It’s the trend to extend the operating license time, called Operating License Extension (OLE) in China, of nuclear power plants (NPPs) in the future. It needs to be adequately demonstrated by licensees and approved by the regulator to gain an extended license time, such as 20 years. The demonstration methods for OLE are different among countries due to the different management systems for NPPs. Safety assessment, environment effect evaluation and update of the final safety analysis report (FSAR) will be the main aspects during OLE demonstration of NPPs in China according to the technical policy issued by National Nuclear Safety Administration (NNSA). Technical methods for scoping and screening, aging management review and time-limited aging analyses, which are the main contents of safety assessment are established based on the technical policy drafted by NNSA and international experiences in order to assist the operators to implement the safety assessment for OLE of NPP.

This paper provides a case study of diagnosing helicopter swashplate ball bearing faults using vibration signals. We develop and apply feature extraction and selection techniques in the time, frequency, and joint time-frequency domains to differentiate six types of swashplate bearing conditions: low-time, to-be-overhauled, corroded, cage-popping, spalled, and case-overlapping. With proper selection of the features, it is shown that even the simple k -nearest neighbor ( k -NN) algorithm is able to correctly identify these six types of conditions on the tested data. The developed method is useful for helicopter swashplate condition monitoring and maintenance scheduling. It is also helpful for testing the manufactured swashplate ball bearings for quality control purposes.

In this paper, we develop and apply feature extraction and selection techniques to classify tool wear in the shaving process. Because shaving tool condition monitoring is not well-studied, we extract both traditional and novel features from accelerometer signals collected from the shaving machine. We then apply a heuristic feature selection technique to identify key features and classify the tool condition. Run-to-life data from a shop-floor application is used to validate the proposed technique.

This article presents a novel model reference adaptive control of fractional order nonlinear systems, which is a generalization of existing method for integer order systems. The formulating adaptive law is in terms of both tracking and prediction errors, whereas existing methods only depends on tracking error. The transient performance of the closed-loop systems with the proposed control strategy improves in the sense of generating smooth system output. The stability and tracking convergence of the resulting closed-loop system are analyzed via the indirect Lyapunov method. Meanwhile, the proposed controller is implemented by employing some fractional order tracking differentiator to generate the required fractional derivatives of a signal. Numerical examples are provided to illustrate the effectiveness of our results.

Since the introduction of the Edmonton Protocol in 2000, islet transplantation has been emerging as promising therapy for Type I diabetes mellitus (T1DM) and currently is the only therapy that can achieve glycemic control without the need for exogenous insulin. Transplanting islet cells has several advantages over transplanting a whole pancreas in that it involves only a minor surgical procedure with low morbidity and mortality, and at a significantly lower cost. However, an obstacle to realizing this goal is a lack of an islet potency index as required by the U.S. Food and Drug Administration (FDA) biologics licensing, as well as a more complete understanding of the physiological mechanisms governing islet and β-cell physiology. Recently, the University of Illinois at Chicago (UIC) has developed a microfluidic platform that can mimic in vivo islet microenvironments through precise and dynamic control of perifusing culture media and oxygen culture levels; all while measuring functionally relevant factors including intracellular calcium levels, mitochondrial potentials, and insulin secretion. By developing an understanding of the physiology and pathophysiology of islets we can more effectively develop strategies that reduce metabolic stress and promote optimization in order to achieve improved success of islet transplantation and open new clinical avenues. The presentation begins by introducing key issues in the field of pancreatic islet transplantation as a clinical therapy for T1DM. This is followed by brief review various technologies that have been developed to study islet cells. The presentation then describes the design, application, and evolution of UIC’s microfluidic-based multimodal islet perifusion and live-cell imaging system for the study of pancreatic islet and β-cell physiology. The article then concludes presenting initial findings from studies seeking to develop an islet potency test.

This paper proposes a framework for addressing challenges of joint production and energy modeling of sustainable manufacturing systems. The knowledge generated is used to improve the technological readiness of manufacturing enterprises for the transition towards sustainable manufacturing. Detailed research tasks of the framework are on the modeling of production, energy efficiency, electricity demand, cost, and demand response decision making. Specifically, the dynamics and performance measures of general manufacturing systems with multiple machines and buffers are modeled to integrate energy use into system modeling. The expressions of electrical energy efficiency and cost are then established based on the electricity pricing profile. Finally, joint production and energy scheduling problem formulations and the solution technique are discussed. New insights are acquired based on the applications of the established model in system parameter selection, rate plan switching decision making, and demand response scheduling. Appropriate implementation of this research outcome may lead to energy-efficient, demand-responsive, and cost-effective operations and thus improve the sustainability of modern manufacturing systems.

Electric vehicle (EV) technologies have received great attention due to the potential contributions to relieving the energy dependence on petroleum and reducing carbon dioxide emissions. The advancement of EV technologies greatly relies on the development of battery technologies. Lithium-ion (Li-ion) batteries have recently become the main choice as the power source for major EV manufacturers. Previous research on EV Li-ion batteries is mainly focused on materials and chemical properties of single cells, while the effects of manufacturing processes on the performance of entire battery packs have almost been neglected. In practice, EV batteries are used in packs containing multiple cells, which may not be ideally manufactured. This research proposes a novel modeling method for analyzing the effects of manufacturing processes on the dynamics of EV Li-ion battery packs. The method will help engineers gain a deeper understanding of the roles of manufacturing processes in improving EV Li-ion battery performance.

In order to research the applicability of turbulence model on characteristics prediction of centrifugal pumps at the design condition, standard k-ε turbulence model, k-ω turbulence model and SST turbulence model are selected, which are commonly used in the numerical prediction for head, efficiency and NPSHr of the centrifugal pumps. By using commercial code ANSYS CFX, the all three turbulent models are used to predict the characteristics of six centrifugal pumps with the different specific speeds at the design condition, which are varied from 34.3 to 260.5. The calculation results are compared with the experimental data, and the comparison indicates that all the prediction results obtained from different turbulence models are more or less different from the experimental data. The head and efficiency predicted by SST turbulence model and k-ω turbulence model are closer and they are all bigger than that predicted by k-ε turbulence model. For low specific speed centrifugal pumps, the head and efficiency predicted by SST model and the NPSHr predicted by k-ε turbulence model are more closer to the experimental values; while for the medium and high specific speed centrifugal pumps, the head and efficiency predicted by k-ε turbulence model are better than that predicted by other models. The k-ω turbulence model and k-ε turbulence model are the best choice to predict NPSHr of medium and high specific speed centrifugal pumps respectively.