Latin Hypercube Research Articles

Optimization Design of Medium-Voltage High-Frequency Transformers Based on Parallel Sampling Kriging Surrogate Model

In the design process of medium-voltage high-frequency transformers, numerical simulation methods often result in long design cycles and significant computational resource consumption. To achieve rapid and high-precision design for medium-voltage high-frequency transformers, a parallel kriging surrogate model is proposed based on the maximum expected improvement criterion. Initially, Latin hypercube sampling is employed to establish the initial sampling points, and corresponding three-dimensional models of medium-voltage high-frequency transformers are constructed for each sampling point configuration. Finite element simulation software is then used to compute the transformer loss and volume for each model. Subsequently, a kriging surrogate model is developed based on the initial sampling point configuration, and an enhanced parallel sampling strategy is implemented to enhance the fitting accuracy of the kriging surrogate model. Finally, the high-precision kriging surrogate model is utilized as the fitness function for a particle swarm optimization algorithm, with the objectives of minimizing transformer loss and volume. The Pareto optimal solution is sought under multi-objective optimization. The effectiveness of the proposed optimization method is verified through experimental measurements of the loss in a prototype of a medium-voltage high-frequency transformer.

A conditioned Latin hypercube sampling design methodology for ground-truthing transient EM resistivity models

Translating 3D resistivity models of the subsurface to relevant hydrogeological parameters such as sediment texture requires applying a rock physics transform that is ideally calibrated using co-located 1D sediment texture logs. It is difficult to select optimal logging sites in the model that span the range of observed resistivity values using expert opinion alone. Instead, we demonstrate a methodology for selecting logging sites using conditioned Latin hypercube sampling (cLHS), a maximally stratified sampling design methodology. cLHS employs simulated annealing to select sampling sites that minimize an objective function by reproducing the statistics of the resistivity model. We implement numerous preprocessing steps to isolate desirable regions within the model for logging, and we use principal component analysis to reduce the dimensionality of the model to aid the convergence of the simulated annealing procedure while preserving the majority of the model variance. Additionally, we impose an additional objective function condition to penalize the selection of sites in the resistivity model that exhibit a high degree of lateral variance. This condition is used to increase the likelihood that the sediment texture logs acquired at the selected sites correlate well with the resistivity model. We tested our approach on a resistivity model obtained using a towed transient EM system in an almond orchard in California’s Central Valley, comparing our selected sets of logging sites against the five cone penetrometer testing logs originally acquired at the site, as well as randomly selected logging sites. The sample sets selected with cLHS were consistently able to reproduce the statistics of the resistivity model and did so more effectively than the true sampling locations or the random sampling locations.

Structural optimization design of ice abrasive water jet nozzle based on multi-objective algorithm

To enhance the cleaning efficiency of the ice abrasive water jet (IAWJ), the nozzle in the jet structure was optimized. The cleaning effect of nozzle with different structural design is different. The design variables include the geometric parameters of the nozzle's structure and the flow process parameters of the jet. The maximum wall static pressure, outlet velocity and wash width are the optimization objectives. Based on the Optimal Latin Hypercube Sampling (OLHS) method, 50 sets of sample capacity were designed for simulation calculation. The parameter functions associated with the three objectives were fitted by MATLAB. The Competitive mechanism multi-objective particle swarm optimizer (CMOPSO) algorithm was used to optimize the nozzle to improve the cleaning effect, which was verified with the test results. The inlet pressure and outlet diameter are critical factors influencing the cleaning effect of the IAWJ. The maximum wall static pressure produced by the optimized nozzle is about 4.5% higher than that of the ordinary cosine nozzle. The cleaning effect of the optimized nozzle is 10% higher than that of the ordinary cosine nozzle, which is verified by the experiments. The optimization of the nozzle serves as a valuable reference for the nozzle structure design of the cleaning machine.

Mutations make pandemics worse or better: modeling SARS-CoV-2 variants and imperfect vaccination.

COVID-19 is a respiratory disease triggered by an RNA virus inclined to mutations. Since December 2020, variants of COVID-19 (especially Delta and Omicron) continuously appeared with different characteristics that influenced death and transmissibility emerged around the world. To address the novel dynamics of the disease, we propose and analyze a dynamical model of two strains, namely native and mutant, transmission dynamics with mutation and imperfect vaccination. It is also assumed that the recuperated individuals from the native strain can be infected with mutant strain through the direct contact with individual or contaminated surfaces or aerosols. We compute the basic reproduction number, , which is the maximum of the basic reproduction numbers of native and mutant strains. We prove the nonexistence of backward bifurcation using the center manifold theory, and global stability of disease-free equilibrium when , that is, vaccine is effective enough to eliminate the native and mutant strains even if it cannot provide full protection. Hopf bifurcation appears when the endemic equilibrium loses its stability. An intermediate mutation rate leads to oscillations. When increases over a threshold, the system regains its stability and exhibits an interesting dynamics called endemic bubble. An analytical expression for vaccine-induced herd immunity is derived. The epidemiological implication of the herd immunity threshold is that the disease may effectively be eradicated if the minimum herd immunity threshold is attained in the community. Furthermore, the model is parameterized using the Indian data of the cumulative number of confirmed cases and deaths of COVID-19 from March 1 to September 27 in 2021, using MCMC method. The cumulative cases and deaths can be reduced by increasing the vaccine efficacies to both native and mutant strains. We observe that by considering the vaccine efficacy against native strain as 90%, both cumulative cases and deaths would be reduced by 0.40%. It is concluded that increasing immunity against mutant strain is more influential than the vaccine efficacy against it in controlling the total cases. Our study demonstrates that the COVID-19 pandemic may be worse due to the occurrence of oscillations for certain mutation rates (i.e., outbreaks will occur repeatedly) but better due to stability at a lower infection level with a larger mutation rate. We perform sensitivity analysis using the Latin Hypercube Sampling methodology and partial rank correlation coefficients to illustrate the impact of parameters on the basic reproduction number, the number of cumulative cases and deaths, which ultimately sheds light on disease mitigation.

Aerodynamic modeling of a delta-wing UAV for model-based navigation

This study aims to identify an optimal, as well as practical, parametric structure for a delta-wing UAV aerodynamic model for the purpose of model-based navigation. We present a comprehensive procedure for characterizing the aerodynamics of this platform, utilizing a hybrid approach that combines open-air wind-tunnel experiments with the processing of real flight data using filter error method. The experimental design employs Latin Hypercube Sampling to maximize the observability of aerodynamic coefficients while adhering to time constraints. Candidate aerodynamic models are selected through step-wise regression. Numerical values for model coefficients are determined experimentally and subsequently calibrated through a two-phase procedure using real flight data. We then compare these models by assessing their effectiveness in improving navigation in the absence of GNSS signal in four different test flights, with respect to conventional inertial coasting using the autopilot IMU. The experimental evidence demonstrates that the model-based navigation, utilizing the proposed aerodynamic model structures, significantly reduces positioning errors compared to traditional navigation methods during GNSS outages.

Low discrepancy on non-linear sensor deployment in a time-critical linear IIoT network

Linear networks are characterized by their long and narrow topology, which transmits data in a linear manner. For time-critical linear IIoT networks, stable connectivity is paramount. A good deployment strategy ensures stable connectivity in the network and maximize the lifetime and coverage of the network. In order to achieve this goal, deployment strategies should address tunnelling effects in linear networks. The proposed method tackles three major issues: 1. minimizing tunnelling effects using non-linear sensor deployment method, 2. maximizing network coverage by introducing a modified Latin Hypercube Sequence (LHS), and 3. Minimizing data interference in the multichannel TSCH network using a neighbour count-based sensor node deployment technique. The simulation validates the efficacy of the suggested approach in prolonging the network’s lifespan by mitigating the tunnelling effect near the sink. Moreover, it optimally utilizes over 70% of the mean energy within the network system. This is a significant improvement over previous algorithms which often failed to even utilizing 40% of the energy, and many a times much lesser. In terms of network coverage, it surpasses other algorithms. Consequently, the proposed method delivers economic advantages by extending the network’s longevity, achieved through a higher number of nodes concentrated around the sink, and significantly reduces the tunnelling effect in the network.

Four chemometric models enhanced by Latin hypercube sampling design for quantification of anti-COVID drugs: sustainability profiling through multiple greenness, carbon footprint, blueness, and whiteness metrics

Montelukast sodium (MLK) and Levocetirizine dihydrochloride (LCZ) are widely prescribed medications with promising therapeutic potential against COVID-19. However, existing analytical methods for their quantification are unsustainable, relying on toxic solvents and expensive instrumentation. Herein, we pioneer a green, cost-effective chemometrics approach for MLK and LCZ analysis using UV spectroscopy and intelligent multivariate calibration. Following a multilevel multifactor experimental design, UV spectral data was acquired for 25 synthetic mixtures and modeled via classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm-PLS (GA-PLS) techniques. Latin hypercube sampling (LHS) strategically constructed an optimal validation set of 13 mixtures for unbiased predictive performance assessment. Following optimization of the models regarding latent variables (LVs) and wavelength region, the optimum root mean square error of cross-validation (RMSECV) was attained at 2 LVs for the 210–400 nm spectral range (191 data points). The GA-PLS model demonstrated superb accuracy, with recovery percentages (R%) from 98 to 102% for both analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.0943, 0.1872) and (0.1926, 0.1779) for MLK and LCZ, respectively, as well bias-corrected mean square error of prediction (BCMSEP) of -0.0029 and 0.0176, relative root mean square error of prediction (RRMSEP) reaching 0.7516 and 0.6585, and limits of detection (LOD) reaching 0.0813 and 0.2273 for MLK and LCZ respectively. Practical pharmaceutical sample analysis was successfully confirmed via standard additions. We further conducted pioneering multidimensional sustainability evaluations using state-of-the-art greenness, blueness, and whiteness tools. The method demonstrated favorable environmental metrics across all assessment tools. The obtained Green National Environmental Method Index (NEMI), and Complementary Green Analytical Procedure Index (ComplexGAPI) quadrants affirmed green analytical principles. Additionally, the method had a high Analytical Greenness Metric (AGREE) score (0.90) and a low carbon footprint (0.021), indicating environmental friendliness. We also applied blueness and whiteness assessments using the high Blue Applicability Grade Index (BAGI) and Red–Green–Blue 12 (RGB 12) algorithms. The high BAGI (90) and RGB 12 (90.8) scores confirmed the method's strong applicability, cost-effectiveness, and sustainability. This work puts forward an optimal, economically viable green chemistry paradigm for pharmaceutical quality control aligned with sustainable development goals.

Metabolic symbiosis between oxygenated and hypoxic tumour cells: An agent-based modelling study.

Deregulated metabolism is one of the hallmarks of cancer. It is well-known that tumour cells tend to metabolize glucose via glycolysis even when oxygen is available and mitochondrial respiration is functional. However, the lower energy efficiency of aerobic glycolysis with respect to mitochondrial respiration makes this behaviour, namely the Warburg effect, counter-intuitive, although it has now been recognized as source of anabolic precursors. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from glycolysis. We employed a multi-scale approach that integrates multi-agent modelling, diffusion-reaction, stoichiometric equations, and Boolean networks to study metabolic cooperation between hypoxic and oxygenated cells exposed to varying oxygen, nutrient, and inhibitor concentrations. The results show that the cooperation reduces the depletion of environmental glucose, resulting in an overall advantage of using aerobic glycolysis. In addition, the oxygen level was found to be decreased by symbiosis, promoting a further shift towards anaerobic glycolysis. However, the oxygenated and hypoxic populations may gradually reach quasi-equilibrium. A sensitivity analysis using Latin hypercube sampling and partial rank correlation shows that the symbiotic dynamics depends on properties of the specific cell such as the minimum glucose level needed for glycolysis. Our results suggest that strategies that block glucose transporters may be more effective to reduce tumour growth than those blocking lactate intake transporters.

An enhanced automated machine learning model for optimizing cycle-to-cycle variation in hydrogen-enriched methanol engines

Renewable methanol and hydrogen have emerged as great potential alternative fuels for internal combustion engines in recent research. Hydrogen exhibits the ability to mitigate cycle-to-cycle variations in methanol engines thanks to its rapid combustion rate. Furthermore, the integration of machine learning with genetic algorithms provides a solution to achieve efficient and clean combustion in hydrogen-enriched methanol engines. Nevertheless, this approach is constrained due to the limited predictive accuracy in small-sample learning. This study aims at introducing an enhanced model based on the automated machine learning framework, and provide a comparative analysis of the enhanced model and computational fluid dynamics simulations in terms of multi-objective optimization performance and computational time. Additionally, this study further considered the effect of engine cycle-to-cycle variations on performance, ultimately attaining optimal operating parameters for stable combustion. The results indicate that datasets actively created by genetic algorithms are more effective than those generated through Latin hypercube sampling. The introduction of a classification model for the identification of misfires has enhanced the efficiency of dataset utilization. Through feature construction, the automated machine learning model demonstrates improvements in R-square and mean-squared error for all parameters on the test dataset. Furthermore, by employing a larger overall population size, the automated machine learning model has not only achieved a higher-quality Pareto fronts compared to the computational fluid dynamics method but has also significantly reduced computation time, particularly upon introducing additional engine cycle-to-cycle variations constraints.

Sample Size Optimization for Digital Soil Mapping: An Empirical Example

In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes for a 2790 km2 area in Ontario, Canada. Utilizing 1791 observations, we generated maps for cation exchange capacity (CEC), clay content, pH, and soil organic carbon (SOC). We then assessed sample sets ranging from 50 to 4000 through conditioned Latin hypercube sampling (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS) to calibrate random forest models, analyzing performance via concordance correlation coefficient and root mean square error. Findings reveal DJS as a robust estimator for optimal sample sizes—865 for cLHS, 874 for FSCS, and 869 for SRS, with property-specific optimal sizes indicating the potential for enhanced DSM accuracy. This methodology facilitates a strategic approach to sample size determination, significantly improving the precision of large-scale soil mapping. Conclusively, our research validates the utility of DJS in DSM, offering a scalable solution. This advancement holds considerable promise for improving soil management and sustainability practices, underpinning the critical role of precise soil data in agricultural productivity and environmental conservation.

A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint

Accounting for aeroelastic phenomena, such as flutter, in the conceptual design phase is becoming more important as the trend toward increasing the wing aspect ratio forges ahead. However, this task is computationally expensive, especially when utilizing high-fidelity simulations and numerical optimization. Thus, the development of efficient computational strategies is necessary. With this goal in mind, this work proposes a surrogate-based optimization (SBO) methodology for wing design using a predefined machine learning model. For this purpose, a custom-made Python framework was built based on different open-source codes. The test subject was the classical Goland wing, parameterized to allow for SBO. The process consists of employing a Latin Hypercube Sampling plan and subsequently simulating the resulting wing on SHARPy to generate a dataset. A regression-based machine learning model is then used to build surrogate models for lift and drag coefficients, structural mass, and flutter speed. Finally, after validating the surrogate model, a multi-objective optimization problem aiming to maximize the lift-to-drag ratio and minimize the structural mass is solved through NSGA-II, considering a flutter constraint. This SBO methodology was successfully tested, reaching reductions of three orders of magnitude in the optimization computational time.

Multi-objective structural optimization and degradation model of magnesium alloy ureteral stent

BackgroundMg alloys have attractive properties, including biocompatibility, biodegradability, and ideal mechanical properties. Moreover, Mg alloys are regarded as one of the promising candidates for manufacturing ureteral stents. This study proposed a multi-objective optimization method based on the Kriging surrogate model, NSGA-Ⅲ, and finite element analysis to improve the degradation performance of Mg alloy ureteral stents. MethodsThe finite element model for the degradation of Mg alloy ureteral stents has been established to compare the degradation performance of the stents under different parameters. Latin hypercube sampling was adopted to generate train sample points in the design space. Meanwhile, the Kriging surrogate model was constructed between strut parameters and stent degradation behavior. The NSGA-Ⅲ was utilized to determine the optimal solution in the global design space. ResultsThe optimized stent achieved 5.52 ​× ​degradation uniformity (M), 10 ​× ​degradation time (DT), and 4 ​× ​work time (FT). The errors between the Kriging surrogate model and the finite element calculation results were less than 6%. ConclusionThe optimized stent achieved better degradation performance. The degradation behavior of stents was dependent on the design parameters. The multi-objective optimization method based on the Kriging surrogate model and finite element analysis was effective in stent design optimization problems.

Structural analysis and optimal design of a spherical thin-walled stainless steel water tank without reinforced tie ribs

A spherical thin-walled stainless steel water tank without reinforced tie ribs has been designed to address the issues of easy fracture and corrosion of the ribs, difficulty in maintenance and cleaning, and short service life exposed during the use of thin-walled stainless steel water tanks with reinforced tie ribs. Firstly, an analytical model of a flat steel thin-walled water tank without reinforced tie ribs was established and subjected to static analysis under water pressure. The deformation and stress distribution patterns of the side molded plate of the flat box were obtained. Secondly, a spherical non ribbed thin-walled stainless steel water tank structure was designed with circular cross-section under different box bulge parameters, and its mechanical response characteristics under water pressure load were analyzed. A strengthening scheme was designed for the bottom box molded plate. Once again, optimize the combination design of the box scheme and the reinforcement scheme, and analyze their static, thermodynamic, and thermal solid coupling performance. Finally, the Latin Hypercube Sampling method was used to generate experimental design samples, and a response surface approximation model of a spherical thin-walled stainless steel water tank without reinforced tie ribs was constructed. The wall thickness of the box molded plate, skeleton, and reinforcement were used as design variables, and the maximum deformation and maximum equivalent stress were used as constraints. The lightweight design was carried out with the goal of minimizing mass. The research results indicate that the design program and parameter selection method for spherical thin-walled stainless steel water tanks without reinforced tie ribs proposed in the article are efficient and feasible, and can provide technical reference and theoretical support for the layout and overall optimization design of non ribbed thin-walled stainless steel water tank structures.

Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

The energy consumption, daylighting, and thermal comfort of buildings directly affect the three key goals of residents. However, there is little research on the optimization of energy consumption, daylighting, and thermal comfort in residential buildings in China. Therefore, this study proposes an optimization framework that combines Bayesian optimization with extreme gradient boosting trees (BO-XGBoost) and non-dominated genetic algorithm-II (NSGA-II) to study the multi-objective optimization of residential building performance. This paper first uses Grasshopper to simulate and obtain a dataset through Latin hypercube sampling (LHS). BO-XGBoost is used to establish the regression relationship between building envelope design parameters and residential building performance. Then, the obtained regression model is used as the fitness function of NSGA-II to get the Pareto optimal solution set. Finally, the ideal point method is used to obtain the optimal combination of building envelope structure parameters for residential buildings. Taking a residential building in a hot summer and cold winter area as an example, the effectiveness of this method is verified. The results show that (1) BO-XGBoost has excellent predictive performance, with R2 values of 0.997, 0.960, and 0.994 for energy consumption, thermal comfort, and daylighting, respectively. (2) The proposed BO-XGBoost-NSGA-II can effectively achieve multi-objective optimization. Compared with the initial scheme of the case building, energy consumption is reduced by 44.1%, thermal comfort index is reduced by 10.9%, and daylighting performance is improved by 1.7%. Therefore, the proposed method can effectively optimize the performance goals of residential buildings and provide practical ideas for similar problems.

A chimp algorithm based on the foraging strategy of manta rays and its application.

To address the issue of poor performance in the chimp optimization (ChOA) algorithm, a new algorithm called the manta ray-based chimpa optimization algorithm (MChOA) was developed. Introducing the Latin hypercube method to construct the initial population so that the individuals of the initial population are evenly distributed in the solution space, increasing the diversity of the initial population. Introducing nonlinear convergence factors based on positive cut functions to changing the convergence of algorithms, the early survey capabilities and later development capabilities of the algorithm are balanced. The manta ray foraging strategy is introduced at the position update to make up for the defect that the algorithm is prone to local optimization, which effectively improves the optimization performance of the algorithm. To evaluate the performance of the proposed algorithm, 27 well-known test reference functions were selected for experimentation, which showed significant advantages compared to other algorithms. Finally, in order to further verify the algorithm's applicability in actual production processes, it was applied to solve scheduling problems in three flexible workshop scenarios and an aviation engine job shop scheduling in an enterprise. This confirmed its efficacy in addressing complex real-world problems.

On the estimation of interior walls in the district-scale Life Cycle Assessment of buildings

The large-scale Life Cycle Assessment (LCA) of buildings supports decision-making processes in reaching the climate goals of the building sector. Yet, the considerable amount of data required for detailed assessments hinders the method. This is particularly the case for buildings’ interior, which oftentimes surpasses the normative cutoff criteria’s negligibility threshold, and thus has to be considered in building LCA. In light of interior walls’ contribution to buildings’ overall environmental impacts, and a lack of cohesive determination methods thereof, this article proposes a parametric approach to complement an existing district-scale LCA workflow for residential buildings. The approach is applied to an exemplary residential district, using a Latin Hypercube Sampling (LHS) method, to establish a range of interior wall areas and resulting effects on LCA calculations. Thereafter, the outcome is compared with the results of a preexisting approach and validated project data. The novel method is capable of significantly reducing the tool’s overestimation of interior wall areas from 400% to a value between 7% and 53%. However, it is evident that the area estimation in terms of Life Cycle Inventory (LCI) needs to be complemented with a more accurate material setup, which could be differentiated by building archetypes.

A methodology to quantify effects of constitutive equations on safety analysis using integral effect test data

To improve the predictive capability of a nuclear thermal hydraulic safety analysis code by developing a better constitutive equation for individual phenomenon has been the general research direction until now. This paper proposes a new method to directly use complex experimental data obtained from integral effect test (IET) to improve constitutive models holistically and simultaneously. The method relies on the sensitivity of a simulation result of IET data to the multiple constitutive equations utilized during the simulation, and the sensitivity of individual model determines the direction of modification for the constitutive model. To develop a robust and generalized method, a clustering algorithm using an artificial neural network, sample space size determination using non-parametric statistics, and sampling method of Latin hypercube sampling are used in a combined manner. The value of the proposed methodology is demonstrated by applying the method to the ATLAS DSP-05 IET experiment. A sensitivity of each observation parameter to the constitutive models is analyzed. The new methodology suggested in the study can be used to improve the code prediction results of complex IET data by identifying the direction for constitutive equations to be modified.

Sustainable chemometric methods boosted by Latin hypercube technique for quantifying the recently FDA-approved combination of bupivacaine and meloxicam in the presence of bupivacaine carcinogenic impurity: Comprehensive greenness, blueness, and whiteness assessments

Addressing sustainability requires developing analytical methods that minimize hazards, waste, and energy utilization per the principles of green and white chemistry. Herein, we present a sustainable spectrophotometric chemometrics technique for quantifying the recently approved Bupivacaine (BUP) and Meloxicam (MEL) combination and the potential BUP carcinogen, 2,6-dimethylaniline (DMA). Our models, including partial least squares (PLS), principal component regression (PCR), genetic algorithm-PLS (GA-PLS), and GA-PCR, were established through a comprehensive experimental design involving 25 mixtures as the calibration set. A key innovation is the utilization of the Latin Hypercube Sampling (LHS) technique, which enables the creation of a robust validation set for evaluating the performance and generalizability of these models. The GA-PLS model demonstrated excellent accuracy, with recovery percentages (R%) from 98 to 102% for all analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.097, 0.050, and 0.112) and (0.119, 0.044 and 0.131) for BUP, DMA, and MEL, respectively. The model also showed a negligible bias-corrected mean square error of prediction (BCMSEP) of (-0.014, −0.003, and 0.018), with relative root mean square error of prediction (RRMSEP) reaching (0.992, 0.752, and 0.659), and limits of detection (LOD) reaching (0.153, 0.081, and 0227) for BUP, DMA, and MEL, respectively. Comprehensive greenness, blueness, and whiteness assessments were performed and compared between the suggested and reported methods. This research pioneers a green–blue-white alternative to conventional approaches, serving as a model for developing sustainable techniques that reduce the usage of resources and chemical waste while meeting the global appeal for environmentally accountable solutions.

CFD simulation of cannabidiol delivery through microneedle patches

This study investigates the efficiency and influence of microneedle parameters, specifically Needle Point Angle (a) and Needle Height (h), on the diffusion of Cannabidiol (CBD) across varying skin depths. Utilizing the Latin Hypercube Sampling method, twelve distinct cases were analyzed. Observations reveal a consistent high concentration of CBD delivered via the microneedle patch, with a notable decrease in concentration as the depth increases, displaying a non-linear trend. Multivariate polynomial regression offers a quantitative relationship between the variables, with the third-order bivariate fitting providing the most accurate representation. Compared to other CBD delivery mechanisms, microneedle patches present enhanced CBD concentrations, circumventing challenges faced by other methods such as dosage inaccuracy, systemic absorption issues, and CBD degradation. The results highlight the potential of microneedle patches as a promising avenue for optimized transdermal drug delivery.

Application of data-driven model reduction techniques in reactor neutron field calculations

High-order harmonic techniques can be used to recreate neutron flux distributions in reactor cores using the neutron diffusion equation. However, traditional source iteration and source correction iteration techniques have sluggish convergence rates and protracted calculation periods. The correctness of the implicitly restarted Arnoldi method (IRAM) in resolving the eigenvalue problems of the one-dimensional and two-dimensional neutron diffusion equations was confirmed by computing the benchmark problems SLAB_1D_1G and two-dimensional steady-state TWIGL using IRAM. By integrating Galerkin projection with Proper Orthogonal Decomposition (POD) techniques, a POD-Galerkin reduced-order model was developed and the IRAM model was used as the full-order model. For 14 macroscopic cross-section values, the TWIGL benchmark problem was perturbed within a 20% range. We extracted 100 sample points using the Latin hypercube sampling method, and 70% of the samples were used as the testing set to assess the performance of the reduced-order model The remaining 30% were utilized as the training set to develop the reduced-order model, which was employed to rebuild the TWIGL benchmark problem. The reduced-order model demonstrates good flexibility and can efficiently and accurately forecast the effective multiplication factor and neutron flux distribution in the core. The reduced-order model predicts keff and neutron flux distribution with a high degree of agreement compared to the full-order model. Additionally, the reduced-order model's computation time is only 10.18% of that required by the full-order model.The neutron flux distribution of the steady-state TWIGL benchmark was recreated using the reduced-order model. The obtained results indicate that the reduced-order model can accurately predict the keff and neutron flux distribution of the steady-state TWIGL benchmark.Overall, the proposed technique not only has the potential to accurately project neutron flux distributions in transient settings, but is also relevant for reconstructing neutron flux distributions in steady-state conditions; thus, its applicability is bound to increase in the future.