Rapid Optimization Research Articles

Surrogate modeling of heat transfers of nanofluids in absorbent tubes with fins based on deep convolutional neural network

In this paper, we propose and investigate a deep convolutional neural network-based surrogate model for fast prediction of heat transfer of nanofluid in absorbent tubes with fins. Inspired by Unet, the proposed model consists of a contracting path and an expanding path, and skip connections are replaced by residual blocks to enhance prediction performance. The model can predict temperature field of tubes’ specified cross-sections in any quantity. The nanofluid-filled absorbent tubes with two types of fins (rectangular and semiepllise) are investigated, where the shapes of the fins are variable. The results show that the proposed model can predict the temperature field with very high accuracy and extremely fast speed: the average prediction error is less than 0.15% for all studied cases, and the prediction speed is more than 4 orders faster than numerical simulation with OpenFOAM. In addition, we analyze the main factors affecting the network model, including the learning rate, data size, network's layer, activation function and input presentation. It is found that the influence of activation function is significant, where ReLU6 is able to reduce the max prediction error to 2.7%, while the Tanh and Sigmoid functions have two and four times larger errors, respectively. The results of our current work show great application potential of the deep neural network-based surrogate model for rapid 3D geometry optimization of the nanofluid-filled fined absorbent tube of heat exchangers and solar collectors.

Performance analysis on the specific impulse and specific thrust of scramjet with a quasi-one-dimensional model

A quasi-one-dimensional model for performance analysis of scramjet has been developed for rapid design and optimization in the current paper. The heat release process along the supersonic combustor is based on the assumption of chemical equilibrium. Furthermore, the effects of flight Mach number ranging from 6 to 14, equivalence ratio ranging from 0.1 to 1.0, and three types of fuel (including boron-B, hydrocarbon-CH, and hydrogen-H2) on the performance of the scramjet have been studied. The energy specific impulse is proposed for the first time to evaluate the energy conversion ability of chemical energy to mechanical energy for different fuels. The results show that mass specific impulse not only depends on the heat value of the fuel but is also closely related to the energy conversion ability of the fuel. And the condensation heat and specific heat capacity of combustion products, and the mole ratio of the gaseous products to the gaseous reactants are the three main factors affecting the energy conversion ability for different fuels. Compared with the CH-fueled scramjet and H2-fueled scramjet, the B-fueled scramjet has the potential for miniaturization and can cruise at a higher Mach number, due to its larger volume specific impulse and specific thrust, respectively.

CaExTun: Mitigating Cas9-Related Toxicity in Streptomyces through Species-Specific Expression Tuning with Randomized Constitutive Promoters.

The CRISPR/Cas9 system provides an efficient tool for engineering genomes. However, its application to Streptomyces genome engineering has been hampered by excessive toxicity associated with overexpression of Cas9 protein. As the level of Cas9 toxicity varies significantly between Streptomyces species, species-specific optimization of Cas9 expression is a strategy to mitigate its toxicity while maintaining sufficient double-strand break (DSB) activity for genome engineering. Using a pool of randomized constitutive promoters and a blue pigment indigoidine biosynthetic gene (IndC) as a reporter, we developed the CaExTun (Cas9 Expression Tuning) platform, which enables rapid screening of a large pool of promoter-Cas9 constructs to quickly recover the one with high DSB activity and no apparent toxicity. We demonstrate the utility of CaExTun using four model Streptomyces species. We also show that CaExTun can be applied to the CRISPRi system by allowing the construction of a library of promoter-dCas9 constructs that confer a wide range of gene repression levels. As demonstrated here, CaExTun is a versatile tool for the rapid optimization of the CRISPR/Cas9 system in a species-specific manner and thus will facilitate CRISPR/Cas9-based genome engineering efforts in Streptomyces.

Compact QEPAS CO2 sensor system using a quartz tuning fork-embedded and in-plane configuration

A quartz-enhanced photoacoustic spectroscopy (QEPAS) system was developed based on a novel quartz tuning fork-embedded, and in-plane QEPAS technique, while increasing the efficiency of assembly alignment with the aid of 3D printing technology. In this paper, it was verified that the simplified numerical model enabled rapid optimization of the dimensions of the acoustic micro-resonator (AmR), and this was validated by experimental results. The optimized results are as follows: outer diameter = 3.2 mm, inner diameter = 0.5 mm, length = 6.77 mm. The acoustic transducer used a commercial quartz tuning fork with a resonance frequency of 30.69 kHz. After optimizing the laser wavelength modulation amplitude and the dimensions of the AmR, a minimum CO2 detection limit of 1.06 ppm was obtained, corresponding to a normalized noise equivalent absorption of 5.5 × 10−9 cm−1 WHz−1/2.

K-RBBSO Algorithm: A Result-Based Stochastic Search Algorithm in Big Data

Clustering is widely used in client-facing businesses to categorize their customer base and deliver personalized services. This study proposes an algorithm to stochastically search for an optimum solution based on the outcomes of a data clustering process. Fundamentally, the aforementioned goal is achieved using a result-based stochastic search algorithm. Hence, shortcomings of existing stochastic search algorithms are identified, and the k-means-initiated rapid biogeography-based silhouette optimization (K-RBBSO) algorithm is proposed to overcome them. The proposed algorithm is validated by creating a data clustering engine and comparing the performance of the K-RBBSO algorithm with those of currently used stochastic search techniques, such as simulated annealing and artificial bee colony, on a validation dataset. The results indicate that K-RBBSO is more effective with larger volumes of data compared to the other algorithms. Finally, we describe some prospective beneficial uses of a data clustering algorithm in unsupervised learning based on the findings of this study.

STRONG start for implementation of guideline-directed medical therapies

Several publications have proposed potential strategies for novel, rapid optimisation of guideline-directed medical therapy to address the sizeable gap between evidence and implementation in patients with heart failure.1–4 The benefit of an optimised guideline-directed medical therapy regimen consisting of an angiotensin receptor-neprilysin (ARN) inhibitor, β blocker, mineralocorticoid receptor antagonist, and SGLT2 inhibitor is substantial, adding an estimated 6·3 years of additional survival for individuals aged 55 years with heart failure and reduced ejection fraction compared with suboptimal medical regimens containing angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) and β blockers.

Optimized Colorectal Cancer Patients’ Management During the COVID-19 Pandemic: Triage and Treatment

# Background The outbreak of coronavirus disease 2019 (COVID-19) has created significant challenges in the management of oncology patients, including patients with colorectal cancer (CRC). We suspect that the COVID-19 pandemic had a major impact on the number of CRC inpatients and outpatients, which might leave many CRC patients unable to get timely medical treatment. At the time, the most important task was to satisfy the imperious demand for rapid optimization of processes and the development of efficient and effective triage and treatment strategies, as well as emergency distant clinical reasoning. # Methods The number of outpatients and inpatients, as well as surgeries performed in Shanghai East Hospital from December 2019 to February 2020 were collected. Using December 2019 data as the baseline status before the pandemic, the changes during this period were analyzed which can reflect the impact of COVID-19 on the treatment of CRC patients. In addition, a triage system and management strategy for patients with CRC during COVID-19 were designed and implemented. To evaluate their effectiveness, we assessed COVID-19 infection rates among CRC patients in relation to total patients and healthcare staff. # Result Compared with the pre-COVID-19 period (December 2019), a drastic decline in the number of outpatient visits (2,789 to 120) and inpatient hospitalizations (207 to 50) for all the CRC patients, as well as in non-emergency colorectal surgeries (133 to 23), was observed in February 2020 at our hospital. # Conclusion A multidisciplinary triage strategy aligned with regional guidance and digital, artificial intelligence (AI)-technology solutions can help increase the efficacy in patient management, allow efficient access to care and reduce the incidence of COVID-19 among CRC patients.

On-chip analysis of glycolysis and mitochondrial respiration in human induced pluripotent stem cells.

Recent advances in microfluidic engineering allow the creation of microenvironments in which human cells can be cultured under (patho-)physiological conditions with greater reality than standard plastic tissue culture plates. Microfluidic devices, also called Organs-on-Chip (OoC), allow complex engineering of the cellular compartment, yielding designs in which microfluidic flow can be precisely controlled. However, it is important that cellular physiology is not only controlled but can also be monitored in these devices. Here, we integrated oxygen and pH sensors into microfluidics, allowing close monitoring of the extracellular flux from the cells, enabling constant assessment of features such as glycolysis and mitochondrial oxidative phosphorylation in situ. Using human-induced pluripotent stem cells (hiPSCs) as an exemplar of a highly metabolic and relatively challenging cell type to maintain, we showed that monitoring the extracellular environment allowed rapid optimization of the seeding protocol. Based on the measurements, we implemented earlier and more frequent media refreshment to counteract the rapid acidification and depletion of oxygen. The integrated sensors showed that hiPSCs in the devices exhibited mitochondrial and glycolytic capacity similar to that measured with the Seahorse extracellular flux system, the most widely used standard for these types of assays in conventional cell culture. Under both conditions, hiPSCs showed greater reliance on glycolysis than mitochondrial OXPHOS and the absolute values obtained were similar. These results thus pave the way for the assessment of cell metabolism in situ under conditions of fluidic flow with the same precision and relevance as current standard static cell cultures.

Process Optimization for Up-Facing Surface Finish of AlSi10Mg Alloy Produced by Laser Powder Bed Fusion

This work investigates the effects of various processing parameters (laser power, scanning speed, hatch distance and beam offset) on the resultant inclined up-facing surface roughness of AlSi10Mg alloys produced by laser powder bed fusion (LPBF). A two-step approach, orthogonal test followed by the Doehlert matrix design (DMD) test is used to efficiently optimize the up-facing surface and contour parameters. The former method aims to determine the significance of variables while the latter one facilitates a rapid optimization. The results show that the interaction and interdependency among the parameters are of great significance to the obtainable surface roughness. Using a rational design of experiments, the optimized up-facing surface roughness of Ra of 5.4 μm is achieved. This is attributed to the elimination of the laser partition track and the reduction in irregularities at the edges of the parts. This work demonstrates an effective approach of experimental processing parameter optimization to improve the surface finish of LPBF parts.

Optimal coupling design for organic Rankine cycle and radial turbine rotor using CFD modeling, machine learning and genetic algorithm

The organic Rankine cycle (ORC) as a potential technology can be used to recover low-grade waste heat. This study provides a rapid and systematic methodology to design ORC system coupling with the radial turbine rotor. Firstly, the coupled model of ORC and the radial turbine is established, and the thermodynamic and economic performance is obtained to optimize the preliminary design parameters. Then, the CFD analysis for the turbine rotor is performed to generate the databases, and the machine learning algorithms (random forest, XGBoost, decision tree, and support vector machine) are used to train the surrogate models. And finally, the optimal shape of the meridional surface is obtained to improve ORC’ power output. The results indicated that the system net power output and the cost rate at the decision point achieved 393.06 kW and 13.59 $/h, respectively. The support vector machine exhibited the highest value of R2 (coefficient of determination) among the regression algorithms. The optimized results showed a more uniform pressure distribution is founding on the suction surface of the optimized structure compared with the basic structure’s, leading to the power output was increased by 7.5 %. The findings can provide references for the rapid coupling design and optimization of ORC and the turbine.

Plug and Pop: A 3D-Printed, Modular Platform for Drug Delivery Using Clinical Ultrasound and Microbubbles.

Targeted drug and gene delivery using ultrasound and microbubbles (USMB) has the potential to treat several diseases. In vitro investigation of USMB-mediated delivery is of prime importance prior to in vivo studies because it is cost-efficient and allows for the rapid optimization of experimental parameters. Most in vitro USMB studies are carried out with non-clinical, research-grade ultrasound systems, which are not approved for clinical use and are difficult to replicate by other labs. A standardized, low-cost, and easy-to-use in vitro experimental setup using a clinical ultrasound system would facilitate the eventual translation of the technology to the bedside. In this paper, we report a modular 3D-printed experimental setup using a clinical ultrasound transducer that can be used to study USMB-mediated drug delivery. We demonstrate its utility for optimizing various cargo delivery parameters in the HEK293 cell line, as well as for the CMT167 lung carcinoma cell line, using dextran as a model drug. We found that the proportion of dextran-positive cells increases with increasing mechanical index and ultrasound treatment time and decreases with increasing pulse interval (PI). We also observed that dextran delivery is most efficient for a narrow range of microbubble concentrations.

Characterization of the spray droplets and spray pattern by means of innovative optical microscopy measurement method with the high-speed camera

Liquid atomization plays an important role in the manufacturing of the pharmaceutical products. It is used in all production steps where liquid is applied. In some productions steps precise control over spraying parameters is essential. These steps include fluid bed granulation, film coating of tablets and pellet coating. Precise atomization of liquids enables control over the granule formation in granulation steps and film formation in coating steps. Atomization process includes breakup of liquid and formation of droplets, which is a complex and important phenomenon that should be carefully evaluated for each particular use. This study focuses on characterization of atomization process by the means of combining high-speed imaging, optical microscopy and continuous backlight illumination together with robust image processing algorithm. With pure water and three different hypromellose (HPMC) polymer dispersions that are frequently used for film coating of solid dosage forms, we successfully measured mean droplet size (Sauter mean diameter between 49 and 413 μm), droplet size distribution and droplet speed (in average 10–46 m/s). It was shown that the shape of cumulative volumetric droplet size distribution can be used to evaluate the successfulness of ligament breakup (i.e., formation of droplets) and by that the spray quality. Moreover, the obtained trends and empirical correlations between droplet size and other process parameters are useful in practice in order to select appropriate settings for optimal coating process parameters. Due to the simplicity of proposed method and data processing, the entire system is potentially suitable for near real-time coating monitoring and for rapid optimization of the atomization process.

A Single-Arm, Open-Label, Multicenter Study to Assess Molecular Response of P1101 Therapy in Patients with Polycythemia Vera and Elevated Hematocrit

Background: Polycythemia Vera (PV) is the commonest myeloproliferative neoplasm, characterized by clonal stem cell proliferation of the erythroid, myeloid, and megakaryocytic lines, and risk for thrombotic events and transformation into post-PV myelofibrosis and acute myeloid leukemia. The excessive myeloproliferation in PV is driven by JAK2 somatic driver mutations. Although hydroxyurea (HU) remains the myelosuppressive agent of first choice, HU treatment can be associated with cytopenias and often unsatisfactory hematological control over time, aphthous and leg ulcers, and concern for the second primary malignancy. Recently, ropeginterferon alfa-2b, a monopegylated IFNa, which had an extended elimination half-life, enabling less frequent dosing and improved tolerability, developed and evaluated the efficacy and safety in comparison to hydroxyurea treatment. An open-label, randomised phase III study (PROUD-PV and CONTINUATION-PV) showed is more effective in achieving durable hematological and molecular remissions than hydroxyurea and is well tolerated during long-term application. However, there are limited data on the clinical relevance of molecular response during ropeginterferon alfa-2b and the efficacy and safety of ropeginterferon alfa-2b in Eastern Asian patients. Aims: The aim of this study is to evaluate clinical and molecular responses, and the association between efficacy and molecular response as assessed by reduction in JAK2 Val617Phe allele burden. In addition, the safety and tolerability of ropeginterferon alfa-2b were also collected. Methods: This single-arm, open-label, multicenter study has been performed in 13 hospitals in Korea. Patients were eligible if 19 years or older with PV diagnosed by WHO's 2016 criteria, requiring cytoreductive therapy and elevated hematocrit (hematocrit >45%). Patients were treated with ropeginterferon alfa-2b, subcutaneously every 2 weeks, at a starting dose of 250 µg, followed by 350 µg at week 2, 500 µg at week 4, and thereafter until week 48. The quantitative JAK2 Val617Phe allele burden was assessed every 3 months Results: With a data cut-off date of 19 Jul 2022, a total of 74 patients were enrolled including 42 (56.8%) patients in HU naïve and 32 (43.2%) patients in HU-resistance/intolerance. The median age was 58 years (range, 26-81) and 58.1% were male. The percentages of patients with low and high risk were 55.4% and 44.6%, respectively. Among the 74 patients, 70 patients continue in the study, and 4 dropped out. Until now, 45, 20, and 6 patients were evaluable at 3, 6, and 9 months, respectively. During the dose escalation period, 4.44% of evaluable patients needed the dose reduction. At baseline, mean hematocrit (%) was 50.53 ± 3.99, which significantly decreased to 47.00 ± 5.58, 42.60 ± 5.92, and 41.68 ± 4.60 at 3, 6, and 9 months, respectively. WBC and PLT also decreased steadily over time; at baseline, mean WBC (x 109/L) was 14.16 ± 6.01, which significantly decreased to 7.17 ± 3.40, 6.16 ± 3.50, and 4.45 ± 1.57 at 3, 6, and 9 months, respectively, and at baseline, mean PLT (x 109/L) was 560.03 ± 299.21, which significantly decreased to 336.53 ± 234.11, 274 ± 167.59, and 249.50 ± 256.18 at 3, 6, and 9 months, respectively. The JAK2 Val617Phe allele burden changes, which were analyzed with available samples so far, showed a trend for rapid decreasing pattern; at baseline, mean JAK2 Val617Phe allele burden (%) was 59.70 ± 27.91(n=39), which significantly decreased to 42.41 ± 23.83 (n=16) and 37.14 ± 13.41 (n=5) at 3 and 6 months, respectively. Updated data with molecular response per individual patient will be presented in the meeting. In addition, although grade 1/2 adverse events (n=20) and grade 3 adverse events (n=4) were reported, there were no drug-related serious adverse events. Conclusion: Our data demonstrated that ropeginterferon alfa-2b therapy with rapid dose optimization induced hematological response and reduction of JAK2 Val617Phe allele burden, and was well tolerated in Korean patients with polycythemia vera and elevated hematocrit. Updated data with longer follow-up will be presented in the meeting.

Development of machine learning models for the prediction of the compressive strength of calcium-based geopolymers

Compressive strength is an important mechanical index that determines the mixture design of geopolymer, and its accurate prediction is essential. The existing experiment-based and statistical methods are time-consuming, labor-intensive and inaccurate. This study aims to develop an effective, reliable and interpretable machine learning (ML) model for predicting the compressive strength of calcium-based geopolymers. Feature engineering was constructed with molar ratios of raw material oxide composition, curing system, and mixing design. A total of eight algorithms in three types, traditional ML algorithms, integrated tree-based ML algorithms, and deep neural network algorithm, were employed to predict the compressive strength, and their differences, advantages, and disadvantages were compared. The importance of input variables in model training was evaluated. The contribution and influence pattern of input features on the development of compressive strength were revealed using the SHapley Additive exPlanations (SHAP) and inverse prediction. The results demonstrate that among the eight models proposed, the XGB model had the highest prediction accuracy (91%) and the lowest root mean squared error (3.85 MPa). Based on the importance analysis and the SHAP value, the parameters that had the greatest impact on the compressive strength were curing age, n(H2O)/n(Na2O), curing temperature, n(SiO2)/n(CaO) and the mass ratio of alkali activation solution to solid powder (L/S). The effects of input features on the compressive strength development of calcium-based geopolymers captured by SHAP and inverse predictions based on the best predictive model were consistent with the experimental results and theoretical understanding. The research in this paper facilitates the rapid prediction, improvement and optimization of the proportioning design and application of calcium-based geopolymers, and also provides a theoretical basis for the utilization of industrial and construction waste, in line with sustainable and low-carbon development strategies.

Anchor Chain Optimization Design of a Catenary Anchor Leg Mooring System Based on Adaptive Sampling

A catenary anchor leg mooring system (CALM) is one of the main kinds of offshore transport terminals. Its buoyancy is connected by multiple anchor chains fixed on the seabed. Because a large deformation characterizes the anchor chain, it is prone to fatigue failure under dynamic loads in marine environments. These factors lead to many challenges in the design optimization of anchor chains. This study aims to develop a rapid optimization design framework for a mooring anchor chain with cost minimization as the design objective. In this framework, an approximation model of the mooring analysis is built based on an adaptive sampling method, and a combination of a genetic algorithm and a sequential quadratic programming method is utilized to optimize the established approximation model. The results show that the framework significantly reduces the computation time, and the error of the final optimization result obtained by adaptive sampling is significantly reduced. We found that using a quadratic or elliptic function as an adaptive function is good enough to obtain the optimization result.

MEPSi: A tool for simulating tomograms of membrane-embedded proteins

The throughput and fidelity of cryogenic cellular electron tomography (cryo-ET) is constantly increasing through advances in cryogenic electron microscope hardware, direct electron detection devices, and powerful image processing algorithms. However, the need for careful optimization of sample preparations and for access to expensive, high-end equipment, make cryo-ET a costly and time-consuming technique. Generally, only after the last step of the cryo-ET workflow, when reconstructed tomograms are available, it becomes clear whether the chosen imaging parameters were suitable for a specific type of sample in order to answer a specific biological question. Tools for a-priory assessment of the feasibility of samples to answer biological questions and how to optimize imaging parameters to do so would be a major advantage. Here we describe MEPSi (Membrane Embedded Protein Simulator), a simulation tool aimed at rapid and convenient evaluation and optimization of cryo-ET data acquisition parameters for studies of transmembrane proteins in their native environment. We demonstrate the utility of MEPSi by showing how to detangle the influence of different data collection parameters and different orientations in respect to tilt axis and electron beam for two examples: (1) simulated plasma membranes with embedded single-pass transmembrane αIIbβ3 integrin receptors and (2) simulated virus membranes with embedded SARS-CoV-2 spike proteins.

Research on Parameter Matching of the Asymmetric Pump Potential Energy Recovery System Based on Multi-Core Parallel Optimization Method

Aiming at the parameters of the different displacements and related components of the variable-displacement asymmetric axial piston pump (VAPP) required by the energy-recovery system of excavator booms of different tonnages, a rapid multi-process parallel optimization method of complex hydraulic products based on a multi-core CPU was proposed for parameter matching. The parameter matching was used to reasonably select relevant parameters so that the excavator’s boom energy-recovery and utilization system can improve operational efficiency and energy-saving efficiency under the premise of satisfying the normal working conditions of the working mechanism, and achieving the purpose of serializing VAPP products. A multi-objective optimization model was put forward according to energy-saving efficiency and operational efficiency. First, the accuracy of the acceleration method of the CVODE, a solver for stiff and non-stiff ordinary differential equation (ODE) systems, was verified by a physical prototype test. The results showed that the test and simulation results were in good agreement. A particle swarm optimization algorithm (PSO) was used to optimize the main parameters of the boom energy-recovery system to obtain the appropriate energy-saving efficiency and obtain the VAPP displacement and related component parameters required by the energy-recovery system of excavator booms of different tonnages. The simulation results showed that a motor working condition was necessary in the guaranteed descending stage, and the process of lifting–descending–lifting was completed under the condition that the total time did not exceed a certain value. The energy-saving rates of the 7-ton (7T), 12-ton (12T), 20-ton (20T), and 30-ton (30T) excavator boom energy-recovery systems reached 29.8%, 35.3%, 31.25%, and 27.88%, respectively. In the eight-core CPU workstation under the simulation conditions, compared with the Simulation X platform simulation method, the simulation efficiency of the multi-core CPU parallel method was improved by more than 80 times.

Static Mixers Producible by Additive Manufacturing: Novel Rapid Automatic Optimisation and Practical Evaluation.

In the extrusion of plastics, the thermal and material homogeneity of the plastic melt at the die entry are of high importance for the extrudate quality. While static mixers are widely used to improve the melt homogeneity, previous attempts at optimisation for reduced pressure loss and improved mixing had to be performed by hand and human experience, limiting the degrees of freedom and efficiency. A new automatic optimisation method based on the open source software OpenFOAM was developed. Using immersed boundary methods, new target functions in the pre-existing routine adjointShapeOptimizationFoam and an additional algorithm checking the suitability for additive manufacturing and fixing the geometry during run-time is presented. The new algorithm is used to optimise an existing static mixer based on an X-type geometry with integrated oil channels, maximising the heat exchange between oil and melt. Based on the results of these simulative optimisations, the best candidates were manufactured using selective laser melting and experimental trials were run. Experimental validation shows that with our optimisation algorithm, a pressure loss reduction of 10% could be achieved. The core melt temperature was reduced by 6 ∘C, improving the thermal homogenisation as well. While the main advantage of this method is the rapid optimisation taking the operating point into account, the trials also showed positive results in off-design operating points. This allows the low-cost design and manufacture of individualised static mixers.

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.

Rapid synthesis and optimization of UV-photopolymerized cassava starch-based superabsorbent hydrogels as a biodegradable, low-cost, and effective adsorbent for MB removal

In this work, eco-friendly superabsorbent hydrogel (SAH) films based on cassava starch (CSt) and polyacrylic acid (PAA) were successfully fabricated and optimized via a one-pot synthesis and UV-photopolymerization under an air atmosphere to obtain the highest swelling ratio (Seq) and to be employed as an effective adsorbent for removing methylene blue (MB). The optimal CSt-g-PAA film exhibited uniform porous structures with a high specific surface area (24.99 m2/g), acceptable Young’s modulus (60.9 MPa), and a relatively high Seq value (35,645% or 356 g/g). It also revealed selective adsorption toward MB dye. The adsorption process of MB onto CSt-g-PAA SAH followed the pseudo-second-order and Langmuir isotherm models. The maximum adsorption capacity (qm), partition coefficient (PC), and removal efficiency for MB adsorption were evaluated to be 1,044 mg/g, 4.08 mg/g.µM−1, and 95%, respectively, making the CSt-g-PAA SAH film one of the most efficient starch-based adsorbents for MB removal. Thermodynamic studies indicated spontaneous and endothermic adsorption. The plausible adsorption mechanism was mainly described by electrostatic interaction and hydrogen bonding. Furthermore, the prepared CSt-g-PAA SAH film displayed excellent reusability, remarkable biodegradability, good biosafety, and low cost. Therefore, it may be a promising and sustainable adsorbent for removing dye pollutants and enhancing the ecosystem.