Evaluation of missile electromagnetic launch system based on effectiveness

To solve the problems of strong infrared radiation, poor continuous combat capability of the system, serious ablation of the launching device, and environmental pollution of the existing missile launching system, electromagnetic launch system (EMLS) has been studied for missile launch system. Combining the situation that the current research on missile electromagnetic launch system (MEMLS) mainly focuses on the key technical points and the deficiencies in the previous research on MEMLS, this paper establishes an effectiveness prediction model based on GRA-PCA-LSSVM, and discusses the investment efficiency of the system based on DEA. The experimental results prove that the established model is reasonable, effective and superior, and provides a reference for the further improvement and development of MEMLS.

Better Prediction of Mutation Score

<div><div><div><p>Mutation score is widely accepted to be a reliable measurement for the effectiveness of software tests. Recent studies, however, show that mutation analysis is extremely costly and hard to use in practice. We present a novel direct prediction model of mutation score using neural networks. Relying solely on static code features that do not require generation of mutants or execution of the tests, we predict mutation score with an accuracy better than a quintile. When we include statement coverage as a feature, our accuracy rises to about a decile. Using a similar approach, we also improve the state-of-the-art results for binary test effectiveness prediction and introduce an intuitive, easy-to-calculate set of features superior to previously studied sets. We also publish the largest dataset of test-class level mutation score and static code features data to date, for future research. Finally, we discuss how our approach could be integrated into real-world systems, IDEs, CI tools, and testing frameworks.</p></div></div></div>

Better Prediction of Mutation Score

<div><div><div><p>Mutation score is widely accepted to be a reliable measurement for the effectiveness of software tests. Recent studies, however, show that mutation analysis is extremely costly and hard to use in practice. We present a novel direct prediction model of mutation score using neural networks. Relying solely on static code features that do not require generation of mutants or execution of the tests, we predict mutation score with an accuracy better than a quintile. When we include statement coverage as a feature, our accuracy rises to about a decile. Using a similar approach, we also improve the state-of-the-art results for binary test effectiveness prediction and introduce an intuitive, easy-to-calculate set of features superior to previously studied sets. We also publish the largest dataset of test-class level mutation score and static code features data to date, for future research. Finally, we discuss how our approach could be integrated into real-world systems, IDEs, CI tools, and testing frameworks.</p></div></div></div>

On the Extrapolation of Rim Sealing Performance From Test Bench to Real Engine: A Numerical Survey

The main annulus hot gas ingress into turbine wheel-spaces is still one of the most challenging problem designers face. During the decades, several experimental test benches were developed worldwide to improve the knowledge associated to the rim seal flow physics. Even if in some cases quite complex and advanced rig configurations were proposed, limitations in the operating conditions and in the reproduction of the real engine geometries/characteristics into the rig are present. In this paper, validated CFD computations are used to explore the impact of some experimental rigs design choices/limitations on the sealing effectiveness prediction and their ability to mimic the real engine configuration behaviour. Attention is paid on several test rig related aspects such as operating conditions, flow path configuration (blade and vane count) and accuracy in the real engine rim seal geometry reconstruction applied to the rig. From the computations it emerges that a scaled geometry operated at lab conditions is able to mimic pretty well the real engine sealing performance when rig and engine experience the same flow path ΔCp. The ability of the rig to match the engine data is not affected by the differences in main annulus Mach number between test bench and engine. A further result that emerges from the computation regards the fact that the Φ0 - ΔCp0.5 curve is not linear, proving that the linear extrapolation of rim sealing performance from test bench to real engine when rig and engine are characterized by different ΔCp0.5 values is not of general application and an alternative approach is given. Finally, it is found that the impact of vane count on the rim sealing effectiveness is significant, making the extrapolation of data from rig to engine difficult.

Effectiveness prediction of abrasive jetting stream of accelerator tank using normalized sparse autoencoder-adaptive neural fuzzy inference system

Abrasive jetting stream generated from accelerator tank is crucial to the precision machining of industrial products during the process of strengthen jet grinding. In this article, its effectiveness prediction using normalized sparse autoencoder-adaptive neural fuzzy inference system is carried out to provide an optimal result of jetting stream. A normalized sparse autoencoder-adaptive neural fuzzy inference system capable of calculating the concentration density of abrasive impact stress by normalized sparse autoencoder and identifying the effectiveness indexes of abrasive jetting by adaptive neural fuzzy inference system is proposed to predict the stream effectiveness index in grinding practices, indicating that when turbulence root-mean-square velocity ( VRMS) is 420 m/s, turbulence intensity ( Ti) is 570, turbulence kinetic energy ( Tc) is 540 kJ, turbulence entropy ( Te) is 620 J/K, and Reynolds shear stress ( Rs) is 430 kPa (Error tolerance = ± 5%, the same as follows), the optimized effectiveness quality of abrasive jetting stream could be ensured. The effectiveness prediction involve the following steps: measuring the jet impact data on the interior boundary surface of accelerator tank, calculating the concentration density of abrasive impact stress, establishing the descriptive analytical frame work of normalized sparse autoencoder-adaptive neural fuzzy inference system, adaptive prediction of abrasive jetting stream effectiveness through normalized sparse autoencoder-adaptive neural fuzzy inference system computation, and performance verification of actual effectiveness prediction in the efficiency quantification and quality assessment when it compared to that of alternative approaches, such as genetic, simulated annealing–genetic algorithm, Taguchi, artificial neural network–simulated annealing, and genetically optimized neural network system methods. Objective of this research is to adaptive predict the abrasive jetting stream effectiveness using a new-proposed prediction system, a stable and reliable abrasive jetting stream therefore can be achieved using jetting pressure ( Pw) at 320 MPa, mass of cast steel grits ( Mc) at 270 g, mass of bearing steel grits ( Mb) at 310 g, mass of brown-fused alumina grits ( Ma) at 360 g, and mass rate of abrasives ( Fa) at 0.46 kg/min. It is concluded that normalized sparse autoencoder-adaptive neural fuzzy inference system owns an outstanding predictive capability and possesses a much better working advancement in typical calibration indexes of accuracy and efficiency, meanwhile a high agreement between the fuzzy predicted and actual measured values of effectiveness indexes is ensured. This novel method could be promoted constructively to improve the quality uniformity for abrasive jetting stream and to facilitate the productive managements of abrasive jet machining consequently.