White light-emitting diodes (WLEDs) composed of blue LED chip, yellow phosphor, and red quantum dots (QDs) are considered as a potential alternative for next-generation artificial light source with their high luminous efficiency (LE) and color-rendering index (CRI). While, QDs’ poor temperature stability and the incompatibility of QDs/silicone severely hinder the wide utilization of QDs-WLEDs. To relieve this, here we proposed a separated QSNs/phosphor structure, which composed of a QSNs-on-chip layer with a yellow phosphor layer above. A silica shell was coated onto the QDs surface to solve the compatibility problem between QDs and silicone. With CRI > 92 and R9 > 90, the newly proposed QDs@silica nanoparticles (QSNs) based WLEDs present 16.7 % higher LE and lower QDs working temperature over conventional mixed type WLEDs. The reduction of QDs’ temperature can reach 11.5 °C, 21.3 °C and 30.3 °C at driving current of 80 mA, 200 mA and 300 mA, respectively.

As the ultimate protection of a pressure system, pressure safety valves (PSV) can respond in an instable manner such as flutter and chatter which will affect service life, reliability and performance. In order to study the dynamic instability caused by multi-source forces including the flow force, the spring compressing force and the pressure wave force, a more realistic CFD model containing a PSV and different connected pipes as well as the pressure vessel is developed, in which advanced techniques in Fluent such as User Defined Function (UDF) and Dynamic Layering method are combined to allow the PSV to operate. Based on this model, the process of the valve’s opening and reclosing is monitored to examine the influence of design parameters on the dynamic instability of the PSV. Specifically, the propagation of pressure waves along the connecting pipes is successfully captured, which is of great help to explain the instability mechanism and optimize the design and setup of pressure relief systems.

Structural shape reconstruction is a critical issue for real-time structural health monitoring in the fields of engineering application. This paper shows how to implement structural shape reconstruction using a small number of strain data measured by fiber Bragg grating (FBG) sensors. First, the basic theory of structural shape reconstruction is introduced using modal superposition method. A transformation is derived from the measured discrete strain data to global displacement field through modal coordinate, which is the same for strain mode shape superposition and displacement mode shape superposition. Then, optimization of the sensor layout is investigated to achieve the effective reconstruction effect. Finally, structural shape reconstruction algorithm using modal superposition method is applied in experiments. The experiment results show that the reconstructed displacements match well with those measured by a laser displacement sensor and the proposed approach is a promising method for structural shape reconstruction.

With the introduction of digital instrumentation system, the cyber security threat to nuclear power plants is becoming more and more serious. The existing cyber security standards of nuclear power plants still need to be improved, and the technology practice of defensive strategies is lacking all over the world. In this paper, based on the comparison of domestic and foreign regulations and standards, combined with the technical practice of I&C system overall plan, a defense-in-depth model based on data flow is proposed. The overall technical requirements, hierarchy, network model, cyber security basic requirements, cyber security interface and protection of digital assets are introduced, the application of the model and the direction of research on cyber security of nuclear power plant are prospected.

In this paper, a new flux expansion nodal method for hexagonal-z geometry is presented to solve multi-group neutron diffusion equations. In each three dimensional node and each group, the intra-nodal flux is approximated by the linear combination of exponential functions and orthogonal polynomials up to the second order. The coefficients are obtained by the weighted residual methods and the coupling conditions of the nodes, which satisfy the continuity of both the zero- and first-order moments of fluxes and currents across the nodal surfaces. A series of benchmark problems including the three dimensional cases are used to test this method. The numerical results verify that it is a rather accurate and efficient for the estimation of the eigenvalue and power distribution.

Attaining lightweight design of mechanical parts and components is an ever-lasting goal of designers during design process. In this paper, a systematic lightweight design methodology is proposed for mould and die design enabling us to avoid conventional ways of empirical approaches, leading to substantial cost reduction in part’s material utilization and minimizing environmental impacts in product realization. At the initial stage, the characteristics of a lightweight mould or die are identified using the so-called lightweight coefficient defined as the ratio of the part’s functional performance to its mass. The proposed design methodology was implemented to attain an optimum lightweight coefficient resulting in desirable mass reduction as well as overall functional performance improvements of the final products. In the case study, the design was started with numerical simulation to model the process-part interaction for a typical extrusion operation, which allowed the identification of the numerical design parameters that affect the required performances as well as the threshold values that must be met. Structural optimization scheme was then employed to achieve the targeted performances with structural components of minimum mass by applying the design criterion of reducing the thickness of components’ walls. The study was done on the redesign of an actual polymer pipe extrusion die. The results showed that 21.67% (3325.26 kg) mass reduction of the underlying extrusion die, together with the evaluated increase of lightweight coefficient by 25.23%, could be obtained with the specified performances of productivity, static stiffness, compressive strength and assembly property satisfied. All in all, this work provides a foundation for dealing with lightweight mechanical parts design aimed at conventional heavy duty machine components with complex functionalities such as moulds and dies. The proposed lightweight coefficient design methodology proves to be effective in reducing parts’ weight without sacrificing the part’s specifications and desired functionalities. In future work other design criteria such as shortening the length of the flow channel or reducing the number of assembly parts are to be investigated to further improve the design outcomes. Moreover, life cycle assessment (LCA) will be performed to evaluate the positive impact of lightweight design on the environment due to material saving and machining wastes reduction in realizing the parts.

Patient-specific computational study of aortic disease provides a powerful means for diagnosis and pre-operative planning. However, creating patient-specific computational models can be time-consuming due to the fact that anatomical geometries extracted from clinical imaging data are often incomplete and noisy. This paper presents an approach for constructing statistical shape models (SSMs) for aortic surfaces with the eventual goal of mapping the mean aorta geometries to raw surface data obtained from the clinical images for each new patient so that patient-specific models can be automatically constructed. The input aortic models in this study come in the form of triangle meshes generated from CT scans on 6 patients. Statistical models with modes that characterize the variation pattern are found after optimizing the group-wise correspondence across the aorta training set. We use the direct reparametrization approach to efficiently manipulate shape correspondence. We use B-spline based differentiable shape representation for the training set and use the adjoint method for deriving analytical gradients in a gradient based approach for manipulating the shape correspondence to minimize the description length of the resulting SSM. Our numerical result shows that the evaluation measures of the optimized statistical model have been significantly enhanced.