Optimal Flow Rate Research Articles

Research on supercritical CO2-based filled type U-pipe solar evacuated tubular collector: A thermodynamically parametric optimization and implementation machine learning modeling

Using supercritical CO2 as a working fluid in a heat and power system operating on solar energy is an advantageous approach that achieves higher efficiency, carbon capture, and emissions reduction. Therefore, the present work focused on researching the effectiveness of developing a supercritical CO2-based U-pipe solar evacuated tubular collector (ETC) by a filled layer that includes contributions to thermodynamic parametric optimization and machine learning modeling to forecast the outlet temperature intelligently. Results indicated that the filled type increase the outlet temperature by 24.29 % compared with the conventional type. The evaluation of the optimization research findings shows that an optimal mass flow rate of 8.0 kg/hr achieves a maximum exergy efficiency of 13.70 % to 14.51 %. Furthermore, the results recommended that arranging 13 tubes with a length of 1800 mm is optimal for exergy efficiency within a mass flow rate range of 6.0–12.0 kg/hr. Based on machine learning implementations, Gradient Boosting, Support Vector Machine, and Multi-Layer Perceptron neural networks can intelligently predict with R2|Tout = 1.000, 0.997, and 0.995, respectively. A polynomial-based GMDH/ML with R2|Tout = 0.986 was presented to intelligently forecast the outlet temperature using five input (Tamb,ṁ,ηopt,Ltube,It) parameters.

Optimizing Microfluidic Channel Design with High-Performance Materials for Safe Neonatal Drug Delivery.

Designing the microfluidic channel for neonatal drug delivery requires proper considerations to enhance the efficiency and safety of drug substances when used in neonates. Thus, this research aims to evaluate high-performance materials and optimize the channel design by modeling and simulation using COMSOL multiphysics in order to deliver an optimum flow rate between 0. 3 and 1 mL/hr. Some of the materials used in the study included PDMS, glass, COC, PMMA, PC, TPE, and hydrogels, and the evaluation criterion involved biocompatibility, mechanical properties, chemical resistance, and ease of fabrication. The simulation was carried out in the COMSOL multiphysics platform and demonstrated the fog fluid behavior in different channel geometries, including laminar flow and turbulence. The study then used systematic changes in design parameters with the aim of establishing the best implementation models that can improve the efficiency and reliability of the drug delivery system. The comparison was based mostly on each material and its appropriateness in microfluidic usage, primarily in neonatal drug delivery. The biocompatibility of the developed materials was verified using the literature analysis and adherence to the ISO 10993 standard, thus providing safety for the use of neonatal devices. Tensile strength was included to check the strength of each material to withstand its operation conditions. Chemical resistance was also tested in order to determine the compatibility of the materials with various drugs, and the possibility of fabrication was also taken into consideration to identify appropriate materials that could be used in the rapid manufacturing of the product. The results we obtained show that PDMS, due to its flexibility and simplicity in simulation coupled with more efficient channel designs which have been extracted from COMSOL, present a feasible solution to neonatal drug delivery. The present comparative study serves as a guide on the choice of materials and design of microfluidic devices to help achieve safer and enhanced drug delivery systems suitable for the delicate reception of fragile neonates.

An efficient low-grade thermal energy dual-driven elastocaloric cooling system: Thermodynamic cycle analysis, economic and sustainability assessment

Elastocaloric cooling is regarded as the most promising emerging refrigeration technology, with heat driven elastocaloric cycles at the forefront of research. However, existing systems often have actuators with a mass of 4–20 times that of the refrigerant, resulting in bulky structures and low thermodynamic efficiency. Enhancing system compactness and efficiency has thus become a critical research challenge. This study proposes an innovative low-grade thermal energy dual-driven elastocaloric cooling system, achieving a 1:1 mass ratio (mActuator/mRefrigerant) between the actuator and refrigerant, significantly improving system compactness and coefficient of performance while effectively reducing costs. A three-dimensional numerical and geometric model was developed to analyze the thermodynamic cycle, examining the effects of material, operational, and geometric parameters on cooling performance, and a “category-wise optimization” strategy was employed to determine optimal parameter combinations. The cost and environmental impacts of the new system were evaluated and compared with vapor-compression air conditioning. Results show that the difference in transformation temperatures between austenite and martensite phases significantly impacts cooling performance. Optimal flow rates and cycling frequencies are essential to avoid performance loss. Considering the coefficient of performance and heat transfer, a 1:1 mass ratio is optimal for geometric parameters. The system achieves a maximum coefficient of performance of 0.158, exceeding the existing two schemes by more than 2 times and 7 times, with a maximum temperature rise of 13.1 K. The proposed system is economically feasible and environmentally friendly, providing valuable theoretical foundations and technical solutions for developing sustainable alternative refrigeration technologies.

A novel 3D opto-electro-thermal coupling numerical model for photovoltaic/thermal systems

Photovoltaic/thermal (PV/T) systems offer a promising solution for enhancing energy conversion performance by capturing waste heat and reducing the operating temperature of photovoltaic cells. The performance of these PV/T systems could be further optimized by numerical simulation. However, currently reported PV/T models utilize simplified models and and fail to consider the electro-thermal coupling in local areas of solar cells, thereby inadequately representing the underlying physical mechanisms. To address this issue, a novel opto-electro-thermal coupling numerical model of perovskite PV/T systems based on drift–diffusion equations is developed in this work. This model enables the investigation of the energy conversion mechanism of optical, electrical and thermal energy at grid cell level. Building on the developed model, the energy conversion performance at system level under various operational conditions is analyzed via a modified weighted total efficiency evaluation indicator. The results indicate that: the total efficiency is significantly compromised by elevated environmental temperature, with an efficiency loss of 8.45 % observed when the temperature increases by 40 °C. Meanwhile, as irradiation intensity rises, the efficiency gained from utilizing waste heat would become increasingly significant in PV/T systems. More importantly, the optimal flow rate for various irradiation intensities is also derived. This work could offer theoretical guidance for investigating more comprehensive energy conversion mechanisms and optimizing energy conversion performance for PV/T systems.

Nozzle structure optimisation in a photovoltaic irrigation system

AbstractOn the basis of the significant impact of the sprinkler nozzle structure in photovoltaic irrigation systems, the nozzle parameters are optimised to improve sprinkler adaptability and irrigation uniformity under different light intensities. An experimental test and numerical simulation were conducted to analyse the influence of nozzles with distinct parameters on the spray irrigation effect. Taking the irrigation uniformity coefficient as the optimisation goal and the nozzle outlet shape, outlet area and straight flow channel length as the optimisation variables, an orthogonal experimental design was employed, and the optimal solution was obtained via range analysis. The experimental results indicate that the sprinkler performs best when the outlet shape is circular, the nozzle outlet area is 7 mm2 and the flow channel length is 2 mm. The optimal nozzle characteristics were tested and compared with those of the original design, and numerical simulation was used to demonstrate the optimised internal flow mechanism. The results demonstrate that the optimised flow rate and spray range are improved, and the working pressure and rotation period are significantly reduced, enabling the system to adapt to a more extensive range of light intensities. The radial water distribution structure is better, and the combined application rate and uniformity coefficient are also significantly improved. In addition, the sprinkler outlet speed is more consistent, and the area of the high‐speed zone in the spray plate increases, which is conducive to reducing energy loss and enhancing sprinkler irrigation uniformity.

A new approach to cooling photovoltaic panels: Electrospray cooling

In recent years, rise in carbon emissions has increased the focus on renewable energy. Solar energy systems are widely used for energy production due to their easy applicability and being an unlimited resource. There are factors affecting the efficiency of these systems. One of the most prominent of these is temperature. Enabling thermal control of the solar panels not only increases their electrical efficiency but also extends their economic life. In this study, electrospray cooling, which is not found to be used in the literature for cooling PV panels, was analyzed. Electrospray cooling performance in PV panels was examined for different flow rates and irradiance values using water as the coolant fluid. As a result, it was determined that the optimum flow rate was 80 ml/h water flow rate, and the panel temperature at this flow rate was reduced from approximately 77 °C–32.2 °C at 1200 W/m2 radiation. The output power obtained increased by 56.6 % at optimum flow rate and radiation. Compared to the studies in which PV panels were cooled by spray, it was found that electrospray requires approximately 1.5–137 times less water.

Design, optimization, simulation, and implementation of a 3D printed soft robotic peristaltic pump

Abstract This study presents an innovative approach to fluidic pumping using soft robotics, designed to circulate fluid through soft conduits for delicate environments like blood streams where traditional peristaltic pumps may not be feasible. A novel soft robotic peristaltic pump is optimized and implemented, featuring 3D printed ring-shaped actuators and a PDMS pipe housing a Newtonian fluid. The design includes a three-stage actuator ring structure, actuated sequentially for peristaltic motion. A parametric finite element model predicts the required pressure, and the Mooney-Rivlin 5 Parameters hyper-elastic material model ensures accurate material properties. Optimization uses response surface analysis in Minitab and MATLAB Simulink Simscape simulations to achieve maximum flow rate with minimal power and pressure. Experimental validation confirms the simulations, achieving an optimal flow rate of 0.27 ml s−1 at a 450 ms cycle, with minor discrepancies due to friction and measurement errors. This study demonstrates the scalability of linearly sequenced soft squeeze actuators into an effective pump, validated by both simulation and experiments. Future applications include medical devices addressing deep venous thrombosis, with further research exploring control theory for optimization and comparing performance with conventional pumps to enhance practical applicability.

The Effect of O2 Flow Rate on Nanostructured TiO2 Thin Films Prepared by Reactive DC Magnetron Sputtering with OAD Technique

In this project, we explored how different oxygen flow rates influenced the creation of TiO2 nanostructures in thin films using magnetron sputtering preparation with oblique angle deposition (OAD) technique. The experiment involved oxygen flow rates of 20, 40, 60, 80 and 100 sccm along with a constant argon flow at 20 sccm. Morphological analysis by FE-SEM was performed to determine film thickness and deposition rate. In a subsequent stage, thin films thickness was confirmed to be 100 nm. After coating, specimens were annealed at 300°C for 2 h using various O2 flow rates. FE-SEM revealed morphological changes, GIXRD to explore crystal structures, and UV-Vis spectrophotometry to measure light transmittance. The results showed an optimal oxygen flow rate at 60 sccm, with annealed specimens exhibiting anatase structure and promising light transmittance of 73-83%.

ESTIMATION OF THE CLEANING TIME OF THE BOTTOM-HOLE ZONE OF WELLS IN CONDITIONS OF UNCERTAINTY

The aim of the work is to analyze changes in the maximum optimal flow rates of wells operating various groups of productive formations of the Volga-Ural oil and gas province under conditions of uncertainty of geological and field data. The relevance of the work is due to the deterioration of the structure of residual oil reserves and the need for the most efficient extraction of liquid hydrocarbon resources, optimization of design methods for various treatments of the bottomhole formation zone. Using geological and statistical modeling systems, the timing of cleaning the bottomhole zones of wells from drilling products was determined at the time they reached their maximum optimal flow rate in conditions of deposits confined to various stratigraphic horizons, and appropriate models of changes in productivity coefficients were constructed. The factors influencing the recovery time of the real properties of the reservoir at the point of opening it by the well, which, among other things, determines the profitability of the process of selecting residual oil reserves, have been established. Among them, the most relevant and necessary for the implementation of an integrated approach to computer support procedures for the development of oil fields are the productivity of wells, fracturing of formations, their proximity to a particular tectonic-stratigraphic complex, geological, physical and physico-chemical properties of formations and their saturating fluids. For the deposits of the Famensk tier of deposits of the Volga-Ural oil and gas province, a multiple decrease in the time of removal of reaction products after hydrochloric acid exposure at objects with low fracturing has been established. At the same time, additional processing of the bottomhole formation zone within areas with high drilling density does not affect the total cleaning time of the bottomhole zone of wells. Stimulation of the removal of drilling mud filtrate and additional products of formation reactions by injection of hydrochloric acid compositions under certain conditions can lead to a decrease in the actual productivity of wells due to the introduction of less developed and dead-end cracks into the drainage process. The existence of the specifics of the procedure for cleaning the bottomhole formation zone depending on the geological and physical parameters of productive formations is substantiated and various patterns of changes in the productivity coefficient depending on the time of wells reaching the maximum optimal flow rate are revealed. The results obtained make it possible to make informed technological decisions to improve the efficiency of oil production, including in conditions of uncertainty, which may be caused by the lack of the necessary volume of hydrodynamic studies of wells.

Application of Nano Bubble Aeration Technology in Domestic Wastewater Treatment: Optimization of Gas Flow and Reaction Time

Nano bubble aeration is an emerging technology with significant potential in wastewater treatment, particularly for removing suspended solids and organic matter. This study investigates the application of nano bubble aeration in the treatment of domestic wastewater, focusing on the reduction of chemical oxygen demand (COD) and total suspended solids (TSS). Nano bubbles, with diameters ranging from tens of nanometers to a few micrometers, exhibit slower rising velocities and prolonged stability in water due to their negatively charged surfaces. Various nano bubble air flow rates were tested over 90-minute intervals to determine the optimal treatment conditions. The results indicate that a flow rate of 2 liters per minute achieves the highest treatment efficiency, reducing COD by 84.73% and TSS by 77.51%. Increasing the flow rate beyond this level showed minimal improvement, demonstrating that 2 liters per minute is the optimal flow rate for efficient wastewater treatment using nano bubble aeration. This research highlights the advantages of nano bubble technology in enhancing the treatment efficiency of suspended solids and organic matter in wastewater.

Enhancing Transfection Efficacy in Glioma Cells: A Comparison of Microfluidic versus Manual Polypropylenimine Dendriplex Formation.

Gene therapy is a promising therapeutic approach for treating various disorders by introducing modified nucleic acids to correct cellular dysfunctions or introduce new functions. Despite significant advancements in the field, the effective delivery of nucleic acids remains a challenge, due to biological barriers and the immune system's ability to target and destroy these molecules. Due to their branched structure and ability to condense negatively charged nucleic acids, cationic dendrimers have shown potential in overcoming these challenges. Despite this, standardized scalable production methods are still lacking. This study investigates the use of microfluidics to formulate generation 3-diaminobutyric polypropylenimine (DAB) dendriplexes and compares their characteristics and in vitro gene delivery efficacy to those prepared using conventional manual mixing. DAB dendriplexes were produced by both microfluidic and manual approaches and characterized. Their cellular uptake and gene expression were evaluated on C6 glioma cancer cells in vitro. Dendriplexes formed using microfluidics at the optimal flow rate and ratio demonstrated enhanced DNA condensation over time, achieving up to 97% condensation at 24hours. Both preparation methods produced positively charged dendriplexes, indicating stable formulations. However, dendriplexes prepared through hand mixing resulted in smaller particle sizes, significantly higher cellular uptake and gene expression efficacy compared to those prepared by microfluidics. Nonetheless, microfluidic preparation offers the advantage of standardized and scalable production, which is essential for future applications. This study highlights the potential of microfluidic technology to improve precision and scalability in gene delivery, paving the way for future advancements in gene therapy. Our findings suggest that, with further optimization, microfluidic systems could provide superior control over dendriplex formation, expanding their potential use in gene therapy applications.

Adaptive jet flow control for performance improvement via coupling stator-blade with rotor-tip injections in a transonic compressor

With the increase in the blade load of modern compressors for aircraft engines and gas turbines, flow separation is always generated in stator and rotor blade passages. This not only deteriorates the compressor efficiency but also blocks the flow path that may induce instability such as stall or surge. In order to tackle these issues, an adaptive jet flow control system coupling stator-blade with rotor-tip injections is proposed, and the goal of this paper is to investigate its effects on the aerodynamic performance of an in-house 1.5 stage transonic compressor. The developed flow control system integrates a jet configuration on the stator-blade surface and on the rotor casing surface respectively. The first step to construct this system is designing the jet configurations, and the Coanda effect is adopted to achieve wall-attachment injection. Second, the flow control system is intended to execute real-time monitoring on the operating condition of the compressor and can inject air with appropriate mass flow rate to eliminate flow separation and rotating stall. To do this, an optimal injection mass flow rate prediction model is established via a back propagation neural network algorithm. It aids to ensure an adaptive jet mass flow rate control during the compressor operation. Then, the effectiveness of the adaptive jet flow control system is evaluated using numerical simulations, and the influence mechanism of the injection mass flow on flow fields and aerodynamic performance is analyzed. Results indicate that compared to the prototype compressor, the compressor with adaptive stator-blade and rotor-tip injections can significantly increase efficiency and improve stability margin.

Optical and stress properties of ZrO2/SiO2 and TiO2/SiO2 anti-reflective coatings deposited by ion-beam-assisted deposition on a flexible substrate

Dielectric films of ZrO2,TiO2, and SiO2 were deposited on flexible polycarbonate (PC) and polyethylene terephthalate (PET) substrates by using ion-beam-assisted deposition (IBAD). Each layer had a thickness ranging from 30 to 210 nm. The optical and anisotropic stress properties were investigated. Two anti-reflective coatings (ARCs), ZrO2/SiO2 and TiO2/SiO2, were selected and deposited on the PET flexible substrate. The anisotropic stresses of the single layer and ARCs were measured using a phase-shifting moiré interferometer. Experimental results showed that the optimal oxygen flow rates for the ZrO2,TiO2, and SiO2 films deposited with IBAD were 10, 10, and 15 sccm, respectively. The refractive index (n) was TiO2(2.37)>ZrO2(2.05)>SiO2(1.46), and the extinction coefficient (k) for all samples was below 10−3. The thermal expansion coefficient of the PC substrate was three times that of the PET substrate, and the high-refraction ZrO2 and TiO2 single-layer films presented cracks and distortions on the PC substrate. Only the low-refractive-index SiO2 sample did not present cracks. The three dielectric films did not crack or distort when deposited on the PET substrate. The anisotropic stress analysis provided the maximum principal and shear stresses for the three dielectric films on the PET substrate. Therefore, the maximum principal stress of the 210 nm single-layer film on a PET substrate is TiO2>ZrO2>SiO2. It was also discovered that the principal stress of the AR multilayer film is significantly decreased due to the damping stacking effect (DSE) of the high- and low-refractive-index materials, ZrO2/SiO2 ARC (−297.3MPa)>TiO2/SiO2ARC(−132.6MPa). Thus, the high packing density of TiO2 gives a better DSE than ZrO2.

Comparative Numerical and Experimental Analyses of Conical Solar Collector and Spot Fresnel Concentrator

This paper aims to compare the thermal performances of the conical solar collector (CSC) system and the spot Fresnel lens system (SFL) using water and CuO nanofluid as the working fluids. The studied CFD models for both systems were validated using experimental data. At an optimal flow rate of 6 L/min, the SFL system showed higher optical and thermal performance in comparison with that of the CSC system. In the case of the SFL system, the availability of a greater amount of solar energy per unit collector area caused an increase in thermal energy. Moreover, in the case of the CSC system, the non-uniform distribution of solar flux on the absorber’s outer surface leads to an increase in temperature gradient and heat losses. As a heating medium, the CuO nanofluid outperformed the water in terms of higher thermal conductivity and heat capacity. The average thermal efficiencies of 64.7% and 61.2% were achieved using SFL with and without CuO nanofluid, respectively, which were 2.4% and 0.5% higher than those of the CSC with and without nanofluid. CFD simulations show a 2.80% deviation for SFL and 2.92% for CSC, indicating acceptable accuracy compared to experimental data.

Developing a neural network model to predict the optimal minimum flow rate for effective hole cleaning

The object of the study is effective well cleaning during drilling. The subject of the study is the development of a machine learning model based on a neural network for predicting the optimal minimum drilling fluid flow rate. The challenge is the need to improve well cleaning efficiency to prevent stuck pipes and the associated downtime and costs. During the study, a neural network model was developed and tested to predict the minimum flow rate for cleaning wells. The model was trained and tested on data showing its high accuracy and reliability. The mean square error (MSE) reached 0.019169 for LSTM and 0.0828 for GRU, indicating the accuracy of the predictions. Neural network architectures such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to efficiently process time series of data and consider long-term dependencies. The results are explained using advanced neural network architectures and machine learning algorithms, which made it possible to achieve good accuracy of predictions. These architectures enable efficient model training on large amounts of data, allowing complex dependencies and influencing factors to be considered. Distinctive features of the results include good accuracy of predictions and the ability to use the model in real-world conditions. The model demonstrates good performance and reliability in predicting the minimum flow rate. The results of the study can be used to optimize the processes of well drilling. Practical applications include using the model to predict the optimal minimum flow rate in various conditions, which will reduce the risks of stuck pipes and increase the efficiency of drilling operations. The model can be integrated into existing monitoring and control systems for drilling processes to improve their performance

A new method development and validation for simultaneous estimation of ramipril and telmisartan by RP-HPLC method in combined dosage form

The present research work was development and validation of new RP-HPLC method for simultaneous estimation of Ramipril and Telmisartan in combined dosage form. In simultaneous RP-HPLC method development, Waters 2695 Separations Module with PDA Detector and column used is C8 SB ZORBAX (150 X 4.6mm) column with 3.5-micron particle size. Injection volume of 10 µL is injected and eluted with the mobile phase selected after optimization was Phosphate buffer and Acetonitrile in the ratio of 70:30 was found to be ideal. The optimized flow rate was at 1.0 mL/min. Detection was carried out at 230 nm. This system produced symmetric peak shape, good resolution and reasonable retention times of Ramipril and Telmisartan were found to be 2.275 and 4.261 minutes respectively. The Ramipril and Telmisartan showed linearity in the range of 20-60 µg/mL and 160-480 µg/mL respectively. The %RSD values for precision was found to be within the acceptable limit, which revealed that the developed method was precise. The developed method was found to be robust and the %RSD value for percentage recovery of Ramipril and Telmisartan was found to be within the acceptance criteria. The results indicate satisfactory accuracy of method for simultaneous estimation of the Ramipril and Telmisartan.

Fizzy extraction secondary electrospray ionization mass spectrometry for analysis of volatile solutes in high-matrix samples

Fizzy extraction (FE) is a rapid way to prepare liquid samples containing volatile organic compounds (VOCs) for analysis. The sample is first infused with a carrier gas, and subsequent rapid decompression of the sample chamber causes it to effervesce spontaneously. Secondary electrospray ionization (SESI) enables analysis of VOCs using mass spectrometry (MS). Here, we report direct hyphenation of FE with SESI-MS. In order to adjust the flow rate of the gaseous extract to match the optimum input flow rate of the ion source, flow was split with a tee junction. The following parameters have been optimized: pressure inside the sample chamber (during saturation), high voltage applied to the nano-ESI solution, temperature of desolvation line, temperature of heated block, temperature of the SESI chamber, gas pressure applied to the headspace of nano-ESI solution, and length of tubing connected to the tee junction. The optimum parameters were 150 kPa, +4.0 kV, 225 °C, 200 °C, 90 °C, 18 kPa, and 6 cm, respectively. The developed method was characterized using MS and high-speed imaging. The limits of detection for ethyl butyrate, ethyl pentanoate, ethyl hexanoate, ethyl octanoate, and ethyl decanoate are 1.11 × 10−7 M, 4.33 × 10−7 M, 8.13 × 10−8 M, 1.09 × 10−7 M, and 2.19 × 10−7 M, respectively. The analysis time was only 2.5 min per sample (not counting the cleaning step). The method was further applied for profiling VOCs in selected dairy products. Interestingly, the FE-SESI-MS method is much more tolerant to the sample matrix (milk) than headspace vapor analysis using the same ion source.

Numerical and experimental investigation of the correlation between inlet flow rates, temperature, and [formula omitted] emissions in pure hydrogen combustion

Many industrial burners currently in use have been designed for fossil fuels and have not been tested with hydrogen. The successful utilization of hydrogen fuel in industrial applications demands the optimization of operating conditions. Optimizing the inlet flow rate is important particularly for industrial burners to ensure safe and efficient operation over a long period. This study investigates the effects of inlet flow rate on temperature distribution and NOx emissions in a multi-purpose pure hydrogen-fueled industrial burner through numerical simulations and experiments. The numerical simulations, employing the realizable k-ε turbulence model and the eddy dissipation concept for turbulence and combustion modeling, are validated against experimental data, achieving a mean absolute percentage error of approximately 5 % for temperature and less than 2 % for NOx emissions at 4 % O2. The results demonstrated that increasing the inlet flow rate enhances reaction intensity and heat release, subsequently increasing combustion temperatures and promoting NOx formation. However, with a substantial increase in the inlet flow rate, the rate of temperature growth diminishes, and NOx emissions reach a plateau. This suggests that the burner is approaching maximum capacity, with the combustion process becoming limited by the burner’s ability to efficiently mix and react the fuel and oxidizer. Finally, an optimal inlet flow rate of 50.4 LPM for hydrogen in the fuel inlet and 200 LPM for air in the oxidizer inlet, balancing temperature and NOx emissions, was identified through the weighted sum performance indicator proposed in this work. This study provides critical insights into pure hydrogen burner performance, advancing previous efforts by offering quantitative data essential for improving burner efficiency and reducing harmful emissions in carbon-free combustion.

Artificial intelligence assisted prediction of optimum operating conditions of shell and tube heat exchangers: A grey‐box approach

AbstractIn this study, a Grey‐box (GB) model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger (STHE) under varying process conditions. Aspen Exchanger Design and Rating (Aspen‐EDR) was initially used to construct a first principle model (FP) of the STHE using industrial data. The Genetic Algorithm (GA) was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions. A dataset comprised of optimum process conditions was generated through FP‐GA integration and was utilised to develop an Artificial Neural Networks (ANN) model. Subsequently, the ANN model was merged with the FP model by substituting the GA, to form a GB model. The developed GB model, that is, ANN and FP integration, achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP model. Performance of the GB framework was also comparable to the FP‐GA approach but it significantly reduced the computation time required for estimating the optimum process conditions. The proposed GB‐based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.

Optimum flow rate of actively deformable particles in the overdamped regime

In this study, we investigate the behavior of actively deformable particles in a two-dimensional system as they flow through a narrow constriction under overdamped conditions. The model simulates particles that oscillate by harmonically changing their radius over time, with dynamics and interaction forces reflecting general cellular systems. We identify an optimal self-oscillation frequency at which the flow rate is maximized, occurring when the oscillation period matches the time needed for a particle to traverse a few of its own radii. While the model is a highly simplified abstraction and not intended to replicate the complexity of biological systems, it offers valuable insights into the mechanisms that may underlie efficient movement in crowded cellular contexts.