Ultrasensitive all-fiber inline Fabry-Perot strain sensors for aerodynamic measurements in hypersonic flows.

This work proposes an ultrasensitive, temperature-insensitive, all-fiber inline Fabry-Perot (FP) strain sensor for aerodynamic coefficients measurements of a hypervelocity ballistic correlation model 2 in a Φ1 hypersonic wind tunnel. The FP sensors fabricated using 157 nm laser micromachining system are structurally simple, small-sized, and high-temperature resistance. 16 FP sensors are installed on a six-force balance, which is mounted inside the model, to sense the aerodynamic forces and moments of the model, and then the model's aerodynamic coefficients are calculated based on aerodynamic theory according to the test data. A new temperature-compensated method is proposed to improve measurement accuracy of aerodynamic coefficients via eliminating temperature-induced measurement errors. Experimental results show, at high temperatures, the FP sensors based on the balance (FP balance) exhibits a high-repeatability precision of the aerodynamic coefficients measurement of less than 1%, and match well with the results of the traditional method using foil-resistive strain sensors. This enhanced-sensitivity FP sensor is currently the most promising alternative to foil-resistive strain sensors for aerodynamic tests among kinds of fiber-optic strain sensors to the best of our knowledge. The FP balance satisfies the requirements of practical application of aerodynamic characteristic tests, and opens up another test system of the field.

A multidimension cloud model-based approach for water quality assessment.

Lakes are vitally important, because they perform a multitude of functions, such as water supply, recreation, fishing, and habitat. However, eutrophication limits the ability of lakes to perform these functions. In order to reduce eutrophication, the first step is its evaluation. The process of evaluation entails randomness and fuzziness which must therefore be incorporated. This study proposes an eutrophication evaluation method, named Multidimension Normal Cloud Model (MNCM). The model regards each evaluation factor as a one-dimension attribute of MNCM, chooses reasonable parameters and determines the weights of evaluation factors by entropy. Thus, all factors of MNCM belonging to each eutrophication level are generated and the final eutrophication level is determined by the certainty degree. MNCM is then used to evaluate eutrophication of 12 typical lakes and reservoirs in China and its results are compared with those of the reference method, one-dimension normal cloud model, related weighted nutrition state index method, scoring method, and fuzzy comprehensive evaluation method. Results of MNCM are found to be consistent with the actual water status; hence, MNCM can be an effective evaluation tool. With respect to the former one-dimension normal cloud model, parameters of MNCM are improved without increasing its complexity. MNCM can directly determine the eutrophication level according to the degree of certainty and can determine the final degree of eutrophication; thus, it is more consistent with the complexity of water eutrophication evaluation.