401,876 research outputs found
On the relationship between hurricane cost and the integrated wind profile
It is challenging to identify metrics that best capture hurricane destructive potential and costs. Although it has been found that the sea surface temperature and vertical wind shear can both make considerable changes to the hurricane destructive potential metrics, it is still unknown which plays a more important role. Here we present a new method to reconstruct the historical wind structure of hurricanes that allows us, for the first time, to calculate the correlation of damage with integrated power dissipation and integrated kinetic energy of all hurricanes at landfall since 1988. We find that those metrics, which include the horizontal wind structure, rather than just maximum intensity, are much better correlated with the hurricane cost. The vertical wind shear over the main development region of hurricanes plays a more dominant role than the sea surface temperature in controlling these metrics and therefore also ultimately the cost of hurricanes
Thermal gradient driven domain wall dynamics
The issue of whether a thermal gradient acts like a magnetic field or an
electric current in the domain wall (DW) dynamics is investigated. Broadly
speaking, magnetization control knobs can be classified as energy-driving or
angular-momentum driving forces. DW propagation driven by a static magnetic
field is the best-known example of the former in which the DW speed is
proportional to the energy dissipation rate, and the current-driven DW motion
is an example of the latter. Here we show that DW propagation speed driven by a
thermal gradient can be fully explained as the angular momentum transfer
between thermally generated spin current and DW. We found DW-plane rotation
speed increases as DW width decreases. Both DW propagation speed along the wire
and DW-plane rotation speed around the wire decrease with the Gilbert damping.
These facts are consistent with the angular momentum transfer mechanism, but
are distinct from the energy dissipation mechanism. We further show that
magnonic spin-transfer torque (STT) generated by a thermal gradient has both
damping-like and field-like components. By analyzing DW propagation speed and
DW-plane rotation speed, the coefficient ( \b{eta}) of the field-like STT
arising from the non-adiabatic process, is obtained. It is found that \b{eta}
does not depend on the thermal gradient; increases with uniaxial anisotropy
K_(||) (thinner DW); and decreases with the damping, in agreement with the
physical picture that a larger damping or a thicker DW leads to a better
alignment between the spin-current polarization and the local magnetization, or
a better adiabaticity
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A novel improved model for building energy consumption prediction based on model integration
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models
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A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost
Due to reducing the reliance of buildings on fossil fuels, Passive House (PH) is receiving more and more attention. It is important that integrated optimization of passive performance by considering energy demand, cost and thermal comfort. This paper proposed a set three-stage multi-objective optimization method that combines redundancy analysis (RDA), Gradient Boosted Decision Trees (GBDT) and Non-dominated sorting genetic algorithm (NSGA-II) for PH design. The method has strong engineering applicability, by reducing the model complexity and improving efficiency. Among then, the GBDT algorithm was first applied to the passive performance optimization of buildings, which is used to build meta-models of building performance. Compared with the commonly used meta-model, the proposed models demonstrate superior robustness with the standard deviation at 0.048. The optimization results show that the energy-saving rate is about 88.2% and the improvement of thermal comfort is about 37.8% as compared to the base-case building. The economic analysis, the payback period were used to integrate initial investment and operating costs, the minimum payback period and uncomfortable level of Pareto frontier solution are 0.48 years and 13.1%, respectively. This study provides the architects rich and valuable information about the effects of the parameters on the different building performance
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