Search Results (824)

With buildings accounting for 40% of global energy consumption, heating, ventilation, and air conditioning (HVAC) systems represent the single largest opportunity for emissions reduction, consuming up to 60% of commercial building energy while maintaining occupant comfort. This critical balance between energy efficiency and human comfort has traditionally relied on rule-based and model predictive control strategies. Given the multi-objective nature and complexity of modern HVAC systems, these approaches fall short in satisfying both objectives. Recently, reinforcement learning (RL) has emerged as a method capable of learning optimal control policies directly from system interactions without requiring explicit models. However, standard RL approaches frequently violate comfort constraints during exploration, making them unsuitable for real-world deployment where occupant comfort cannot be compromised. This paper addresses two fundamental challenges in HVAC control: the difficulty of constrained optimization in RL and the challenge of defining appropriate comfort constraints across diverse conditions. We adopt a safe RL with a neural barrier certificate framework that (1) transforms the constrained HVAC problem into an unconstrained optimization and (2) constructs these certificates in a data-driven manner using neural networks, adapting to building-specific comfort patterns without manual threshold setting. This approach enables the agent to almost guarantee solutions that improve energy efficiency and ensure defined comfort limits. We validate our approach through seven experiments spanning residential and commercial buildings, from single-zone heat pump control to five-zone variable air volume (VAV) systems. Our safe RL framework achieves energy reduction compared to baseline operation while maintaining higher comfort compliance than unconstrained RL. The data-driven barrier construction discovers building-specific comfort patterns, enabling context-aware optimization impossible with fixed thresholds. While neural approximation prevents absolute safety guarantees, reducing catastrophic safety failures compared to unconstrained RL while maintaining adaptability positions this approach as a developmental bridge between RL theory and real-world building automation, though the considerable gap in both safety and energy performance relative to rule-based control indicates the method requires substantial improvement for practical deployment. Full article

Hypertension is a major risk factor for heart failure. Acetylation of p53 is known to regulate its activities. We have previously identified that p53 acetylation is required for cardiac remodeling in a mouse model of pressure overload-induced heart failure. Acetylation mutant p53 (p53aceKO) mice have been shown to have the ability to regulate SIRT3 KO-induced cardiac fibrosis. In the present study, we hypothesized that p53aceKO mice would exhibit cardiac protection and blunt cardiac fibrosis when subjected to Ang-II-induced hypertension. Control and p53aceKO mice received either a micro-osmotic pump implant administering Ang-II for 28 days or a sham procedure. Blood pressure was measured weekly, and echocardiography was performed every two weeks. Mice were euthanized and hearts were processed for histological analysis. While both control and p53aceKO mice receiving Ang-II exhibit increased systolic and diastolic blood pressures, control mice also demonstrate increases in ejection fraction and fractional shortening compared to the sham, while p53aceKO mice do not. Furthermore, control mice receiving Ang-II exhibit decreased left ventricular diameter and volume at end-systole and end-diastole, as well as thickening of both the anterior and posterior walls, while p53aceKO mice exhibit no significant changes in any of these parameters. Additionally, p53aceKO mice do not exhibit the Ang-II infusion-induced cardiac fibrosis seen in control mice treated with Ang-II. Mutation of p53 acetylation is protective against Ang-II infusion-induced cardiac fibrosis and dysfunction in mice. Acetylated p53 may, therefore, be a novel therapeutic target to address complications in the heart associated with hypertension. Full article

Drilling fluid loss is a common and complex downhole problem that occurs during drilling in deep fractured formations, which has a significant negative impact on the exploration and development of oil and gas resources. Establishing a drilling fluid loss model for the quantitative analysis of drilling fluid loss is the most effective method for the diagnosis of drilling fluid loss, which provides a favorable basis for the formulation of drilling fluid loss control measures, including the information on thief zone location, loss type, and the size of loss channels. The previous loss model assumes that the drilling fluid is driven by constant flow or pressure at the fracture inlet. However, drilling fluid loss is a complex physical process in the coupled wellbore circulation system. The lost drilling fluid is driven by dynamic bottomhole pressure (BHP) during the drilling process. The use of a single-phase model to describe drilling fluids ignores the influence of solid-phase particles in the drilling fluid system on its rheological properties. This paper aims to model drilling fluid loss in the coupled wellbore–-fracture system based on the two-phase flow model. It focuses on the effects of well depth, drilling pumping rate, drilling fluid density, viscosity, fracture geometric parameters, and their morphology on loss during the drilling fluid circulation process. Numerical discrete equations are derived using the finite volume method and the “upwind” scheme. The correctness of the model is verified by published literature data and experimental data. The results show that the loss model without considering the circulation of drilling fluid underestimates the extent of drilling fluid loss. The presence of annular pressure loss in the circulation of drilling fluid will lead to an increase in BHP, resulting in more serious loss. Full article

With increasing global energy demand and depletion of onshore oil–gas resources, deep-sea hydrocarbon exploration and development have become strategically vital. As core subsea transportation equipment, the performance of helico-axial multiphase pumps directly determines the efficiency and economic feasibility of deep-sea extraction. However, non-uniform inflow patterns caused by uneven gas–liquid distribution in pipelines degrade pressure-boosting capability and reduce pump efficiency under actual operating conditions. To address this, an optimization method employing helical static mixers was developed. A mixer with a 180° helical angle was designed and installed upstream of the pump inlet. Numerical simulations demonstrate that the mixer enhances gas-phase distribution uniformity in stratified flow, improving efficiency and head across varying gas void fractions (GVFs). At a stratification height ratio (Ψ) of 0.32, efficiency increased by 15.41% and head rose by 15.64 m, while turbulent kinetic energy (TKE) at the impeller outlet decreased by up to 50%. For slug flow conditions, the mixer effectively suppressed gas volume fraction fluctuations, consistently improving efficiency under different slug flow coefficients (φ) with a maximum head increase of 9.82%. The optimized flow field exhibits uniform gas–liquid velocity distribution, stable pressure boosting, and significantly reduced TKE intensity within impeller passages. Full article

Background/Objectives: After pneumonectomy, the right ventricular stroke volume is pumped into pulmonary vessels whose volume has been reduced by approximately 50%. To sustain conditions for pulmonary flow, the flow reserve is increased in the remaining lung, which is conducive to the development of pulmonary hypertension symptoms. This study sought to examine pulmonary flow in one lung and the size of the right atrium (RA), right ventricle (RV) and pulmonary artery (PA) in patients who had undergone pneumonectomy and to establish the influence of time since pneumonectomy on these parameters, as well as their potential mutual dependencies. Methods: The retrospective analysis included 34 patients who had undergone pneumonectomy. Pulmonary flow was measured by means of perfusion scintigraphy. The diameters of the RA, RV and PA were evaluated based on computed tomography with contrast. Results: We observed complete or near-complete utilization of flow reserve in 38.2% (13/34) of patients, enlarged transversal and longitudinal dimensions of the RA in 17.6% (6/34) and 32.3% (11/34) of patients, respectively, and enlarged transversal and longitudinal dimension of the RV in 67.6% (23/34) and 44.1% (15/34) of patients, respectively. Dilatation of the PA was discovered in 23.5% (8/34) to 26.5% (9/34) of patients, as well as the presence of an extensive complex of radiographic features of pulmonary hypertension (PH) syndrome in 23.5% (8/34) of cases. Conclusions: Radiological features of PH were present in a significant number of patients. These features developed at varying rates but were present in all patients followed >10 years after the procedure. Full article

Nanofluids are an important technology for enhancing heat transfer in industrial applications by incorporating high thermal conductivity nanoparticles into base fluids. However, they often require higher pumping power and energy consumption. This study employs a two-dimensional (2D) approximation of vortex generators (VGs) in a turbulent trapezoidal channel with nanoparticle concentrations of ${\mathrm{Al}}_2{\mathrm{O}}_3$, ${\mathrm{SiO}}_2$, and ${\mathrm{TiO}}_2$. Simulations are performed using ANSYS Fluent 2021 with the Finite Volume Method (FVM) and the k–$\epsilon$ turbulence model to capture turbulence characteristics, eddy viscosity, and turbulent kinetic energy production. The introduction of vortex generators improves fluid mixing and reduces the thermal boundary layer, resulting in enhanced heat transfer, with a performance evaluation criterion (PEC) of 1.08 for water (baseline case without nanofluids). The single nanofluids further optimize heat transfer, increasing the Nusselt number and pressure drop while balancing thermal performance, reaching a PEC of 1.6 for ${\mathrm{SiO}}_2$ at 3% concentration, representing a 48% improvement over the baseline. A hybrid mixture of 1% ${\mathrm{Al}}_2{\mathrm{O}}_3$ and 2% ${\mathrm{SiO}}_2$ achieves the same PEC of 1.6 as single ${\mathrm{SiO}}_2$ nanoparticles, but with higher heat transfer and lower pressure drop, demonstrating improved thermal performance. Full article

Assessing the variation in the thermal conductivity of heterogeneous rock materials can be critical when upscaling models to simulate geothermal system operation, especially for petrothermal systems, where conduction dominates over convection. This study’s objective was to evaluate heterogeneity effects when assessing the thermal conductivity of geological materials, in this case, metamorphic rocks from Kuujjuaq (Canada), where the installation of a ground-coupled heat pump system is expected. Four core samples of gneissic rocks were analyzed in detail and compared to results obtained from a thermal response test. Thermal conductivity measurements in dry conditions were performed on the cylindrical surface of the samples with an optical thermal conductivity scanner. The 2D thermal conductivity spatial distribution was obtained by linear interpolation and used for numerical modeling to simulate steady-state conductive heat transfer along the sample vertical direction. Then, the effective thermal conductivity was computed according to Fourier’s law, using the simulated temperature to investigate the effect of scale variation with the heterogeneity. Results indicate the importance of distinguishing between the sample section’s effective thermal conductivity and local average thermal conductivity. Significant scale effects were identified with a variation ratio comprised between −10% and +16% when varying the length of the sample section. The representative elementary volume for the effective thermal conductivity was determined equivalent to half of the sample length. This volume gave a thermal conductivity that is equal to the harmonic mean of the laboratory-assessed values with a relative error <5%. A comparison between the in situ and laboratory-assessed thermal conductivity indicates that the thermal conductivity inferred from the thermal response test is adequate for sizing a geothermal system, assuming a range of variability equivalent to 1.5 times its standard deviation. Full article

This paper presents an innovative and low-cost approach to the dispensing of multiple liquids on a microfluidic chip with the aim of dispensing liquids in a controlled sequence. The project focused on the design and development of a microfluidic liquid dispensing system that is an integral part of the Lab-on-Chip (LOC). Liquids are often dispensed into LOCs through blisters, syringes, or electric microfluidic pumps, but these can be impractical for Point-of-Care (POC) settings, especially in remote areas. Additionally, incorrect volumes of biochemical reagents and the introduction of reagents outside the sequence can distort the results of the diagnosis. The process undertaken involved designing and 3D printing prototypes of the dispensing system, along with laser cutting and manufacturing the Polymethyl Methacrylate (PMMA) LOC devices intended for receiving the liquids. The proposed novel low-cost dispensing system uses manually operated actuators and cams to disperse metered fluids sequentially to minimise end-user errors at POC settings. Full article

The transportation and automotive industries are slowly integrating biocomposite materials into products where the economics make sense; this typically means a short manufacturing cycle time, not using expensive prepreg, and with little waste generated from the process. In a previous investigation into the use of biocomposites for electric bus seats and backs, three different material systems (hemp, flax, and pure cellulosic fibers, each paired with a high-bio-content epoxy) and two manufacturing processes (wet layup followed by compression molding, vacuum-assisted resin transfer molding) were investigated, but neither process proved to be viable. In this paper, a relatively obscure process called Wet Compression Molding (WCM) is considered for economical production of the biocomposite bus seats using the same three material systems. Darcy’s law predictions of full impregnation time for a nominally 3.5 mm thick part using experimentally determined permeability values are all less than 2 s. Furthermore, prepreg is not used, and net-shape parts without excess resin show potential. Important design details of the WCM mold set, used in the manufacturing of flat test panels from each material system, that are generally not discussed in the literature include a high-pressure O-ring seal, and semi-permeable membranes covering injection pins and vacuum vents (evacuates trapped air) to prevent resin ingress. Biocomposite laminate specimens are fabricated using the mold set in a thermal press and a vacuum pump. Part characterization includes fiber volume fraction estimates and measurements of thickness, density, flexural modulus, and outer fiber maximum stress at failure. Due to its rapid impregnation with just enough resin, WCM should be considered for the economical manufacture of parts similar in shape and size to electric bus seats and backs. Full article

Vacuum membrane-based air dehumidification (MAD) is potentially more efficient than refrigeration cycles. Air permeance through a membrane is inevitable, especially when there is a large pressure difference between the supply and permeate sides. Given the high specific gas volume under vacuum conditions, removing the permeating air from the dehumidifier is crucial for the stable operation of the vacuum compressor. Energy-efficient air removal techniques are still lacking, thereby hindering the development of MAD technology. This paper proposes a novel MAD approach using a vacuum mixing condenser. The cooling water directly condenses moisture from the vacuum compressor without any heat exchanger. The permeating air and water mixture in the condenser then experiences a quasi-isothermal pressurization process through a multiphase pump, enabling continuous dehumidification and air removal with low power consumption. The fundamentals of the proposed approach are illustrated, and mathematical models are built. Influences of air permeance rate, cooling water flow rate, condenser pressure, membrane area, and gravitational work are investigated. The results show that a COP of 8~12 is achievable to dehumidify air to 50%RH, 25 °C. The vacuum compressor consumes about 80% of the power. A low air permeance rate, low condenser pressure, large membrane area, and high gravitational work positively impact the COP, while the cooling water flow rate has a more complex effect. The proposed dehumidifier can use less selective membranes for higher permeability and cost-effectiveness. Full article

Crystallization is one of the approximately twenty unit operations and is considered to be among the most important due to the large number of chemical compounds it produces, as well as due to the enormous quantities of these substances being manufactured around the world. This article aims to present a mathematical model for the shortcut design of a cooling crystallization unit consisting of the crystallizer and auxiliary equipment, such as an evaporator with its preheater and condenser, a heat pump that acts as the cooling system of the crystallizer, and a crystallizer pressure regulator modeled as an expansion valve. The model estimates an extensive series of variables, including mass and volume flow rates of the streams, heat duties of each piece of equipment, sizing variables such as heat transfer areas of heat exchangers and volumes of the vessels, and product flow rates for each specific feed. It embraces equations for the calculation of a series of stream properties, such as density, specific heat capacity, and latent heat of vaporization. For the sizing of the crystallizer, which is the main equipment of the unit, both flow rates and crystallization kinetics are taken into account. The latter is estimated by experimental data taken in a laboratory crystallizer and includes the crystal’s growth rate as a function of residence time. Full article

This article presents a proposal for blending natural gas and biogas with a control system with feedback to ensure a constant mixture density. To achieve this, we propose the following: a mathematical model to determine the gas density based on its composition; a control system whose main components are a gas mixer, valves, and a natural gas storage tank to regulate the biogas density, where its inputs are gases from biomass plants and the natural gas grid; mathematical models to calculate the volume of natural gas required in the storage tank. It is assumed that the composition at the outlet of the biogas plants is measured and that there are no losses of any kind; a case study simulation is then performed. All models consider random variation in gas composition over time. The main results are as follows: (a) reduced natural gas consumption, the promotion of biogas production and use and of mixtures of lower methane compared to natural gas, and the facilitation of the pumping of the gas mixtures; (b) all the biogas produced is used; (c) different piping, sources, storage tanks, consumers, and mixer schemes, considering the concepts of cities, microgrids, smart grids, and smart cities. Full article

Climate change is projected to significantly reduce water availability, particularly in arid and semi-arid regions, which makes hydrological planning essential given the increasing competition for water resources. Inefficient consumption patterns exacerbate water depletion and highlight the importance of water quality management. Promoting sustainable practices, reducing consumption, and enhancing water recycling contribute to a more resilient approach. The aim of thIS study is to evaluate the reuse of reclaimed water in the Vega Alta region, which accounts for almost 15% of the total water mix (about 99 hm³/year) as it reuses 92.52% of the treated wastewater, most of which is pumped to irrigation areas targeted for consolidation, also creating new landscapes. These figures place the Region of Murcia as the leading autonomous community in Spain for water reuse, although challenges remain regarding water volumes and the associated costs, investment, and maintenance. Full article

This article is devoted to the design of a real-time gain scheduling (adaptive) proportional–integral (PI) controller for the displacement volume regulation of a swash plate-type axial piston pump. The pump is intended for open circuit hydraulic drive applications without “secondary control”. In this type of pump, the displacement volume depends on the swash plate swivel angle. The swash plate is actuated by a hydraulic-driven mechanism. The classical control device is a hydro-mechanical type, which can realize different control laws (by pressure, flow rate, or power). In the present development, it is replaced by an electro-hydraulic proportional spool valve, which controls the swash plate-actuating mechanism. The designed digital gain scheduling controller evaluates control signal values applied to the proportional valve. The digital controller is based on the new linear parameter-varying mathematical model. This model is estimated and validated from experimental data for various loading modes by an identification procedure. The controller is implemented by a rapid prototyping system, and various real-time loading experiments are performed. The obtained results with the gain scheduling PI controller are compared with those obtained by other classical PI controllers. The developed control system achieves appropriate control performance for a wide working mode of the axial piston pump. The comparison analyses of the experimental results showed the advantages of the adaptive PI controller and confirmed the possibility for its implementation in a real-time control system of different types of variable displacement pumps. Full article

The development of unconventional oil and gas resources highly relies on hydraulic fracturing technology, and the fracturing effect directly affects the level of oil and gas recovery. Carrying out fracturing evaluation is the main way to understand the fracturing effect. However, the current fracturing evaluation methods are usually carried out after the completion of fracturing operations, making it difficult to achieve real-time monitoring and dynamic regulation of the fracturing process. In order to solve this problem, an intelligent prediction method for fracture propagation based on the attention mechanism and Long Short-Term Memory (LSTM) neural network was proposed to improve the fracturing effect. Firstly, the GOHFER software was used to simulate the fracturing process to generate 12,000 groups of fracture geometric parameters. Then, through parameter sensitivity analysis, the key factors affecting fracture geometric parameters are identified. Next, the time-series data generated during the fracturing process were collected. Missing values were filled using the K-nearest neighbor algorithm. Outliers were identified by applying the 3-sigma method. Features were combined through the binomial feature transformation method. The wavelet transform method was adopted to extract the time-series features of the data. Subsequently, an LSTM model integrated with an attention mechanism was constructed, and it was trained using the fracture geometric parameters generated by GOHFER software, forming a surrogate model for fracture propagation. Finally, the surrogate model was applied to an actual fracturing well in Block Ma 2 of the Mabei Oilfield to verify the model performance. The results show that by correlating the pumping process with the fracture propagation process, the model achieves the prediction of changes in fracture geometric parameters and Stimulated Reservoir Volume (SRV) throughout the entire fracturing process. The model’s prediction accuracy exceeds 75%, and its response time is less than 0.1 s, which is more than 1000 times faster than that of GOHFER software. The model can accurately capture the dynamic propagation of fractures during fracturing operations, providing reliable guidance and decision-making basis for on-site fracturing operations. Full article