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Recently, the application of building information modeling (BIM) in building energy efficiency design programs has been emphasized in research. In China, faced with requirements in building energy efficiency design standards and specifications, researchers have been using BIM as a platform for breaking up bottlenecks in energy simulation and design software, and BIM is considered the key to improve the efficiency of building energy efficiency design. Firstly, based on the trade-off energy consumption judgment method proposed in the ‘Public Building Energy Efficiency Design Standard (GB50189-2015)’, this study combines the BIM platform with an orthogonal simulation design adopting an experimental building construction project in a cold region of China, to thereby study the influences of different envelope structural parameters on cooling and heating loads. Then, this study analyzes the continuity and consistency of data exchange between different BIM-related software, through the modeling software Revit and the sustainable analysis software Ecotect. Finally, through the experiment, the critical factors of the optimized design of the building envelope are investigated and applied for the optimized design of the building thermal system. In addition, this study uses BIM to complete an energy efficiency design for a laboratory heating, ventilation and air conditioning (HVAC) system. According to the experimental results, the following conclusions can be drawn: this study proposes a BIM-based optimized design method of building thermal system, which complies with various Chinese building energy efficiency standards and polices and can be used as a reference for Chinese designers. Also, the issues and challenges in building consumption simulation based on BIM technology and the application of BIM technology in the design of building thermal HVAC systems are discussed, and the development trends of BIM technology in China are analyzed. Building thermal system and HVAC system design are the key points of building efficiency design. The optimized design method proposed in this paper is of great significance to this system design. The optimized design method can improve the design efficiency of designers while complying with China’s standards and policies. Therefore, it can save costs, improve the economic benefits of the projects and, at the same time, obtain an ideal building efficiency design.

The wettability of catalyst plays an important role in regulating catalytic performance in heterogenous catalysis because the microenvironment around the catalytic sites directly determines the mass transfer process of reactants. Inspired by gas trapped on the surface of subaquatic spiders, amphiphilic micro-organohydrogels with tunable surface wettabilities were developed by anchoring various alkane chains onto a poly(2-(dimethylamino)ethyl methacrylate) (p(DMAEMA)) hydrophilic microgel network. Palladium nanoparticles (Pd NPs) were encapsulated in amphiphilic microgels (amphiphilic Pd@M) to catalyze hydrogenation reaction, achieving higher activities than pristine monohydrophilic Pd@M composite. The underwater oleophilicity and aerophilicity of Pd@M composites were quantified by oil/gas adhesion measurements and computational simulations. The higher amphiphilic catalytic activities are attributed to the formation of a gas-oil-solid reaction interface on the catalyst surfaces, allowing rapid transport of H2 and organic substrates through water to the Pd catalytic sites. Additionally, amphiphilic Pd@M composites also exhibit more superior catalytic performance in multi-substrates reaction.

Performance or efficiency evaluation is of great importance for effective supply chain management, especially in the context of sustainable development and platform economy. In existing literature,...

Central air-conditioning systems predominantly operate under partial load conditions. The optimization of a differential pressure setpoint in the chilled water system of a central air-conditioning system leads to a more energy-efficient operation. Determining the differential pressure adjustment value based on the terminal user’s real-time demand is one of the critical issues to be addressed during the optimal control process. Furthermore, the online application of the differential pressure setpoint optimization method needs to be considered, along with the stability of the system. This paper proposes a variable differential pressure reset method with an adaptive adjustment algorithm based on the Mamdani fuzzy model. The proposed method was compared with differential pressure reset methods with reference to the chilled water differential temperature, outdoor temperature, and linear model based on the adjustment algorithm. The energy-saving potential, temperature control effect, and avoidance of the most unfavorable thermodynamic loop effects of the four methods were investigated experimentally. The results indicated that, while satisfying the terminal user’s energy supply demand and ensuring the avoidance of the most unfavorable thermodynamic loop, the proposed adaptive adjustment algorithm also decreased the differential pressure setpoint value by 25.1%–59.1% and achieved energy savings of 10.6%–45.0%. By monitoring the valve position and supply air temperature of each terminal user, the proposed method exhibited suitable online adaptability and could be flexibly applied to buildings with random load changes.

In this paper, we will focus on the retrofit of the ventilation system setup on the premise of reserving the original VAV system. Therefore, a combination of the existing VAV system and the dedicat...

ER2319 welding wires were used to fabricate a wall structure using CMT-PAdv-based wire and arc additive manufacturing (WAAM). Heat treatment was used after the WAAM process. The microstructure and mechanical property anisotropy before and after the heat treatment were studied. There were three areas with different grain morphology along building direction in the as-deposited wall structure. Microstructure difference was clear between interlayer regions and intra-layer regions both in as-deposited and heat-treated state. Netlike second phases became discontinuous after heat treatment, but pores and Fe-rich phases basically unchanged. Mechanical property presented isotropy before heat treatment because α-Al with lower Cu content had low resistance on crack initiation or growth and crack mainly grew via netlike second phases which had no directionality. Mechanical property presented anisotropy after heat treatment because α-Al was strengthened and brittle Fe-rich phases which concentrated in interlayer regions played a leading role in crack initiation. Moreover, the tensile loading force along the building direction was perpendicular to interlayer regions. Thus, mechanical properties in building direction were lower than that in deposition direction.

In this paper, a new parametric approach is developed for disturbance attenuation in observer design of linear time-invariant systems. First, based on the general solution to a type of generalised ...

Synthetic materials with tunable mechanical properties have great potential in soft robotics and biomedical engineering. However, current materials are limited to the mechanical duality altering their mechanical properties only between soft and hard states and lack of consecutively programmable mechanics. Herein, the magnetic-programmable organohydrogels with heterogeneous dynamic architecture are designed by encasing oleophilic ferrofluid droplets into hydrogel matrix. As magnetic field increases, the mechanical properties of organohydrogels can be consecutively modulated owing to the gradual formation of chain-like assembly structures of nanoparticles. The storage modulus G′ increases by 2.5 times when magnetic field goes up to 0.35 T. Small-Angle X-ray Scattering (SAXS) confirms the reconfigurable orientation of nanoparticles and the organohydrogels show reversible modulus switching. Besides, the materials also exhibit high stretchability, magnetic actuation behavior and effective self-healing capability. Furthermore, the organohydrogels are applied into the design of effectors with mechanical adaptivity. When subjected to serious external perturbations, the effector can maintain mechanical homeostasis by regulating modulus of organohydrogel under applied magnetic field. Such materials are applicable to homeostatic systems with mechanically adaptive behaviors and programmed responses to external force stimuli.

Owing to design and operation limitations, most multi-zone variable air volume (VAV) systems in existing buildings still follow traditional techniques for ventilation. In recent ventilation researc...

A new energy-optimised switching control law is proposed for a type of nonlinear systems based on orthogonal decomposition Hamiltonian realisation (ODHR). The proposed method guarantees the global ...

Cooling water (CW) system with strong nonlinear characteristics is a critical component of central air conditioning systems. Further, variable flow control of CW pumps is a critical measure for ach...

In comparison with constant static pressure and total air volume control strategies, a static pressure reset strategy for variable static pressure control of Variable Air Volume (VAV) air condition...

Anisotropic composite hydrogels have wide applications in the fields of materials for actuators and sensors. Herein, we report an anisotropic composite hydrogel prepared by a mechanical-strain-induced method. Polymer networks including poly( N -isopropylacrylamide) (PNIPAM) and sodium alginate (SA), as well as carbon nanotubes (CNTs) are found to align simultaneously by stretching, and then fixed by physical crosslinking through non-covalent bonds. Composite hydrogels with doubly aligned polymer networks showed anisotropic optical and mechanical properties. The actuation performance of the anisotropic composite hydrogels as compared with the isotropic ones was found to be enhanced, which showed the capability of lifting 100 times its weight with 20% contraction strain. Besides, a bilayer hydrogel was designed to bend with a maximum of 390° to mimic the tendril behavior of plants.

Based on the quaternion theory, a novel algorithm named non-circular augmented quaternion MUSIC (NCAQ-MUSIC) is proposed for DOA and range estimation of noncircular signals impinging on a concentered orthogonal loop and dipole (COLD) array. Firstly, based on the augmented quaternion, the proposed algorithm uses the noncircular characteristic of the signals to achieve the virtual array expansion; secondly, the DOA and range parameters can be completely separated in the principle of rank reduction, and finally, the parameters of DOA and range are estimated through one dimensional search. Compared with direct mutil-dimensional (M-D) searching algorithms, the proposed method merely requires several one-dimension (1-D) spectral peak search which does not need parameter pairing. Simulation results verify the performance promotion of the proposed approach.

As many studies prefer to focus on the accuracy of prediction models in model-based problems of chiller plants, the practicality and feasibility of the models used for field applications are often compromised for a superior performance. We herein present an online predictive control method based on linear regression model for a typical chiller plant from the perspective of real-time application to optimize its overall performance and energy consumption. A multi-input–single-output (MISO) controller is developed for predicting the global coefficient of performance (COP) of the chiller plant to reflect integral characteristics and interactions of components based on the multivariable linear regression (MLR) model. The input variables are much easier to obtain in a practical system compared to variables such as water flow and part load ratio, thereby rendering an excellent model capable of easy implementation and duplication. The performance of this approach is tested and evaluated in a real chiller plant system by comparing it to that obtained using a local control strategy. The results of this study indicate that the online predictive control strategy can enhance the global COP values by 5.35% on average and reduce electricity consumption by 2.70% daily compared to the local control strategy.

The automatic control of Heating, ventilation and Air Conditioning (HVAC) systems aims to achieve the thermal comfort requirements of occupants with minimum energy consumption. The automatic control strategy of existing HVAC systems determines a set value for creating a thermal environment in accordance with relevant design principles and/or occupants’ preferences. An overly-cooled indoor environment may reduce the occupant's thermal comfort and result in excessive energy consumption. In order to improve thermal comfort and save energy, this study proposes an indoor thermal environment optimal control method based on the online monitoring of thermal sensation. First, a smart wristband collects the human physiological data, including wrist skin temperature and heart rate. This is for predicting human thermal sensation, where a fuzzy comprehensive evaluation method is employed to determine the integrated thermal sensation of multi occupants. Then, a linear adjustment algorithm is developed to optimize the indoor temperature set point. In order to evaluate the performance of the thermal sensation-based control method, a series of experiments were conducted using the thermal sensation-based control and set point-based control. The results show that the thermal sensation-based control can adjust the temperature setting in a timely fashion according to the occupants’ integrated thermal sensations, although they do not necessarily state their subjective perception. It is also revealed that the thermal sensation-based control can achieve a more comfortable thermal environment than the set point-based control. Furthermore, the thermal sensation-based control saves 13.8% in daily energy consumption compared to the set point-based control method.

Nanocomposite fibers have attracted intensive attentions owing to their promising applications in various fields. However, the fabrication of nanocomposite fibers with super toughness and strong strength under mild conditions remains a great challenge. Here we present a facile flow-induced assembly strategy for the development of super-tough and strong nanocomposite fibers with highly ordered carbon nanotubes (CNTs), which can be induced by directional and fast flow on a grooved hydrogel surface. The prepared nanocomposite fibers show excellent mechanical properties, with a tensile strength up to 643± 27 MPa and toughness as high as 77.3±3.4 MJ m−3 at ultimate strain of 14.8±1.5%. This versatile and efficient flow-induced alignment strategy represents a promising direction for the development of high-performance nanocomposites for practical applications.

In variable air volume air(VAV) conditioning systems, conventional methods for static pressure reset depends on accurate mathematics models which are often difficult to be built. On the contrary, controls, which are based on a series of control rules derived from the operating and commissioning experience, have the capacity to achieve better effects. A rule-based fuzzy control method (RBFCM) for static pressure reset is proposed using improved Mamdani model and functioning fuzzy subset inference method, which reflects the physical characteristics of the controlled object and is convenient for the engineers to understand and use. The development and implementation of the proposed RBFCM are necessary to demonstrate in detail. Through comparative experiments , the proposed RBFCM is compared with the conventional fixed-step method and itself with different actual domain in a control experiment platform for the VAV air conditioning system. Experimental results indicate that the proposed RBFCM has better control performances and outstanding energy-saving potential if the actual domain can be selected reasonably. A minimum of 33% energy saving rate of the proposed RBFCM with the optimal actual domain is shown through experimental study.

In as-welded state, each region of 2219 aluminum alloy TIG-welded joint shows different microstructure and microhardness due to the different welding heat cycles and the resulting evolution of second phases. After the post-weld heat treatment, both the amount and the size of the eutectic structure or θ phases decreased. Correspondingly, both the Cu content in α-Al matrix and the microhardness increased to a similar level in each region of the joint, and the tensile strength of the entire joint was greatly improved. Post-weld heat treatment played the role of solid solution strengthening and aging strengthening. After the post-weld heat treatment, the weld performance became similar to other regions, but weld reinforcements lost their reinforcing effect on the weld and their existence was more of an adverse effect. The joint without weld reinforcements after the post-weld heat treatment had the optimal tensile properties, and the specimens randomly crack in the weld zone.

This paper presents a novel methodology to study the characteristic of the sleeve electric two-way regulating valves, applied in the HVAC systems. In this paper, the valve admittance is introduced ...

Energy-dissipation elastomers relying on their viscoelastic behavior of chain segments in the glass transition region can effectively suppress vibrations and noises in various fields, yet the operating frequency of those elastomers is difficult to control precisely and its range is narrow. Here, we report a synergistic strategy for constructing polymer-fluid-gels that provide controllable ultrahigh energy dissipation over a broad frequency range, which is difficult by traditional means. This is realized by precisely tailoring the relaxation of confined polymer fluids in the elastic networks. The symbiosis of this combination involves: elastic networks forming an elastic matrix that displays reversible deformation and polymer fluids reptating back and forth to dissipate mechanical energy. Using prototypical poly (n-butyl acrylate) elastomers, we demonstrate that the polymer-fluid-gels exhibit a controllable ultrahigh energy-dissipation property (loss factor larger than 0.5) with a broad frequency range (10−2 ~ 108 Hz). Energy absorption of the polymer-fluid-gels is over 200 times higher than that of commercial damping materials under the same dynamic stress. Moreover, their modulus is quasi-stable in the operating frequency range., In most cases the frequency range of a damping material is adapted to a specific application. Huang et al. design a gel filled with a polymeric fluid that bypasses this problem and offers an unusually broad window over which vibrational energy is effectively dissipated.

The combination of photothermal therapy (PTT) and gene therapy (GT) shows great potential to achieve synergistic anti-tumor activity. However, the lack of a controlled release of genes from carriers remains a severe hindrance. Herein, peptide lipid (PL) and sucrose laurate (SL) were used to coat single-walled carbon nanotubes (SCNTs) and multi-walled carbon nanotubes (MCNTs) to form bifunctional delivery systems (denoted SCNT-PS and MCNT-PS, respectively) with excellent temperature-sensitivity and photothermal performance. CNT/siRNA suppressed tumor growth by silencing survivin expression while exhibiting photothermal effects under near-infrared (NIR) light. SCNT-PS/siRNA showed very high anti-tumor activity, resulting in the complete inhibition of some tumors. It was highly efficient for systemic delivery to tumor sites and to facilitate siRNA release owing to the phase transition of the temperature-sensitive lipids, due to PL and SL coating. Thus, SCNT-PS/siRNA is a promising anti-tumor nanocarrier for combined PTT and GT.

Among sub-items of energy consumption in public buildings, lighting sockets play an important role in energy-saving analysis. So, the energy consumption data quality of lighting sockets is important. However, limited by the initial cost of energy monitoring platform, it is difficult to install electricity meters covering all branches and to retrofit the incompact classification electricity branches, which results in a mixture of the lighting socket energy consumption and other components. In this study, a separation methodology is proposed. First, the abnormal data in the energy monitoring platform are cleaned and screened using a clustering algorithm. Second, the average outdoor air temperature partitioning model (OATPM) method and the k-nearest neighbor (KNN) clustering algorithm method are proposed for identifying and separating the abnormal data. These two methods have complementary advantages in the best applicable scenarios, including calculation accuracy and other aspects. The verification results for three buildings show that the relative error of this separation methodology is less than 15%. Finally, this paper presents the optimization parameters of the KNN method. Through this methodology, building managers need only historical data in an energy monitoring platform to separate the combined power consumption of the lighting sockets and air-conditioning online, independent of detailed information statistics.

Adaptive soft-matter machines are developed using the complex multiphase organohydrogels with multistable mechanical states., Many biological organisms can tune their mechanical properties to adapt to environments in multistable modes, but the current synthetic materials, with bistable states, have a limited ability to alter mechanical stiffness. Here, we constructed programmable organohydrogels with multistable mechanical states by an on-demand modular assembly of noneutectic phase transition components inside microrganogel inclusions. The resultant multiphase organohydrogel exhibits precisely controllable thermo-induced stepwise switching (i.e., triple, quadruple, and quintuple switching) mechanics and a self-healing property. The organohydrogel was introduced into the design of soft-matter machines, yielding a soft gripper with adaptive grasping through stiffness matching with various objects under pneumatic-thermal hybrid actuation. Meanwhile, a programmable adhesion of octopus-inspired robotic tentacles on a wide range of surface morphologies was realized. These results demonstrated the applicability of these organohydrogels in lifelike soft robotics in unconstructed and human body environments.

Current thermal sensation estimation models mostly are suitable for the sedentary condition, failing to consider the difference of human thermal sensation in different activity states. This has caused critical limitations in accurately predicting thermal sensation. Moreover, the development method of current models primarily relied on regression analysis, which ignored the non-linear characteristics between the skin temperature and thermal sensation. This paper aimed to identify the significant parameters that can accurately estimate human thermal sensation in different activity states by experimenting and developing the estimation model based on the Takagi–Sugeno (T–S) fuzzy model. A series of human subject experiments were carried out in an environment chamber. The results indicated the feasibility of using wrist skin temperature and its time differential and heart rate as variables for developing thermal sensation estimation model. After that, the T–S fuzzy model was used to develop the thermal sensation estimation models, taking into account the influence of gender. To analyze the applicability of the estimation models in an unstable condition, several experiments were further carried out in the actual built environment. The study revealed that the thermal sensation estimation model based on skin temperature and its time differential and heart rate showed a high degree of accuracy, while the estimation model based only on skin temperature and heart rate also indicated good prediction effect. In addition, the verification results illustrated that the proposed models can predict the human thermal sensation in the unstable environmental condition.

Stimuli-responsive hydrogels have become one of the most popular artificial soft materials due to their excellent adaption to complex environments. Thermoresponsive hydrogels triggered by temperature change can be efficiently utilized in many applications. However, these thermoresponsive hydrogels mostly cannot recover their mechanical states under large strain during the process. Herein, we utilize the heterogeneous comb-type polymer network with semicrystalline hydrophobic side chains to design self-recovery semi-crystalline hydrogels. Based on hydrophilic/hydrophobic cooperative complementary interaction and heterogeneous polymer network, hydrogels can be endowed with excellent thermosensitive properties and mechanical performance. The hydrogels exhibit high compressive strength (7.57 MPa) and compressive modulus (1.76 MPa) due to the semi-crystalline domains formed by association of the hydrophobic poly (e-caprolactone) PCL. The melting-crystalline transition of PCL and elastic polymer network provide the hydrogels excellent thermomechanical performance and self-recovery property. Furthermore, the hydrogels exhibit shape memory behavior, which can be realized by simple process and smart surface patterning. With these excellent properties, our hydrogels can be applied in sensors, flexible devices and scaffolds for tissue engineering.

The flow mechanism of natural gas in the shale matrix is closely related to the high efficient development of shale gas reservoirs. However, the flow characteristic of shale gas in nanometer pores is scale dependent, and is also affected by the formation condition, adsorption layer, residual water saturation and detailed pore structure. In this study, a lattice Boltzmann model with a regularization procedure was applied to simulate shale gas flow in a 2D porous medium, and the effects of slippage, adsorption layer, residual water saturation and surface area on the apparent permeability were discussed. Simulation results indicate that the apparent permeability decreases with the increasing formation pressure, and increases with the increasing formation temperature and characteristic pore size. The adsorption layer and residual water affect the apparent permeability by changing the effective pore space for gas flow. And the effect is more obvious under the large Knudsen number conditions. When the porosity of the porous media is constant, the apparent permeability decreases with the increasing surface area, and the large surface area would even offset the positive effect of slippage on the apparent permeability.

Existing automatic control of building thermal environments do not consider the individual's real-time thermal sensation, which could reduce the occupants’ thermal comfort. Therefore, it is very important to accurately obtain an individual's thermal sensation and real-time reflect on the control logic of air-conditioning systems. Current thermal sensation estimation models mostly apply to sedentary condition without considering human sensation in different activity states, which caused these models have critical limitations in accurately predicting human thermal sensation. In this paper, an intelligent wristband device is used for online monitoring of human thermal characteristics in different active states. The wrist skin temperature and its time differential as well as the heart rate are used for the evaluation index of human thermal sensation, and a series of environmental chamber experiments are carried out to obtain the relationship between the wrist skin temperature and thermal sensation in different activity states in summer. The correlation models of human thermal sensation, wrist skin temperature and its time differential, and heart rate has been formulated by statistical analysis and correlation analysis. In order to verify the feasibility of correlation models in the unstable environmental condition, several tests were conducted in the actual built environment. This study indicates that the wrist skin temperature and its time differential and heart rate can be used for estimating human thermal sensation with a high degree of accuracy in the different activity states. In addition, results of this study also demonstrate the promising applicability of obtained correlation models in the unstable environmental condition.

Due to the mathematical modeling principle deficiency, the data-driven neural network and support vector machine methods have become the powerful basic methods for the exchange rate prediction. Based on the analysis of the characteristics of exchange rate time series data, the exchange rate prediction performance of Artificial neural network (ANN) and Least squares-Support vector machine (LS-SVM) is explored. The parameter optimization method of the two-times training is proposed. The fundamental principle of LS-SVM prediction is analysed in detail. By virtue of daily, monthly and quarterly data of three currency exchange rates, the prediction performance of LS-SVM is examined. The comparison is made with ANN prediction results based on the same data in relevant literature review. According to the experimental result, LS-SVM has better short-term prediction performance, and it is superior to ANN in most cases in terms of prediction precision.

Tight oil sandstones have obscured flow characteristics because of their complex pore structure and ultralow permeability. In this study, representative elementary volume (REV) scale lattice Boltzmann method (LBM) was applied to simulate fluid flow in the multiscale tight porous media generated by coupling digital cores from two scales, and the effects of clay permeability, clay volume fraction and interparticle pores (interP) volume fraction on the permeability of the multiscale tight porous media were analyzed. The tight multiscale porous media were generated by assigning the nanometer scale digital core of clay to each pixel of clay constituent in the micron scale digital cores containing three constituents of matrix, interP and clay. The porosity of the generated multiscale tight porous media ranges from 11.12% to 14.98%, among which the porosity contributed by the interP is 4.06–5.00%, and the porosity contributed by the clay is 6.40–9.98%. The pore size distribution curves are bimodal with the pore size ranging from 0.05 to 100 μm, and show good agreement with our previous experimental results. The porosity and permeability inputted to the REV scale LBM were from pore scale simulation results of fluid flow in the nanometer scale digital cores. The simulated permeability of the multiscale tight porous media is 0.05 × 10−3–0.23 × 10−3 μm2, which is close to our experimental results. The permeability of the multiscale tight porous media shows positive linear correlation with the clay permeability and clay volume fraction, but is unrelated to the interP volume fraction in the reasonable porosity range. It is hoped that this study can provide some new insights into the core analysis of tight oil sandstones.

Based on realistic kerogen model, the effects of water content on methane (CH4) adsorption capacity were studied qualitatively and quantitatively using the molecular dynamics (MD) and Monte Carlo (MC) simulation methods. The methane single component adsorption process under the dry condition, methane and water two components competitive adsorption on dry kerogen, and methane single component adsorption on moist conditions (0.6, 1.2, 1.8, 2.4, and 3.0 wt%) were simulated. Adsorption processes under different temperatures (298, 323, and 348 K) were modeled, and the pressure was up to 20 MPa. Simulation results show that the absolute adsorption capacity of CH4 on the dry kerogen increases with the increasing pressure but decreases with the increasing temperature. The excess adsorption capacity increases up to a specific pressure and then declines with the increasing pressure. The competitive adsorption simulation results indicate that kerogen prefers to adsorb water (H2O) than CH4. At the pressure of 20 MPa and temperature of 298 K, the competitive adsorption capacity of CH4 (0.53 mmol/g) is only a seventh of the single component adsorption capacity of CH4 (3.92 mmol/g) on dry kerogen. The CH4 adsorption capacity on moist kerogen also decreases with the increasing moist content. Compared with the absolute adsorption capacity on the dry kerogen (3.92 mmol/g), the adsorption capacity on the moist kerogen reduces about 16%, 30%, 40%, 47%, and 55% at 20 MPa, respectively. The reduction of the adsorption capacity is attributed to the strong attraction between kerogen and water. At last, the effect of the water content on the shale gas adsorption is discussed under the micro-scale, and the adsorption capacity reduction curves were calculated under the reservoir conditions. The results show that the adsorption capacity of shale gas is significantly affected by water at the economically recoverable depth. The dry condition is considered as the situation of the maximal adsorption capacity of the shale gas reservoir. This study can serve as a reference for better understanding of the transportation and storage mechanism of shale gas under the reservoir conditions.

Contact angle (CA) is the most important parameter used to quantify the wettability of solid surfaces. In order to evaluate wettability performance, numerous methods have been developed to measure the CA of solid surfaces. Recent years have seen increased focus on the special wettability performance of various biological materials. Biomimetic wettability has become one of the most popular research fields, and novel CA measurements have been invented accordingly. In this protocol, we introduce several CA measurement techniques mainly based on the image capture method, which is commonly to investigate the wettability of natural materials. According to the solid/liquid/gas context, we classify CA measurements into three types: in air, under liquid, and air bubble measurements, and describe methods for each. The precise measurement of CA together with study of surface structure can reveal the mechanisms of special wettability, thus accelerating the investigation of biomaterials.

Since there are lots of influencing factors in indoor temperature regulation process of air conditioning system in the large-public building, it is difficult to establish an accurate mathematical model and to implement model-based online control. This paper proposes an online modeling method for indoor temperature regulation characteristic according to building thermal process. A physical-data driven model, which takes the state-space equation solution representation form as the reference structure, is proposed, and the model parameters are identified based on the multiple linear regression analysis . Then, experimental and simulation studies in online and offline have been carried out to validate the proposed modeling method. The contribution of this study is to provide an online model, which can not only retain system physical property but also be updated online simply. The proposed model with building thermal process property will be benefit for terminal controller design for indoor temperature regulation in the air conditioning system.

Superhydrophobic carbon nanotube (CNT) films have demonstrated many fascinating performances in versatile applications, especially for those involving solid/liquid interfacial processes, because of their ability to affect the material/energy transfer at interfaces. Thus, developing superhydrophobic CNTs has attracted extensive research interests in the past decades, and it could be achieved either by surface coating of low-free energy materials or by constructing micro/nanohierarchical structures via various complicated processes. So far, developing a simple approach to fabricate stable superhydrophobic CNTs remains a challenge because the capillary force induced coalescence frequently happens when interacting with liquid. Herein, drawing inspirations from the lotus leaf, we proposed a simple one-step chemical vapor deposition approach with programmable controlled gas flow to directly fabricate a CNT film with rather stable superhydrophobicity, which can effectively prevent even small water droplets from permeating into the film. The robust superhydrophobicity was attributable to typical lotus-leaf-like micro/nanoscale hierarchical surface structures of the CNT film, where many microscale clusters composed of entangled nanotubes randomly protrude out of the under-layer aligned nanotubes. Consequently, dual-scale air pockets were trapped within each microscale CNT cluster and between, which could largely reduce the liquid/solid interface, leading to a Cassie state. Moreover, the superhydrophobicity of the CNT film showed excellent durability after long time exposure to air and even to corrosive liquids with a wide range of pH values. We envision that the approach developed is advantageous for versatile physicochemical interfacial processes, such as drag reduction, electrochemical catalysis, anti-icing, and biosensors.

Aiming at the prediction control for indoor temperature time-delay in variable air volume (VAV) air conditioning system, this paper presents an indoor temperature prediction control method based on Elman neural network multi-step prediction model. Firstly, this paper introduces basic control principles of pressure-dependent and pressure-independent VAV terminal through comparable analysis and points out significance of indoor temperature prediction control based on pressure-dependent VAV terminal. Then, Elman neural network multi-step prediction model and corresponding indoor temperature prediction control method for pressure-dependent VAV terminal are proposed based on the fundamental principle of periodic prediction control for time-delay system. Finally, the effect of proposed prediction control method is validated by the experimental study according to the test data of supply air volume regulating process, provided that the supply air volume control loop adopts constant static pressure control method. Experimental results indicate the proposed indoor temperature prediction control method based on pressure-dependent VAV terminal could change the conventional regulating mode of the VAV air conditioning system, which will be benefit for improving the control stability of indoor temperature control loop and other corresponding control loops.

The existing control systems for the parallel pumps in HVAC systems are mostly based on a centralized structure in which all the data from sensors and control instructions are processed by a master...

This article applied the multiple relaxation time multicomponent/multiphase pseudopotential lattice Boltzmann model to simulate two immiscible fluids flow in 2D porous media, and analyzed the effects of capillary number (Ca), viscosity ratio (M) and wettability on the relative permeability curves. Simulation results indicate that the nonwetting phase (NWP) relative permeability increases with increasing Ca; while the effect of Ca on the wetting phase (WP) relative permeability depends on the wettability. When M > 1, the NWP relative permeability increase with increasing M in a strong wetting condition because of the lubricating effect. The amplitude of the NWP relative permeability may even exceed the single phase permeability. However, the exact value of the amplitude and where it occurs depends on M and the structure of the porous media. The WP relative permeability is insensitive to M. When the porous media converts from strong wetting condition to neutral wetting condition, the NWP relative permeability decreases while the WP relative permeability increases.

In reality, projects usually consume complex resources. Making good use of the various resources is vital for optimal project selection and maximum profit earning. This paper proposes a new project selection model from the perspective of complex resource constraints. In the model, the resources are divided into non-renewable and renewable categories, and some resources of the two categories can both be shared by different projects. In addition, the paper considers the situation where the company has resources in stock and can purchase them in the marketplace if they are out of stock. The paper proves that the proposed model which considers renewable resource and resource sharing produces higher profit than the ones that do not consider renewable resource and resource sharing. To solve the complex model problem, an improved genetic algorithm is presented. For the sake of illustration, a case study is provided.

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

The adsorption characteristics of shale under the reservoir conditions have significant effects on the original gas in place and productivity. In this study, based on realistic kerogen models, methane (CH 4 ) adsorption isotherms were simulated considering the influence of maturity (immature, oil window mature and postmature) and moisture content (0.6, 1.2, 1.8, and 2.4 wt.%) using molecular dynamics (MD) and Monte Carlo (MC) simulation methods. The effects of the maturity and moisture content on the CH 4 adsorption capacities and thermodynamic properties were discussed. Simulation results indicate that the adsorption capacities of CH 4 on dry kerogen increase with maturity but decrease with temperature. The presence of the moisture content sharply decreases the CH 4 adsorption capacities and has a greater effect on the kerogen with high maturity than on that with low maturity. The reduction of the adsorption capacity decreases with increasing pressure and increases with increasing moisture content until reaching a limiting value that depends on the maturity. The heat of CH 4 adsorption shows no obvious relationship with the maturity, but is mainly affected by the differences in chemical compositions (especially related to sulfur) on dry kerogen. The heats of CH 4 adsorption on moist kerogens (both in immature, oil window mature and postmature) also reduce with increasing moisture content until reaching a limiting value, after which the moisture content has little effect on the heat of CH 4 adsorption. Interestingly, this limiting moisture value is in accordance with the limiting value found in the water effect on CH 4 adsorption capacities. This study provides a quantitative understanding of the effects of moisture content and maturity on CH 4 adsorption and thermodynamic properties from a microscopic perspective.

Chiller plants are often operated in drastically different conditions other than the design. Consequently, the optimal control has to be developed and implemented based on the real operation conditions to improve the efficiency. This study presents a Web-based online control system implementation in a chiller plant for a hospital as a case study. The primary optimization measures include an optimal number of chillers in operation, optimal chilled water and condensing water pump speeds, and optimal cooling tower fan speed. The measured chiller plant savings is 24.6%.

Engineering surface wettability is of great importance in academic research and practical applications. The exploration of hydrogel‐based natural surfaces with superior properties has revealed new design principles of surface superwettability. Gels are composed of a cross‐linked polymer network that traps numerous solvents through weak interactions. The natural fluidity of the trapped solvents confers the liquid‐like property to gel surfaces, making them significantly different from solid surfaces. Bioinspired gel surfaces have shown promising applications in diverse fields. This work aims to summarize the fundamental understanding and emerging applications of bioinspired gel surfaces with superwettability and special adhesion. First, several typical hydrogel‐based natural surfaces with superwettability and special adhesion are briefly introduced, followed by highlighting the unique properties and design principles of gel‐based surfaces. Then, the superwettability and emerging applications of bioinspired gel surfaces, including liquid/liquid separation, antiadhesion of organisms and solids, and fabrication of thin polymer films, are presented in detail. Finally, an outlook on the future development of these novel gel surfaces is also provided.

This paper proposed an optimization algorithm for torque sharing function (TSF) based on numerical iterative algorithm for switched reluctance motor (SRM). This optimization problem takes minimum rms voltage as the minimum optimization objective, current-limiting and bus voltage as the constraint. It gets free from a particular fitting function, making it has advantages of wide universality. Detailed design process of algorithm is illustrated in the paper. The results show that current-limiting is the main constraint at low speed. While at medium and high speeds, bus Voltage is the main limiting factor. The optimized TSF is applied to a 6/4 SRM. Simulations and experiment have been carried out to verify the effectiveness a of the proposed algorithm.

We propose a proactive dynamic network slicing scheme that utilizes a deep-learning based short-term traffic prediction approach for 5G transport networks. The demonstration shows utilization efficiency improvement from 46.33% to 71.53% under the evaluated scenario.

Correlation models between human behavior and energy consumption of teaching buildings are crucial for devising energy efficiency strategies. Especially in teaching buildings in campus, on one hand, the investment is limited and the level of operation and maintenance is low. On the other hand, the certain management system exists, and the flow of occupant in campus is also regular obviously. So, it is difficult for large-scale technical retrofits and possible for management retrofits. In order to the low-cost management retrofits, the model between human behavior and energy consumption are studied in this paper. According to the models, on the one hand, managers of campus can predict energy consumption based in historical, for matching energy consumption supply and demand each other. On the other hand, managers can judge future human behaviors based on historical energy consumption, for conducting management to reduce energy consumption. In this paper, first, correlation models between the energy consumption and human behavior are proposed and the lighting management level coefficient K is defined to evaluate the lighting management level. With the aid of the correlation models and K, managers can obtain management strategies to increase energy efficiency without large-scale technical retrofits. The verification results show that the proposed strategies could bring a 13.96% economic benefit ratio. Second, correlation models between human behavior and influencing factors of human behavior are proposed for predicting human behavior. Finally, a technical route of energy efficiency retrofit by management is suggested and validated. Although the energy-saving ratio is not conspicuous, the cost is low and the operation is easy. It means that the technical route is worthy for research and promotion.

Semantic Web has recently gained traction with the use of Linked Open Data (LOD) on the Web. Although numerous state-of-the-art methodologies, standards, and technologies are applicable to the LOD cloud, many issues persist. Because the LOD cloud is based on graph-based resource description framework (RDF) triples and the SPARQL query language, we cannot directly adopt traditional techniques employed for database management systems or distributed computing systems. This paper addresses how the LOD cloud can be efficiently organized, retrieved, and evaluated. We propose a novel hybrid approach that combines the index and live exploration approaches for improved LOD join query performance. Using a two-step index structure combining a disk-based 3D R*-tree with the extended multidimensional histogram and flash memory-based k-d trees, we can efficiently discover interlinked data distributed across multiple resources. Because this method rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. We also propose a hot-cold segment identification algorithm to identify regions of high interest. The proposed method is compared with existing popular methods on real RDF datasets. Results indicate that our method outperforms the existing methods because it can quickly obtain target results by reducing unnecessary data scanning and reduce the amount of main memory required to load filtering results.

The establishment of an accurate energy consumption prediction model is an important way to analyze the energy consumption of a campus building, thereby providing a reliable basis for the selection of energy-saving transformation measures. To establish a high-precision campus energy consumption model, this paper proposes an improved bottom-up energy consumption prediction method based on energy consumption monitoring data and the Bayesian theory. The measured data of the monitoring platform are used as sample information in the Bayesian analysis. Considering a campus complex in China as an example, a traditional model using the area expansion method and the improved model has been established in this paper. For a certain type of building, the prediction results indicate that the maximum monthly error rate of the traditional model is 54% while that of the improved model is within 15%. This indicates that the improved model has advantages with regard to prediction accuracy. In addition, considering both the construction cost of the energy consumption monitoring platform and the prediction accuracy, this study explores the optimal sample number in the Bayesian analysis. Based on the analysis of different prior information and different research objects, with an error of 20% as the standard, the optimal sample size was finally determined to be four.

An effective indoor temperature model would assist in improving energy efficiency and indoor thermal comfort of air conditioning system. However, it is difficult to build an accurate model due to lag response characteristic in the regulation process of indoor temperature. To solve this problem, the modeling and prediction methods for indoor temperature lag response characteristic based on time-delay neural network (TDNN) and Elman network neural (ENN) are presented. Then, taking variable air volume (VAV) air conditioning system as the study object, the effectiveness and practicability of proposed methods are validated using simulation sampling data and real-time operating data. Results indicate that ENN could be considered as a better modeling method for indoor temperature prediction for its simpler network structure, smaller storing space and better prediction accuracy. The contribution of this study is to provide an applicable online ANN modeling method for indoor temperature lag characteristic, and detailed training and validation for online implementation are presented, which will benefit for engineers and technicians to use in practical engineering. Meanwhile, this study provides the reference for online application of advanced intelligent algorithms in the building engineering.

The automatic control of the existing indoor thermal environment is usually based on the user's set points without considering their real-time thermal feeling, causing an uncomfortable thermal environment and high energy consumption. In this study, the wristband device's physiological data is utilized to propose a thermal sensation prediction-based fuzzy control method of indoor temperature to improve the indoor environment's thermal comfort and achieve an energy saving of air-conditioning system. The linear regression model-based updating method is utilized to update the thermal sensation prediction model in real-time. Moreover, Mamdani fuzzy model and FFSI method are adopted to develop the fuzzy control algorithm for indoor temperature set point optimization. Several comparative experiments are performed to compare control performance, thermal comfort, and energy consumption of thermal sensation prediction-based control with thermal sensation feedback-based control and temperature set point-based control. The results show that thermal sensation prediction-based control can adjust the temperature set point by monitoring physiological data from the subjects without interfering with their regular works. It is also revealed that the thermal comfort performance of thermal sensation prediction-based control is superior to the temperature set point-based control, while it is similar to the thermal sensation feedback-based control. Furthermore, the proposed control approach provides 20.07% and 10.73% savings in daily energy consumption compared with the temperature set point-based control and thermal sensation feedback-based control, respectively.

This paper aims to analyze the effects of mineralogy on petrophysical properties and evaluate the performance of eight permeability estimation models in tight oil sandstones. The results include mineralogy, pore size distribution, porosity, and permeability. The evaluated eight permeability models are Purcell, Winalnd, Swanson, Thomeer, Wells-Amaefule (W-A), Katz-Thompson (K-T), Pittman and Dastidar models. The experimental results indicate that the samples are rich in quartz (averages 41.5%), feldspar (30.0%), and clays (18.5%); have a wide pore size distribution mainly in the nanometer scale (9.2 nm–1 μm); and have low porosity (11.07% on average) and ultralow permeability (0.22 mD). Quartz and feldspar preserve the primary pores by supporting the overburden pressure, and feldspar further enhances the porosity by forming secondary dissolution pores. Mixed layer of illite/smectite (I/S) and illite significantly decrease porosity while chlorite preserves the primary pores by inhibiting silica overgrowth. The Pittman model has the best estimation accuracy ( R adj 2 = 0.968); the Winland, K-T and Dastidar models give reasonable estimations ( R adj 2 > 0.92); others have poor performance ( R adj 2