Engine Size Research Articles

CFD Analysis into the Breakdown of Catamaran Resistance Based on the Original Formula by Insel and Molland

Over the last 40 years, the use of fast catamaran has progressively developed attributed to its unique characteristics on resistance and seakeeping. The advantages have also been applied to military vessels to gain both resistance and seakeeping benefits. The current study focuses on the resistance of catamaran warships to provide less resistance, therefore, the size of engine and emission of toxic gases to the atmosphere. The total resistance of a catamaran will be different from a monohull of equal displacement. There are several factors including viscous interference factors such as φ, which is introduced to take account of the pressure field change around the hull, σ takes account of the velocity augmentation between the two hulls and calculated from an integration of local frictional resistance over the wetted surface, and τ is the wave resistance interference factor change. Those resistance components were developed by Insel and Molland in the 1990s. The investigation discusses the derivation of those components numerically using Computational Fluid Dynamics (CFD) approach. The speeds (hence, the Froude numbers) are varied from 0.2 to 0.6 and the separations between the hulls (S/L) are made between 0.2 and 0.4 so the comparative purposes can be done against the classical work of Insel and Molland and other published data.

Automated Design Process of a Fixed Wing UAV Maximizing Endurance

Unmanned aerial vehicle (UAV) design necessitates significant effort in prototyping, testing, and design iterations. To reduce design time and improve wing performance, an automated design and optimization framework is proposed utilizing open-source software, including OpenVSP: VSPAERO & Parasite Drag Tool, XFOIL, and Python. This study presents a preliminary UAV wing design methodology, emphasizing weight estimation, drag analysis, stall prediction, and endurance optimization. The maximum takeoff weight of the UAV was calculated after estimating the empty weight using a linear regression from data from 20 existing similar UAVs. The wing and engine sizing were determined using the matching plot technique. A solver with low-fidelity models, combining the Vortex Lattice Method (VLM) and analytical expressions, was used to predict the drag coefficient and maximum lift coefficient of the designed wing. An optimization process using a genetic algorithm was applied to maximize endurance while satisfying requirements such as rate of climb, stall, and maximum speeds. The optimized wing was analyzed with computational fluid dynamics (CFD), and its aerodynamic characteristics were compared with those obtained using VLM and the suggested aerodynamic solver. According to the CFD results, the proposed aerodynamic solver estimated the drag coefficient at zero angle of attack with an error of 17.2% compared to 63.1% using the VLM classic method. The error on the maximum lift coefficient estimation was limited to 5.3%. In terms of optimization, the framework showed an increase in the endurance ratio of up to 2% compared to the Artificial Neural Network method coupled with XFLR5. The primary advantage of the suggested framework is the utilization of open-source software, giving a cost-effective and accessible solution for small and medium-sized startups to design and optimize UAVs to achieve mission objectives.

Heat Propagation Across Turbocharger Unit with Variable Turbine Inlet Temperature

Currently, issues about emission gains serious attention among the lawmakers and governments. Multiple regulation and enforcements have been applied to reduce the level of emission that released to the environment. Vehicle emissions have been identified as one of the major polluters thus forcing the carmakers to explore new technology to reduce the vehicle emissions. One of the technologies that have been preferred by the carmakers is the applications of forced induction system to the internal combustion engine. By using the forced induction system where currently turbocharger system is more often used compared to supercharger, two benefits can be achieved which are improvements of engine efficiency and engine size reduction. However, the ability of turbocharger unit to deliver compressed air can be affected by the amount of heat that travels along the turbocharger unit itself. In this paper, the effect of heat that originated from the exhaust gas that enters the turbine inlet towards the turbocharger performance was measured and analysed. An automotive turbocharger unit manufactured by Garrett Turbocharger model GT2056 was used in the study. The rotational speed is set between 20 000 rpm to 70 000 rpm and the temperature at turbine inlet is set between 40℃ to 100℃. The parameters that measured are the temperature difference at internal turbine, internal bearing temperature difference, internal compressor temperature difference, turbine casing temperature difference, bearing housing temperature difference and compressor housing temperature difference. From the results obtained, it can be observed that the heat travels from turbine towards the compressor side through the conduction between the contacting parts between the turbine casing, bearing housing and lastly at compressor casing. The heat that arrives at the compressor side through the casing will give small effect to the air mass flow that delivered by the compressor.

Research on Dimension Reduction Method for Combustion Chamber Structure Parameters of Wankel Engine Based on Active Subspace

The combustion chamber structure of a rotary engine involves a combination of interacting parameters that are simultaneously constrained by engine size, compression ratio, machining, and strength. It is more difficult to study the weight of the effect of the combustion chamber structure on the engine performance using traditional linear methods, and it is not possible to find the combination of structural parameters that has the greatest effect on the engine performance under the constraints. This makes it impossible to optimize the combustion chamber structure of a rotary engine by focusing on important structural parameters; it can only be optimized based on all structural parameters. In order to solve the above problems, this paper proposes a method of dimensionality reduction for the structural parameters of a combustion chamber based on active subspace and combining a probability box and the EDF (Empirical Distribution Function). This method uses engine performance indexes such as explosion pressure, maximum cylinder temperature, and indicated average effective pressure as the influence proportion analysis targets and quantitatively analyzes the influence proportion of combustion chamber structure parameters on engine performance. Eight main structural parameters with an influence of more than 85% on the engine performance indexes were obtained, on the basis of which three important structural parameters with an influence of more than 45% on the engine performance indexes and three adjustable structural parameters with an influence of less than 15% on the engine performance indexes were determined. This quantitative analysis work provides an optimization direction for the further optimization of the combustion chamber structure in the future.

Measuring, interpreting and monitoring economic efficiency in South Australia’s Spencer Gulf and West Coast Prawn Fisheries

Fishers and fishing vessels are not homogeneous, with their level of catch affected by the choice of physical inputs such as engine size, boat size and the type of fishing technology employed, as well as less tangible factors such as skipper skill and experience. Economic output also vitally depends upon the value of the product in the market and the cost of inputs. These differences in the ability of individual fishers to catch fish, and create economic returns, can be assessed through the application of efficiency analysis. This paper provides an empirical examination of efficiency indicators in the South Australian Spencer Gulf and West Coast Prawn Fisheries. Applying data envelopment analysis (DEA) to a unique vessel-level dataset that includes both quantity and value information, we estimate measures of efficiency in these input-controlled fisheries where effort decisions are made in real-time using a participative management approach. We find that quantity and value-based efficiency measures are relatively high but that the variability of value-based measures is increasing. This increasing heterogeneity in value, despite the homogeneity in quantities, has potential implications for pressures on cooperative management going forwards as fishers pursue different business strategies. Comparing the measures of efficiency to direct measures of individual profit, we find a divergence: technical and cost measures of efficiency are more strongly positively related to short-run economic performance while revenue-efficiency is more strongly positively correlated with long-run economic performance, suggesting that different efficiency metrics may be required for short- and long-run monitoring.

Car Price Prediction Using Enhanced Technique

The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user‘s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user‘s inputs. The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user‘s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user‘s inputs.

Comparisons between full molecular dynamics simulation and Zhang's multiscale scheme for nanochannel flows.

In a very small surface separation, the fluid flow is actually multiscale consisting of both the molecular scale non-continuum adsorbed layer flow and the intermediate macroscopic continuum fluid flow. Classical simulation of this flow often takes over large computational source and is not affordable owing to using molecular dynamics simulation (MDS) to model the adsorbed layer flow, if the flow field size is on the engineering size scale such as of 0.01-10mm or even bigger like occurring in micro or macro hydrodynamic bearings. The present paper uses full MDS to validate Zhang's multiscale flow model, which yields the closed-form explicit flow equations respectively for the adsorbed layer flow and the intermediate continuum fluid flow. Here, full MDS was carried out for the pressure-driven flow of methane in the nano slit pore made of silicon respectively with the channel heights 5.79nm, 11.57nm, and 17.36nm. According to the number density distribution, the flow areas were respectively discriminated as the adsorbed layer zone and the intermediate fluid zone. The values of the characteristic parameters for Zhang's multiscale scheme were extracted from full MDS and input to Zhang's multiscale flow equations respectively for calculating the flow velocity profile and the volume flow rates of the adsorbed layers and the intermediate fluid. It was found that for these three channel heights, the flow velocity profiles calculated from Zhang's model approximate those calculated from full MDS, while the total flow rates through the channel calculated from Zhang's model are close to those calculated from full MDS. The accuracy of Zhang's multiscale flow model is improved with the increase of the channel height. The recent modification of the optimized potential for liquid simulation (MOPLS) model was used to calculate the interaction force between two methane molecules. In order to calculate the interaction force between the wall atoms and the methane molecules accurately, our previous non-equilibrium multiscale MDS was used. The interaction forces between the methane molecule and the wall atom were obtained from the coupled potential function by the L-B mixing rule when the fluid molecules arrived at near wall. The methane molecule diameter was obtained from the radial distribution function by using equilibrium MDS under the same initial conditions. The local viscosities across the adsorbed layer were obtained from the local velocity profile by using the Poiseuille flow method. The motion equation of the methane molecule was solved by the leapfrog method. The temperature of the simulation system was checked by Bhadauria's method, i.e., the system temperature was rectified by the velocities in the y- and z-directions. The flow velocity distributions across the channel height and the volume flow rates through the channel were also calculated from Zhang's closed-form explicit flow equations respectively for the adsorbed layer flow and the intermediate fluid flow. The results respectively obtained from full MDS and Zhang's multiscale flow equations were then compared.

Modeling of an Adaptive HHO Gas Controller Based on Fuzzy Logic and Polynomial Function Controls to Improve Engine Torque of Gasoline Engine

Typical hydrogen-hydrogen-oxygen gas (HHO) usage to improve gasoline engine performance refers to the fixed HHO flow rate method, providing 0.25‒0.5 liters/minute of HHO for every 1000 cc engine size. However, the arising hypothesis expresses that the fixed HHO flow rate method does not optimally improve engine torque for various loads. The research objective is to propose an adaptive HHO gas controller that manages the HHO generator to produce an appropriate HHO flow rate for engine operation by adapting to load and engine speed variations. Hence, the engine torque improvement optimally occurs for various loads. The adaptive HHO controller combines fuzzy logic and polynomial function controls involving real-time engine data, such as mass airflow (MAF), air-fuel ratio (AFR), and the commanded AFR from the engine control unit (ECU), whose values vary with load and engine speed. A system simulation based on Matlab-Simulink investigates engine performance improvement due to the controller. The results show that the adaptive HHO flow rate due to the proposed adaptive HHO controller improves engine torque during small, medium, and big-loaded engine operations, respectively, by 1.5%‒4.7%, 6.8%‒26.8%, and 21.1%‒72.8% depending on engine speed 2500 rpm‒4000 rpm) and the commanded AFR (12.6‒15.4). Conversely, under the same condition, the fixed HHO flow rate with 0.75 liters/minute HHO for a 1500 cc engine, used for comparison, improves the engine torque by 0%‒0.6%, 0.3%‒14.2%, and 9.3%‒50.1%, respectively. The data show that the adaptive HHO controller provides better improvement. Moreover, the adaptive HHO controller improves engine thermal efficiency and reduces AFR error against the commanded AFR.

Techno-economic assessment for the practicability of on-board CO2 capture in ICE vehicles

The transport sector is a major energy-intensive and significant contributor to CO2 emissions. With heavy-duty internal combustion engine vehicles (HD-ICEVs) set to remain a primary mode of transport for goods and passengers, the European Union plans to implement CO2 emission rights starting in 2027 to address this issue. To mitigate CO2 emissions, on-board carbon capture technologies can be an innovative solution, but a comprehensive analysis from several perspectives must be carried out. To tackle the existing knowledge gap on this subject, this paper presents the techno-economic assessment of a CO2 capture and storage system hybridised with an organic Rankine cycle (CCS-ORC) integrated into an HD-ICEV whose technical ability has been previously demonstrated. In this paper, the capture installation based on temperature swing adsorption is designed for two engines with different displacement volumes, at varying carbon capture rates (CCR), and using three different sorbents. The size of the thermal devices is estimated as the determinant factor for the required space and the installation costs. The results show that the heat exchangers can achieve a minimum area density of 100 m2/m3, while the most significant temperature swing adsorption device obtained is scarcely 0.26 m3. The carbon abatement cost (CAC) obtained for the CCS-ORC system is less than 35 €/tCO2 at 100 % of CCR, and with an engine size greater than 18 and 21 L, the CAC of the CCS-ORC system is zero at 100 and 70 % of CCR, respectively. Furthermore, with a CO2 emissions right price above 71 €/tCO2, the projected payback of the initial investment of the CCS-ORC system is achieved in the lifespan of the HD-ICEV for all sorbents evaluated at 100 % of CCR.

Effects of the droplet size and engine size on two-phase kerosene/air rotating detonation engines in flight operation conditions

The numerical and experimental research of rotating detonation engines is usually conducted in ground environmental conditions. Although many breakthroughs have been made, there are still deficiencies in understanding engines operating in real flight conditions. In order to reveal the effect of engine size and initial kerosene droplet size on the rotating detonation engines in flight conditions, the Eulerian-Lagrangian model is adopted, and a series of two-phase kerosene/air rotating detonation cases are simulated in the conditions of Mach 5 and 24 km altitude. When 5 μm and 10 μm droplets are adopted, the RDE behaves like gaseous rotating detonations with clear cellular structures. When the 20 μm and 30 μm droplets are adopted, the rotating detonation waves tend to be divided into two layers and form the λ-shaped shock structure. The results indicate that droplet size and engine size influence detonation wave propagation and flow field mainly through droplet heat absorption and evaporation height. Increasing the engine sizes can promote the two-phase rotating detonation and broaden the initial diameter range capable of obtaining rotating detonation waves. However, as droplet size increases, the fresh mixture layer becomes stratified, with an upper layer predominantly comprising fuel vapor and a lower layer of fuel droplets, which results in an λ-shaped shock structure at the detonation front. The flight operation condition brings in differences in features such as detonation wave height and velocity deficit. The detonation height in flight conditions is higher than that in ground conditions. The most velocity deficit in flight conditions observed in our study is below 15 %, which is lower than the results (22.5 %) in ground operating conditions. These results indicate that the optimal design of the rotating detonation engine must consider the real operation conditions and the effect of engine sizes and droplet sizes.

Seven Things to Know About Exercise Classification With Inertial Sensing Wearables.

Exercise monitoring with low-cost wearables could improve the efficacy of remote physical-therapy prescriptions by tracking compliance and informing the delivery of tailored feedback. While a multitude of commercial wearables can detect activities of daily life, such as walking and running, they cannot accurately detect physical-therapy exercises. The goal of this study was to build open-source classifiers for remote physical-therapy monitoring and provide insight on how data collection choices may impact classifier performance. We trained and evaluated multi-class classifiers using data from 19 healthy adults who performed 37 exercises while wearing 10 inertial measurement units (IMUs) on the chest, pelvis, wrists, thighs, shanks, and feet. We investigated the effect of sensor density, location, type, sampling frequency, output granularity, feature engineering, and training-data size on exercise-classification performance. Exercise groups (n = 10) could be classified with 96% accuracy using a set of 10 IMUs and with 89% accuracy using a single pelvis-worn IMU. Multiple sensor modalities (i.e., accelerometers and gyroscopes), high sampling frequencies, and more data from the same population did not improve model performance, but in the future data from diverse populations and better feature engineering could. Given the growing demand for exercise monitoring systems, our sensitivity analyses, along with open-source tools and data, should reduce barriers for product developers, who are balancing accuracy with product formfactor, and increase transparency and trust in clinicians and patients.

Gas engine CCHP system optimization: An energy, exergy, economic, and environment analysis and optimization based on developed northern goshawk optimization algorithm

This paper aims to enhance the design and operation of a Combined Cooling, Heating, and Power (CCHP) system utilizing a gas engine as the primary energy source for a residential building in China. An Energy, Exergy, Economic, and Environment (4E) analysis is employed to assess the system's performance and impact based on energy, exergy, economic, and environmental criteria. The effectiveness of the DNGO algorithm is evaluated on a case study site and compared with Northern Goshawk Optimization (NGO) and Genetic Algorithm (GA). The findings demonstrate that the DNGO algorithm identifies the optimal gas engine size of 130 kW. The algorithm's search capabilities are greatly enhanced by this unique blend, surpassing what traditional methods can offer. The DNGO algorithm brings several advantages, including unparalleled energy efficiency, reduced exergy destruction, and a substantial decrease in CO2 emissions. This not only supports environmental sustainability but also aligns with global standards. Economically, the algorithm enhances the performance of the CCHP system, evident through a reduced payback period and increased annual profit. Additionally, the algorithm's rapid convergence rate allows it to reach the optimal solution faster than its counterparts, making it advantageous for time-sensitive applications. Incorporating innovative methods like chaos theory, the DNGO algorithm effectively avoids local optima, enabling a broader search for the best solution. The utilization of Lévy flight further enhances the algorithm's ability to escape local optima and navigate the search space more efficiently. Additionally, swarm intelligence is employed to simulate the collective behavior of decentralized systems, aiding in problem-solving. This research represents a significant advancement in optimization techniques for CCHP systems and offers a fresh perspective to the field of swarm-based optimization algorithms.

Calibrating the Livengood–Wu integral knock model for differently sized methanol engines

Experimental test campaigns have begun to demonstrate the potential of methanol as an alternative fuel for heavy-duty spark-ignited engines. However, there is no consensus yet on the scope of this solution in terms of maximum power and engine size. A zero-dimensional combustion model is therefore being developed outside the scope of this work. Its main objective will be to predict key performance parameters such as power and efficiency as function of engine size. Due to the high loads typically encountered in heavy-duty engines, knock will be the main constraint to maximize the engine's potential. This work therefore aims to find an accurate knock model that can be implemented in the modelling framework. The Livengood–Wu knock integral model is being considered as a good candidate, as it is computationally inexpensive and thus allows for a large number of engine configurations to be modelled within a reasonable time. Due to a lack of autoignition delay times of methanol at conditions relevant to heavy-duty engines, a large database was created using chemical kinetics calculations. A neural network model was trained with the tabulated data for fast data retrieval. To validate whether the knock integral approach is robust enough to be applied to a wide range of engine sizes, a calibration constant was added to match the knock predictions to experimental data. Its value was calculated for three different engines, a light and heavy-duty SI engine and a large-bore dual-fuel engine. They highlight a remarkable difference in calibration constant across the different engines investigated.

A Forecast of CO2 Emissions Based on the Fuel Consumption Rating of a Particular Vehicle

The accumulation of carbon dioxide will not only cause global warming but also cause a series of catastrophic consequences and cause irreversible damage to the earth's ecology. The prediction of carbon dioxide emissions from light-duty vehicles can provide decision-making support for consumers when purchasing cars and is beneficial to consumers in purchasing environmentally friendly cars. This paper constructs a vehicle carbon dioxide emission model based on a multiple linear regression model, using the four indicators of vehicle engine size, vehicle fuel consumption, Hwy (L/100km), and Comb (L/100km) as output variables. The quantity is the predictor variable. The correlation coefficient of this study is 0.9675. The regression equation of this linear regression model is Y=22.2344+2.7157X1+32.9843X2+26.6259X3-39.1429X4. The order of impact of the four indicators studied on carbon emissions is Comb (L/100km), vehicle fuel consumption, Hwy (L/100km), and vehicle engine size. The determination of carbon emissions of specific models of new light vehicles can refer to the multiple linear regression model proposed in this study.

Review of Organic Rankine Cycles for Internal Combustion Engine Waste Heat Recovery: Latest Decade in Review

The last decade (2013–2023) was the most prolific period of organic Rankine cycle (ORC) research in history in terms of both publications and citations. This article provides a detailed review of the broad and voluminous collection of recent internal combustion engine (ICE) waste heat recovery (WHR) studies, serving as a necessary follow-on to the author’s 2013 review. Research efforts have targeted diverse applications (e.g., vehicular, stationary, and building-based), and it spans the full gamut of engine sizes and fuels. Furthermore, cycle configurations extend far beyond basic ORC and regenerative ORC, particularly with supercritical, trilateral, and multi-loop ORCs. Significant attention has been garnered by fourth-generation refrigerants like HFOs (hydrofluoroolefins), HFEs (hydrofluoroethers), natural refrigerants, and zeotropic mixtures, as research has migrated away from the popular HFC-245fa (hydrofluorocarbon). Performance-wise, the period was marked by a growing recognition of the diminished performance of physical systems under dynamic source conditions, especially compared to steady-state simulations. Through advancements in system control, especially using improved model predictive controllers, dynamics-based losses have been significantly reduced. Regarding practically minded investigations, research efforts have ameliorated working fluid flammability risks, limited thermal degradation, and pursued cost savings. State-of-the-art system designs and operational targets have emerged through increasingly sophisticated optimization efforts, with some studies leveraging “big data” and artificial intelligence. Major programs like SuperTruck II have further established the ongoing challenges of simultaneously meeting cost, size, and performance goals; however, off-the-shelf organic Rankine cycle systems are available today for engine waste heat recovery, signaling initial market penetration. Continuing forward, next-generation engines can be designed specifically as topping cycles for an organic Rankine (bottoming) cycle, with both power sources integrated into advanced hybrid drivetrains.

Exploring Mega‐Nourishment Interventions Using Long Short‐Term Memory (LSTM) Models and the Sand Engine Surface MATLAB Framework

AbstractCoastal protection is of paramount importance because erosion and flooding affect millions of people living along the coast and can largely influence countries' economy. The implementation of nature‐based solutions for coastal protection, such as sand engines, has become more popular due to these interventions' adaptability to climate change. This study explores synergies between Artificial Intelligence (AI) and hydro‐morphodynamic models for the creation of efficient decision‐making tools for the choice of optimal sand engines configurations. Specifically, we investigate the use of long‐short‐term memory (LSTM) models as predictive tools for the morphological evolution of sand engines. We developed different LSTM models to predict time series of bathymetric changes across the sand engine as well as the time‐decline in the sand engine volume as a function of external forces and intervention size. Finally, a MATLAB framework was developed to return LSTM model results based on users' inputs about sand engine size and external forcings.

Vibrational Dynamics of Rice Precision Hole Seeders and Their Impact on Seed Dispensation Efficacy

This investigation considered the effects of both internal and external excitation vibrations on the efficacy of the seed dispenser in a rice precision hole seeder. Through comprehensive field tests, we analyzed vibrational characteristics during direct seeder operations and established a vibration seeding test bed for systematic examination of these effects. Time-domain analysis of the vibration data revealed a predominantly vertical vibration direction, with notably higher levels in sandy loam soil compared to clay loam. A correlation was observed between increased engine size and rotary ploughing speeds, as well as forward speed and elevated vibration amplitudes. Frequency domain analysis pinpointed the primary vibration frequency of the machinery within the 0–170 Hz range, remaining consistent across different operating conditions. Crucially, bench test results indicated that seeding accuracy and dispersion were significantly influenced by vibration frequencies, particularly within the 70–130 Hz range, where a decrease in accuracy and increase in dispersion were noted. A regression model suggested a complex, non-linear relationship between seeding performance and vibration frequency. These insights highlight the necessity for a robust mechanism to effectively address these vibrational impacts. This study paves the way for enhancing the operational efficiency of the rice precision hole seeder, aiming to achieve the design goals of minimized vibrations in the paddy power chassis.

Performance Analysis of a Waste Heat Recovery System for a Biogas Engine Using Waste Resources in an Industrial Complex

To achieve carbon neutrality and address global energy supply issues by 2050, there is active progress in the industrial sector for waste energy recovery and commercialization projects. It is necessary to consider both the energy recovery efficiency and economic feasibility based on the production volume for the resource utilization of waste energy, along with eco-friendly processing methods. In this study, a waste heat recovery system was designed to recover a large amount of thermal energy from high-temperature exhaust gases of gas engines for power generation by using biogas produced from organic waste in industrial complexes. Types and sizes of components for a waste heat recovery system that were suitable for various engine sizes depending on biogas production were designed, and the energy recovery efficiency was analyzed. The waste heat recovery system consisted of a smoke tube boiler that generated superheated steam at 161 °C under 490 kPa of pressure from the exhaust gas as the heat source, along with two economizers for heating both supply water and hot water. Heat exchangers that were suitable for three different engine sizes were configured, and their performance and energy flow were calculated. In particular, when operating two engines with a power output of 100 kW, the boiler showed the highest steam production efficiency, and the superheated steam production from high-temperature exhaust gas at 600 °C was designed to be 191 kg/h, while hot water at 58 °C was designed to be produced at 1000 kg/h. In addition, further research on the heat exchanger capacity ratio confirmed that it was within a certain range despite the difference in heat exchanger capacity and efficiency depending on the engine size. It was confirmed that the heat exchange capacity ratio of the boiler was important as an optimal-capacity design value for the entire system, as it ranged from 46% to 47% of the total heat exchanger size.

Corona-heated Accretion-disk Reprocessing: Frequency-resolved Lag Predictions for UV/Optical Reverberation Mapping of Active Galactic Nuclei

Continuum reverberation mapping with high-cadence, long-term UV/optical monitoring of active galactic nuclei (AGNs) enables us to resolve the AGN central engine sizes on different timescales. The frequency-resolved time lags of NGC 5548 (the target for the AGN STORM I campaign) are inconsistent with the X-ray reprocessing of the classical Shakura–Sunyaev disk model. Here we show that the frequency-resolved time lags in NGC 5548 can be well produced by the Corona-Heated Accretion-disk Reprocessing (CHAR) model. Moreover, we make CHAR model predictions of the frequency-resolved time lags for Mrk 817, the source of the AGN STORM II campaign. We also obtain frequency-resolved time lags as a function of the BH mass and Eddington ratio, which is valid for BH masses from 106.5 to 109 M ⊙ and Eddington ratios from 0.01 to 1. Moreover, we demonstrate that with the time spans of current continuum reverberation-mapping campaigns, the lag–luminosity relation of the CHAR model can be τgz∝L51000.55±0.04 , which is consistent with observations. Future observations can test our results and shed new light on resolving the AGN central engine.

Application of Deep Learning Initiatives for CO2 Emissions Forecasting

This work models and forecasts vehicle CO2 emissions, a major source of atmospheric changes and climate disruptions, using cutting-edge artificial intelligence. The CO2 emission by vehicle dataset from Kaggle, which includes several features such vehicle class, engine size, cylinder transmission, fuel type, fuel consumption, city, highway, comb, and CO2 emissions, was used to build the model. To predict CO2 emissions, a hybrid model (CNN-LSTM-MLP) was developed based on long short-term memory network (LSTM), convolution neural network (CNN), and multi-layer perceptron (MLP). The proposed model shows superior results compared with CNN, MLP, LSTM, Light Gradient Boosting Machine (LGBM) Regressor, support vector machine (SVM), Linear Regression, and Random Forest.