• Energy Research

Tourism and agriculture can contribute to destination sustainability by increasing tourist-stakeholder satisfaction. Grown agriculture is an important component of a rural destination attraction. Nonetheless, tourists’ satisfaction with locally grown agricultural food has been unexplored when it comes to linking tourism with agriculture. The purpose of this study is to develop an understanding of the links between demand for locally grown agricultural products at the destination and the levels of satisfaction among visitors. Theoretical views concerning tourist consumption of local agricultural products and its effects were reviewed. Unstructured in-depth interviews were conducted onsite in Tiantangzhai to investigate the relationship between tourism and agriculture with visitor satisfaction. The sample of respondents consisted of 71 participants who shared their views on the relationship between agriculture and tourism at the destination. Findings were presented in a two-level analysis including the industry focus and a soft-laddering technique that revealed deeper insights. Findings indicate that tourist-stakeholders present a demand-driven economy that can be exploited by local agricultural producers and the tourism economy itself. Theoretical implications point to a more sustainable destination image and practical implications highlight the demand-driven implications of tourist expectations and experience satisfaction.

Abstract Reinforcing steel corrosion is a significant factor leading to the durability deterioration of reinforced concrete (RC) structures. The on-line monitoring of the corrosion of RC structures in a long-term, human-free manner is not only valuable in industry, but also a significant challenge in academia. This paper presents the first of its kind corrosion-monitoring approach that only exploits three heterogeneous tiny energy sources to power commercial-off-the-shelf wireless sensor motes such that the corrosion-related data are automatically and autonomously captured and sent to users via wireless channels. We first investigated the availability of these three tiny energy sources: corrosion energy, a cement battery, and a weak solar energy. In particular, the two former energy sources inherently exist in RC structures and can be generated continually in the service-life of RC structures, which beneficial for the prospects of long-term corrosion monitoring. We then proposed a proof-of-concept prototype, which consisted of a Telosb wireless sensor mote and an energy harvester in order to evaluate the feasibility and effectiveness of the ultralow-power ambient energy as a type of power supply in corrosion monitoring applications. The critical metrics for the holographic monitoring of RC structures, including electrochemical noise, humidity and temperature, were successfully acquired and analysed using a post-processing program. This paper describes a unique and novel approach towards the realisation of smart structural monitoring and control system in the practical engineering.

Wall-model large eddy simulations (WMLES) are conducted to investigate the spatial features of large-scale and very-large-scale motions (LSMs and VLSMs) in turbulent boundary flow in different surface roughnesses at a very high Reynolds number, O (106–107). The results of the simulation of nearly smooth cases display good agreement with field observations and experimental data, both dimensioned using inner and outer variables. Using pre-multiplied spectral analysis, the size of VLSMs can be reduced or even disappear with increasing roughness, which indirectly supports the concept that the bottom-up mechanism is one of the origins of VLSMs. With increases in height, the power of pre-multiplied spectra at both high and low wavenumber regions decreases, which is consistent with most observational and experimental results. Furthermore, we find that the change in the spectrum scaling law from −1 to −5/3 is a gradual process. Due to the limitations of the computational domain and coarse grid that were adopted, some VLSMs and small-scale turbulence are truncated. However, the size of LSMs is fully accounted for. From the perspective of the spatial correlation of the flow field, the structural characteristics of VLSMs under various surface roughnesses, including three-dimensional length scales and inclination angles, are obtained intuitively, and the conclusions are found to be in good agreement with the velocity spectra. Finally, the generation, development and extinction of three-dimensional VLSMs are analyzed by instantaneous flow and vorticity field, and it shows that the instantaneous flow field gives evidence of low-speed streamwise-elongated flow structures with negative streamwise velocity fluctuation component, and which are flanked on each side by similarly high-speed streamwise-elongated flow structures. Moreover, each of the low-speed streamwise-elongated flow structure lies beneath many vortices.

Large-eddy simulation (LES) is performed to investigate self-similarity in a wind turbine wake flow. The turbine is represented using an actuator line model in a pseudo-spectral method-based solver. A new hybrid approach of smoothed pseudo-spectral method and finite-difference method (sPSMFDM) is proposed to alleviate the Gibbs phenomenon caused by the jump of velocity and pressure around the turbine. The LES is validated with the mean velocity and turbulence statistics obtained from wind-tunnel measurement reported in the literature. Through an appropriate choice of characteristic scales of velocity and length, self-similarity is elucidated in the normalized mean velocity and Reynolds stress profiles at various distances. The development of self-similarity is categorized into three stages based on the variation in the characteristic scales and the spanwise distribution of normalized velocity deficit. The mechanisms responsible for the transition of self-similarity stages are analyzed in detail. The findings of the flow physics obtained in this study will be useful for the modeling and fast prediction of wind turbine wake flows.

Coherent structures in the turbulent boundary layer were investigated under different stability conditions. Qualitative analyses of the flow field, spatial correlation coefficient field and pre-multiplied wind velocity spectrum showed that the dominant turbulent eddy structure changed from small-scale motions to large- and very-large-scale motions and then to thermal plumes as the stability changed from strong stable to neutral and then to strong unstable. A quantitative analysis of the size characteristics of the three-dimensional turbulent eddy structure based on the spatial correlation coefficient field showed that under near-neutral stability, the streamwise, wall-normal and spanwise extents remained constant at approximately 0.3 δ , 0.1 δ and 0.2 δ ( δ , boundary layer height), respectively, while for other conditions, the extent in each direction varied in a log-linear manner with stability; only the spanwise extent under stable conditions was also independent of stability. The peak wavenumber of the pre-multiplied wind velocity spectrum moves towards small values from stable conditions to neutral condition and then to unstable conditions; thus, for the wind velocity spectrum, another form is needed that takes account the effects of the stability condition.

Reynolds-stress closure modeling is critical to Reynolds-averaged Navier-Stokes (RANS) analysis, and it remains a challenging issue in reducing both structural and parametric inaccuracies. This study first proposes a novel algebraic stress model named as tensorial quadratic eddy-viscosity model (TQEVM), in which nonlinear terms improve previous model-form failure due to neglection of nonlocal effects. Then a data-driven regression model based on a fully-connected deep neural network is designed to determine the TQEVM coefficients. The well-trained data-driven model using high-fidelity direct numerical simulation (DNS) data successfully learned the underlying input-output relationships, further obtaining spatial-dependent optimal values of these coefficients. Finally, detailed validations are made in wall-bounded flows where nonlocal effects are expected to be significant. Comparative results indicate that TQEVM provides improvements both for the stress-strain misalignment and stress anisotropy, which are clear advantages over linear and quadratic eddy-viscosity models. TQEVM extends to the scope of resolution to the wall distance y + ≈ 9 as well as provides a realizable solution. RANS simulations with TQEVM are also carried out and the obtained mean-flow quantities of interest agree well with DNS. This work, therefore, results in a high-fidelity representation of Reynolds stresses and contributes to further understanding of machine-learning-assisted turbulence modeling and regression analysis.