The Strain Energy Release Rate for Stack of Coated Conductors with Interface Crack in Perpendicular Magnetic Field

Due to the ability of transporting huge current in the stack of high-temperature superconducting conductors, the electromagnetic body force generated by the interaction of magnetic field and current may affect the mechanical stability of structure. In this paper, the fracture behavior of the stack of coated conductors which contains an interface crack is studied for increasing field and decreasing field. The body forces are obtained with variational formulation for the Bean’s critical state model. Based on the virtual crack closure technique (VCCT), the strain energy release rate of the stack of coated conductors with an interface crack is determined. The strain energy release rates are compared for different crack positions, crack lengths, magnetic fields and the thicknesses of substrate, respectively. These results may be useful for the practical application

Effects Comparison of Different Resilience Enhancing Strategies for Municipal Water Distribution Network: A Multidimensional Approach

Water distribution network (WDN) is critical to the city service, economic rehabilitation, public health, and safety. Reconstructing the WDN to improve its resilience in seismic disaster is an important and ongoing issue. Although a considerable body of research has examined the effects of different reconstruction strategies on seismic resistance, it is still hard for decision-makers to choose optimal resilience enhancing strategy. Taking the pipeline ductile retrofitting and network meshed expansion as demonstration, we proposed a feasible framework to contrast the resilience enhancing effects of two reconstruction strategies—units retrofitting strategy and network optimization strategy—in technical and organizational dimension. We also developed a new performance response function (PRF) which is based on network equilibrium theory to conduct the effects comparison in integrated technical and organizational dimension. Through the case study of municipal WDN in Lianyungang, China, the comparison results were thoroughly shown and the holistic decision-making support was provided

How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint

Abstract—Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of the pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users ’ information are known a priori. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arrive, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of the services (assumed to be a non-negative monotone submodular function) provided by selected users under a budget constraint. We design two online mecha-nisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms. I

A New Method for Impeller Inlet Design of Supercritical CO2 Centrifugal Compressors in Brayton Cycles

Supercritical Carbon Dioxide (SCO2) is considered as a potential working fluid in next generation power and energy systems. The SCO2\ua0Brayton cycle is advantaged with higher cycle efficiency, smaller compression work, and more compact layout, as compared with traditional cycles. When the inlet total condition of the compressor approaches the critical point of the working fluid, the cycle efficiency is further enhanced. However, the flow acceleration near the impeller inducer causes the fluid to enter two-phase region, which may lead to additional aerodynamic losses and flow instability. In this study, a new impeller inlet design method is proposed to achieve a better balance among the cycle efficiency, compressor compactness, and inducer condensation. This approach couples a concept of the maximum swallowing capacity of real gas and a new principle for condensation design. Firstly, the mass flow function of real gas centrifugal compressors is analytically expressed by non-dimensional parameters. An optimal inlet flow angle is derived to achieve the maximum swallowing capacity under a certain inlet relative Mach number, which leads to the minimum energy loss and a more compact geometry for the compressor. Secondly, a new condensation design principle is developed by proposing a novel concept of the two-zone inlet total condition for SCO2\ua0compressors. In this new principle, the acceptable acceleration margin (AAM) is derived as a criterion to limit the impeller inlet condensation. The present inlet design method is validated in the design and simulation of a low-flow-coefficient compressor stage based on the real gas model. The mechanisms of flow accelerations in the impeller inducer, which form low-pressure regions and further produce condensation, are analyzed and clarified under different operating conditions. It is found that the proposed method is efficient to limit the condensation in the impeller inducer, keep the compactness of the compressor, and maintain a high cycle efficiency

Parameter Sensitivity Study on Inflow Distortion of Boundary Layer Ingested Turbofans

The inflow distortion to the fan introduced by the ingestion of the fuselage boundary layer is the most critical challenge in realizing the benefits of boundary later ingesting (BLI) concepts. Minimizing the level of distortion while maintaining the desired amount of ingested boundary layer and free stream flow is crucial in minimizing the penalties to fan efficiency and noise emissions. In this paper, a parametric sensitivity study is performed to examine the integration of two semi-buried BLI turbofans at the rear end of a typical tube-and-wing body (TWB) fuselage. The key parameters influencing BLI, such as the nacelle installation positions, wing position, fuselage length, rear fuselage shape, intake shape and operating conditions were evaluated by computational fluid dynamics (CFD). Among the investigated parameters, increasing the nacelle spanwise installation spacing improved inflow distortion by reducing the diffusion separation, but this needs to be offset against the added weight and nacelle drag. A high wing position variant showed strong interference between the wing and the nacelle, which must be avoided as this significantly increases the complexity of the inflow distortion. A moderate angle of attack (AOA) variation did not affect the fan inflow distortion but there was a tendency for interference from the wing to increase when the AOA was increased. The general conclusions from this study will be useful in the conceptual design of a similar type of BLI configuration, as well as a more comprehensive optimization of this type of aircraft–engine integration

Merino: Entropy-driven Design for Generative Language Models on IoT Devices

Generative Large Language Models (LLMs) stand as a revolutionary advancement in the modern era of artificial intelligence (AI). However, directly deploying LLMs in resource-constrained hardware, such as Internet-of-Things (IoT) devices, is difficult due to their high computational cost. In this paper, we propose a novel information-entropy framework for designing mobile-friendly generative language models. Our key design paradigm is to maximize the entropy of transformer decoders within the given computational budgets. The whole design procedure involves solving a mathematical programming (MP) problem, which can be done on the CPU within minutes, making it nearly zero-cost. We evaluate our designed models, termed MeRino, across nine NLP downstream tasks, showing their competitive performance against the state-of-the-art autoregressive transformer models under the mobile setting. Notably, MeRino achieves similar or better zero performance compared to the 350M parameter OPT while being 4.9x faster on NVIDIA Jetson Nano with 5.5x reduction in model size. Code will be made available soon

Lightweight Vision Transformer with Cross Feature Attention

Recent advances in vision transformers (ViTs) have achieved great performance in visual recognition tasks. Convolutional neural networks (CNNs) exploit spatial inductive bias to learn visual representations, but these networks are spatially local. ViTs can learn global representations with their self-attention mechanism, but they are usually heavy-weight and unsuitable for mobile devices. In this paper, we propose cross feature attention (XFA) to bring down computation cost for transformers, and combine efficient mobile CNNs to form a novel efficient light-weight CNN-ViT hybrid model, XFormer, which can serve as a general-purpose backbone to learn both global and local representation. Experimental results show that XFormer outperforms numerous CNN and ViT-based models across different tasks and datasets. On ImageNet1K dataset, XFormer achieves top-1 accuracy of 78.5% with 5.5 million parameters, which is 2.2% and 6.3% more accurate than EfficientNet-B0 (CNN-based) and DeiT (ViT-based) for similar number of parameters. Our model also performs well when transferring to object detection and semantic segmentation tasks. On MS COCO dataset, XFormer exceeds MobileNetV2 by 10.5 AP (22.7 -> 33.2 AP) in YOLOv3 framework with only 6.3M parameters and 3.8G FLOPs. On Cityscapes dataset, with only a simple all-MLP decoder, XFormer achieves mIoU of 78.5 and FPS of 15.3, surpassing state-of-the-art lightweight segmentation networks.Comment: Technical Repor