Experimental Design Analysis Research Articles

Experimental and numerical investigation on slurry erosion performance of hybrid glass/steel fiber reinforced polymer composites for marine applications

AbstractThe accelerating growth in the utilization of industrial waste to prepare sustainable material is gaining interest of researchers globally owing to their synergistic effect with enhanced performance and cost‐effective approach. This study mainly discussed the development of hybrid polymer composites by partial replacement of steel fiber in place of synthetic glass fiber to investigate the slurry erosion performance in an erosive environment. Initially, fabrication of hybrid glass/steel fiber reinforced polymer composites with different weight percentages (38, 37, 36, 35 wt%) glass fiber and (11, 12, 13, 14 wt%) steel fiber are fabricated through vacuum assisted resin transfer modeling (VARTM) technique. After that, steady‐state slurry erosion performance analysis is carried out by varying impact velocity (10–40 m/s), fiber loading (4:1 to 10:4), impingement angle (30°–90°), and slurry concentration (160–265 g/min) respectively in the slurry erosive environment. The observation indicates that at steady‐state conditions the maximum erosion rate lies within the range of 45°–75° by varying the impingement angle whereas the other factors remain constant. The experimental results are validated with different theoretical models as well as a numerical simulation model using computational fluid dynamics approach. Finally, Taguchi's design of experimental analysis (L16 orthogonal array design) is studied by varying all the four factors at‐a‐time to get the erosion rate of the hybrid composites. At the end, the simulated results depicted a clear picture of erodent particle trajectories, eroded surface, and streamlines, and all are in good agreement with the experimental results.

Response surface methodology for design of gas turbine combustor

• Automated design space exploration with CFD quantifies combustor performance. • SST - kω turbulence model with partially premixed FGM combustion model used. • Removing blow-off design points improves response surface prediction accuracy by 57.2% • Pareto fronts from multi objective optimization used for trade-off decisions. • Six sigma DOE analysis quantifies uncertainty in combustor performance. Gas turbine combustor design is a complex multi-objective problem. In the present study, parametric design space study and optimization of a gas turbine combustor using computational fluid dynamics (CFD) simulations is presented. Baseline case validation, automated workflow setup for geometry modification, meshing, boundary condition specification, CFD solution and output parameter calculations are discussed. Response surface methodology is used to study combustor performance based on combustion efficiency, pattern factor, total pressure drop, Carbon monoxide (CO) and Nitrogen oxides (NOx) with variations in three design variables: swirl number, secondary hole diameter and dilution hole diameter. We use central composite design for design of experiments (DOE) and genetic aggregation for response surface generation. Design space refinement is carried out to identify the blow-off region and limit the search space for optimal designs to swirl number greater than 0.9. Exclusion of blow-off design points while generating the response surface resulted not only in 57.2% average reduction of root mean square error in the response surface predictions but also smoother trends away from the blow-off region. A candidate optimal design point with swirl number = 1.0, secondary hole diameter = 12.24 mm and dilution hole diameter = 15.26 mm is chosen using multi objective genetic algorithm on the response surface. Finally, uncertainty quantification with six sigma DOE analysis quantifies the confidence intervals for performance parameters based on the variations in geometric design variables. This preliminary design methodology can be used to improve existing combustors and guide the design of novel combustor concepts.

Small scale in vitro method to determine a potential bioequivalent equilibrium solubility range for fed human intestinal fluid

Intestinal drug solubility is a key parameter controlling oral absorption but varies both intra and inter individuals and between the fasted and fed states, with food intake known to alter the bioavailability of many compounds. Intestinal solubility can be measured in vitro either using sampled fed human intestinal fluid (FeHIF) or simulated fed intestinal fluid (SIF) but neither approach is optimal. FeHIF is difficult to obtain and variable, whilst for fed SIF multiple recipes are available with no consensus on the ideal version. A recent study characterised FeHIF aspirates using a multidimensional approach and calculated nine simulated media recipes that covered over ninety percent of FeHIF compositional variability. In this study the equilibrium solubility of thirteen drugs have been measured using the nine simulated media recipes and compared to multiple previous design of experiment (DoE) studies, which have examined the impact of fed SIF media components on solubility. The measured nine media solubility data set is only statistically equivalent to the large scale 92 media DoE in 4 out of 13 drug comparisons, but has improved equivalence against small scale DoEs (9 or 10 media) with 6 out of 9 or 10 out of 12 (9 and 10 media respectively) equivalent. Selective removal of non-biorelevant compositions from the 92 media DoE improves statistical equivalence to 9 out of 13 comparisons. The results indicate that solubility equivalence is linked to media component concentrations and compositions, the nine media system is measuring a similar solubility space to previous systems, with a narrower solubility range than the 92 point DoE but equivalent to smaller DoE systems. Phenytoin and tadalafil display a narrow solubility range, a behaviour consistent with previous studies in fed and fasted states and only revealed through the multiple media approach. Custom DoE analysis of the nine media results to determine the most statistically significant component influencing solubility does not detect significant components. Indicating that the approach has a low statistical resolution and is not appropriate if determination of media component significance is required. This study demonstrates that it is possible to assess the fed intestinal equilibrium solubility envelope using the nine media recipes obtained from a multi-dimensional analysis of fed HIF. The derivation of the nine media compositions coupled with the results in this study indicate that the solubility results are more likely to reflect the fed intestinal solubility envelope than previous DoE studies and highlight that the system is worthy of further investigation.

A Hybrid Approach Using Design of Experiment and Artificial Neural Network in a Camless Heavy-Duty Engine

Abstract Increasingly stricter emission regulations and fleet CO2 targets drive the engine development toward clean combustion and high efficiency. To achieve this goal, planning and conducting experiments in a time- and cost-effective way play a vital role in finding the optimal combinations of all selectable parameters. This study investigated the effects of five engine parameters on two engine-out responses in a camless variable valve actuation (VVA) heavy-duty engine. Five engine parameters were intake valve lift (IVL), inlet valve closing (IVC), injection pressure, start of injection (SOI), and exhaust gas recirculation (EGR). Initially, a design of experiment (DoE) model was generated to predict both engine-out responses: brake-specific fuel consumption (BSFC) and BSNOx emissions. Due to a poor fit of the BSFC regression model from DoE analysis, an artificial neural network (ANN) model was developed to predict BSFC instead. A d-optimal design with five engine parameters at five levels was used to design the experiment. Extra test points together with d-optimal design points were utilized to train the ANN model. The well-trained ANN model for BSFC and DoE model for BSNOx were combined with a genetic algorithm (GA) to generate the Pareto-optimal front. The results proved the concept of using a hybrid statistical approach (DoE + ANN) with GA as an effective tool to generate a range of compromise design solutions. By extracting designs along the Pareto-optimal front, the impact of engine parameters on the system can be explained.

Analysis of factors influencing on performance of solid tires: Combined approach of Design of Experiments and thermo-mechanical numerical simulation

Excessive loading conditions and internal heat build-up are the major causes of failures in solid resilient tires. Both operational and design related factors can affect tire performance including internal stress generation and heat build-up. In spite of their limitations, experimental techniques are still the most common method of identifying the factors that affect tire performance. This study explores a combined approach of numerical modelling and Design of Experiments (DoE) to identify the impact of several key design and operational factors on the performance of solid tires under different temperature levels. First, 3D Finite Element (FE) static and thermal models of the solid resilient tires are developed by incorporating suitable temperature dependent hyperelastic material models and relaxation properties. The temperature-dependent mechanical behaviours of rubber material are obtained by using Dynamic Mechanical Analyzer (DMA) and the William Landel Ferry (WLF) parameters. The developed FE thermal model is used to predict the tire performance, including the tire blasting region, which is then validated with experimental data. Secondly, a two-level fractional factorial DoE analysis is developed using response readings of maximum stresses, deformations and contact pressure obtained from the validated FE thermal model. The factors used in the DoE include the number of rubber layers, applied load, rolling speed, ramp angle and aperture level. The results provide optimal operational and design parameters of the solid resilient tire at different environmental temperatures and show the potential for considerable stress reduction with minimal changes to tire deformation and contact area. These results highlight the capability of the combined FEM and statistical design approach to drastically reduce the number of physical experiments required for obtaining optimal tire performance thus saving significant time and cost.

Statistical approach to optimize crashworthiness of thermoplastic commingled composites

A design of experiments analysis was developed to relate the processing conditions to the impact energy absorption capability of thermoplastic commingled composites. Based on low velocity impact test responses and statistical modeling, a factorial design was implemented to investigate the interaction of processing parameters with the polymer viscosity. The influence of thermal consolidation on materials performances were evaluated and quantified. The dataset was obtained according to the impact energy capability of carbon fiber/polyamide and carbon fiber/poly (ether-ether-ketone) commingled composites. The results identified the best combination of variables which improves crashworthiness of structural composites and leads to high impact energy absorption capabilities. The results revealed that the commingled composites crashworthiness strongly depends on the polymer yarn dispersion in the carbon fiber tow, that was identified and classified in different distribution patterns. This work represents a significant step towards manufacturing of thermoplastics composites with target structural performance.

Systemizing and Automating the Concept Development Process Based on Product Configuration and User Feedback: Case Study on Automating the Design Process of Creating Concepts for a Kitchen Stand Mixer

Optimizing the development and evaluation of concepts during the design of consumer products through, e.g., topology optimization, often excludes areas associated with user needs (e.g., usability). This paper reports on an exploratory study of developing a system which can create new product concepts based on users’ preferences, requirements, and a clearly defined product structure. The 3D model of the product (structured using top-down design and resilient modeling) was integrated with calculations of the performance of the user needs (performance indicators). Different designs were developed based on design of experiment analyses and optimization analyses of the 3D model of the product (a kitchen stand mixer). The outcome of the analyses was a range of concepts which scored differently in the performance indicators. The best designs (based on Pareto front) were evaluated by six potential customers. Half of the participants preferred the same design, suggesting that this tool can be used to develop a design which a specific customer segment prefers. The process of creating the model and using it for customer interviews contributed a set of qualitative findings to the literature on combining parametric design, customization, and systematic design with user needs.

Evaluation of extraction parameters in authentic hair reference material using statistical design of experiments.

Optimal methods for forensic hair analysis are often debated, especially regarding extraction parameters that include incubation time, temperature, and size of extracted hair particles. To assess hair pretreatment parameters for analysis of drugs of abuse, the statistical technique known as Design of Experiments (DoE) is useful. DoE evaluates both the individual roles and the combinatorial associations between multiple variables and overall drug extraction efficiency. Previous reports have focused on incorporated hair reference material (HRM), which is prepared in the lab at a specified drug concentration. In contrast, authentic HRM, which is prepared by diluting hair from drug users with blank hair to achieve specific drug concentrations, is an effective matrix for standardization of forensic hair testing, since the drug is incorporated into the hair matrix via uptake from systemic distribution. In the present study, extraction parameters for authentic HRM samples containing multiple drugs (diazepam, alprazolam, cocaine, methamphetamine, oxycodone, hydrocodone, and morphine) and metabolites (nordiazepam, cocaethylene, norcocaine, hydroxycocaine, and 6-monoacetylmorphine) were optimized based on recovery using a 23 full factorial DoE matrix. The factors evaluated included extraction solvent volume/sample weight ratio (12.5 or 25 μL/mg), particle size (pulverized or cut into 1 mm snippets), and extraction time (2 or 24 h) using solvent swelling. DoE analysis revealed significant differences in the optimal combinations of extraction parameters for maximum recovery. However, for the majority of drugs and metabolites, the most effective extraction method consisted of pulverizing hair prior to a 2-h extraction with a 12.5μL/mg extraction solvent volume/sample weight ratio.

Impact of Select Geometric and Operational Parameters on Hydrodynamics in Dissolution Apparatus 2 (Paddle Apparatus): A Design of Experiments Analysis Based on Computational Fluid Dynamics Simulations

PurposeA Design of Experiments (DOE) analysis driven by Computational Fluid Dynamics (CFD) simulations was used to evaluate individual and two-factor interaction effects of varying select geometric and operational parameters on the hydrodynamics in dissolution apparatus 2 (paddle apparatus).MethodsSimulations were run with meshing controls and solution strategies retained from a mesh-independent validated baseline model. Distance between vessel and impeller bottom surfaces, impeller offset, vessel radius and impeller rotation speed were considered as input parameters. The velocity magnitudes at four locations near the vessel bottom surface were considered as output parameters. Response surfaces and Pareto charts were generated to understand individual and two-factor interaction effects of input parameters on the output parameters.ResultsImpeller offset has a dominating influence of a linear and quadratic nature on the output parameters and affects overall hydrodynamics. Changes to other input parameters have limited influence on velocity magnitudes at locations closest to the vessel axis and on overall hydrodynamics. However, these parameters have important influences of varying degrees on velocity magnitudes at locations away from the vessel axis.ConclusionsThe hydrodynamics in Apparatus 2 is influenced differently by different parameters and their combinations. Impeller offset has a stronger influence when compared to parameters that do not alter apparatus symmetry.

Taguchi’s DOE and artificial neural network analysis for the prediction of tribological performance of graphene nano-platelets filled glass fiber reinforced epoxy composites under the dry sliding condition

For the present study, the tribological properties of glass-fibre reinforced epoxy (GFRE) composites and GFRE doped with 0.5 and 1 wt% of graphene nano-platelets (GNPs), were evaluated under the dry sliding condition by using pin-on-disc tribometer. Taguchi’s design of experiment (DOE) was applied to find the optimal parameters for minimum specific wear rate (SWR) and coefficient of friction (COF) which occurs at load of 40 N and GNP wt% of 1. The GNP wt% was the most significant factor determined by applying analysis of variance (ANOVA). An artificial neural network (ANN) model was developed for predicting the specific wear rate and coefficient of friction with a coefficient of determination (R2) equal to 0.965 and 0.986, respectively. Surface roughness, X-ray diffraction (XRD), Field-emission scanning electron microscope (FESEM) and Electron dispersive Spectroscopy (EDS) analysis of the worn specimen were conducted to examine surface morphology and the wear behaviour.

Optimization of welding application parameters of thin sheet blocks used in the new-generation ship hull

The main material of merchant ships is steel. Generally, block sheets used in forming a ship hull are between 7 and 12 mm. On a 60 m ship, the hull consists of approximately 25–35 mega-blocks and 45–60 blocks. Welding operations cause expansion and shrinkage on the part due to regional temperature differences. The purpose of this study is determining the optimum welding parameter of panels with 48 × 1200 cm dimensions and 5 mm thickness. Because the thickness of the sheet metal used is very thin, the blocks to be welded are very large and the mega-blocks formed by welding are used in large numbers and include uncertainties such as optimal parameters in the process application and mechanical behavior on the weld. To reduce the number of experiments within the scope of the study, welding application conditions were determined by using Taguchi experimental design (L9). In the determined parameters, submerged arc welding was carried out on the panels in a horizontal position. Taking 10 × 48 cm sections as head–middle–end from each combined panel, visual control and radiographic testing (ISO 5580) was performed. Tensile (TS EN ISO 4136) and notch-impact (TS EN ISO 9016) mechanical tests were also conducted. By conducting statistical analysis (analysis of variance) of the selected parameters used in the Taguchi experimental design, percentage effects of each parameter on yield strength, tensile strength and notch-impact tests were obtained. The impact percentage of the highest current was determined; the results were 50.24% for yield strength, 51.43% for tensile strength and 57.53% for notched-impact test results. Data means were checked with design of experiment analysis. The best welding results were obtained at parameters of 650 A, 32 V and 150 cm/min.

Optimization of the coupling groove parameters of composite porous vapor chamber

The recently developed composite porous vapor chamber (CPVC) with central and radial grooves shows small thermal resistance and good thermal performance under high heat fluxes. However, the groove parameters are difficult to optimize due to their coupling relationship and the lack of sound scientific ground. This paper addresses the issues by proposing an optimization method based on design of experiments (DOE) and response surface methodology (RSM) with the help of a developed decoupling strategy. In this method, a decoupling strategy inspired by the golden section rule is proposed to break the coupling relationship between groove parameters. Then DOE and RSM analyses are performed to provide data for single-objective and multi-objective optimization of groove parameters. Results indicate that, due to the decoupling strategy, the total number of sampling points is reduced by about 40% and the ratio of effective sampling points is increased by about 65.43% in DOE analysis. The influence of the groove parameters is nonmonotonic, and groove depth (T) has the greatest effect on the thermal hydraulic characteristics of CPVC, while the influence of central circular diameter (D) and groove width (W) is similar. The effect trend of the number of grooves (N) is contrary to that of W at small values due to the dramatic decrease of W. The thermal hydraulic performances can not reach the minimum or maximum value simultaneously, and the groove parameter configuration of D = 29 mm, W = 3.7 mm, T = 2.1 mm and N = 16 can be chosen as the optimal, leading to the decrease of the maximum temperature and the maximum temperature difference on the condensing surface, and the liquid pressure drop by 0.005 K, 0.015 K and 9.68%, respectively, compared with the initial design. The proposed method may pave a more objective and scientific alternative to design the structural parameters of CPVC.

An Investigation to Reduce the Effect of Moisture on Injection-Molded Parts through Optimization of Plasticization Parameters

Plastic materials can absorb moisture from the atmosphere even after drying. This absorbed moisture can cause many defects in the molding process. Conventionally, a dryer is used to reduce the moisture content. However, the use of a drying unit involves huge daily power consumption and is also time-consuming. Therefore, this study proposed a new method to reduce the impact of moisture on molded product part quality through the optimization of plasticization parameters while reducing drying time. Two plastic materials, polyurethane (TPU) and polycarbonate (PC), were used for the experimental verification. Key plasticization parameters, including back pressure, screw rotation speed, and barrel temperature were chosen; their influence was investigated by the design of experiments (DOE). Moisture-induced defects, such as air bubble area, part surface gloss, and appearance were measured and correlated with the plasticization parameters. It was found, after optimization via a three-level DOE and factorial design multivariate statistical analysis using TPU, that the gloss (GU) of the PC part without drying was very close to that (98.4 GU) of the fully dried PC. The proposed methodology may help molders to improve production efficiency and achieve cost savings.

A Simulation-Based Digital Design Methodology for Studying Conjugate Heat Transfer in Tundish

The successful design of refractory lining for a tundish is critical due to the demand of superheat control, improvement of steel cleanliness and reduction in material cost during continuous casting. A design of experiment analysis, namely, the Taguchi method, was employed to analyze two-dimensional heat transfer through refractory linings of a single-strand tundish, with the consideration of the thickness and the thermal conductivity of lining materials. In addition, a three-dimensional conjugate heat transfer model was applied in the tundish, taking in account the molten steel flow and heat conduction in the linings. A special focus of this study was to demonstrate the analysis methodology of combining Taguchi and CFD modelling to explore lining design in terms of thickness and thermal conductivity for the given process conditions during tundish operations.

Performance of electrokinetic treatment of fine-grained problematic soils

Electrokinetic (EK) treatment is an innovative, cost-effective in situ ground modification technology. The EK treatment uses a combination of low-voltage direct-current, electrodes, and ionic solutions across problematic soil to improve the ground conditions. This study aims to model the effect of changing electrode length (le) on the performance of the EK treatment on the engineering properties of fine-grained problematic soils. The consideration of the changing electrode lengths (le), varying soil depths (ds), and lengthwise anode to cathode distances (dA↔E), in the soil block samples, is in the form of the laboratory model test tank. The significant performance of the experimental tests was with changing electrode lengths of 0.25le (7.5 cm), 0.50le (15.0 cm), 0.75le (22.5 cm), and 1.0le (30.0 cm). The study analyzed the test data obtained from the Atterberg limit and one-dimensional swelling tests at different extraction points of the EK treated soils in the test tanks. Furthermore, the study carefully analyzed the effect of changing electrode length (le) on the performance of the EK treatment. The results of the Design of Experiment (DOE) model analysis revealed that the effect of changing electrode length (le) on the plasticity index (PI), and swelling potential (SP) of the EK treated soils, was significant. For a specific soil depth (ds), the electrode lengths (le) of 0.50le and 0.75le were significantly effective in reducing the PI, and the SP of the EK treated soils. Unlike other studies in the literature, the use of DOE analysis in the present study enabled the detection of the significant input factors and their interactive effects on the PI and the SP, thus, enabling the practicing engineers to navigate accurate design models for large in situ applications.

Design of Experiment (DOE) Analysis with Response Surface Method (RSM) to Optimize the Electroplating Parameter

Identifying optimum process parameters, their effects, and contributions to the outcomes of electroplating thickness in the electroplating process is very time-consuming and requires high cost. Small Medium Enterprises (SMEs) use a traditional approach in determining optimum process parameters that can lead to an inefficient result, such as a high variation in the response. Design of Experiment (DOE) can identify the significant factors in the process, show the correlation of each factor, and determine the optimum process parameter to achieve the targeted response (thickness). The research aimed to use DOE analysis with Response Surface Method (RSM) to optimize the electroplating parameter. It was experimental research using real production part as the DOE sample and Minitab statistic software to analyze the result. The used sample in the experiment was the continuous product order from a home appliance manufacturer. Then, four factors during the electroplating process were chosen: electrolyte concentration, electric current, duration of timing, and surface of the electroplated area. The results show that to reach thickness at 40 microns, it needs the optimum parameter with 5 minutes duration, electrolyte density of 22 Baume, electricity of 5 Volt, and surface area product of 415 cm2. This condition leads to capacity improvement of up to 100%. Hence, it decreases overtime costs and contributes to reducing energy consumption.

Design of experiment analysis on tensile properties of PLA samples produced by fused filament fabrication

The fused filament fabrication (FFF) is one of the most common forms of 3D printing with many hobbyists and as well as professional printers adopting this technology. With numerous printing parameters available for each print, having the knowledge to optimize the printing process to obtain a custom mechanical properties is clearly advantageous. This paper aims to analyze the elastic mechanical properties of PLA specimens manufactured through FFF process. To reduce experimental runs, the L27 Taguchi orthogonal array was used to analyze the influence of seven parameters, (i) infill pattern, (ii) layer height, (iii) infill density, (iv) printing velocity, (v) raster orientation, (vi) outline overlap, and (vii) extruder temperature, and three interactions, (i) infill pattern/layer height, (ii) infill pattern/infill density and, (iii) layer height/infill density on both Young’s modulus (E) and yield strength (Rp0.2). To remove any doubt about certain parameters, a two-level fractional factorial design with four factors (24–1) was used to supplement Taguchi approach. Results show that the infill density, infill pattern, printing velocity, and printing orientation are the most influential parameters, whereas layer height, extruder temperature, and outline overlap have no significant influence on Young’s modulus and Yield strength. We show that the analysis of interactions could play a leading role in optimization parameters by removing doubt concerning some parameters. This work could be further developed to propose a model to help designers to obtain either tailor-made or a robust mechanical property with minimum variation and uncertainty in product.

Optimization of process parameters for the selective leaching of copper, nickel and isolation of gold from obsolete mobile phone PCBs

The sequential separation of base and precious metals from the end-of-life mobile phone printed circuit boards (PCBs) is a significant challenge for the development of recycling process that exhibit material circularity. In this contribution, a simple, eco-friendly, and efficient leaching process is developed for the dissolution of copper and nickel from obsolete mobile phone PCBs to facilitate the isolation of a gold-rich residue. Chemical pre-treatment of downsized PCBs produced a metallic portion for which the leaching parameters for copper and nickel were optimised, including the type of leaching reagent, temperature, time, pulp density and agitation speed. It was found that quantitative dissolution of base metals occurred using 3.0 M nitric acid at 30 °C with a 50 g/L pulp density, 2 h residence time, and 500 rpm stirring speed; under these conditions, no gold was dissolved. Design of Experiment analysis using Response Surface Methodology was also undertaken to validate the process. Finally, the kinetics of the leaching process were studied and shown to conform to the chemically controlled surface reaction model, with activation energies of 39.7 and 18.4 kJ/mol for copper and nickel. Importantly, the leaching process optimised in this work avoids the need for high temperatures and reduces energy consumption and effluent generation, leading to the cleaner processing of obsolete mobile phone PCBs for the separation of gold from the dominant base metals.

Design of Experiments for Optimization of Polyoxometalate Syntheses

Design of experiments (DOE) is a key method for optimizing physical processes by altering multiple variables at once to assess their effect. In chemistry, DOE explores a wider parameter space than the dominant “One Factor at a Time” (OFAT) method providing greater opportunity to explore the factors that can be used to optimize yield, purity, and to explore chemical space for new compounds. One area of chemistry that suffers from low yields and poor reproducibility but is full of hard to predict and interesting materials is polyoxometalate cluster science. Herein, we developed a DOE analysis methodology to explore the parameter space of polyoxometalate cluster formation to explore the subtle input effects that are known to have an impact on the product discovery, purity, and stability under the preparation conditions. Using a Plackett–Burman screening design, we analyzed the effect of six synthetic parameters in only 12 experiments, following up with a full factorial analysis of the three most significant factors to identify the key parameters in the successful synthesis of each. Based on this, we provide a useful template that produces the input data for automated synthesis based on DOE on other synthetic procedures. In our POM test cases, redox agent stoichiometry was found in three of the four systems studied to be significant factors with pH and temperature, which also found to be commonly important. The insights derived from this analysis were applied to design optimized synthetic procedures and improve the yield of the product by on average >33% from the highest reported literature yield. Thus, the DOE methodology outlined here is shown to yield insights into reaction optimization rapidly with facile experimental design and analysis even for complex multivariate synthetic procedures.

Optimization of the aerodynamic characteristics of a NACA air intake based on DoE and numerical methods

The Design of Experiment (DoE) method combined with numerical simulations is employed to optimize the aerodynamic characteristics of a National Advisory Committee for Aeronautics (NACA) air intake. The drag force, total pressure recovery coefficient, mass flow rate at the outlet, and Mach number at the outlet are used to evaluate the performance of the air intake based on computational fluid dynamics (CFD) numerical methods. Three critical geometric parameters, namely the ramp angle a, ramp divergence angle θ, and turning corner radius R, are considered as variables in the DoE design. Results under the baseline conditions indicated that axial vortexes are formed near the side edge close to the ramp wall. DoE analysis indicated that above four performance parameters are largely influenced by the ramp angle a, while the ramp divergence angle θ has negligible effects. A small radius of the turning corner is beneficial to enhance the pressure recovery coefficient and the mass flow rate. The Mach number at the outlet declines and the velocity uniformity on a certain section increases with decreasing value of ramp angle a. Meanwhile, larger total pressure recovery coefficient, mass flow rate, and vortex tube length are acquired under a smaller value of a. Thus, the aerodynamic performance of this air intake can be improved by a set of optimized geometric parameters.