BP network and FGA‐ANN is developed to optimize initial weights and to enable fast convergence of the BP network, and lastly, this algorithm is applied to the classification of dissolved gas analysis (DGA) data and power transformer fault diagnosis.

If possible, the results should be accompanied by significance. For comparative studies, the proposed scheme does not require the three ratio code and high diagnosis accuracy is obtained. In addition, useful information is provided for future fault trends and multiple faults analysis.

Accessibility and availability of data are the main limitations which model will be applied.

This paper provides useful advice for power transformer fault diagnosis method based on DGA data.

The new method of optimal choice of options of pattern samples due to GRA. The paper is aimed at optimized samples data classified and abandons the traditional ratio method.

Even though austenitic stainless steels have been extensively used in industries, owing to some of the characteristics of the material, its performance in machining is difficult to understand, in particular at high cutting speeds. There is no availability of dependable and in-depth studies pertinent to this matter. In this work, performance of AISI 304 austenitic stainless steel was studied in terms of surface roughness (Ra) and material removal rate (MRR) at high cutting speeds. Subsequently, parametric optimization and prediction for responses were carried out.

Turning operations were conducted using L9 orthogonal array and the outcomes were analyzed to attain optimal set of machining parameters for the responses using signal-to-noise (S/N) ratio and Pareto analysis of variance (ANOVA). In the present work, the cutting speed values were considered beyond the recommended range as designated by tool manufacturers. Finally, multiple regression models were developed to predict responses.

From the results, 350 m/min was found to be a significant speed. The investigation reveals that even though the speeds are taken beyond the recommended values, the results are favorable. The optimal machining parameters values for surface quality obtained were cutting speed of 350 m/min, feed of 0.15 mm/rev and depth of cut of 2.0 mm. In case of MRR, the optimal values were: cutting speed of 400 m/min, feed of 0.25 mm/rev and depth of cut of 2.0 mm. It was found out that there was an improvement in Ra and MRR (around 15 and 4%) due to optimization. The results indicate that Pareto ANOVA is easier than S/N ratio. This revealed that the feed rate and depth of cut were mostly affected parameters for Ra and MRR. The developed models are capable of predicting the responses accurately.

The outcome of the work reveals that even though the speeds were taken beyond the recommended value, the results are favorable for manufacturing industries when the tool cost is considered insignificant.

No work was reported on machining of the chosen material beyond the recommended cutting speed. Moreover, it was observed from the past works that cutting speeds were limited to 100–300 m/min.