Processing Generation Research Articles

Effect of ignition timing on the combustion process of a port injection free piston linear generator: A system level multi-physics coupling method

Free piston linear generators (FPLGs) have the advantages of high power density, high compactness and flexible compression ratio, rendering FPLGs as potential power sources in pure electric range extension, hybrid power and various outdoor power fields. In the present study, the influence of ignition timing on the performance of a dual-piston dual-cylinder free-piston engine generator was experimentally investigated. A FPLG multi-physics coupled simulation-experimental bench was specially developed, and the effect of ignition timing on the engine performance was investigated through the three-dimensional numerical simulation method. The simulation results showed that with the increase of spark advance, both the peak in-cylinder pressure and the heat release rate raised, and the maximum peak pressure could reach over 30 bar under given scenarios. With the increase of ignition timing, the start of combustion was significantly advanced, and the maximum advance in combustion initiation was about 19°ECA over the investigated engine speeds. When the ignition timing ranges were −35°ECA ∼ -20°ECA, the combustion durations were roughly varied between 20°ECA ∼ 30°ECA. At the specified engine speed, when the ignition timing was earlier than − 20°ECA, the indicated power and indicated thermal efficiency were not sensitive to ignition timing. As the engine speed and spark advance increased,the NOx mass fraction in the combustion process were raised. The heat transfer loss decreased with the retarding of the ignition timing, and it increased as the engine speed decreased.

It's better when I see it: Students benefit more from open‐book than closed‐book teaching

AbstractTeaching previously learned contents to (fictitious) peers is regarded as a beneficial activity that aids learning. However, it is still an open question which cognitive mechanism (generating versus retrieving information) account for this learning‐by‐teaching effect. To examine the role of retrieval during generative processing while learning‐by‐teaching, we conducted an experiment. After a learning phase about the human respiratory system, university students (N = 108) either taught the learned contents with (open‐book teaching) or without (closed‐book teaching) access to the learning material. Students in the control group restudied the contents. Results indicated that teaching was not more beneficial than restudying regarding students' learning outcome (i.e., retention, drawing knowledge, transfer knowledge). However, open‐book teaching was more effective than closed‐book teaching regarding students' retention. This effect was mediated by the richness (i.e., number of concepts) and completeness of students' explanations. The findings suggest that retrieval failure may constrain potential effects of closed‐book explaining.

Socio-affective stimulation of primary school students

In the post-pandemic context, the growing level of complexity of Cuban social and educational environments, as well as the low perception of risk with minors from home, generated the need for educational practices, although they are based on the principle of the unity of the cognitive with the affective, to ponder the socio-affective content in the formative process of the new generations. Faced with this challenge, the objective of this research was declared: to favor the socio-affective stimulation of primary school students from a sociopedagogical conception. The diagnosis carried out with a mixed approach revealed cognitive and procedural deficiencies in the socio-affective stimulation of primary schoolchildren, which led to the structuring of a sociopedagogical proposal. It was based on the theoretical methods inductive-deductive, analytical-synthetic, historical-logical and systemic-structural-functional. It had a flexible, participatory and contextualized vision, whose feasibility to enter the educational praxis was corroborated by the criterion method of specialists and the user method.

Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression

Electric bus energy consumption prediction is conducive to bus route scheduling and charging optimization management. However, existing prediction models do not comprehensively consider vehicle parameters, auxiliary power, speed profile and external factors. In particular, there is a lack of in-depth research on generating speed profile based on low-resolution data for electric bus. The aim of this study involves predicting bus trip-level energy consumption. Thus, the temperature effect on powertrain efficiency and auxiliary power was first analyzed, and then a Markov-based speed profile generation method considering the speed state transition spatiotemporal characteristics and driving style was developed. Reliable process-related variables extracted from the aforementioned model development in conjunction with external-related variables are used as the input of Markov-based Gaussian processing regression model (M-GPR). Finally, 7919 regular bus driving samples on the same route were extracted from the raw bus operation dataset to verify the model effectiveness in improving the prediction accuracy of trip-level energy consumption. The results indicate that M-GPR exhibits a better prediction performance with an average prediction error of 8.69%, which decreases by 0.62%–5.41% compared with other models. Furthermore, M-GPR presents wide applicability, with average errors between 7.01% and 9.90% in different operating scenarios.

Recovery of Fluoride-Rich and Silica-Rich Wastewaters as Valuable Resources: A Resource Capture Ultrafiltration-Bipolar Membrane Electrodialysis-Based Closed-Loop Process.

Traditional technologies such as precipitation and coagulation have been adopted for fluoride-rich and silica-rich wastewater treatment, respectively, but waste solid generation and low wastewater processing efficiency are still the looming concern. Efficient resource recovery technologies for different wastewater treatments are scarce for environment and industry sustainability. Herein, a resource capture ultrafiltration-bipolar membrane electrodialysis (RCUF-BMED) system was designed into a closed-loop process for simultaneous capture and recovery of fluoride and silica as sodium silicofluoride (Na2SiF6) from mixed fluoride-rich and silica-rich wastewaters, as well as achieving zero liquid discharge. This RCUF-BMED system comprised two key parts: (1) capture of fluoride and silica from two wastewaters using acid, and recovery of the Na2SiF6 using base by UF and (2) UF permeate conversion for acid/base and freshwater generation by BMED. With the optimized RCUF-BMED system, fluoride and silica can be selectively captured from wastewater with removal efficiencies higher than 99%. The Na2SiF6 recovery was around 72% with a high purity of 99.1%. The aging and cyclic experiments demonstrated the high stability and recyclability of the RCUF-BMED system. This RCUF-BMED system has successfully achieved the conversion of toxic fluoride and silica into valuable Na2SiF6 from mixed wastewaters, which shows great application potential in the industry-resource-environment nexus.

PERANCANGAN PROYEK LOW RISE APARTMENT/FLAT DENGAN PENDALAMAN GREEN DESIGN OLEH KONSULTAN E STUDIO

Population growth that continues to increase in the midst of limited land is a paradoxical essential problem in facing the development of the world of modern architectural and interior design today. This condition will lead to a negative impact on the environment, thus making the existence of these three problems very crucial to be responded to in an architectural and interior design strategy that is concerned about the environment. E Studio as an architectural and interior consulting company that focuses on implementing the principles of building design that is functional, environmentally friendly (green design), and has character (ambiance) is the answer in its active contribution to responding to these existing problems, i.e., limited land, environmental damage, and the density of the population that is indicated to occur in the current modern era. The method used for design exploration uses the concept of programmatic form processing with a green design approach to answering the problems and needs of the design users. Bringing up one theme, PARADOX, to realize that the presence of a building can essentially make it non-contradictory to the surrounding natural environment. The application of green design is applied to the processing of building mass generation, optimization of natural lighting and ventilation, concern about user health and comfort, and materials selection that can be recycled to reduce project waste in the future.
 Keywords: Architecture, E Studio, Green Design, Interior, Low Rise Apartment

Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil.

Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013–2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (Rsum), mean temperature (Tmean), mean relative humidity (RHmean), and mean normalized difference vegetation index (NDVImean). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.

A Review on Biodiesel Production from Various Feedstocks by Transesterification

Since fossil fuel emissions will continue indefinitely, we must find a suitable and long-term alternative, owing to the fact that it is biodegradable, non-toxic, and eco-friendly, biodiesel an excellent substitute for diesel engines. EASAC classifies the evolution of biodiesel into four generations. Biodiesel feedstocks and their advantages and disadvantages for different generations of the fuel are thoroughly analysed in this article. An in-depth investigation is provided in this article, of the benefits and drawbacks of various feedstocks used in the manufacturing process of different generations of biodiesel.In terms of the production of biodiesel, transesterification is the best method because it produces high-yield biodiesel with comparable properties to diesel, making it an ideal choice. As far as economics are concerned, this process is also viable. It is possible to meet the energy requirements of the future by blending different oil feedstocks. The system used and the cost of feedstock have the most significant impact on the cost of biodiesel production. Characteristics of biodiesel such as the oxidation stability, cold flow and cetane number, viscosity, and density, are some of the most important characteristics of biodiesel. Biodiesel’s performance in diesel engines was also discussed in this paper, and it was suggested that biodiesel is safer for the environment than Petro-diesel. Unlike Petro-diesel, it degrades four times faster and has with a higher flash point, making storage and handling easier. It’s also nontoxic and causes less irritation to the skin than soap and water. The paper also looked at the production of biodiesel using feedstocks from the first through the fourth generation.

A coupling optimization method of production scheduling and computation offloading for intelligent workshops with cloud-edge-terminal architecture

With the rapid development and application of Industrial Internet of Things (IIoT) technology, the amount of data generated during the operations of intelligent workshops shows an explosive growth trend. Real-time analysis and processing of these data is the premise of rapid intelligent decision-making. Therefore, the cloud-edge-terminal (CET) architecture is usually adopted in manufacturing sites, and the overall delay of data processing can be reduced by designing a reasonable offloading strategy for computing tasks. In general, computing tasks are generated by demand-driven monitoring, measurement, analysis, and decision-making during the executions of production jobs. There is a strong coupling relationship between them, and the scheduling scheme of production jobs largely determines the generation sequence and processing requirements of computing tasks. However, the current optimization methods for production scheduling and computation offloading are carried out separately to pursue their own goals, resulting in that the balance between production efficiency and computing delay cannot be achieved. To this end, this paper proposes a Production scheduling-Computation offloading Coupling Optimization (PCCO) model for intelligent workshops with CET architecture for the first time, which takes the maximum completion time of production jobs and the minimum total offloading delay time of computing tasks as dual optimization objectives. Then a four-layer coding method is designed and an enhanced multi-population multi-objective whale optimization algorithm (EMMOWOA) is proposed to solve the PCCO model. In EMMOWOA, the arithmetic exploration mechanism is adopted to strengthen the exploration ability, and a new leader selection method is proposed to lead the search of population to avoid prematurely trapping into the local optimum; to avoid the loss of diversity, an adaptive multi-population strategy is adopted. A series of comparative experimental results based on multi-objective standard test functions show that the proposed EMMOWOA has better convergence and diversity than the newly proposed multi-objective optimization algorithms, which proves the effectiveness of the proposed algorithm improvement strategies. Finally, simulation cases and a practical production case prove that the proposed coupling optimization method can balance production efficiency and computing delay well, and better meet the optimization requirements of intelligent manufacturing sites.

Machining of high temperature heat resistant super alloys- a concise review on cooling aspects

Analyzing and comprehending cutting process mechanisms is a crucial step in creating a cost-effective, long-lasting, and safe machining process.In modern manufacturing, it is very challenging to achieve a high surface finish product with high dimensional accuracy. To reduce the high production cost as well as for the sustainable manufacturing selection of both cutting tool materials and cutting environments is necessary. Nowadays high-temperature alloys are in very demand in industries like power generation, gas turbines, and chemical processing. Superalloys are recognised as a significant problem in a high-temperature work environment with the sophisticated material technology in space and aviation industries. At the time of machining of these superalloys, a high amount of heat developed at the cutting area which affects the microstructure of the work specimen and also the cutting inserts. To minimize that excess heat, researchers and manufacturing industries are adopting different cooling lubrication techniques. When the lubricant penetrated the cutting region, the temperature is reduced since cooling effect. Several studies focus on the optimization of individual performance features in the machining processes and in recent past, Super-alloys and cutting materials like ceramic, carbide, and hybrid tools are gaining popularity. Since the application of the superalloy in various sectors is ever- increasing, therefore it is necessary to evaluate a machining behavior during the experimental investigation. In this contemporary review, the effects of various cooling and lubrication strategy on machining outputs will be discussed considering the sustainable and greenway of manufacturing.

Deep Canonical Correlation Analysis Using Sparsity-Constrained Optimization for Nonlinear Process Monitoring

This article proposes an efficient nonlinear process monitoring method (DCCA-SCO) by integrating canonical correlation analysis (CCA), deep autoencoder neural networks (DAENNs), and sparsity-constrained optimization (SCO). Specifically, DAENNs are first used to learn a nonlinear function automatically, which characterizes intrinsic features of the original process data. Then, the CCA is performed in that low-dimensional representation space to extract the most correlated variables. In addition, the SCO is imposed to reduce the redundancy of the hidden representation. Unlike other deep CCA methods, the DCCA-SCO provides a new nonlinear method that is able to learn a nonlinear mapping with a sparse prior. The validity of the proposed DCCA-SCO is extensively demonstrated on the benchmark Tennessee Eastman (TE) process and the diesel generator process. In particular, compared with the classical CCA, the fault detection rate is increased by 8.00% for the fault IDV(11) in the TE process.

Screw analysis and experimental study of spatial noncircular gear compound transmission.

Curve face gear pair and noncircular bevel gear pair are collectively referred to as spatial noncircular gear pair (SNCGP). Different from the traditional gear transmission with fixed shafts, spatial noncircular gear (SNCG) compound transmission can realize compound motion with variable transmission ratio between intersecting axes, and has a broad engineering application prospect. It includes two categories named speed reduction and speed increase. Based on the gear meshing theory, screw theory and calculus, a screw analysis method of SNCG compound transmission was proposed, which directly and comprehensively reflected the compound motion characteristics of rotation and axial movement. Firstly, a unified coordinate system was established. The screw geometric characteristics of compound motion were discussed, including instant screw axis, axode, pitch surface and its normal equidistant surface. The screw principle of compound motion was revealed. Then, according to this method, combined with the motion process of generator with different tooth profiles, the tooth surface of each SNCG was obtained. Finally, gear machining, tooth surface measurement, transmission ratio measurement and axial displacement measurement were carried out, which verified the correctness of this screw analysis method for SNCG compound transmission, and laid a foundation for further study and application.

Soil Pollution Assessment of a Gas Flare Site in the Niger Delta Region

Aim: The aim of this research was to investigate the level of pollutants deposition on soil generated from a gas flaring site.
 Study Design: The Soil Samples for this study were picked from two sets of locations (a proposed gas flare site situated at Ogbogu Gas plant and two control sites sfrom a primary source of local farm lands). A total number of eighteen samples were collected from the five different sampling spots as designated using the global positioning system (GPs).
 A contaminated Site and three farm lands within Ogbogu and Ebocha community were mapped at 20m, 50m, 100m, 500m & 700m from the gas flare site and mapped with GPs location at two different sampling depths (0-15 & 15-30) cm.
 A Control Site and two (2) farm lands within Idu Ekpeye community were mapped outside the area of concentration from the gas flare sites. These locations were chosen to determine by comparative assessment, the impact of gas flaring on the soil properties of the contaminated sites.
 Place and Duration of Study: This study was carried out in Ogba/Egbema/Ndoni Local Government Area of Rivers State. This area was chosen for this study because of the high volume of petroleum activities. Oil and gas processing, production, transportation and utilization for power generation occur daily in the area resulting in numerous cases of environmental degradation and pollution.
 Methodology: This research explores and presents a method of analysing the environmental impact of gas flaring in the Niger Delta so as to provide the data required for the complete analysis and evaluation of the various observed and noted health and environmental effects of gas flaring in Niger Delta. Different samples at various proximities at 200m, 500m, 1000m, 5000m and 7000m from the flare site using a hand aguar equipment sampled at depths of (0-15)cm and (15–30)cm were taken and measurements and experimentations were meticulously carried out.
 Results: The results obtained in this research show a marked trend as all the parameters considered indicated a gradient away from the flare points in all the flow stations such as soil pH changing from acidic (4.0-4.2) to near neutral (6.4-6.6) away from the flare points and the average low soil moisture content of (17% - 23%) as against 40% for the (10m and 20m) and control distance. The quantity of carbon emitted by these flares are about 2,525,000.00 tonnes of carbon per day. These values portray a bad omen for the affected communities.
 Conclusion: Contamination of agricultural soils by trace metals creates a serious risk of the introduction of these xenobiotics into human food chain. The soil fertility is being reduced as this study has revealed an increase in temperature along-side an increase in gas flaring and flare distances.
 This study recommends that gas flaring should be seen as a violent action against the people and that the flared gas should be channelled to meeting the ever increasing demand for energy in the industrial sector of the economy.

A cladding-pumping based power-scaled noise-like and dissipative soliton pulse fiber laser

We report a high-average-power noise-like pulse (NLP) and dissipative soliton (DS) pulse fiber laser. Average power as high as 4.8 W could be obtained at the fundamental mode-locked repetition rate. The NLP can also be transformed into a more powerful DS mode-locking state by optimizing the polarization and losses of intra-cavity pulses in the nonlinear polarization evolution regime. The operation mode between the NLP and DS can be switched, and the laser output performance in both modes has been studied. The main advantage of this work is switchable high-power operation between the NLP and DS. In comparison with conventional single-mode NLP fiber lasers, the multi-function high-power optical source will greatly push its application in supercontinuum generation, coherence tomography, and industrial processing.

Simulation of hydraulic power matching–based risk and economic evaluation among cascade hydropower stations in spot transactions

The electricity market is evolving rapidly from pre-bid to spot markets, where the transactions between producers and consumers have become crucial and are vulnerable to safety risks. In addition, the new modes of transaction are also becoming popular and are said to have some risks. To reveal the impact of the new electricity transaction mode—spot market—on the safety risk and generation benefit, this article presents a simulation method connecting the transaction results and operation process. To verify the effectiveness of the proposed method, an actual cascade hydropower station (CHS) in the Dadu River basin, Southwestern China, which comprises eight hydropower stations, is selected. Hydraulic power matching among the CHSs is discriminated from four aspects with multiple indicators combined with the reality of a hydropower-dominated market in Sichuan, China. The dispatching decision-making process of hydropower generators is described with a multi-objective optimization model and then solved with a fast search strategy based on the classical output calculation method in hydrology. The security risks and economic damage hidden in the day-ahead market on a certain day are discovered and analyzed. The technology proposed provides support for bidding decisions in spot transactions and satisfies the reality during the transition period of electricity reform.

Electrical and Optical Test of a Semi-Combustible Plasma Generator via a Plasma Ignition Simulator

Electrothermal-chemical (ETC) launch technology can considerably increase the ballistic performance and optimize the fire range and range overlap of a 155-mm unimodular charge howitzer. To discuss the plasma ignition process of a semi-combustible plasma generator (SCPG) with a special tubular semi-combustible medium, a plasma ignition simulator (PIS) for the 155-mm unimodular charge howitzer is designed and manufactured to provide a realistic chamber environment. The optical and electrical measurements of a hybrid arc controlled ablation (ACA) plasma are operated via several sensors, a high-speed digital camera, and a compact spectrometer. Test results show that the PIS can satisfy the design requirements of the research of the plasma ignition process. When the discharge voltage is 6.5 kV, hybrid plasma propagation in the simulator chamber is observed and detected. The electronic excitation temperature of the hybrid plasma is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 15$ </tex-math></inline-formula> 860 K, and the hybrid plasma propagation velocity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 1000$ </tex-math></inline-formula> m/s. These transient propagating hybrid plasmas are enough to ignite all unimodular charges in the chamber within 1.0 ms. The results provide an important reference for future ETC launch tests.

New trends in the ultrasonic imaging of concrete structures by means of 3D-FMC technology

The novel technologies in ultrasound generation and data processing offer new opportunities for three-dimensional imaging of concrete structures. Especially the Full-Matrix-Capture (FMC) technique with its real-time imaging capability can be successfully utilized for quality assurance in the construction industry. Although, the available testing instruments on the market implement the principle of linear transducer array with its two-dimensional reconstruction of B-Scan images based on the Synthetic Aperture Focusing Technique (SAFT) principle. The Dry-Point-Contact (DPC) transducers utilized in commercially available instruments for concrete testing with their matrix-like layout offer a direct opportunity to implement a three-dimensional Full-Matrix-Capture (FMC) data acquisition cycle. The obtained data can be in real-time processed by 3D-TFM reconstruction and visualized and evaluated three-dimensionally. In the present contribution both state-of-the-art and novel true-3D imaging approaches and data assessment are considered on real concrete inspection objects and the advantages of enhanced data processing with respect to improved information content and easiness of result, interpretation is discussed.

Optimal distribution of heat exchanger area for maximum efficient power of thermoelectric generators

Based on the multi-element thermoelectric generator model established in the previous literature, finite-time thermodynamic theory is used to deduce the efficient power expression of the thermoelectric generator. For a fixed number of thermoelectric elements and total heat exchanger area, the maximum efficient power is obtained by optimizing heat exchangers area distribution and output current. The effects of the number of thermoelectric elements and the total heat exchanger area on the maximum efficient power are analyzed. The results show that there is an optimal heat exchangers area distribution and an optimal output current to maximize the efficient power of the thermoelectric generator. Thermal efficiency at maximum efficient power is always better than at maximum power output. The total heat exchanger area is increased from 0.03 m2 to 0.09 m2, the maximum efficient power is increased from 0.02 W to 0.26 W, and the corresponding optimal output current and optimal heat exchangers area distribution are increased by 138% and 6.7%, respectively. In the actual designing process of the thermoelectric generator, the influences of the number of thermoelectric elements, the total heat exchanger area, the output current and the heat exchangers area distribution on its performance should be considered.

The Promise and Pitfalls of Learning in Immersive Virtual Reality

Can immersive virtual reality (IVR) serve as an effective venue for learning and training? The promise of learning in IVR lies in its affordances for motivating learners to engage in generative processing (i.e., cognitive processing aimed at making sense of the material). The pitfall of learning in IVR is that it can distract learners so they engage in extraneous processing (i.e., cognitive processing that does not support the instructional goal). This paper reviews (1) media comparison research we have conducted on the effectiveness of learning academic content and skills in IVR versus learning with conventional media and (2) value-added research we have conducted concerning which features can improve the instructional effectiveness of learning in IVR. The paper includes implications for practice and for further work in the area. Overall, the paper focuses on the challenges associated with determining how to reduce the distracting aspects of IVR, maintain the motivating aspects of IVR, and guide the learner towards the core instructional material.

Intergenerational transmission of historical memory of volcanic risk in Mexico

Collective memory is built on concepts and meanings that a population shares and builds over time. These contents are perpetuated through oral transmission from generation to generation. In the case of populations exposed to volcanic risk, coexistence with the volcano is part of their history and daily life. The present study focuses on understanding the meanings, perceptions, and responses facing (before and after) volcanic crises. All these are taken from the collective memory that has been transmitted intergenerationally after the new stage of activity of the Popocatepetl volcano in 1994. It was carried out with qualitative methodology, based on symbolic interactionism, integrating elements of ethnography and grounded theory. People of different age groups from a Mexican community near the Popocatepetl volcano participated. The techniques used were in-depth interviews, focus groups, and participant observation. The historical memory of the volcano’s activity before and after the last reactivation was reconstructed, thus evidencing the generational processes.