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A New Model of Anisotropic Field Dependence for HTS Conductor on Round Core (CORC) Cables
No full textThis paper shows a new model of the anisotropic field dependence for modeling the High Temperature Superconductor (HTS) based Conductor on Round Core (CORC) transmission Cables. The modeling of CORC Cable was carried out using the finite-element method (FEM) and H-formulation model which put into the software COMSOL. This new model with the field dependence model was tested for both the 2D and 3D models of CORC cables. The simulations were performed with different conditions, e.g. different transport current and the gap angle between each HTS wires. This new field dependence model can contribute a good approximation of the real operating performance of CORC cables, and also build an upper limit of the AC loss. This model could be really beneficial for the design of CORC cables
Control of Interlinking Converters in Hybrid AC/DC Grids: Network Stability and Scalability
No full textHybrid AC/DC networks are an effective solution for future power systems, due to their ability to combine advantages of both AC and DC networks. However, they bring new technological challenges, one key area being the control of such a network. The network, and especially the interlinking converter (ILC), must be controlled to ensure that the DC and AC subsystems coordinate to stabilize the network and allocate power appropriately. This is an area which has attracted considerable recent interest due to the non-triviality of the control design. One promising tool is passivity theory which allows the derivation of decentralized conditions through which the stability of the network can be guaranteed. This paper investigates the application of a passivity framework to AC/DC grids, using a typical lossless line assumption. By ensuring that an appropriately formulated passivity condition is satisfied by the AC and DC buses, and the interlinking converter, the stability of the interconnection can be guaranteed. We also discuss how the ILC controller may be designed to achieve an appropriate power allocation between AC and DC sources. Simulation results demonstrate that the proposed ILC control design regulates the frequency and voltages of the hybrid AC/DC network with a stable operation maintained
Surface wave propagation from drop-projectile tests: Physical and numerical modelling
No full textSurface wave propagation can be investigated with the help of centrifuge and numerical modelling. An electromagnetic drop-projectile apparatus was used to generate surface waves in soil upon the impact of a spherical metal ball with a shallow foundation. A 3D LS-DYNA FE model was developed and first calibrated against the analytical solutions for the soil displacement at the ground surface due to the arrival of Rayleigh waves. The 3D model was then validated with the results of vertical acceleration obtained from geotechnical centrifuge test for homogeneous soil layer profile. The numerical results show that LS-DYNA can reliably be used as a numerical tool to simulate surface wave propagation. Following the validation, a parametric numerical study was performed to assess the impact of stiffness contrast in soil layers on surface wave propagation. In this parametric study, soil layer with relatively lower stiffness (shear wave velocity) was modelled below a stiffer upper layer. The attenuation of vertical acceleration of the surface waves at an increasing distance away from the source were investigated and compared between the results of geotechnical centrifuge test and numerical models. The results of parametric analysis tend to suggest that the presence of soft soil at shallow depth can amplify the amplitude of vertical accelerations within the stiffer upper layer. This effect however is likely to be localised near the source of vibration
CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets
No full textShape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, it is also a very labor intensive step, which counteracts the perceived value of the resulting model. The state-of-the-art method for automating cylinder detection can detect cylinders with 62% precision and 70% recall, while other shapes must then be segmented manually and shape segmentation is not achieved. This performance is promising, but it is far from drastically eliminating the manual labor cost. We argue that the use of class segmentation deep learning algorithms has the theoretical potential to perform better in terms of per point accuracy and less manual segmentation time needed. However, such algorithms could not be used so far due to the lack of a pre-trained dataset of laser scanned industrial shapes as well as the lack of appropriate geometric features in order to learn these shapes. In this paper, we tackle both problems in three steps. First, we parse the industrial point cloud through a novel class segmentation solution (CLOI-NET) that consists of an optimized PointNET++ based deep learning network and post-processing algorithms that enforce stronger contextual relationships per point. We then allow the user to choose the optimal manual annotation of a test facility by means of active learning to further improve the results. We achieve the first step by clustering points in meaningful spatial 3D windows based on their location. Then, we apply a class segmentation deep network, and output a probability distribution of all label categories per point and improve the predicted labels by enforcing post-processing rules. We finally optimize the results by finding the optimal amount of data to be used for training experiments. We validate our method on the largest richly annotated dataset of the most important to model industrial shapes (CLOI) and yield 82% average accuracy per point, 95.6% average AUC among all classes and estimated 70% labor hour savings in class segmentation. This proves that it is the first to automatically segment industrial point cloud shapes with no prior knowledge at commercially viable performance and is the foundation for efficient industrial shape modeling in cluttered point clouds
Variation diminishing Hankel operators
No full textThis paper studies the variation diminishing property of linear time-invariant Hankel k-positive systems, i.e., systems whose Hankel operator maps inputs with k - 1 sign changes to outputs with at most the same variation. Our main result is that these systems have a dominant approximation in the form of a parallel interconnection of k positive lags, that is, first order positive systems. This is shown by expressing the k-positivity of a LTI system as the external positivity (that is, 1-positivity) of k compound LTI systems. Our characterizations are generalizations of the well known properties of positive systems (k = 1) and Hankel totally positive systems (k = 8)
Data assimilation applied to pressurised water reactors
No full textBest estimate plus uncertainty is the leading methodology to validate existing safety margins. It remains a challenge to develop and license these approaches, in part due to the high dimensionality of system codes. Uncertainty quantification is an active area of research to develop appropriate methods for propagating uncertainties, offering greater scientific reason, dimensionality reduction and minimising reliance on expert judgement. Inverse uncertainty quantification is required to infer a best estimate back on the input parameters and reduce the uncertainties, but it is challenging to capture the full covariance and sensitivity matrices. Bayesian inverse strategies remain attractive due to their predictive modelling and reduced uncertainty capabilities, leading to dramatic model improvements and validation of experiments. This paper uses state-of-the-art data assimilation techniques to obtain a best estimate of parameters critical to plant safety. Data assimilation can combine computational, benchmark and experimental measurements, propagate sparse covariance and sensitivity matrices, treat non-linear applications and accommodate discrepancies. The methodology is further demonstrated through application to hot zero power tests in a pressurised water reactor (PWR) performed using the BEAVRS benchmark with Latin hypercube sampling of reactor parameters to determine responses. WIMS 11 (dv23) and PANTHER (V.5.6.4) are used as the coupled neutronics and thermal-hydraulics codes; both are used extensively to model PWRs. Results demonstrate updated best estimate parameters and reduced uncertainties, with comparisons between posterior distributions generated using maximum entropy principle and cost functional minimisation techniques illustrated in recent conferences. Future work will improve the Bayesian inverse framework with the introduction of higher-order sensitivities
Surrogate model optimization of a 'micro core' PWR fuel assembly arrangement using deep learning models
No full textThis paper investigates the applicability of surrogate model optimization (SMO) using deep learning regression models to automatically embed knowledge about the objective function into the optimization process. This paper demonstrates two deep learning SMO methods for calculating simple neutronics parameters. Using these models, SMO returns results comparable with those from the early stages of direct iterative optimization. However, for this study, the cost of creating the training set outweighs the benefits of the surrogate models
Optimized installation flow – A strategy for substantial cycle time reduction
No full textIndustrial system infrastructure installations, such as those in semiconductor fabrication plants, are complex, short-term and mission critical. They frequently encounter productivity, predictability and performance problems. We propose a strategic approach to manage such projects and substantially reduce their durations. The method, called Optimized Installation Flow (OIF), builds on lean and associated theories in the realm of production planning and control, synthesizing a method with seven principles. The results of implementation of OIF in 108 such projects show marked and consistent improvement in project duration when compared with the results of 91 other projects managed using the same company’s previous best practice “Two-week buffer” approach. On average, cycle time durations for tool installation projects were reduced by 42%-48%, without any overtime on site. The method is gradually being adopted as new standard practice throughout the construction management portfolio of the case study company. OIF is an operating strategy that has demonstrated improvement, shifting mindsets, behaviours and organization’s culture
Engineering the Cavity modes and Polarization in Integrated Superconducting Coherent Terahertz Emitters
No full textOn-chip, solid-state terahertz (THz) devices based on superconducting BiSrCaCuO+δ(BSCCO) can coherently and continuously radiate electromagnetic waves with frequencies tunable between 100 GHz and 11 THz. Their huge frequency tunability observable by the application of an applied voltage of as small as 0 < V_{\mathrm{d}\mathrm{c}}(\mathrm{V}) < 1.5 covers the entire THz gap. Here, we report on a novel approach towards engineering the THz waves in such devices, with pentagonal cavities, by performing the numerical simulations/analytical calculations of the cavity resonances. We investigate the radiation of the intense and coherent THz waves in pentagonal emitters by keeping the bias feed point in the middle and changing the device geometry. We compare the results with the experiment and find a good agreement
(Invited) Optimizing Material Systems for All-Inkjet-Printed Organic Thin-Film Transistors
No full textAll-inkjet-printed organic thin-film transistors enjoy advantages of low-cost printability and mechanical flexibility, thereby enabling newly emerging application areas. Recent developments have shown devices with low operating voltages and steep subthreshold slopes, using a bottom-gate bottom-contact structure. However, it would be also interesting to understand the optimization of the underlying materials system. This study investigates the composition and associated solvents for semiconductor inks, as well as the necessity of encapsulation. Basically, we compare the semiconductor inks of 6,13-bis(triisopropylsilylethynyl)pentacene with and without polystyrene binder, and find the importance of the polymer binder in lowering trap state density. Comparing semiconductor inks of different boiling points, e.g. toluene (low) and anisole (high), suggests that using a solvent with a high boiling point can enhance semiconductor crystallization. Using encapsulation with a fluoropolymer CYTOP is essential to reduce the trap state density. These results are important for further development of novel all-inkjet-printed organic thin-film transistors