Engineering Parameters Research Articles

Experimental investigation of camless air engine to improve performance by the novel controller and exhaust pressure predicting technique

In the face of the climate crisis, hike in prices of petroleum products, and limited gasoline sources, it is imperative to explore the possible opportunities in the field of energy transformation technology. Non-conventional and non-hazardous green fuel-air engine technology has proven to have a promising future in propulsion technology. In the air engine, the camless mechanism has created scope for research by replacement option of cam-follower with variable valve train (solenoid valve), which can be controlled electronically. In this paper, work is done on the operational parameters (i.e., cycle time, flow rate, and crank position) of camless air engines and proposed a controlling mechanism with experimental results to improve the system's efficiency. Control of intake duration maximizes the utilization of input compressed air power by allowing the optimum amount of air to expand fully. The novel controller was designed, developed, and tested, which controls the engine intake duration by estimating exhaust pressure. A prototype model was built with an electronic control circuit and pneumatic system, which records signals of operational parameters with the help of feedback sensors and controls the engine's intake duration at different operating pressure ranging from 3 to 6.5 bar. Experiments were conducted, and the results of two cycles were compared, case-I, where compressed air is continuously supplied from 0° to 170° of crank angle, and case-II controlled cycle, which controls intake duration by estimating exhaust pressure. Results show the average efficiency improvement of 11.91 % in case-II compared to case-I and also improvement in work done and RPM (Revolution per Minute) of the cycle. Thus, the control system in a camless air engine increases the feasibility of the air engine as one of the propulsion technologies in a city vehicle and even in the Compressed Air Energy Storage System (CAES).

Numerical investigation of slip effects on heat and mass transfer in a vertical channel with immiscible micropolar and viscous fluids of variable viscosity

AbstractThis study investigates the fluid flow, heat, and mass transfer phenomena within a vertical channel containing two immiscible fluids, with a particular focus on slip effects. These effects include no slip, velocity slip, thermal slip, and multiple slips, each analyzed with appropriate boundary conditions. The study thoroughly examines key characteristics, such as variations in thermal conductivity and viscosity. Using a sixth‐order Runge–Kutta numerical method implemented using Mathematica, the study achieves precise solutions for complex scenarios. The detailed results show how the various slip mechanisms and relevant parameters interact with each other in complex ways. These findings are useful for both theoretical understanding and application in real‐life engineering situations. This study also gives important information about how fluid flow, heat transfer, and mass transfer change under different slip effects. It looks at these effects and shows how they change visually. It also carefully calculates and analyzes engineering parameters like the Nusselt number, shear stress, and Sherwood number using bar charts, showing how they affect and behave. The study resulted in velocity slip having a minimal impact on temperature, whereas thermal slip resulted in higher temperatures. Both velocity and thermal slip conditions simultaneously result in the lowest temperatures.

Parameterization of H2SO4 and organic contributions to volatile PM in aircraft plumes at ground idle

ABSTRACT Volatile Particulate Matter (vPM) emissions are challenging to measure and quantify, since they are not present in the condensed form at the engine exit plane and they evolve to first form in the aircraft plume and then continue to grow and change as they mix and dilute in the ambient atmosphere. To better understand the issues associated with the initial formation and growth of vPM, a modeling study has been undertaken to examine several key parameters that affect the formation and properties of the vPM that is created in the initial cooling and dilution of the aircraft exhaust. A modeling tool (Aerosol Dynamic Simulation Code, ADSC) that was developed and enhanced over a series of past research projects supported by NASA, DoD’s SERDP/ESTCP, and FAA was used to perform a parametric analysis of vPM. The parameters of fuel sulfur content (FSC), emitted condensable hydrocarbon (HC) concentrations, and the species profile of the HCs were used to construct a computational matrix that framed a wide range of expected parameter values. This computational matrix was executed for two representative commercial aircraft engines at ground idle and results were obtained for distances of 250 m and 1000 m downstream. From prior results, the most significant vPM emissions occur at the lowest power settings, so an engine power condition of 7% rated thrust was used. A primary goal of the parametric study is to develop an updated vPM modeling methodology and also to help interpret data collected in experimental campaigns. The parameterization proposed here allows the vPM emission composition and particle numbers to be estimated in greater detail than current methods. The aim is to provide additional understanding on how the vPM properties vary with fuel and engine parameters to increase the utility of vPM predictions. Implications: Volatile Particulate Matter (vPM) is an important contribution to the total PM emitted by aviation engines. While vPM is not currently a part of engine emissions certification regulations, vPM is used in aviation environmental impact assessments and for air quality modeling in and around airports. Current methods in use, such as FOA, were developed before many recent advances in experimental data acquisition and in understanding of vPM processes. The parameterization proposed here allows the vPM emission composition and particle numbers to be estimated in greater detail than current methods. These estimates can be used to develop inventories and provide a better estimate of total emission for most aviation engines. Its use in international regulatory tools can inform possible future regulatory actions regarding vPM.

RSM based optimization of lambda and mixed fuel concentration parameters of an LTC mode engine

In this study, the effects of lambda and premixed ratio (PR) parameters on the response parameters of an engine operating in LTC (low temperature combusiton) mode is investigated and optimized using a hybrid method. RSM (response surface method), which is a combination of experimental and statistical method, is used as hybrid method. Depending on the variable parameters of lambda and PR, an experimental set was created with CCD (central composite design) and experiments were performed. The effects of the variable parameters on the response parameters were investigated and evaluated in detail on the generated counter graphs, optimization of the variable parameters was carried out. As lambda increased, a decrease in engine torque and thermal efficiency values, and improvements in CO2 and NOx emissions were observed. As PR increased, thermal efficiency and specific fuel consumption values worsened. The optimum premixed ratio was determined as 33.619 % and lambda value as 2.576. At these conditions, the values of response parameter were obtained as torque 12.115 Nm, BSFC 346.764 g/kWh, ITE 21.447 %, CA10 3.503, CA50 8.005, CO 0.169 %, HC 191.467 ppm, CO2 5.301 %, NOx 404.564 ppm and soot 12.687 %. The optimization desirability value was determined as 0.665 and strengthened the goodness of the optimization.

Modeling of CI engine performance and emission parameters using artificial neural network powered by catalytic co-pyrolytic renewable fuel

Emission and performance parameters of a 4-stroke CI engine operated on a blend of catalytic co-pyrolysis oil with pure diesel, produced through Azadirachta indica seed, waste LDPE (low-density polyethylene), and aluminium oxide (Al2O3) as a catalyst, are modelled in the current work using an Artificial Neural Network (ANN). At 500°C temperature, the highest oil output obtained was 93.91 wt%. The produced liquid fuel possessed similar physical features to that of pure diesel, including density (794 kg/m3) and heating value (44.42 MJ/kg), but lower flash and fire points that would assist in a better and complete combustion of the fuel blend resulting in a better performance and combustion characteristics. Using inputs including brake mean effective pressure, load, brake power, and torque, a developed ANN model was applied to forecast the performance (Brake Thermal Efficiency and Brake Specific Fuel Consumption) along with emission characteristics (Smoke and NOx). The Levenberg-Marquardt back-propagation training technique was applied for emissions and performance characteristics prediction having the best accuracy. Regression coefficients (R2) for predicting BTE, BSFC, NOx, and smoke were all very near to 1: 0.99801, 0.9983, 0.95753, and 0.97467. The study determines that the proposed alternative fuel could be utilized in blend with the pure diesel to in an unmodified diesel engine. It has also been found that artificial neural networks (ANN) could prove to be useful to model and forecast the performance or emissions of renewable fuels in diesel engines, with the potential for these fuels to be employed in transportation.

Design of main parameter of modular multilevel converter applied in power grid simulator

Abstract In the context of application in a power grid simulator, we investigated the parameter design issues of several crucial components of the Modular Multilevel Converter (MMC) topology applied to the power grid simulator system. These components include the bridge arm inductance and submodule capacitance. Based on the fundamental circuit of the MMC applied to the power grid simulator system, we introduced an equal discharge time constant H. We analyzed the capacitor voltage fluctuation rate and the equivalent capacitance discharge time constant “H”, discovering that they still exhibit an inversely proportional relationship across different engineering scenarios. Consequently, a general method for determining submodule capacitance parameters was proposed, and in conjunction with previous engineering parameters, recommended values for an equal discharge time constant of 40 ms were provided. Furthermore, we introduced the concepts of double-frequency circulating resonant angular frequency and phase resonant angular frequency, outlining the principles for determining the bridge arm reactance values. Through the analysis of corresponding parameters in previous engineering studies, universally applicable recommended values for the bridge arm reactance parameters were proposed, considering the unit resonant angular frequency as the power grid frequency angular frequency 1.0ω.

Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions.

The article's main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies.

Parameter fine tuning on CRDI engine operated with blends of grape biodiesel and diesel

This study investigates the feasibility of using grape biodiesel from the wine industry as a sustainable and cost-effective alternative to traditional fuels. The research focuses on optimizing key parameters like blend ratio, injection timing, injection pressure, engine load, and exhaust gas recirculation in a Common Rail Direct Injection engine to achieve low emissions without compromising performance. Due to grape biodiesel's higher viscosity, different energy content, and varying ignition delays compared to diesel, precise adjustments are essential for complete combustion and reduced emissions. This study uses response surface methodology and a Central Composite Design matrix to find the best values for a number of parameters in modern CRDI engines that use advanced electronic control units. Through fifty input combinations, the study aims to minimize specific fuel consumption, hydrocarbons, nitrogen oxides, and carbon monoxide while maximising brake thermal efficiency, brake mean effective pressure, and mechanical efficiency. The optimal configuration includes a fuel injection timing of 6° bTDC, an engine load of 82 %, 6.7 % EGR, 1000 bar injection pressure, and a 33 % grape biodiesel blend. These optimum input conditions yielded outputs of 3.55 bar BMEP, 31.85 % BTE, 64 % mechanical efficiency, 0.278 kg/kWh SFC, 0.127 % CO, 357 ppm NOx, and 8 ppm of HC. These adjustments ensure low emissions and efficient engine operation, highlighting grape biodiesel's potential as a viable alternative fuel.

Prediction, optimization, and validation of the combustion effects of diisopropyl ether-gasoline blends: A combined application of artificial neural network and response surface methodology

This research study mainly focuses on identifying the significant factors to be considered to discover the accuracy and reliability of the predictive models. The experimental results were employed to develop three different models: an artificial neural network (ANN), a response surface methodology (RSM), and a hybrid model. Brake thermal efficiency, specific fuel consumption, and regulated emissions were predicted using ANN, and inputs such as fuel blend concentration, CR, and engine speed were optimized using the RSM and hybrid models. The accuracy and reliability of the model results were validated with the least mean square error, mean absolute percentage error, and a higher signal-to-noise ratio. The higher R2 between 0.99426 and 0.9998 was observed by ANN whereas R2 by RSM and the hybrid model were relatively less. Similarly, the mean square error of ANN was relatively less compared to RSM and hybrid. However, the mean absolute percentage error observed in the validation test results for the optimized input parameters discovered by RSM, was less than 5 % for all the responses and higher in the hybrid model. Thus, the authors concluded that the ANN's predictive ability was much higher and RSM is the best suited for optimizing the engine parameters compared to the hybrid model.

Microfluidics single-cell encapsulation reveals that poly-l-lysine-mediated stem cell adhesion to alginate microgels is crucial for cell-cell crosstalk and its self-renewal

Microfluidic cell encapsulation has provided a platform for studying the behavior of individual cells and has become a turning point in single-cell analysis during the last decade. The engineered microenvironment, along with protecting the immune response, has led to increasingly presenting the results of practical and pre-clinical studies with the goals of disease treatment, tissue engineering, intelligent control of stem cell differentiation, and regenerative medicine. However, the significance of cell-substrate interaction versus cell-cell communications in the microgel is still unclear. In this study, monodisperse alginate microgels were generated using a flow-focusing microfluidic device to determine how the cell microenvironment can control human bone marrow-derived mesenchymal stem cells (hBMSCs) viability, proliferation, and biomechanical features in single-cell droplets versus multi-cell droplets. Collected results show insufficient cell proliferation (234 % and 329 %) in both single- and multi-cell alginate microgels. Alginate hydrogels supplemented with poly-l-lysine (PLL) showed a better proliferation rate (514 % and 780 %) in a comparison of free alginate hydrogels. Cell stiffness data illustrate that hBMSCs cultured in alginate hydrogels have higher membrane flexibility and migration potency (Young's modulus equal to 1.06 kPa), whereas PLL introduces more binding sites for cell attachment and causes lower flexibility and migration potency (Young's modulus equal to 1.83 kPa). Considering that cell adhesion is the most important parameter in tissue engineering, in which cells do not run away from a 3D substrate, PLL enhances cell stiffness and guarantees cell attachments. In conclusion, cell attachment to PLL-mediated alginate hydrogels is crucial for cell viability and proliferation. It suggests that cell-cell signaling is good enough for stem cell viability, but cell-PLL attachment alongside cell-cell signaling is crucial for stem cell proliferation and self-renewal.

Modelowanie cyfrowe w celu przewidywania zagrożenia osuwania się gruntu na wybranym obszarze; Przypadek testowy Ponzano (Włochy)

Creating a digital model is one of the aims of the geotechnical engineers, to predict the land sliding hazard which occur in different regions in the world. In February 2017, an extensive landslide occurred in the hamlet of Ponzano as a sloped area in the Abruzzo region in Italy. In this regard, predicting the land sliding hazard is one of the important issues to prevent hazard to the civilizations. In this project we created a model in the 3D dimension through the Plaxis 3D numerical solution software from the data based of the region Abruzzo in 2007 to evaluate the land sliding hazard before happened and then compare the results with the data from the drone data surveyed recently in the 2022. In this regard, the data from the Abruzzo resources from 2007 imported into the QGIS as the open-source cross-platform software to analysis the geospatial data and then imported into the Recap software to work on the point cloud data and then imported into the Civil 3D software to create a solid surface from the TIN surface and finally since the solid surface contains a large number of irrelative details, they were imported into the Rhinoceros software to create a NURBS surface to be smoothed for better performance in the analysis. The NURBS surface imported into the Plaxis and all of the geometry and geotechnical engineering parameters by considering the investigated geotechnical survey that was conducted in parallel in the area, defined for the model. The “Hardening Soil” model considered for the 1st layer as the “clay and lime” and the “Hoek-Brown” model defined for the 2nd layer as the “marl-flysch”. A fine mesh elements distribution assessed also for the model. The phases defined as the “gravity” to define the unit weight of the soil layers, the “plastic” phase to calculate the instant deformations and the “consolidation” to analyze the plastic deformations in the sloped area of the model. In parallel, the drone data achieved in 2022, were imported into the CloudCompare as the 3D point cloud processing software and different methods such as the “segment”, “statistical outlier Filter”, “CSF filter”, “noise filter”, “cross section” etc. were performed to clean the data and then imported into the Recap software to work on data and then imported into the Civil 3D software to create solid surface of the current data after the land sliding. In this regard, to evaluate the displacement occurred from the year 2007 toward the 2022, a TIN volume surface as the colored map created through the Civil 3D software to show the displacements in the z direction and all of the results were compared with the Plaxis 3D numerical solution software. The results showed that the colored map with the displacement in the positive and negative direction of the z is the same of the analyzed model and the values match each other’s and we created a digital model of the selected area to predict the land sliding hazard in the region in the following.

Exergy, energy, performance, and combustion analysis for biodiesel NOx reduction using new blends with alcohol, nanoparticle, and essential oil

Biodiesel is considered one of the alternative replacements to fossil fuels. However, the major challenge associated with its application in diesel engines is the higher level of NOx emissions. Fuel modification technologies, where biodiesel is blended with various additives and fuels, have emerged as a distinguished method in recent years to improve engine performance and reduce NOx. The study aims to reduce NOx emissions by fuel modification techniques. Five blends were prepared using Tucuma biodiesel along with ethanol, carbon nanotubes, and eucalyptus oil. The prepared blends were deployed to a test bed engine at rated speed and by varying loads to investigate performance, emission, and combustion characteristics. The results revealed that ethanol-blended fuels such as DE10 and TB10E10 had reduced the NOx emissions by 51.37% and 9%, respectively, at lower loads compared to diesel. Although the nanoparticle blend exhibited increased NOx emissions compared to diesel, it demonstrated reductions of 4.1%, 4.56%, 7.2%, and 3.1% at loads of 25%, 50%, 75%, and 100%, respectively, compared to TB10. The study highlights various tradeoffs observed between operating conditions and engine parameters for the blends, as detailed in this research. The study found that blends TB10E10 and TB10E10CNT20 exhibit improved performance close to that of diesel and reduced NOx and CO emissions compared to that of diesel and TB10. The study recommends further exploring the impact of injection rates with ethanol-blended fuels as they showed longer ignition delays since advancing the injection can create better combustion with ethanol blends.

A potential optical approach for diagnosis of the local magnetic field near the surface of the first wall/divertor tiles by Zeeman effect using polarization-resolved laser-induced breakdown spectroscopy

Magnetic field measurement is the basic diagnosis to obtain the physical engineering parameters of magnetic confinement fusion device and the macro information of plasma discharge. The real-time diagnosis of magnetic field distribution near the plasma-facing components (PFCs) surface provides the important information on the migration and transport model of key elements. In this work, a remote, in-situ approach for the magnetic field measurement near the surface of PFCs by the polarization-resolved laser-induced breakdown spectroscopy (LIBS) based on Zeeman effect is proposed and implemented. The Zeeman characteristics of the emission spectra of laser-induced W, Mo and C plasmas were verified in the laboratory by using different magnetic field configurations. According to the polarization characteristics of the Zeeman sublines of the LIBS spectrum, the intensity and direction of the external local magnetic field were successively identified by using a linear polarizer. Subsequently, a linear array fiber was utilized to determine the polarity of the external magnetic field. And finally, the magnetic field intensity near the lower edge surface of the tungsten baffle of the Experimental Advanced Superconducting Tokamak (EAST) upper divertor was measured when the field coils were demagnetized. This method can supplement the experimental data near the PFCs for the magnetic field configuration of the magnetic confinement fusion device and provide a reference for the wall element analysis model diagnosed by LIBS.

The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks

The research aims to develop a neural network-based lost information restoration method when the complex nonlinear technical object (using the example of helicopter turboshaft engines) sensors fail during operation. The basis of the research is an auto-associative neural network (autoencoder), which makes it possible to restore lost information due to the sensor failure with an accuracy of more than 99%. An auto-associative neural network (autoencoder)-modified training method is proposed. It uses regularization coefficients that consist of the loss function to create a more stable and common model. It works well on the training sample of data and can produce good results on new data. Also, it reduces its overtraining risk when it adapts too much to the training data sample and loses its ability to generalize new data. This is especially important for small amounts of data or complex models. It has been determined based on the computational experiment results (the example of the TV3-117 turboshaft engine) that lost information restoration based on an auto-associative neural network provides a data restoring error of no more than 0.45% in the case of single failures and no more than 0.6% in case of double failures of the engine parameter registration sensor event.

Optimization of CO2 flooding under dual goals of oil recovery and CO2 storage: Numerical case studies of the first-ever CCUS pilot in Changqing oilfield

CCUS (CO2 capture, utilization, and storage) is an important way to reduce carbon emissions and is increasingly applied in the development of hydrocarbon resources. It is crucial to develop proper engineering parameters to achieve the dual goals of enhanced oil recovery (EOR) and CO2 storage. This paper reports systematic numerical case studies of the first-ever CCUS pilot project at Changqing Oilfield to identify further improvement opportunities compared to the current development strategy. The targeted reservoir is located in the Huang 3 (H3) block and is characterized by ultra-low permeability (<1 mD), low oil production (<5 tons/day), and miscible and near-miscible flooding. A three-phase compositional reservoir model is established and a typical well group, consisting of one injector and eight producers, is selected to perform the numerical case studies. We investigate the influence and sensitivity analysis of engineering parameters on oil production and CO2 storage in ultra-low permeability sandstone reservoirs. The results indicate that the well pattern, the timing of switching to water-alternating-gas injection, the gas-water slug size ratio, and the injection rate are controlling factors to achieve favorable performance. The simulation results show that by using optimized engineering parameters, the oil recovery factor of the target reservoir is significantly increased by 25% and the CO2 storage efficiency is increased by 60%. Finally, an optimized development plan is obtained based on the actual conditions of the target reservoir. The reported case studies are supposed to provide insights into the optimal design and operation of CO2 utilization and storage in hydrocarbon reservoirs.

Comparative knock analysis of HCNG fueled spark ignition engine using different heat transfer models and prediction of knock intensity by artificial neural network fitting tool

Hydrogen and its careers have a lot of potential in transportation industry due to lesser emissions and better performance. Performance of hydrogen enriched compressed natural gas (HCNG) fueled engine is limited by Knock. The purpose of this study is to investigate knock intensity (KI) by different convective heat transfer models. This study can be used to train electronic control unit (ECU) of engine operating on different loads from low to high speed. In present study, experimentation have been performed on HCNG fueled spark ignition engine at different operating conditions by varying hydrogen amount in HCNG, by varying load of engine, by varying exhaust gas recirculation (EGR) rate in air and by varying speed of engine to calculate in-cylinder pressure, knock intensity (KI) and heat transfer rate (HTR). Six convective heat transfer models (Assanis, Eichelberg, Han, Hohenberg, Nusselt and Woschni) have been incorporated in quasi dimensional combustion model (QDCM) to calculate the parameters of HCNG engine. Comparative and predictive analysis using artificial neural network fitting tool (ANNFT) of aforementioned simulated models have been performed with experimental findings to attain the efficient model for knock intensity of HCNG engine. Woschni model, predicts the minimum error of 0.37 % & 0.86 % b/w experimental and simulated in-cylinder pressure and indicated mean effective pressure respectively. Han model predicts the minimum error of 3.8 % b/w experimental and simulated knock intensity operating at 11.2 % EGR. Maximum error of 20.3 % at 2.4 % EGR is attained by using Woschni model in knock intensity calculation. The best suited algorithm for present data set is Bayesian regularization utilized in ANNFT to predict knock intensity effectively. The findings of this study can be utilized in the development of HCNG engine.

Optimization of performance and emission of a diesel engine fueled with isopropyl alcohol Blends: A comparative ANN-GA and RSM-HCO application

This study focuses on optimizing the operating parameters of a diesel engine fueled by diesel-isopropyl alcohol blends, with the aim of enhancing engine performance and minimizing emissions. Using data collected from 144 experimental runs, predictive models were developed utilizing artificial neural networks (ANN) and response surface methodology (RSM). The models exhibited impressive accuracy, with an average absolute percentage error below 2 % for each parameter and an R2 value exceeding 0.99. Subsequently, these models were optimized using genetic algorithm (GA) and hill climbing algorithm (HCO) to identify the optimal engine operating conditions. The results indicate that both ANN-GA and RSM-HCO models exhibit satisfactory accuracy in predicting engine parameters. The ANN-GA model demonstrated an average deviation of 2.9 % from experimental data, while the RSM-HCO model exhibited an average deviation of 5.4 %. Both optimization results indicated that, in an emissions-focused approach, desirability above 0.9 could be achieved with isopropyl alcohol-diesel blends and high engine speeds, such as 2600–2700 rpm, with a 3.5 bar engine load, resulting in low emission values and high engine performance.

Research on optimization of engineering parameters of effective action distance of multi-stage alternating injection acid etching

This study calibrated the numerical simulation model by using indoor experiments to measure experimental data related to conductivity, acid rock reaction kinetics and acid fluid loss, and combined it with the numerical simulation model to calculate the multi-condition acid flow under multi-physics coupling . The effective action distance of the acid liquid is finally formed, which can be used at 200℃ and 250℃. The effective action distance of the acid liquid is related to the amount of acid liquid and the number of acid liquid injection stages. A set of quick diagrams that can be quickly applied to multi-stage Alternate injection process design and optimized workflow. After calculation, it is concluded that: for the 200℃ formation, it is recommended that the total amount of acid used in the level 3 injection process should be more than 400m3; if level 2 injection is used, taking into account both cost and benefit, the total acid amount of 300m3 can be close to the same amount of acid in level 3 The effective range of the injection. For the 250℃ formation, it is not recommended to use a solution with small liquid volume and multiple stages. If level 3 injection is used, it is recommended that the acid dosage be more than 500m3; if level 2 injection is used, taking into account both benefits and costs, it is recommended to use 400m3 acid liquid . Injection can obtain an effective distance similar to that of level 3 injection with the same amount of acid.

Research on the dynamic tensile characteristics and surface crack evolution of coal under impact loading

The tensile properties of coal under dynamic loading are important mechanical characteristics of coal and are highly important for controlling coal rock stability under impact loading conditions, selecting blasting engineering parameters, and studying the mechanism of rockburst disasters. To investigate the dynamic tensile failure process of coal subjected to impact loading, this study used high-speed photography and digital image correlation technology to capture the dynamic tensile failure of coal under impact loading. The dynamic tensile evolution was quantitatively analyzed from the beginning of coal sample being loaded to failure. The captured images of the coal were processed, and the fractal dimension was used to quantitatively describe the evolution of the coal surface cracks under impact loading. The following conclusions were drawn from the experimental results: (1) An empirical formula was established to describe the dynamic tensile strength characteristics of coal under different loading rates. (2) Under impact loading, the maximum strain of a Brazilian disc coal sample first appeared at the contact end between the sample and the incident rod. (3) Under impact loading, a Brazilian disc coal sample cracked from the center of the sample outward, and the crack subsequently extended toward both ends. The fractal dimension of the crack exhibited a power function relationship with time, and the variation range of the fractal dimension of the crack was 1.05–1.39.

Impact of injection pressure on a dual-fuel engine using acetylene gas and microalgae blends of chlorella protothecoides

The amount of fossil fuel usage in compression ignition (CI) engines is greatly reduced when biodiesel is used. The primary disadvantage of using biodiesel is that, due to its high viscosity, which causes fuels to remain unburned during the premixed combustion stage, leads to lower brake thermal efficiency (BTE). Gaseous fuels predominantly reducing emissions in CI engines due to its complete burning without leaving any carbon traces. Fuel injection pressure (FIP) is one of the factors which is influencing the combustion phase because they are used to optimize fuel particle atomization. The current study examines engine parameters for a dual fuel engine that runs on biodiesel blends made from 20 % methyl ester of chlorella protothecoides micro algae (B20MEOA) and acetylene gas under variable Fuel injection pressure (FIP) ranging from 200 bar to 240 bar with 10-bar steps. According to the experimental results, when 3 LPM of acetylene gas is supplied along with the intake air and B20MEOA supplied at a FIP of 240 bar, the emissions such as smoke opacity, hydrocarbon (HC), and carbon monoxide (CO) are reduced by 16.9 %, 8.3 %, and 15.4 %, respectively, whereas the oxides of nitrogen (NOx) increases by approximately 7 % when compared to B20MEOA alone operation.