Optimal Settings Research Articles

Cognitive Modeling of the Sustainable Development of the Banking System under the Conditions of Innovative Transformations

The article considers theoretical approaches to modeling the sustainable development of the Russian banking system in the context of innovative transformations and the formation of digital ecosystems. The relevance of the study lies in the fact that in modern conditions, approaches are increasingly being used to ensure the sustainable development of the banking system based on cognitive modeling, the use of artifi cial intelligence. The scientifi c novelty lies in the fact that in the study, a deep learning model DL-model "Random Forest" was formed, which allows you to get a stable forecast for the net profi t of the banking system, in the conditions of innovative transformations. The practical signifi cance of the study is that the results obtained can be recommended for implementation in practice to provide support for managerial decision-making regarding the sustainable development of the banking system. The cognitive model was developed in the GraphViz environment using a semantic frame network in the form of graphs in the DOT programming language. An analysis of the dynamics of both macroeconomic indicators of the real sector of the economy and the parameters of the development of the banking sector of the Russian Federation was carried out. The criterion for the success of the predictive properties of the DL-model was the value of the average forecast error (MAE). The proposed DL model uses the best decision tree that has optimal hyperparameter settings, for example, the depth of the tree is six layers, the number of estimators (trees) in the ensemble is ten. In the experiment, the hyperparameters of the neural network did not change, the input parameters to various trees were randomly selected by the algorithm.

Use of Lactobacillus Bacteria to Degrade Oil and Grease in Restaurant Wastewater

Restaurant wastewater poses a significant environmental challenge, especially concerning the removal of oil and grease, which is known to be highly persistent. To tackle this issue, the research utilized a Lactobacillus bacteria consortium to effectively degrade oil and grease present in the wastewater. The Lactobacillus bacteria consortium was applied in an aerobic environment within the Grease Trap, while various treatment conditions, such as hydraulic retention time (HRT) and aeration rate, were evaluated to determine the optimal settings for the SBR. By employing this combined pre-treatment and SBR approach, distinct aerobic and anaerobic conditions were created, leading to significant removal of pollutant parameters by microorganisms. Remarkably, the study's findings demonstrated that at a 24-hour retention time and a 14 L/minute aeration rate, the Lactobacillus bacteria consortium achieved an impressive 97.42% removal of oil and grease, compared to only 84.54% removal by the Grease Trap without bacteria. The pretreatment of the Lactobacillus bacteria consortium led to even higher reductions in other pollutant parameters. By harnessing the power of the Lactobacillus bacteria consortium and employing the SBR in combination with pre-treatment, this approach holds great potential for addressing the persistent challenge of oil and grease removal in restaurant wastewater and improving overall water quality.

Psychological distance in language learning: a case study in higher education

The purpose of this study is to describe the psychological distance between students and teachers in higher education. This is a qualitative case study. This study includes students from Madura University's English Department. The purposive sampling approach is used to choose a sample. To collect data, major sources of information include observation and interviews, with documents acting as secondary sources. The study's findings show that the psychological distance between Students and lecturers improve students' learning processes and provide input for learning. These tactics are used by teachers to maintain a psychological distance from their students. According to 13 (36.1%) of the students, encouraging youngsters to voice their thoughts, statements, and points of view is one approach for keeping their distance. Motivating students increases the psychological barrier between teachers and students. 14 (41.7%) of students agree, whereas 8 (22.2%) believe professors also say hallo or greet students. It suggests that instructors and members of academic society give students with an appropriate linguistic environment in order to provide optimum classroom settings for students to obtain relevant information as well as social interaction in order to improve students' comprehension of their society. Members of academic societies are urged to be responsive in order to provide useful feedback to students and to interact in a natural way on a regular basis.
 

The grey-based Taguchi method was used to enhance the TIG-MIG hybrid welding process parameters for mild steel

Metal Industry and Machine Technology Development Enterprise is Ethiopia's leading manufacturing industry, producing a diverse range of industrial machinery and products. The main welding process used to join the products is MIG welding, which has several flaws, including low weld-metal toughness, spatter formation, undercut formation, and finally poor tensile strength and toughness. The company also uses TIG welding, which uses an inert gas to produce less smoke and fumes. While this technology produces precise welds, it is a time-consuming operation with a lower production rate. As a result, a special type of welding process is required that incorporates the properties of both types of welding processes. As a result, the hybrid TIG-MIG welding configuration was proposed. The optimization of process parameters of EN24 mild steel material for TIGMIG hybrid welding is presented in this paper. The test was carried out on a 6 mm EN24 mild steel plate. The butt joint configuration was used. MIG welding current, TIG welding current, MIG welding voltage, TIG welding voltage, and welding gun travel speed were used as process parameters. A single-level L27 orthogonal array is used to optimize process parameters. Tensile and hardness tests are used to evaluate the mechanical properties of the weld joint. The optimum level setting of the test, according to the mean effect plot of GRG, is MIG welding current of 200 A, MIG welding voltage of 15 V, TIG welding current of 200 A, TIG welding voltage of 18 V, and welding gun travel speed of 5 mm/s. The significant process parameters were investigated using ANOVA. MIG welding current and MIG welding voltage were significant factors in the ANOVA, with percentage contributions of 44.19% and 49.20%, respectively. Five confirmation tests were performed, and the results show that the mean grey relational grade of the conformation test was 0.7594, which falls within the 90% confidence interval, indicating that the experiment is reliable. Finally, MIG welding, TIG welding, and TIG-MIG hybrid welding processes were compared, with the results indicating that TIG-MIG hybrid welding has the highest hardness and tensile strength of all. Based on the findings, it is possible to conclude that the company should use the hybrid welding method to improve the weld joint's hardness and tensile strength.

Research on the 3D Reverse Time Migration Technique for Internal Defects Imaging and Sensor Settings of Pressure Pipelines.

Although pressure pipelines serve as a secure and energy-efficient means of transporting oil, gas, and chemicals, they are susceptible to fatigue cracks over extended periods of cyclic loading due to the challenging operational conditions. Their quality and efficiency directly affect the safe operation of the project. Therefore, a thorough and precise characterization approach towards pressure pipelines can proactively mitigate safety risks and yield substantial economic and societal benefits. At present, the current mainstream 2D ultrasound imaging technology faces challenges in fully visualizing the internal defects and topography of pressure pipelines. Reverse time migration (RTM), widely employed in geophysical exploration, has the capability to visualize intricate geological structures. In this paper, we introduced the RTM into the realm of ultrasonic non-destructive testing, and proposed a 3D ultrasonic RTM imaging method for internal defects and sensor settings of pressure pipelines. To accurately simulate the extrapolation of wave field in 3D pressure pipelines, we set the absorbing boundary and double free boundary in cylindrical coordinates. Subsequently, using the 3D ultrasonic RTM approach, we attained higher-precision 3D imaging of internal defects in the pressure pipelines through suppressing imaging artifacts. By comparing and analyzing the imaging results of different sensor settings, the design of the observation system is optimized to provide a basis for the imaging and interpretation of actual data. Both simulations and actual field data demonstrate that our approach delivers top-notch 3D imaging of pipeline defects (with an imaging range accuracy up to 97.85%). This method takes into consideration the complexities of multiple scattering and mode conversions occurring at the base of the defects as well as the optimal sensor settings.

Optimization and ANFIS-based modeling of two step FSSW process parameters on tensile strength for triple-sheet joining of aluminum alloy

In this paper, a potential technique is used to weld three sheets of AA 6082 through a two-step friction stir spot welding (TSFSSW). The optimal settings of the process parameters, namely, the rotational speed of the tool, dwell time, and plunge depth during welding have been analyzed. Taguchi’s L9 orthogonal array and ANOVA method is used to get proper material flow, appropriate heat conduction and the stir zone expansion, which helps in preparing sound joint. Microstructural tool analysis and fractography are performed by employing optical microscopy and Scanning Electron Microscopy (SEM). The tool was analyzed before and after use for heat effect along with the observation of the fracture behavior of the joint. To analyze the residual stresses developed in the joint, the XRD analysis is also carried out. The maximum tensile strength of 94.51 MPa was obtained under optimal parameters of the rotational speed of 1200 rpm, dwell time of 10 s, and plunge depth of 2.9 mm. The results of the ANFIS model revealed that a minor difference between experimental and model values (95.62) confirms the validation of the experiment. The resulting experimental findings of the ANFIS model were compared with other similar work done by researchers previously.

A Self-adaptive and Secure Approach to Share Network Trace Data

A large volume of network trace data are collected by the government and public and private organizations and can be analyzed for various purposes such as resolving network problems, improving network performance, and understanding user behavior. However, most organizations are reluctant to share their data with any external experts for analysis, because they contain sensitive information deemed proprietary to the organization, thus raising privacy concerns. Even if the payload of network packets is not shared, header data may disclose sensitive information that adversaries can exploit to perform unauthorized actions. So network trace data need to be anonymized before being shared. Most of the existing anonymization tools have two major shortcomings: (1) they cannot provide provable protection, and (2) their performance relies on setting the right parameter values such as the degree of privacy protection and the features that should be anonymized, but there is little assistance for a user to optimally set these parameters. This article proposes a self-adaptive and secure approach to anonymize network trace data and provides provable protection and automatic optimal settings of parameters. A comparison of the proposed approach with existing anonymization tools via experimentation demonstrated that the proposed method outperforms the existing anonymization techniques.

Technical note: Darkroom lighting for luminescence dating laboratory

Abstract. An optimal lighting setting for the darkroom laboratory is fundamental for the accuracy of luminescence dating results. Here, we present the lighting setting implemented in the new Luminescence Dating Research Laboratory at Stony Brook University, USA. In this study, we performed spectral measurements on different light sources and filters. Then, we measured the optically stimulated luminescence (OSL) signal of quartz and the infrared stimulated luminescence (IRSL) at 50 ∘C (IR50) as well as post-IR IRSL at 290 ∘C (pIR-IR290) signal of potassium (K)-rich feldspar samples exposed to various light sources and durations. Our ambient lighting is provided by ceiling fixtures, each equipped with a single orange light-emitted diode (LED). In addition, our task-oriented lighting, mounted below each wall-mounted cabinet and inside the fume hoods, is equipped with a dimmable orange LED stripline. The ambient lighting, delivering 0.4 lx at the sample position, induced a loss of less than 5 % (on average) in the quartz OSL dose after 24 h of exposure and up to 5 % (on average) in the IR50 dose for the K-rich feldspar samples, with no measurable effect on their pIR-IR290 dose. The fume hood lighting, delivering 1.1 lx at the sample position, induced a dose loss of less than 5 % in quartz OSL and K-rich feldspar IR50 doses after 24 h of exposure, with no measurable effect on their pIR-IR290 dose. As light exposure during sample preparation is usually less than 24 h, we conclude that our lighting setting is suitable for luminescence dating darkrooms; it is simple, inexpensive to build, and durable.

Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture

Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) is identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine are conducted, to compare the performance of OML to traditional Batch Machine Learning. The assessment of contemporary OML algorithms using a real production system is a fundamental innovation in this research. The evaluations clearly indicate that OML adds significant value to CPS, and it is strongly recommended as an extension of related architectures, such as the cognitive architecture for AI discussed in this article. Additionally, surrogate-model-based optimization is employed, to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks.

Optimum test condition setting and sample allocation for accelerated degradation tests under the last-exit-time failure mode

ABSTRACT Accelerated degradation tests (ADTs) have been widely adopted for products whose failure is defined as the first-passage-time (FPT) when the degradation path crosses a given threshold. However, for products whose failure is defined as the last-exit-time (LET), few studies related to ADT have been investigated. In this paper, we focus on designing an optimum test condition setting and sample allocation scheme for ADTs under the LET failure mode. In particular, the widely adopted Wiener process model and the generalized exponential acceleration model are used to describe the fluctuating degradation behaviors of products. By minimizing the asymptotic variance of mean-time-to-failure under the normal use condition, we design a two-level optimum test plan and a three-level compromise plan under the constraints of total sample size and stress region. The optimum plans are given in analytical forms, and we prove that the optimum plans under the FPT failure mode and LET failure mode are equivalent under some situations. Finally, a renewed selection method for the upper limit of the stress level in ADTs is proposed based on the prior ADT information from the perspective of accelerated mechanism equivalence. The proposed method is illustrated using a real-world application to rubber rings.

Multi-objective parametric optimization of electrochemical machining of Inconel-625 superalloy

The high hardness value of Inconel-625 super alloy makes it difficult to machine with traditional machining methods. In this study, the machining of Inconel-625 has been attempted with electrochemical machining. The influence of input process parameters—voltage, feed rate, and flow rate of electrolyte has been studied on the output responses—material removal rate (MRR) and average surface roughness (SR). The experiments were performed following the central composite rotatable design of the response surface methodology. Analysis of variance has been used to test the significance of the process parameters and the model. A comprehensive empirical model representing the relationship between the process parameters and the response parameters has been developed. Further, the desirability function approach was used to acquire the optimal set of parameters. The optimal values of MRR and SR obtained in the study are 0.192837 gm./min and 0.99 μm, respectively, corresponding to the optimum settings of voltage, feed rate of the tool, and flow rate of electrolyte at 13.7 V, 0.20 mm/min, and 2.8 lit/min, respectively. The optimum settings so attained have been substantiated using confirmation experiments. The Confirmation tests show that the error between predicted and experimental values of MRR and SR was 2.02% and 4.8%, respectively, which fall in the acceptable range. Moreover, the surface characteristics of the machined area have been investigated using field emission scanning electron microscopy and 3D optical profilometer to analyze the machined surface's microstructure and 3D surface characteristics.

Optimal settings for different tooth types in the virtual bracket removal technique.

To determine the optimal settings for reconstructing the buccal surfaces of different tooth types using the virtual bracket removal (VBR) technique. Ten postbonded digital dentitions (with their original prebonded dentitions) were enrolled. The VBR protocol was carried out under five settings from three commonly used computer-aided design (CAD) systems: OrthoAnalyzer (O); Meshmixer (M); and curvature (G2), tangent (G1), and flat (G0) from Geomagic Studio. The root mean squares (RMSs) between the reconstructed and prebonded dentitions were calculated for each tooth and compared with the clinically acceptable limit (CAL) of 0.10 mm. The overall prevalences of RMSs below the CAL were 66.80%, 70.08%, 62.30%, 94.83%, and 56.15% under O, M, G2, G1, and G0, respectively. For the upper dentition, the mean RMSs were significantly lower than the CAL for all tooth types under G1 and upper incisors and canines under M and G2. For the lower dentition, the mean RMSs were significantly lower than the CAL for all tooth types under G1 and lower incisors and canines under M, G2, and G0 (all P < .05). Additionally, the mean RMSs of all teeth under G1 were significantly lower than those under the other settings (all P < .001). The optimal settings varied among different tooth types. G1 performed best for most tooth types compared to the other four settings.

Directional versus ring-mode deep brain stimulation for Parkinson’s disease: protocol of a multi-centre double-blind randomised crossover trial

BackgroundThe effectiveness of Deep Brain Stimulation (DBS) therapy for Parkinson’s disease can be limited by side-effects caused by electrical current spillover into structures adjacent to the target area. The objective of the STEEred versus RING-mode DBS for Parkinson’s disease (STEERING) study is to investigate if directional DBS for Parkinson’s disease results in a better clinical outcome when compared to ring-mode DBS.MethodsThe STEERING study is a prospective multi-centre double-blind randomised crossover trial. Inclusion criteria are Parkinson’s disease, subthalamic nucleus DBS in a ‘classic’ ring-mode setting for a minimum of six months, and optimal ring-mode settings have been established. Participants are categorised into one of two subgroups according to their clinical response to the ring-mode settings as ‘responders’ (i.e., patient with a satisfactory effect of ring-mode DBS) or ‘non-responder’ (i.e., patient with a non-satisfactory effect of ring-mode DBS). A total of 64 responders and 38 non-responders will be included (total 102 patients). After an optimisation period in which an optimal directional setting is found, participants are randomised to first receive ring-mode DBS for 56 days (range 28–66) followed by directional DBS for 56 days (28–66) or vice-versa. The primary outcome is the difference between ring-mode DBS and directional DBS settings on the Movement Disorders Society Unified Parkinson’s Disease Rating Scale – Motor Evaluation (MDS-UPDRS-ME) in the off-medication state. Secondary outcome measures consist of MDS-UPDRS-ME in the on-medication state, MDS-UPDRS Activities of Daily Living, MDS-UPDRS Motor Complications–Dyskinesia, disease related quality of life measured with the Parkinson’s Disease Questionnaire 39, stimulation-induced side-effects, antiparkinsonian medication use, and DBS-parameters. Participants’ therapy preference is measured at the end of the study. Outcomes will be analysed for both responder and non-responder groups, as well as for both groups pooled together.DiscussionThe STEERING trial will provide insights into whether or not directional DBS should be standardly used in all Parkinson’s disease DBS patients or if directional DBS should only be used in a case-based approach.Trial registrationThis trial was registered on the Netherlands Trial Register, as trial NL6508 (NTR6696) on June 23, 2017.

Parametric Optimization of Torsional Parameters of Ferrocement "U" Wrapped Beams Using Recent Meta-Heuristic Optimization Algorithms.

Structural elements are subjected to different types of loads, one of which is a torsional load. Due to the complexity of the analysis, torsion was not given much importance in earlier days. With stringent updates in codal provisions and due to architectural modifications, torsion is now considered one of the major parameters for structural design. The main aim of this paper is to analyze distressed elements due to torsion. It highlights different approaches, such as destructive and non-destructive processes, to be adopted to estimate the torsional parameters of a ferrocement "U" wrapped beam. The destructive method is the experimental determination of parameters, which is absolutely necessary. The non-destructive method includes an analytical method based on a softened truss model as well as a soft computing method. The soft computing method is based on the regression coefficient analysis method along with two recent optimization algorithms, i.e., (1) ARO (artificial rabbits optimization) and (2) DAOA (dynamic arithmetic optimization algorithm). The predicted results are found to be in agreement with the experimental values (destructive method). Lastly, the obtained results from both proposed methods are analyzed, and it is found that both algorithms can be utilized in any engineering problem to determine the global optimum value with corresponding input optimal settings. As the experimental method is time-consuming and expensive, analytical, and soft computing methods can be preferred over the experimental method.

Taguchi analysis of the tensile behaviour of unaged and hygrothermally aged asymmetric helicoidally stacked CFRP composites

Taguchi method was used to predict and optimize the effects of hygrothermal aging on the tensile behavior of asymmetric helicoidally stacked Carbon Fiber Reinforced Plastic (CFRP) composites. This research is in furtherance to the previous work, which dealt purely with experiments. MR70 12P carbon fiber epoxy prepreg sheets were manufactured into laminated composites comprising constant inter-ply pitch angles ranging from 0o to 30o. The composites were tested in tension as either dry unaged specimens or following hygrothermal aging in seawater at the constant temperatures of 40 oC and 60 oC for 2000 hrs. Optimizations were conducted based on Taguchi L18 orthogonal array considering two design parameters viz. inter-ply stacking angles and hygrothermal aging temperature. The result depicted that the combination of aging temperature (C) and stacking angles are major factors in determining the tensile behavior of composite materials (p = 0.011). The model explains 86.6% of tensile strength variability, with a predicted R-squared value of 93.04%. The model’s robustness is supported by the adjusted R-squared value of 77.6%. Analysis of variance shows that inter-ply stacking angles are the main significant factor affecting the tensile behaviors at a 95% confidence level. A confirmation test was carried out to validate the optimized results and it was found that there were improvements in S/N ratios from initial to optimal setting.

Optimization of viscoelastic behaviors of bioinspired asymmetric helicoidal CFRP composites using Taguchi Method

The dynamic mechanical properties play an important role in the selection of suitable materials in the manufacturing wing of aircraft and wind turbine blades. In this paper, the standard Taguchi method was used to examine the effect of inter-ply stacking angles of 0° (UD), 0/90° (cross-ply), 5°, 15°, 10°, 20°, 25° 30° and aging temperature (ambient temperature, 40o, 60o) on the dynamic mechanical properties of bioinspired asymmetric helicoidal Carbon Fiber Reinforced Plastic (CFRP) composites. The standard Taguchi’s L18 was used. The signal-to-noise ratio and analysis of variance were introduced to analyze and estimate the optimal combination parameters. The results show the dynamic mechanical properties are linearly correlated to the fiber architecture and aging temperature. Analysis of variance (ANOVA) indicates that inter-ply stacking angles (15°, and 20°) and aging temperature (40o, 60o) are the main significant factors affecting the dynamic mechanical values at a 95% confidence level. Inter-ply stacking angles are finally noted as critical factors affecting the extent of macromolecular mobility within helicoidally stacked continuous fiber CFRP composites. A confirmation test validated the optimized results and it was found that there were improvements in S/N ratios from initial to optimal setting. The experimental and expected results are very close, with an error ratio not exceeding 5%.

A proposed fault identification-based fuzzy approach for active distribution networks with photovoltaic systems

Recently, the issue of protection in active distribution networks has been greater than before due to mushrooming use of photovoltaic (PV) generation units. PV systems coupled to power electronics devices and their control strategies causes significant changes to fault current characteristics leading to maloperation of fault identification schemes. Therefore, this paper proposes a robust self-adaptive fault identification scheme that incorporates the presence of PV systems and considers the effect of uncertainty arising due to their high penetration levels. The real-time voltage and current measurements are captured locally by advanced one-end measuring facilities, considering the uncertain temperature and irradiation conditions, of solar generation units. First, the proposed scheme employs wavelet transform, statistical alienation concept, and fuzzy logic to identify the fault criteria. Then, it uses fuzzy concept-based the system features to obtain the optimal protection scheme setting. For simulation analysis, a 10 MW solar farm-connected to a real-world active distribution system is emulated in PSCAD-EMTDC simulator to create intensive study cases, and the proposed scheme is implemented in MATLAB software. Several fault parameters are examined in the scheme comprising different fault types, fault locations, and transition resistances. Also, the uncertainty arising from PV plants and different loading conditions are taken full consideration in the developed scheme. Simulation results have verified the validity of fault identification scheme; nonetheless, the computational burden is kept reasonable. The proposed scheme is investigated under white gaussian and impulsive noise. Furthermore, sensitivity analysis and comparative study are conducted in terms of accuracy, high decision speed, and sampling frequency variation. The proposed scheme succeeds to detect and classify internal faults for both on/off-grid operation modes and differentiate them from external ones in the case of on-grid mode. The proposed scheme has dominance performance including high speed fault detection within, a half cycle, and provides accuracy more than 98 %.

Robust design modeling and optimization for dealing with a non-convex design space

Classic experimental designs effectively generate experimental design points under standard design spaces such as a circle, a cube, or a sphere. However, for non-convex design spaces, traditional experimental designs are not appropriate due to nonstandard experimental design conditions. No attention has been given to developing an algorithm to generate design points for a non-convex design space in the literature. This paper is three-fold. One, this paper presents a novel exchange algorithm based on an inner approximation approach in order to construct optimal design points where the design space is non-convex. Besides, the proposed algorithm efficiently produces experimental design points for non-convex design space. In addition, it is provided a fair comparison of the proposed algorithm performance. Two, the fitted response functions of the mean, standard deviation, and variance are found using the proposed regression model construction technique where the design space is non-convex. Three, a selected optimal design concept-based robust design optimization model is developed to find Pareto optimal solution settings of chosen design factors for the non-convex design space. Finally, an Industry 4.0 concept-based numerical example is provided to illustrate how to apply the proposed methodology effectively for the non-convex design space.

Aerodynamic optimization of an adaptive flap for next-generation green aircraft

Adaptive, morphing flaps are taking ever-increasing attention in civil aviation thanks to the expected benefits this technology can bring at the aircraft level in terms of high-lift performance improvement and related fuel burnt reduction per flight. Relying upon morphing capabilities, it is possible to fix a unique setting for the flap and adapt the flap shape to match the aerodynamic requirements for take-off or landing. The proper morphed shapes can assure better high-lift performances than those achievable by referring to a conventional flap. Moreover, standing the unique flap setting for take-off and landing, a dramatic simplification of the flap deployment systems may be achieved. As a consequence of this simplification, the deployment system can be fully hosted in the wing, thus avoiding under-wing nacelles with significantly better aerodynamics and fuel consumption. The first step for a rational design of an adaptive flap consists in defining the target morphed shapes and the unique optimal flap setting in the take-off and landing phases. In this work, aerodynamic optimization analyses are carried out to determine the best flap setting and related morphed shapes in compliance with the take-off and landing requirements of a reference civil transport aircraft. Four different initial conditions are adopted to avoid the optimization falling into local optima, thus obtaining four groups of optimal candidate configurations. After comparing each candidate's performance through 2D and 3D simulations, the optimal configuration has been selected. 2D simulations show that the optimal configuration is characterized by a maximum lift increase of 31.92% in take-off and 9.04% in landing. According to 3D simulations, the rise in maximum lift equals 22.26% in take-off and 3.50% in landing. Numerical results are finally verified through wind tunnel tests, and the aerodynamic mechanism behind the obtained improvements is explained by carefully analyzing the flow field around the flap.

Experimental Investigation to Improve Heat Transfer Rate of Transformer Oil Using Cuo Nanoparticles

In this research, we employed a statistical experimental design approach, specifically the Taguchi method using an L9 orthogonal array robust design, to optimize experimental conditions. Our aim was to maximize the Heat Transfer coefficient of CuO-Transformer oil Nano fluid. We carefully selected three controllable factors with different levels: Concentration of Nanoparticles (0.1, 0.2, and 0.3g/lit), Size of nanoparticles (10, 15, and 20 Nm), and Ratio of Surfactant weight to Nano particles weight (0.1, 0.5, and 1).Our analysis of the results highlighted the significant role played by the Size of nanoparticles in influencing the heat transfer coefficient of the Nano fluid, accounting for approximately 88.852% of the variation. We determined the optimal settings for these three factors to maximize the heat transfer coefficient: Concentration of Nano particlesof0.2g/lit, Size of Nano particles of 10 Nm, and Ratio of Surfactant weight to Nano particles weight of 1. The Heat transfer coefficient predicted by ANOVA under these optimized conditions was 559.443W/m2 K and by regression model was 549.45 W/m2 K.To validate our findings, a confirmation test was conducted at these optimal conditions, and the results demonstrated a high degree of consistency between the experimental and predicted values.