Design and Processing of Gas Turbine Blades Based on Additive Manufacturing Technology.

Aiming at the problems of the complex shape, difficult three-dimensional (3D) digital modeling and high manufacturing quality requirements of gas turbine blades (GTB), a method of fitting the blade profile line based on a cubic uniform B-spline interpolation function was proposed. Firstly, surface modeling technology was used to complete the fitting of the blade profile of the GTB, and the 3D model of the GTB was synthesized. Secondly, the processing parameters of the additive manufacturing were set, and the GTB model was printed by fused deposition technology. Then, the rapid investment casting was completed with the printed model as a wax model to obtain the GTB casting. Finally, the blade casting was post-processed and measured, and it was found to meet the requirements of machining accuracy and surface quality.

Design, Fabrication, and Characterization of a Laser-Controlled Explosion-Initiating Device with Integrated Safe-and-Arm, EMP-Resistant, and Fast-Acting Technology Based on Photovoltaic Power Converter.

To augment the intelligence and safety of a rocket or ammunition engine start, an intelligent initiation system needs to be included in the data link. A laser-controlled intelligent initiation system with inherent safety and a laser-controlled explosion-initiating device (LCEID) incorporating electromagnetic pulse (EMP) resistant, safe-and-arms fast-acting modular device based on photovoltaic power converter technology is designed and fabricated in this work. LCEID is an integrated multi-function module consisting of the optical beam expander, GaAs photovoltaic (PV) array, safe-and-arms integrated circuit, and low-energy initiator. These components contribute to EMP resistance, fast-acting, safe-and-arm, and reliable firing, respectively. To achieve intelligent initiation, each LCEID has a unique "identification information" and a "broadcast address" embedded in integrated-circuit read-only memory (ROM), which is controlled by encoded laser addressing. The GaAs PV array was investigated to meet the low-energy initiator firing voltage requirements. Experimental results show that the open-circuit voltage, short-circuit current, and maximum power output of the four-junction GaAs PV array illuminated by a 5.5 W/cm2 laser beam were 220 mA, 21.5 V, and 3.70 W, respectively. When the voltage of the 22 µF energy storage capacitor exceeds 20 V, the laser charging time is found to be shorter than 2.5 s. Other aspects of LCEID, such as laser energy coupling efficiency, the firing process, and the energy-boosting mechanism, were explored. Measurements show that the coupling efficiency of the micro lens with a radius of curvature D = 20 µm and size of r = 50 µm reaches a maximum of 93.5%. Furthermore, for more than 18 V charge voltage, the LCEID is found to perform reliably. The fabricated LCEID demonstrated a high level of integration and intrinsic safety, as well as a finely tailored initiation performance that could be useful in military applications.

Acoustic signal analysis for detecting defects inside an arc magnet using a combination of variational mode decomposition and beetle antennae search.

An accurate, rapid signal analysis is crucial in the acoustic-based detection for internal defects in arc magnets. Benefiting from the adaptive decomposition without the mode mixing, variational mode decomposition (VMD), has emerged as a promising technology for processing and analyzing acoustic signals. However, improper parameter settings are the root cause of inaccurate VMD results, while existing optimization methods for VMD parameters are only applicable to a single signal with exclusive signal characteristics, rather than different signals with similar features. Therefore, we developed a new acoustic signal analysis method combining VMD, beetle antennae search (BAS), and naive Bayes classification (NBC), and then applied it for detecting internal defects of arc magnets. In this method, multiple optimizations for different signals are simplified to a one-time optimization for the whole signal group by a specially designed parameter-related fitness function. Since the coordinates of the function maximum value in a parameter space correspond to the unified parameter setting generating the overall optimal processing effect for all signals, BAS is introduced to achieve a rapid search of coordinates. With the obtained unified parameter setting, each acoustic signal of arc magnets can be consistently processed by VMD. Next, two modes stemmed from VMD are screened out by an energy threshold, and their specific frequency information is extracted as features representing the internal defects. NBC is carried out to learn and identify the extracted features. The experimental validation of the proposed method was conducted by detecting various arc magnets. Experimental results indicate that the identification accuracy reaches 100% and the detection speed per a single arc magnet approximately ranges between 1.7 and 4.5 s. This work provides not only a new strategy for the parameter optimization of VMD, but also a practical solution for the internal defect detection of arc magnets.

Modeling counter-current chromatography with non-ideal injection.

In general counter-current chromatography systems, there are several off-column fittings between injector and column inlet, such as bends, valves, connecting tubes and joints. Due to these off-column fittings, the sample will diffuse in the mobile phase and form an irregular distribution when it flows from the injector to the column inlet. Thus, the concentration distribution of the solutes at the column inlet is a continuous curve (called the injection profile). As some previous research reveals, it is necessary to input actual injection profile into the simulation model to mimic elution profile. Therefore, we built a non-ideal CCC model whose initial value is from the actual injection profile, and validated the rationality of this model with iteration method. The simulation analysis of different injection profiles shows the conditions whereby a discrete injection profile can replace the actual injection profile in the non-ideal CCC model for accurate simulation elution. Simulation elution under such conditions reveal that non-ideal injection model can reflect the relationship between the injection profile and elution profile, and help to explain the reasons of irregular change in elution profile, like the tailed peak and flat peak.

Low-Rank Sparse Preserving Projections for Dimensionality Reduction.

Learning an efficient projection to map high-dimensional data into a lower dimensional space is a rather challenging task in the community of pattern recognition and computer vision. Manifold learning is widely applied because it can disclose the intrinsic geometric structure of data. However, it only concerns the geometric structure and may lose its effectiveness in case of corrupted data. To address this challenge, we propose a novel dimensionality reduction method by combining the manifold learning and low-rank sparse representation, termed low-rank sparse preserving projections (LSPP), which can simultaneously preserve the intrinsic geometric structure and learn a robust representation to reduce the negative effects of corruptions. Therefore, LSPP is advantageous to extract robust features. Because the formulated LSPP problem has no closed-form solution, we use the linearized alternating direction method with adaptive penalty and eigen-decomposition to obtain the optimal projection. The convergence of LSPP is proven, and we also analyze its complexity. To validate the effectiveness and robustness of LSPP in feature extraction and dimensionality reduction, we make a critical comparison between LSPP and a series of related dimensionality reduction methods. The experimental results demonstrate the effectiveness of LSPP.