Search Results (156)

Integrating renewable energy, particularly wind power, into modern power systems introduces challenges concerning stability and reliability. These issues require enhanced regulation to balance power supply with load demand. Flexible loads and energy storage provide viable solutions to stabilize the grid without relying on new resources. This paper proposes building thermostatically controlled loads (BTLs), such as heating, ventilation, and air conditioning (HVAC) systems, as flexible demand-side management tools to address the challenges of intermittent energy sources. A new concept is introduced, portraying BTLs as a stochastic battery with losses, offering a compact representation of their dynamics. BTLs’ thermal characteristics, user-defined set points, and ambient temperature changes determine the power limits and energy capacity of this stochastic battery. The model is simulated using DIgSILENT Power Factory, which includes thermal power plants, gas turbines, wind power plants, and BTLs. A dynamic dispatch strategy optimizes power generation while utilizing BTLs to balance grid fluctuations caused by variable wind energy. Performance analysis shows that integrating BTLs with conventional thermal plants can reduce variability and improve grid stability. The study highlights the dual role of simulating overall flexibility and applying dynamic dispatch strategies to enhance power systems with high renewable energy integration. Full article

This study analyzes the dynamics of temperature and moisture in a cylindrical silo with a conical roof and floor used for storing corn in the Bajío region of Mexico, considering conditions both with and without aeration. The model incorporates external temperature fluctuations, solar radiation, grain moisture equilibrium with air humidity through the sorption isotherm (water activity), and grain respiration to simulate real storage conditions. The model is based on continuity, momentum, energy, and moisture conservation equations in porous media. This model was solved using the finite element method (FEM) to evaluate temperature and interstitial humidity variations during January and May, representing cold and warm environmental conditions, respectively. The simulations show that, without aeration, grain temperature progressively accumulates in the center and bottom region of the silo, reaching critical values for safe storage. In January, the low ambient temperature favors the natural dissipation of heat. In contrast, in May, the combination of high ambient temperatures and solar radiation intensifies thermal accumulation, increasing the risk of grain deterioration. However, implementing aeration periods allowed for a reduction in the silo’s internal temperature, achieving more homogeneous cooling and reducing the threats of mold and insect proliferation. For January, an airflow rate of 0.15 m³/(min·ton) was optimal for maintaining the temperature within the safe storage range (≤17 °C). In contrast, in May, neither this airflow rate nor the accumulation of 120 h of aeration was sufficient to achieve optimal storage temperatures. This indicates that, under warm conditions, the aeration strategy needs to be reconsidered, assessing whether a higher airflow rate, longer periods, or a combination of both could improve heat dissipation. The results also show that interstitial relative humidity remains stable with nocturnal aeration, minimizing moisture absorption in January and preventing excessive drying in May. However, it was identified that aeration period management must be adaptive, taking environmental conditions into account to avoid issues such as re-wetting or excessive grain drying. Full article

This study explores the feasibility of using 3D printing technology to fabricate reference materials for validating compressive strength measurements in construction laboratories. Polylactic acid (PLA) and polyethylene terephthalate glycol-modified (PETG) were selected due to their widespread availability and use in fused deposition modeling (FDM). A series of cubic samples with varying infill levels and dimensions were printed and tested to evaluate the influence of infill density, temperature, and storage time on compressive strength. PLA samples exhibited higher compressive strength values (from 23.5 kN for 10% infill to 70.7 kN for 50% infill) and a steeper increase in strength with rising infill density compared to PETG (from 12.4 kN for 10% infill to 44.1 kN for 50% infill). However, PETG demonstrated superior stability over time, with significantly smaller increases in result variability after 31 days. The results confirm a strong linear correlation between infill level and compressive strength and indicate that even small fluctuations in ambient temperature can influence test outcomes. Despite PLA’s initial mechanical advantage, PETG’s aging resistance makes it a promising candidate for the development of durable and repeatable reference materials (increment of StD for PLA from 0.17 kN to 0.63 kN and 0.25 kN to 0.37 for PET-G). This research contributes to closing the gap in the availability of reliable mechanical reference materials for destructive testing, offering a novel application for 3D printing in quality control in civil engineering. Full article

To further enhance the economic performance of fuel cell vehicles (FCVs), this study develops a model-adaptive model predictive control (MPC) strategy. This strategy leverages the dynamic relationship between proton exchange membrane fuel cell (PEMFC) output characteristics and temperature to track its optimal operating temperature (OOT), addressing challenges of temperature control accuracy and high energy consumption in the PEMFC thermal management system (TMS). First, PEMFC and TMS models were developed and experimentally validated. Subsequently, the PEMFC power–temperature coupling curve was experimentally determined under multiple operating conditions to serve as the reference trajectory for TMS multi-objective optimization. For MPC controller design, the TMS model was linearized and discretized, yielding a predictive model adaptable to different load demands for stack temperature across the full operating range. A multi-constrained quadratic cost function was formulated, aiming to minimize the deviation of the PEMFC operating temperature from the OOT while accounting for TMS parasitic power consumption. Finally, simulations under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) conditions evaluated the OOT tracking performance of both PID and MPC control strategies, as well as their impact on stack efficiency and TMS energy consumption at different ambient temperatures. The results indicate that, compared to PID control, MPC reduces temperature tracking error by 33%, decreases fan and pump speed fluctuations by over 24%, and lowers TMS energy consumption by 10%. These improvements enhance PEMFC operational stability and improve FCV energy efficiency. Full article

Articulated arm coordinate measuring machines are designed for in situ use directly in manufacturing environments, enabling efficient dimensional control outside of climate-controlled laboratories. This study investigates the influence of ambient temperature variation on the accuracy of length measurements performed with the Hexagon Absolute Arm 8312. The experiment was carried out in a laboratory setting simulating typical shop floor conditions through controlled temperature changes in the range of approximately 20–31 °C. A calibrated steel gauge block was used as a reference standard, allowing separation of the influence of the measuring system from that of the measured object. The results showed that the gauge block length changed in line with the expected thermal expansion, while the articulated arm coordinate measuring machine exhibited only a minor residual thermal drift and stable performance. The experiment also revealed a constant measurement offset of approximately 22 µm, likely due to calibration deviation. As part of the study, an uncertainty budget was developed, taking into account all relevant sources of influence and enabling a more realistic estimation of accuracy under operational conditions. The study confirms that modern carbon composite articulated arm coordinate measuring machines with integrated compensation can maintain stable measurement behavior even under fluctuating temperatures in controlled environments. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on techniques such as the finite element method (FEM) or machine learning (ML)—to compare real-time data against expected performance. However, these models often rely on static warning thresholds, which fail to reflect the dynamic conditions affecting dam behavior, including fluctuating water levels, temperature variations, and extreme weather events. This study introduces an adaptive warning threshold methodology for dam safety based on kernel density estimation (KDE). The approach incorporates a boosted regression tree (BRT) model for predictive analysis, identifying influential variables such as reservoir levels and ambient temperatures. KDE is then used to estimate the density of historical data, allowing for dynamic calibration of warning thresholds. In regions of low data density—where prediction uncertainty is higher—the thresholds are widened to reduce false alarms, while in high-density regions, stricter thresholds are maintained to preserve sensitivity. The methodology was validated using data from an arch dam, demonstrating improved anomaly detection capabilities. It successfully reduced false positives in data-sparse conditions while maintaining high sensitivity to true anomalies in denser data regions. These results confirm that the proposed methodology successfully meets the goals of enhancing reliability and adaptability in dam safety monitoring. This adaptive framework offers a robust enhancement to dam safety monitoring systems, enabling more reliable detection of structural issues under variable operating conditions. Full article

Formaldehyde vapor (HCHO) is a harmful chemical substance and a potential air contaminant, with a permissible level in indoor spaces below 0.08 ppm (80 ppb). Thus, highly sensitive gas sensors for the continuous monitoring of HCHO are in demand. The electrical conductivity of semiconducting nanomaterials (e.g., single-walled carbon nanotubes (SWCNTs)) makes them sensitive to chemical substances adsorbed on their surfaces, and a variety of portable and highly sensitive chemiresistive gas sensors, including those capable of detecting HCHO, have been developed. However, when monitoring low levels of vapors (<1 ppm) found in ambient air, most chemiresistive sensors face practical issues, including false responses to interfering effects (e.g., fluctuations in room temperature and humidity), baseline drift, and the need to apply a purge gas. Here, we report an actuator-driven, purge-free chemiresistive gas sensor that is capable of reliably detecting 0.05 ppm of HCHO in the air. This sensor is composed of an HCHO→HCl converter (powdery hydroxylamine salt, HA), an HCl detector (a SWCNT-based chemiresistor), and an HCl blocker (a thin plastic plate). Upon exposure to HCHO, the HA emits HCl vapor, which diffuses onto the adjacent SWCNTs, increasing their electrical conductivity through p-doping. Meanwhile, inserting a plastic plate between HA and SWCNTs makes the conductivity of SWCNTs insensitive to HCHO. Thus, via periodic actuation (insertion and removal) of the plastic plate, HCHO can be detected reliably over a wide concentration range (0.05–15 ppm) with excellent selectivity over other volatile organic compounds. This actuator-driven system is beneficial because it does not require a purge gas for sensor recovery or baseline correction. Moreover, since the response to HCHO is synchronized with the actuation timing of the plate, even small (~0.8%) responses to 0.05 ppm of HCHO can be clearly separated from larger noise responses (>1%) caused by interfering effects and baseline drift. We believe that this work provides substantial insights into the practical implementation of nanomaterial-based chemiresistive gas sensors. Full article

In the context of the global energy transition, thermal management of electric vehicle batteries faces severe challenges due to temperature rise and energy consumption under dynamic operating conditions. Traditional strategies rely on real-time feedback and suffer from response lag and energy efficiency imbalance. In this study, we propose a neural network-based synergistic optimization method for driving conditions prediction and dynamic thermal management, which collects multi-scenario real-vehicle data (358 60-s condition segments) by naturalistic driving data collection method, extracts four typical conditions (congestion, highway, urban, and suburbia) by combining with K-means clustering, and constructs a BP (backpropagation neural network) model (20 neurons in the input layer and 60 neurons in the output layer) to predict the vehicle speed in the next 60 s. Based on the prediction results, the coupled PID control and temperature feedback mechanism dynamically adjusts the coolant flow rate (maximum reduction of 17.6%), which reduces the maximum temperature of the battery by 3.8 °C, the maximum temperature difference by 0.3 °C, and the standard deviation of temperature fluctuation at ambient temperatures of 25~40 °C is 0.2 °C in AMESim simulation and experimental validation. The results show that the strategy significantly improves battery safety and system economy under complex working conditions by prospectively optimizing heat dissipation and energy consumption, providing an efficient solution for intelligent thermal management. Full article

Extreme climatic conditions characterized by drastic temperature fluctuations exacerbate soil erosion through intensified thermo-mechanical weathering processes. Loess-covered regions are particularly vulnerable to such conditions because of the inherent thermo-sensitivity of loess. A comprehensive investigation of mechanisms of thermo-mechanical weathering in loess under extreme temperature regimes holds critical importance for elucidating soil degradation patterns. It is also essential for formulating mitigation strategies in climate-sensitive loess terrains, especially given the increasing frequency of extreme weather events under global warming scenarios. This study employed integrated physical monitoring experiments and numerical modeling. The evolutionary patterns of temperature fields and corresponding thermal stress distributions in loess subjected to both heat shock (rapid heating) and cold shock (rapid cooling) conditions were systematically examined. The key findings are as follows: (1) Soil temperature variations demonstrate phase-lagged responses to ambient thermal variations during both shock scenarios, exhibiting distinct thermal inertia effects. (2) The spatial distribution pattern of thermal stress is predominantly governed by the temperature gradient within the soil matrix. (3) While the magnitude ranges of thermal stress remain comparable between shock types, their directional characteristics fundamentally differ; heat shocks induce surface compressive stresses and internal tensile stresses, whereas cold shocks generate inverse stress patterns. (4) Compared to heat shock, cold shocks trigger obvious surface degradation through tensile stress-induced failure of particle bonds. These mechanically weakened zones establish favorable conditions for subsequent erosion processes in loess landscapes. Full article

Rapid urbanization and industrialization have led to great changes to the climate, such as global warming, urban heat islands, and frequent fluctuations in ambient temperature, and also a large amount of building energy consumption. Adaptive building provides an appropriate solution to maintain low energy consumption under various indoor and outdoor conditions and therefore has increasingly gained attention recently. Yet there is no clear definition on adaptive buildings and the current literature often focuses on the building envelope and overlooks buildings’ mechanical system, which is also an important part of the building system for responding to the indoor requirements and outdoor conditions. This article presents a systematic review on the research and development of adaptive buildings to address the identified research gaps. Firstly, it introduces and discusses the definition and evolution of the concept of adaptive building. Secondly, it reviews the adaptive building envelope technologies of roof, wall and window. Thirdly, it investigates the research progress on the adaptive mechanical system, especially lighting and air-conditioning systems. Lastly, it demonstrates practical applications of adaptive buildings and provides recommendations on future research directions on adaptive buildings. Full article

Accurately estimating the State of Charge (SOC) of power batteries is crucial for the Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, energy management efficiency, and the safety and lifespan of the battery. However, SOC cannot be measured directly with instruments; it needs to be estimated using external parameters such as current, voltage, and internal resistance. Moreover, power batteries represent complex nonlinear time-varying systems, and various uncertainties—like battery aging, fluctuations in ambient temperature, and self-discharge effects—complicate the accuracy of these estimations. This significantly increases the complexity of the estimation process and limits industrial applications. To address these challenges, this study systematically classifies existing SOC estimation algorithms, performs comparative analyses of their computational complexity and accuracy, and identifies the inherent limitations within each category. Additionally, a comprehensive review of SOC estimation technologies utilized in BMS by automotive OEMs globally is conducted. The analysis concludes that advancing multi-fusion estimation frameworks, which offer enhanced universality, robustness, and hard real-time capabilities, represents the primary research trajectory in this field. Full article

The Thermoelectric Window Frame (TEWF) can be adjusted by regulating the operating current to achieve the desired indoor temperature. However, indoor and outdoor ambient disturbances are inevitable, causing indoor temperature fluctuations and preventing them from reaching the set point. To solve the problem, a model-based control method is proposed to maintain the indoor temperature at the set point in this work. This method relies on a computational model for determining the operating current and a transient model for tracking variations in indoor temperature. Experimental results under various working conditions validate the two models. Moreover, indoor interference (e.g., changes in set point or air leaks due to occupants’ behavior) and outdoor interference (e.g., changes in the outdoor temperature) are incorporated into stable-state experiments. When these interferences occur, new operating currents are calculated for the new working conditions and applied to the TEWF. The results show that the indoor temperature significantly deviates from the desired values if the operating currents are not adjusted when disturbances occur. However, the indoor temperature can reach the set point by regulating the new operating currents in time, even during disturbances. Full article

Soil temperature, a key factor in subsurface geochemical processes, is influenced by environmental and geological dynamics. This study analyzed hourly soil temperature variations at depths of 10 to 100 cm near the Sakurajima volcano, alongside concurrent ambient temperature measurements. By applying temperature models and statistical methods, we characterized both seasonal and short-term thermal dynamics, including soil-atmosphere thermal coupling. Our findings revealed a depth-dependent thermal diffusivity, establishing distinct thermal regimes within the soil profile. The soil’s strong thermal buffering capacity, evidenced by increasing amplitude attenuation and temporal lag with depth, allowed us to identify optimal instrument placement depths (80–100 cm) for minimal diurnal temperature influence. We also quantified the relationship between ambient temperature fluctuations and soil thermal response at various depths, as well as the impact of these temperature variations on soil permeability. These results enhance our understanding of subsurface thermal behaviour in volcanic environments and offer practical guidance for environmental monitoring and geohazard studies. Full article

The screening of environmentally friendly, efficient and high-temperature-resistant organic corrosion inhibitors represents a significant means of reducing metal losses in industrial production. In this study, we investigated using aliphatic amine-functionalized perylene-diimide (PDINN) to inhibit Q235 steel in 1 M HCl media. The results show that PDINN significantly inhibits corrosion of Q235 steel in 1 M HCl. It is of greater significance that PDINN’s inhibition is unresponsive to temperature fluctuations in the corrosive environment, maintaining an efficiency of 86.5% at an ambient temperature of 328 K. DFT and MD analyses indicate that the exceptional inhibitory capacity of PDINN is closely associated with the extensive conjugated structure within the molecule, where it is firmly adsorbed on the Fe (110) via π-electrons. Full article