Lift Ratio Research Articles

Investigation on the influence of geometric parameters on two-phase ejector performance with R290

In this paper, the effect of geometric parameters on a two-phase ejector performance was experimentally studied, and the R290 was used as the working fluid. The four geometric parameters of the ejector were discussed in detail, including the diameter of the nozzle throat (Dt), the nozzle exit position (NXP), the diameter of the mixing section (Dm) and the length of the mixing section (Lm). The experimental results indicated that the pressure lift ratio (rp) of the ejector was sensitive to Dt and NXP. Within the increase of Dt and NXP discussed in this paper, the rp of the ejector varied from 1.13 to 1.33 and 1.32 to 1.07, respectively. In addition, a maximum rp of the ejector could be obtained when Dm was 3 mm to 4 mm under different nozzle throat diameter. When the Dt was 0.7 mm, the entrainment ratio of the ejector varied from 0.402 to 1.406 as the Dm varied from 2 mm to 6 mm. The Lm of the ejector diffuser had little effect on the ejector performance. Moreover, the empirical correlations for the efficiencies of R290 two-phase ejector components were summarized based on the experimental result and ejector model.

FP GROWTH ALGORITHM MODELING FOR PRODUCT INVENTORY ANALYSIS

Pusaka Tani is a shop that provides agricultural needs such as fertilizers, rice seeds and plant medicines. Sales transactions at the Farmer's Library still use a manual system, namely by using notes as proof of sales transactions. The amount of data that accumulates results in less useful data. So that Pusaka Tani cannot know the products that are often purchased by consumers simultaneously which results in many products being sold out without the knowledge of Pusaka Tani which results when consumers are not going to buy these products and products that are rarely purchased by consumers become unsold. The purpose of this study is to model FP Growt in the analysis of product stock supply. In the research conducted, the support value and confidence value used is a minimum support value of 30% and a minimum confidence value of 70%. The results obtained from the research conducted are for the highest lift ratio value of 1.67 and the lowest is 1.09

Hybrid-excited magneto-responsive soft actuators for grasping and manipulation of objects

Magneto-responsive soft actuators have attracted significant attention due to their non-contact nature and exceptional responsiveness. However, existing actuation methods relying on a single magnetic source, such as permanent magnets or electromagnets, suffer from an imbalance between flexible regulation and low energy operation. In this study, we propose a novel hybrid-excited magnetic actuation approach that leverages the synergy between permanent magnets and electromagnets. By utilizing the static magnetic field generated by permanent magnets to stabilize the deformation state of soft actuators, and employing the pulsed magnetic field generated by electromagnets to regulate the deformation, we achieve a harmonious combination of flexibility and energy efficiency. Furthermore, we introduce a two-step magnetization method that enhances the deformability of multi-arm soft actuators, enabling complete closure after deformation. To exemplify the practical implications, we develop a magnetic soft gripper system capable of versatile object manipulation, offering precise control over the grasping and releasing processes without the need for electrical energy consumption. Experimental results demonstrate the exceptional performance of the soft gripper, including a high lifting ratio (larger than 50), fast (<0.05s) and adjustable response. Moreover, we substantiate its potential in real-world scenarios through targeted delivery of single objects with different physical features and simultaneous manipulation of multiple objects with selective capability. This research presents a significant advancement in the field of magneto-responsive soft actuators, offering novel design principles, improved performance, and promising applications across various domains.

Machine learning-based optimization of a pitching airfoil performance in dynamic stall conditions using a suction controller

Flow separation control on oscillating airfoils is crucial for enhancing the efficiency of turbine blades. In this study, a genetic algorithm was employed to optimize the configuration of a pure suction jet actuator on an oscillating airfoil at a Reynolds number of 1.35×105. Neural networks based on multilayer perceptrons were used to train the aerodynamic coefficients as functions of the control parameters and reduce the number of simulations. The objective function was the mean performance coefficient, defined as the ratio of the average lift to the average drag during an oscillation period. The control parameters were location, velocity, opening length, and suction jet angle relative to the airfoil surface. The optimal jet had the maximum velocity and opening length and was normal to the airfoil surface. The optimal jet location was near the leading edge vortex (LEV) (between 3% and 6% of the chord). The optimum jet can increase the average performance coefficient (average ratio of lift to drag during a period) by about 24 times. The major part of this improvement is related to reducing drag force. The average lift coefficient increases from about 0.58 to about 0.92 using this jet, while the average drag coefficient decreases from about 0.23 to about 0.02. The optimal jet suppressed the dynamic stall vortex, which resulted from the combination of two clockwise vortices: LEV and turbulent separation vortex. Suppressing this vortex prevented the counterclockwise trailing edge vortex from growing at the end of the airfoil.

Algoritma Apriori Dalam Identifikasi Pola Pembelian Konsumen Pada Produk Minuman

Competition between companies is getting tougher and entrepreneurs indirectly need a better product marketing strategy. Inventory management system is a very popular business in Indonesia, especially in the beverage industry. Related to inventory, there is a technique in the development of science today, one of which is data mining techniques or mining techniques. In data mining, a priori algorithms contain a type of association rules that aim to find interesting relationships between combinations of elements hidden within the dataset. The application of a priori algorithm in this study can help Pekopen tavern managers find the right consumer buying behaviour . So that it can be used as material for business decision-makers to determine marketing strategies and optimize sales of menu variants, especially for several types that are in great demand. These results are used to find new information to determine a better product marketing strategy. Data processing using apriori algorithm is carried out by conducting the highest frequency analysis based on the minimum support value followed by determining the association rules based on the confidence value and then continued with the evaluation process through determining the lift ratio. There are 2000 sales transaction data processed from the data processing process obtained from a combination of itemset {Chizu Redvelvet, Cokolateh} with a support value of 40% and a confidence value of 67%.

Implementation of the apriori algorithm and the topsis method in determining sales patterns at mentaya hafiz furniture

Indonesia's economic growth during the Covid-19 pandemic was not good due to low levels of public consumption. The government and MSME actors are trying to build the economy so that it can survive in all the conditions it faces. This study aims to find out how the implementation of the a priori algorithm and the TOPSIS method determine the sales pattern of a business. The analysis was carried out using two methods, namely the association rule using the a priori algorithm and the TOPSIS method as a decision support system. The analysis of the a priori algorithm produces an itemset of house frames, doors and windows as a combination that meets the 20% support value. Meanwhile, there are three association rules, namely if you buy house frames and doors, then buy windows with a confidence value of 100% and a lift ratio of 1.89. It's the same as the other 2 rules which produce a lift ratio value of more than 1, which means the rule is valid. In the analysis of the TOPSIS method, of the 22 alternatives it is known that alternative A14, namely windows, is ranked 1st as the product that sells the most, followed by alternative A13, namely doors. In addition, there are several products that have the same preference value so that they are also in the same rank, such as cafe chairs, stakes/stones and flower shelves then cafe tables and shoe racks.

Penerapan Algoritma FP-Growth untuk Menentukan Strategi Promosi Berdasarkan Waktu dan Pembelian Produk

Sales is the main activity in every business. In making business decisions, sales patterns can be used to provide useful information such as strategies for promotion. Wandri Mart is a business engaged in the sale of products or goods commonly referred to as minimarkets in the city of Payakumbuh. In conducting promotional strategies, the owner of Wandri Mart does not know when to do promotions and what promotions are needed in order to increase sales. The purpose of this study is to obtain purchasing patterns related to the time of purchase and the type of goods purchased, so that a more effective promotional strategy can be developed. The method used by researchers is data mining techniques with the FP-Growth algorithm. The data used was taken as much as 5471 sales transaction data for 1 year. The results of this study indicate that the FP-Growth algorithm can be used to determine association rules using a minimum support of 1%, 2%, 3% and a minimum confidence of 10%. Experiments using Minimum Support 1% and Minimum Confidence 10% have the highest lift ratio value and produce more rules compared to other experiments so that it is obtained if on Tuesdays in August, customers buy instant noodles and packaged drinks with 6% and 5% support respectively and 50% and 45% confidence respectively with a lift ratio of 1.75 and 1.59 respectively. The lift ratio means that the rules have high association accuracy, and this also has a positive impact on sales and can be used as useful information for Wandri Mart to increase sales

Prediksi Persediaan Barang Tepat Waktu dengan Menerapkan Algoritma Apriori

he Apriori algorithm is a data mining technique for determining associative rules for a combination of components, this study aims to find the pattern frequency of each component that has been ordered by the customer so that the order is in accordance with the number and timely delivery, therefore to support the problems faced by PT. . SMF for the combination of inventory to purchase of goods, PT SMF applies the Apriori Algorithm to predict stock of goods so that when there is an order, the goods are not lacking and the delivery is also on time. The results of the research carried out obtained the most data on goods produced every 1 month, including the Pipe Bracket and Solenoid Bracket pattern of linkages in terms of predicting the number of goods at PT. If SMF produces Pipe Brackets, it must also produce Selenoid Brackets where the resulting confidence is 60%, Nc : 4, and for the lift ratio test it is 2.307. Whereas if you produce the Selenoid Valve Bracket, you must also produce the air pipe bracket for the resulting confidence of 75%, Nc: 5, and the lift ratio test of 2.272.

Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations

Existing transaction data is only recorded and stored as a sales transaction memorandum, so it has not been utilized optimally. The data is only stored and used as transaction history. The availability of a lot of data and having a pattern of sales transactions that are similar to MSME Cafe Over Limit will be utilized by using data mining science. This research uses the association rules method. Implementation of fp-growth to get item combinations. The purpose of this research is to make it easier for MSMEs to determine menu recommendations for customers. The fp-growth algorithm is used to process as many as 2038 transaction data with a minimum support value of 10%, while for a minimum confidence value of 50%. So that there are 3 rules, namely "if you order Mariam chocolate cheese milk then the customer will order Kopsus Overlimit", from this rule it will form a support value of 10.79%, using a confidence value of 54.19% and a lift ratio of 0.93. Furthermore "if you order Kopsus Overlimit then you will order tofu at grandma's house", from the rule it will produce a support value of 34.69%, with a specified confidence value of 59.76%, so the lift ratio value is 1.15. The last rule "if you order tofu at grandma's house, the customer orders Kopsus Overlimit", from the rule that occurs, the support value is 34.69%, with a confidence value of 66.7% and a lift ratio of 1.15. The results of the study found the two best rules, namely "if the customer orders over-limit Kopsus, he will order tofu at grandma's house" and "if he orders tofu at grandma's house, the customer orders over-limit Kopsus". Based on the results of the rules formed, it can be concluded that only two rules can be categorized as valid and can be used as a reference in food and beverage menu recommendations at MSME Cafe Over Limit. So the results of this study can be useful to be applied to MSMEs, especially in terms of menu recommendations.

Identification of Website-Based Product Sales Frequency Patterns using Apriori Algorithms and Eclat Algorithms at Rio Food in Bekasi

Sales reports that are not managed automatically may hinder businesses from accurately determining their progress in the short or long term. With increasing community needs for a product, business owners have an opportunity to market their products to a larger audience. The abundance of data highlights the need for information to produce patterns that can be used as a reference for making decisions in buying products on the website. Data mining algorithms can provide support for analysis, which can help avoid inaccurate business progress reports. In this study, the Apriori and Eclat algorithms were applied to analyze frequent itemsets in association rule mining. The dataset used in this study consists of 20 transaction data from frozen food sales. The results showed that the combination of Nugget and Chicken Sausage itemsets were the most frequent, with higher support, confidence, and lift ratio values than the others. These results can be used as product recommendations that are most in demand by customers.

Numerical study of impact of façade ribs on the wind field and wind force of high-rise building under atmospheric boundary layer flow

Façade appurtenances are found to be able to improve the aerodynamic performance of tall building. In present paper, impact of façade ribs on the flow structure and wind force of high-rise building are evaluated through LES approach under atmospheric boundary layer flow. It is found that horizontal ribs can significantly increase the size of wake vortex and suppress the near-wall vertical flow. Vertical ribs obviously affect the flow separation due to the introduction of local recirculation, which induces a narrow shear layer and near wake. Combination of horizontal and vertical rib can together affect the flow structure. All arranged ribs could alleviate the fluctuating wind near the sidewall. The change of wind filed will alter the wind pressure distribution and overall wind force. Horizontal ribs slightly reduce the mean wind pressure on sidewall. Vertical ribs clearly decrease the mean leeward suction. Facade ribs with different arrangement significantly reduce fluctuating wind pressure on the lateral and leeward wall. All models with ribs have smaller wind force than that of reference model, the maximum reduction ratio of drag and lift reach 26% and 47%, respectively.

Dynamic simulation study on regulation characteristics of transcritical CO2 multi-ejector refrigeration system

This paper presents a dynamic model of transcritical CO2 refrigeration system with multi-ejector which is developed based on the real CO2 properties and considering the internal transonic flow, phase change and choking, and its predictions are in good agreement with experimental data from the literature. The transient response of the system is studied by adjusting the speed of compressor, water flow of heat exchanger, opening of expansion valve and combination of ejector as disturbance conditions. The presented results show that the developed dynamic model has similar response rules with the same type of system. Changes of cooling water flow of gas cooler and chilled water flow of flooded evaporator can cause significant response of other pressures in the system except low-temperature evaporator, and can regulate the cooling and heating load to some extent. Opening of medium and low temperature expansion valve plays a decisive role in the system's medium and low temperature refrigeration capacity respectively, and the low-temperature expansion valve is the only regulation means of the low-temperature cooling capacity. Optimal operating combination of multi-ejector maximizes the system performance and ejector pressure lift ratio. Furthermore, the optimal ejector combination of the system under the refrigeration capacity of 75∼115 kW is studied and compared, a control strategy of adjusting ejector combination by "nozzle mass flow rate" is put forward to adapt to the high-performance operation of the system under different loads. Under the research conditions, the optimal ejector combination satisfies that the nozzle mass flow rate is stable in 0.066∼0.070 kg·s-1.

Comparative designs and optimizations of the steam ejector for a CHP system

High-efficiency heating in the combined heat and power (CHP) system is considered a promising strategy for energy conservation and emission reduction with the explosion of the heating demands. To recover the excessive waste heat from the exhaust steam and reduce the exergy destruction of the heating supply, we proposed a novel CHP cascade heating system integrated with a steam ejector and carried out research about ejector comparative designs and optimizations. Three methods were applied for the ejector designs in a CHP system and the software Fluent was used for numerical simulation to analyze and compare the performance and internal flow characteristics of the ejector. The ejector designed with the optimized thermodynamic method yielded the largest heating capacity and the highest system heating exergy efficiency of 212 MW and 71.0%, thus offering greater strengths for designing ejectors in the CHP cascade heating systems. On this basis, the thermodynamic performance of the designed ejector was further optimized by adjusting the structure of the mixing chamber based on the exergy analysis. The ejector entrainment ratio maximally increased by 11.4% and 6.4% through optimizing the inlet angle and the length of the contraction section of the mixing chamber. Compared with the existing ejector in the CHP heating system, the entrainment ratio of the designed ejector had a significant increment of 24.9% with a higher pressure lift ratio. Meanwhile, the heating capacity of the designed ejector and system heating exergy efficiency improved by 19 MW and 6.1%, respectively. This work provided an applicable method for the ejector designs and optimizations in a CHP system.

Configurational Entropy Regulation in Polyolefin Elastomer/Paraffin Wax Vitrimers by Thermally Responsive Liquid-Solid Transition for Force Storage.

The work output of shape memory polymers during shape shifting is desired for practical application as actuators. Herein, a polyolefin elastomer (POE) and paraffin wax (PW) are co-cross-linked by dynamic boronic ester bonds to enhance the network elasticity and the stress transfer between the two phases, endowing high force storage capacity to the prepared vitrimers. Depending on the phase of PW, one-way force storage is realized by programming at a low temperature (25 °C), owing to which solid PW can promote the locking of POE chains in a low-entropy state, while reversible force storage can be realized by programming at a high temperature (75 °C), owing to which the relaxation of chains facilitated by liquid PW can promote the construction of a stable structure. Based on one-way force storage, a weight-lifting machine with a weight of 20 mg prestrained at 25 °C can lift a 100 g weight, showing a lifting ratio of no less than 5000, with a high work output of 0.98 J/g. A high-temperature alarm can be triggered at varied temperatures (43-56 °C) through controlled force release by adjusting the PW content and programmed prestrains. Based on the reversible force storage, crawling robots and artificial muscles with a work output of 0.025 J/g are demonstrated. The dynamic cross-linking network also confers mold-free self-healing capability to POE/PW vitrimers, and the repair efficiency is enhanced compared with the POE vitrimer due to the improved POE chain motion by liquid PW. The realized one-way and reversible force storage and self-healing by POE/PW vitrimers pave the way for the application of SMPs in the fields of soft robotic actuators.

Performance analysis of dual ejector-expansion air source heat pump cycle with three-stage evaporation for cold region application

In the present study, a modified dual ejector-expansion heat pump cycle with three-stage evaporation has been proposed for air source heat pump applications in cold region. This cycle layout could lead to an improvement in cycle performance due to much expansion work recovery and better temperature variation matching between cooled air and refrigerant. The performance analyses of the proposed cycle and ejectors using refrigerant R290 have been conducted at different operating conditions. In addition, the performance characteristics of the cycle are compared to those of the baseline cycles using a conventional expansion device, as well as a single ejector and a single evaporator. Through thermodynamic analyses, it is found that the improvements in the coefficient of performance of the proposed cycle can reach up to 26.1% and 10.5%, respectively, as compared to the basic vapor-compression heat hump cycle and ejector-expansion heat hump cycle. The exergy destruction of the proposed cycle can be reduced by 28.6% and 13.6% than those of the other two basic cycles. In addition, the performance of ejectors under different operating conditions is studied. The pressure lift ratio of the ejector in the new cycle varies from 1.36 to 1.69, which shows 14.3%∼21.6% improvement compared to the basic ejector-expansion heat pump cycle. Overall, this paper confirms the performance advantages of the proposed new heat pump cycle and its potential application in an air-source heat pump system.

High Step-Up Ratio DC-AC Converter Using Fourth-Order LCLC Resonant Circuit for Ultrasonic Fingerprint-Sensor Drivers

Ultrasonic fingerprint sensors are becoming more widely used in thick or flexible displays. In order to better identify fingerprint information, ultrasonic sensors need to generate more ultrasonic energy, which can be transmitted to the display surface through media with higher acoustic impedance. In this paper, a DC-AC converter with a high lift ratio was proposed to enhance the transmission energy of the ultrasonic fingerprint sensor, thus helping to improve the identification. The converter comprises a full-bridge inverter and two LC resonant circuits. The introduction of an additional LC resonant circuit into the traditional Class-D LC resonant converter effectively increases the boost ratio of the proposed converter from 5 to 22. When used as a part of the ultrasonic fingerprint sensing system, the proposed converter can amplify the 20-V low-voltage DC required to drive the piezoelectric organic film to 376 V high-voltage AC. The voltage of the wave received from this new driver is equal to 970 mV, which greatly exceeds the 376 mV achieved by using the Class-D converter alone. In this paper, the topology proposed by the ultrasonic fingerprint sensor converter driver was experimentally verified, which greatly improved the boost ratio and can be considered suitable for wider applications.

OPTIMIZATION OF MARKET BASKET ANALYSIS USING CENTROID-BASED CLUSTERING ALGORITHM AND FP-GROWTH ALGORITHM

The proliferation of the food and beverage sales business requires the creativity of business owners to offer their flagship products to every consumer, both new and subscribed consumers. A large number of menu choices makes the ordering process long because consumers are confused about which menu will be the best choice. the seller to be able to provide the right recommendations so that orders can take place faster. Shopping cart analysis is an activity that has often been done to find out the items found that are sold simultaneously. The FP-Growth association method is a faster algorithm for generating association rules, but the association process in large dataset sizes tends to add large items so that the accuracy value of association rules decreases. So that in this study, the grouping of datasets was carried out using a clustering model with a centroid-based algorithm, namely k-means, k-medoids, and fuzzy c-means. This research was conducted through dataset collection, dataset preparation, clustering modeling, evaluation of clustering models using DBI and silhouette index, association modeling, and evaluation of association models using lift ratio. The results of this study showed that the clustering model with the best DBI and silhouette index values ​​was at k=3 for k-means, k=2 for k-medoids, and k=7 for fuzzy c-means. The number of association rules is generated from the grouped data set using fuzzy c-means, but the highest average lift ratio is in the association rules generated from the grouping data set using k-means. From the association model using k-means and FP-Growth, 32 unique association rules were found with the 4 most frequently found items, namely cireng chili oil, regal milk coffee, banana cheese, and vietnam drip.

Proposal and performance study on a component-based double-stage compression auto-cascade refrigeration cycle

It is difficult for the conventional single-stage compression auto-cascade refrigeration cycle to achieve higher refrigeration efficiency and lower refrigeration temperature, because the stream rich in high boiling point component from the phase separator bottom undergoes a single-stage compression process of high pressure lift ratio. Based on the concept of the evaporating temperature of the high/low boiling point component matching the grade compression of two streams from the phase separator, a component-baseddouble-stagecompressionauto-cascaderefrigeration (CDACR)cycle using R170/R600a is proposed in this paper. In the novel cycle, one dedicated compressor with two suction ports is used as the substitute for a conventional compressor with the sole suction port to realize grade compression process of the component of the refrigerant mixtures, and the stream enriched with high boiling point component from the separator bottom is sucked into the high-pressure suction port and undergoes the low-pressure-lift-ratio compression process, while the stream rich in low boiling point component from the separator top is sucked into the low-pressure suction port and executes the high-pressure-lift-ratio compression process, so as to cut down the compressor power consumption and obtain a lower refrigeration temperature. The mathematical model of the proposed cycle is developed to evaluate the thermodynamic performance of the system and comparisons with the conventional single-stage compression auto-cascade refrigeration (SCACR) cycle are also discussed. The results indicate that application of the component-based grade compression to the conventional auto-cascade refrigeration cycle dramatically improves the performance of the CDACR cycle, and there is optimum composition ratio of refrigerant mixtures for the CDACR cycle to obtain the highest coefficient of performance (COP). It is also shown that the performance of the CDACR cycle is significantly better than that of the SCACR cycle. As compared with those of the SCACR cycle, the compressor power consumption of the CDACR cycle decreases by 44.83%-53.17%, and its COP increases by 0.34–0.43, as the evaporating temperature at the evaporator outlet ranges from −60°C to −40°C. In addition, at the condenser outlet temperature in the range of 26°C- 40°C, the compressor power consumption for the proposed cycle is 37.74%-47.03% lower than that for the SCACR cycle, while COP of the CDACR cycle is 0.29–0.42 higher than that of the SCACR cycle.

Numerical simulation of the performance of an asymmetrical airfoil under extreme weather conditions

This paper presents a numerical study of icing of a NACA2412 airfoil and the aerodynamic performance of the airfoil with ice accretion. The study analyzes the effects of liquid water content, accretion time and varying angles of attack (AoA) up to 200 on ice accretion and, consequently, on the aerodynamic coefficients of the airfoil. The free stream velocity and air temperature were kept constant in the simulations. Results revealed that the ratio of lift and drag coefficients was the highest after a short accretion time (i.e., about 20 minutes), but then it decreased significantly with accretion time. Ice accretion increased with time; its mass was the greatest at 100 of AoA. The results can be beneficial for designing blade shapes to optimize wind turbine performance during adverse weather conditions.

Prediction and optimization of airfoil aerodynamic performance using deep neural network coupled Bayesian method

In this paper, we proposed an innovative Bayesian optimization (BO) coupled with deep learning for rapid airfoil shape optimization to maximize aerodynamic performance of airfoils. The proposed aerodynamic coefficient prediction model (ACPM) consists of a convolutional path and a fully connected path, which enables the reconstruction of the end-to-end mapping between the Hicks–Henne (H–H) parameterized geometry and the aerodynamic coefficients of an airfoil. The computational fluid dynamics (CFD) model is first validated with the data in the literature, and the numerically simulated lift and drag coefficients were set as the ground truth to guide the model training and validate the network model based ACPM. The average accuracy of lift and drag coefficient predictions are both about 99%, and the determination coefficient R2 are more than 0.9970 and 0.9539, respectively. Coupled with the proposed ACPM, instead of the conventional expensive CFD simulator, the Bayesian method improved the ratio of lift and drag coefficients by more than 43%, where the optimized shape parameters of the airfoil coincide well with the results by the CFD. Furthermore, the whole optimization time is less than 2 min, two orders faster than the traditional BO-CFD framework. The obtained results demonstrate the great potential of the BO-ACPM framework in fast and accurate airfoil shape optimization and design.