Background: Renewable energy resources are becoming more important to meet growing energy demands while reducing pollutants in the environment. In the current market, wind turbines are primarily restricted to rural use due to the large size, noise creation, and physical appearance. However, wind turbines possess the ability to run at any time of the day. Horizontal axis wind turbines remain the most widely used, but there is significant room for improvement in vertical axis wind turbines. Methods: While vertical axis wind turbines are not reaching the same level of efficiency of horizontal axis wind turbines, there are significant benefits to researching improvements. One of the main benefits is to make use of vertical axis wind turbines in urban settings. In order to improve the efficiency of the vertical axis wind turbine, a biological approach was taken to design blades that mimic the shape of maple seeds and triplaris samara seeds. This approach was taken because due to its geometrical properties, typically extra lift is generated. Results: The results obtained through FEA simulations were consistent with the expected results for the application that was considered. The results obtained provide valuable insight for engineers to iterate and design optimum wind turbine blades taking advantage of biological phenomena applied to conventional airfoils. Conclusions: The purpose of this paper is to provide structural analysis details into the design of a vertical axis wind turbine blades that mimic the geometry of maple and triplaris samaras seeds.

Extant literature illustrates that complementary efforts, such as Entrepreneurially Minded Learning, add an important dimension to the training of the next generation of engineers and innovators, providing them with multiple perspectives and a pathway for linking technical concepts to societal challenges. Nationwide initiatives, such as the Kern Entrepreneurial Engineering Network (KEEN), have focused specifically on infusing Entrepreneurially Minded Learning into curriculum content and delivery, training both faculty and students to have the know-why in addition to the know-how of engineering topics. KEEN has established a framework that supplements engineering skills already taught in classrooms with outcomes that support the development of an entrepreneurial mindset. The framework is rooted in fostering the 3Cs of entrepreneurial mindset: Curiosity, Connections, and Creating Value. In this study, we contribute a series of concepts infusing KEEN-inspired modules into a three-course sequence in Dynamics and Controls. We provide an overview on each of the modules, highlighting the KEEN framework objectives. We present postcourse student questionnaire responses illustrating student perception of entrepreneurial mindset and the 3Cs as it relates to engineering and addressing technological challenges. We provide lessons learned and sufficient detail of all modules for replication in other Dynamics and Controls course sequences as well as supporting student data.

Purpose -The purpose of the paper is to present an approach to detect and isolate the sensor failures, using a bank of extended Kalman filters (EKF) using an innovative initialization of covariance matrix using system dynamics. Design/methodology/approach -The EKF is developed for nonlinear flight dynamic estimation of a spacecraft and the effects of the sensor failures using a bank of Kalman filters is investigated. The approach is to develop a fast convergence Kalman filter algorithm based on covariance matrix computation for rapid sensor fault detection. The proposed nonlinear filter has been tested and compared with the classical Kalman filter schemes via simulations performed on the model of a space vehicle; this simulation activity has shown the benefits of the novel approach. Findings -In the simulations, the rotational dynamics of a spacecraft dynamic model are considered, and the sensor failures are detected and isolated.Research limitations/implications -A novel fast convergence Kalman filter for detection and isolation of faulty sensors applied to the three-axis spacecraft attitude control problem is examined and an effective approach to isolate the faulty sensor measurements is proposed. Advantages of using innovative initialization of covariance matrix are presented in the paper. The proposed scheme enhances the improvement in estimation accuracy. The proposed method takes advantage of both the fast convergence capability and the robustness of numerical stability. Quaternion-based initialization of the covariance matrix is not considered in this paper. Originality/value -A new fast converging Kalman filter for sensor fault detection and isolation by innovative initialization of covariance matrix applied to a nonlinear spacecraft dynamic model is examined and an effective approach to isolate the measurements from failed sensors is proposed. An EKF is developed for the nonlinear dynamic estimation of an orbiting spacecraft. The proposed methodology detects and decides if and where a sensor fault has occurred, isolates the faulty sensor, and outputs the corresponding healthy sensor measurement.

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