Sensitivity Analysis Strategy Research Articles

An efficient single-loop strategy for time-variant reliability sensitivity analysis based on Bayes’ theorem

Time-variant reliability sensitivity analysis aims to measure the effect of input variables on the time-variant failure probability, which can then be used to guide structural design and optimization. The traditional methods for calculating the sensitivity index require a nested sampling procedure and their computational complexity depends on the number of input variables, making them computationally the most expensive. To address the above limitations, an efficient single-loop strategy is developed for time-variant reliability sensitivity (TRS) analysis. Based on Bayes' theorem, the TRS index is represented by the difference between the probability density function (PDF) and failure-conditional PDF of the variable. This derivation enables TRS analysis to be implemented using only a single set of samples, with computational costs that don’t depend on the number of input variables. Then, the sensitivity index is normalized to identify influential variables effectively. Several case studies involving numerical and engineering problems are employed to verify the feasibility and effectiveness of the proposed strategy. The direct Monte Carlo simulation is introduced to assess the accuracy of the obtained results. The results indicate that the proposed strategy provides acceptable sensitivity analysis for time-variant events, while greatly conserving computational resources and enhancing efficiency.

A single-loop reliability sensitivity analysis strategy for time-dependent rare events with both random variables and stochastic processes

To deal with the time-dependent reliability sensitivity (TDRS) analysis of rare events with both random variables and stochastic processes, a single-loop sampling procedure combined with the cross entropy-based importance sampling strategy (CEIS), namely SCEIS, is derived to lighten the computational burden without loss of precision. Firstly, the TDRS indices are derived to make them implementable by a single-loop importance sampling (IS) procedure. Then, the quasi-optimal IS density is obtained through the extension of the cross-entropy method, which is an adaptive procedure that overcomes the challenges of determining the position and number of design points in traditional IS. Furthermore, the SCEIS can be easily implemented to estimate the TDRS indices for problems with small failure probability. Three examples involving numerical and engineering problems are analyzed to demonstrate the performance of the proposed method. The results obtained from comparing with other existing methods reveal that the proposed method provides satisfactory TDRS analysis for rare events while significantly reducing computational costs.

An electrochemical biosensor with homogeneous dual amplification strategy for sensitive analysis of Pb2+

Herein, a homogeneous double amplified electrochemical biosensing system was designed on the basis of rolling circle amplification (RCA) and DNAzyme-assisted Pb2+ recycling amplification with the assistance of the signal molecule methylene blue (MB), which was used for highly sensitive analysis of Pb2+. In comparison with the conventional electrochemical biosensor, the homogeneous strategy adopted in this work can avoid the complex electrode modification and immobilization processes, and address the issues of the large steric hindrance and the restricted probe freedom on the sensing interface, thus achieving high recognition efficiency between recognition unit and target molecule as well as excellent detection performance of biosensor. In addition, the double amplified method assembling RCA and DNAzyme-assisted Pb2+ recycling amplification can convert a handful of target Pb2+ into abundant signal molecule MB, which would lead to the double amplification of output signal and the significantly enhanced detection sensitivity. Taking advantages of homogeneous dual amplification strategy, the constructed electrochemical biosensor for Pb2+ analysis emerged a good linear range of 50 fM to 500 nM and a detection limit of 16.7 fM with significant selectivity and desirable stability. Impressively, the result presented herein comprise a valuable reference for establishing homogeneous multiple amplification model and constructing innovative biosensing system for sensitive detection of heavy metal ions.

Multiply robust estimation of principal causal effects with noncompliance and survival outcomes.

Treatment noncompliance and censoring are two common complications in clinical trials. Motivated by the ADAPTABLE pragmatic clinical trial, we develop methods for assessing treatment effects in the presence of treatment noncompliance with a right-censored survival outcome. We classify the participants into principal strata, defined by their joint potential compliance status under treatment and control. We propose a multiply robust estimator for the causal effects on the survival probability scale within each principal stratum. This estimator is consistent even if one, sometimes two, of the four working models-on the treatment assignment, the principal strata, censoring, and the outcome-is misspecified. A sensitivity analysis strategy is developed to address violations of key identification assumptions, the principal ignorability and monotonicity. We apply the proposed approach to the ADAPTABLE trial to study the causal effect of taking low- versus high-dosage aspirin on all-cause mortality and hospitalization from cardiovascular diseases.

CRISPR-Cas12a-activated proximity catalytic hairpin assembly strategy for sensitive gene analysis in Streptococcus mutans

The cnm gene in Streptococcus mutans (S. mutans) mediates bacterial binding to extracellular matrix and invasion within host cells, and has been found to be correlated with the occurrence of brain microbleeds. Hence, it is crucial to develop a reliable method to evaluate cnm-positive S. mutans. Herein, we propose a novel diagnostic platform utilizing Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas12a system for accurate identification of the cnm gene, along with proximity catalytic hairpin assembly (CHA) for enhanced signal amplification, for the detection and analysis of the cnm gene in S. mutans. When the cnm gene is present, it activates trans-cleavage activity of Cas12a to trigger the proximity CHA. In contrast to conventional CHA, the hairpin probes in proximity CHA were linked in a sequential manner using a liner ssDNA sequence. Additionally, the CHA process was facilitated by the Duplex-Specific Nuclease (DSN), which enhanced the efficiency and speed of the reaction. The approach exhibits several notable benefits, including i) the target recognition capability of crRNA in CRISPR-Cas system enhances the detection specificity; ii) Cas12a-crRNA is independent on the PAM during recognizing target dsDNA; iii) the use of the DSN and the proximity design of the H1-H2 complex has significantly enhanced the reactive efficiency of proximity CHA. As a result, the method exhibits a low limit of detection of 47 aM and a high selectivity to cnm gene. Moreover, the approach has been effectively employed in clinical settings to accurately differentiate S. mutans from a combination of microorganisms.

Mathematical model with sensitivity analysis and control strategies for marijuana consumption

The current study focuses on reducing the use of marijuana in the population. Marijuana seems to represent a serious threat to public health in developing countries since it is an illegal narcotic with various harmful health effects. The novelty of this manuscript is to modify the non-user, experimental users, recreational users, and addict's (NERA) model for marijuana consumption by incorporating a new class of addicted users under treatment, called the hospitalized class. This real-world issue is expressed in mathematical terms using first-order non-liner ordinary differential equations, and as a result, a mathematical model for marijuana consumption is established. The general population is divided into two main groups: marijuana users and non-users. Subsequently, four subgroups of abusers are created, each reflecting a different stage or degree of substance misuse. The invariant region and basic reproduction number R0 are those parts that are used for the validation of the proposed modified model and to find out the initial transmission rate of marijuana smoking in the general population, respectively. The essential factors influencing cannabis growth were identified based on the results of sensitivity analysis. Additionally, strategies were constructed in the form of prevention objectives to prevent individuals from experiencing increasing effects of marijuana. The effectiveness of these control strategies was subsequently confirmed through numerical simulations, and the numerical simulations were carried out with the help of MATLAB using the RK-4 method.

Self-Assembled Integrated Nanozyme Cascade Biosensor with Dual Catalytic Activity for Portable Urease Analysis.

In this work, a novel nanozyme (Cu@Zr) with all-in-one dual enzyme and fluorescence properties is designed by simple self-assembly. A nanozyme cascade sensor with disodium phenyl phosphate (PPDS) as substrate was first established by exploiting the dual enzymatic activities of phosphatase and laccase. Specifically, phosphatase cleaves the P-O bond of PPDS to produce colorless phenol, which is then oxidized by laccase and complexed with the chromogenic agent 4-aminoantipyrine (4-AP) to produce red quinoneimine (QI). Strikingly, the NH3 produced by the urease hydrolysis of urea can interact with Cu@Zr, accelerating the electron transfer rate and ultimately leading to a significantly improved performance of the cascade reaction. Moreover, the fluorescence at 440 nm of Cu@Zr is further quenched by the inner filter effect (IFE) of QI. Thus, the colorimetric and fluorescence dual-mode strategy for sensitive urease analysis with LODs of 3.56 and 1.83 U/L was established by the proposed cascade sensor. Notably, a portable swab loaded with Cu@Zr was also prepared for in situ urease detection with the aid of a smartphone RGB readout. It also provides a potentially viable analytical avenue for environmental and biological analysis.

ICP-MS and fluorescence dual-mode detection of ZIKV-RNA based on quantum dot labeling with hybridization chain reaction

The detection of Zika virus (ZIKV) is of great significance to human life and health. Herein, we presented an ICP-MS and fluorescent dual-mode sensor for quantitative analysis of Zika virus RNA fragments (ZIKV-RNA), which employed quantum dots (QDs) as signal tags and combined with hybridization chain reaction (HCR). The dual-mode sensor realized cross-checking of the analysis results and improved the assay accuracy. Firstly, the single-stranded DNA (ssDNA) was anchored on the surface of magnetic beads (MBs). Afterward, HCR was conducted with probe DNA-CdSe quantum dots conjugates (pDNA-QDs) and link DNA (lDNA), producing the MBs-ssDNA-[pDNA-QDs-lDNA]n conjugates. In the presence of target ZIKV-RNA, a strand displacement reaction occurred, leading to the dissociation of the [pDNA-QDs-lDNA]n labels from the conjugates into the solution. Finally, the signal intensity was detected using ICP-MS and fluorescence analysis, with achieved limits of detection of 131 pM and 152 pM, respectively. The inter-assay RSD values of fluorescence and ICP-MS were 3.94 % and 4.26 % at 10 nM level, respectively, showing that the method had good precision. This method showed high selectivity and was applied to the analysis of biological fluids. There was no significant difference between the results of ICP-MS modes and fluorescence mode. This method offers a new strategy for sensitivity analysis of ZIKV-RNA and exhibits promise in clinical applications for diagnosis.

Sensitivity analysis of selection bias: a graphical display by bias-correction index.

In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to develop a sensitivity analysis strategy by using the bias-correction index (BCI) approach for quantifying the influence and direction of selection bias. We used a BCI, a function of selection probabilities conditional on outcome and covariates, with different selection bias scenarios in a logistic regression setting. A bias-correction sensitivity plot was illustrated to analyze the associations between proctoscopy examination and sociodemographic variables obtained using the data from the Taiwan National Health Interview Survey (NHIS) and of a subset of individuals who consented to having their health insurance data further linked. We included 15,247 people aged ≥20 years, and 87.74% of whom signed the informed consent. When the entire sample was considered, smokers were less likely to undergo proctoscopic examination (odds ratio (OR): 0.69, 95% CI [0.57-0.84]), than nonsmokers were. When the data of only the people who provided consent were considered, the OR was 0.76 (95% CI [0.62-0.94]). The bias-correction sensitivity plot indicated varying ORs under different degrees of selection bias. When data are only available in a subsample of a population, a bias-correction sensitivity plot can be used to easily visualize varying ORs under different selection bias scenarios. The similar strategy can be applied to models other than logistic regression if an appropriate BCI is derived.

Fractional optimal control strategies for hepatitis B virus infection with cost-effectiveness analysis

Hepatitis B disease is a communicable disease that is caused by the hepatitis B virus and has become a significant health problem in the world. It is a contagious disease that is transmittable from person to person either horizontally or vertically. This current study is aimed at sensitivity analysis and optimal control strategies for a fractional hepatitis B epidemic model with a saturated incidence rate in the sense of the Caputo order fractional derivative approach. Fundamental properties of the proposed fractional order model are obtained and discussed. A detailed analysis of disease-free equilibrium and endemic equilibrium points is given by applying fractional calculus theory, which is a generalized version of classical calculus. Sensitivity indexes are calculated for the classical order model. Illustrative graphics that show the dependence of the sensitivity index on fractional order derivative for alpha in (0,1) are provided. Based on the results of the sensitivity analysis and using Pontryagin’s Maximum Principle, optimal control strategies for preventing hepatitis B infection with vaccination and treatment are considered. Fractional Euler’s method is used to carry out the numerical simulation for the proposed fractional optimal control system and the obtained results are analyzed. The results of the analysis reveal that hepatitis B disease can be prevented if necessary precautionary is taken or effective vaccination and treatment control measures are applied. The analysis of cost-effectiveness is also conducted and discussed.

Two-stage multi-objective optimization of reinforced concrete buildings based on non-dominated sorting genetic algorithm (NSGA-III)

The colossal environmental footprint of the construction industry is a paramount ecological concern, especially in the wake of climate change threats and natural resources depletion. The present study performs a novel two-stage optimization approach for the design of reinforced concrete (RC) frame buildings to minimize the water footprint (WF) and cost, based on non-dominated sorting genetic algorithm (NSGA-III). The first stage includes multi-objective optimization (MOO) with respect to the cost and capacity of cross-sections of RC beam and column elements for any possible combination of design variables using section-based database schema (SBDBS). The second stage is MOO based on NSGA-III to minimize the volumes of reinforcing steel and concrete in RC structure considering the optimal sections generated by SBDBS as a pre-determined list for elements. Four structural archetypes consisting of 4, 8, 12, and 16-story frames were considered in the design optimization problems. The main objective of this optimization is to calculate the cost and WF of RC structures for various unit cost and WF of materials, and as a result, identify the optimal design of RC structures. The result of this optimization framework allows the decision maker to identify the optimal RC structural design for any cost or WF condition by performing a sensitivity analysis, without need for additional structural analysis or optimization. The proposed approach further allows the decision maker to consider the uncertainty of WF and cost of materials that emanate from regional or spatiotemporal variables. The results show that the shape of the optimal design Pareto front (PF) is more related to the values of unit WF of materials than the costs. To highlight the advantages of the proposed procedure, probabilistic sensitivity analysis and decision-making strategy were performed and pertinent conclusions were drawn.

Topology optimization of geometrically nonlinear structures using reduced-order modeling

High computational costs are encountered in topology optimization problems of geometrically nonlinear structures since intensive use has to be made of incremental-iterative finite element simulations. To alleviate this computational intensity, reduced-order models (ROMs) are explored in this paper. The proposed method targets ROM bases consisting of a relatively small set of base vectors while accuracy is still guaranteed. For this, several fully automated update and maintenance techniques for the ROM basis are investigated and combined. In order to remain effective for flexible structures, path derivatives are added to the ROM basis. The corresponding sensitivity analysis (SA) strategies are presented and the accuracy and efficiency are examined. Various geometrically nonlinear examples involving both solid as well as shell elements are studied to test the proposed ROM techniques. Test cases demonstrates that the set of degrees of freedom appearing in the nonlinear equilibrium equation typically reduces to several tenth. Test cases show a reduction of up to 6 times fewer full system updates.

Multifaceted Insight into Sensitivity Analysis and Environmental Impact on Human Health of Soil Contamination Risk Assessment.

Sensitivity analysis is a valuable method for evaluating the impact of model parameters on health risk characterization, thereby supporting the prediction of critical uncertainty factors. However, limitations arise in terms of cross-disciplinary discussions and in-depth analyses of previous research. To overcome these limitations, a systematic and multifaceted approach was introduced for analyzing the parameter sensitivities in soil contamination risk assessment. This approach specifically targeted the 12 main parameters associated with 65 soil contaminants for health risk assessment, employing detailed authoritative statistics for risk assessment. Screening analysis revealed that identified heavy metals and organics were influenced by key parameters, such as PM10, body weight of adults (BWa), daily air inhalation rate of adults (DAIRa), air exchange rate (ER), and typical soil parameters. PM10 showed a positive 100% correlation with inorganics and metals, but BWa and DAIRa exhibited different impacts on different chemicals, with an increase in potential risk observed with higher BWa and lower DAIRa. Furthermore, incorporating soil parameters in the analysis showed that compact soil could improve the protection against vapor organic compounds for human health. This refined study presents a comprehensive strategy for sensitivity analysis in health risk assessment of soil contamination, thereby offering substantial support for the protection and preservation of human health. A logical framework also was provided for addressing the limitations of sensitivity analysis and facilitating an understanding of the complex relationships between model parameters and the health risk of soil contamination.

Sensitive misalignment oriented loaded contact pressure regulation model for spiral bevel gears

In full consideration of misalignment having strong sensitivity with respect to meshing transmission performances, an innovative data-oriented loaded contact pressure regulation model is established for spiral bevel gears by fine-modifying the optimal selected misalignment. Here, accurate loaded contact pressure evaluations includes: i) geometric boundary considering size, direction, and position; ii) loaded contact pressure distribution; iii) the maximum loaded contact pressure. Firstly, an improved tooth contact analysis with error (eTCA) is developed to establish data-oriented functional relationships. Loaded contact pressure prediction considering the different evaluations is performed for the initial misalignments and its performances. Moreover, considering accurate prediction and feedback, data-oriented regulation model is established by using numerical loaded tooth contact analysis (NLTCA). Focusing on accurate loaded contact pressure distribution, the constrained sub-objectives are the geometric boundary and the maximum one. Finally, the sensitivity coefficients of each misalignment to each loaded contact pressure evaluation at each contact point are calculated. The sensitivity analysis strategy is provided to get an accurate and effective data-oriented loaded contact pressure regulation model. Given numerical instances can verify that the proposed method get an important access to the meshing transmission performance monitoring and optimization for complex spiral bevel gears.

The prototype, mathematical model, sensitivity analysis and preliminary control strategy for Adaptive Tuned Particle Impact Damper

The paper presents a novel approach for prototyping and modelling of the Adaptive Tuned Particle Impact Damper (ATPID). After introducing the operation and potential disadvantages of the classical Particles Impact Dampers (PIDs) the authors propose the concept of single-grain controllable damper, which can adapt to actual dynamic excitation by a real-time change of the container height. The investigations focus on the methodology of simplified mathematical modelling of the ATPID damper based on grain physical properties, nonlinear soft contact theory, and control function of the absorber height being a novel component used to optimize dynamic response of the system. The proposed ATPID model is positively verified against the experimental results obtained from the developed test stand including a vibrating beam equipped with the proposed innovative attenuator. The conducted analyses clearly reveal the operating principles of the ATPID damper, the types of grain movement, the influence of shock absorber parameters on the vibrating system response and the energy balance of the system. The solution of the formulated optimization problem aimed at minimization of vibration amplitudes allows to find the optimal damper height for various physical parameters of the grain and the external excitation and to achieve a high efficiency of the proposed damper reaching 90%. In addition, a real-time control strategy providing adaptation of the ATPID damper to changing amplitude of kinematic excitation and effective mitigation of steady-state vibrations is proposed and verified experimentally.

Sensitivity Analysis and Multi-Objective Optimization Strategy of the Curing Profile for Autoclave Processed Thick Composite Laminates.

To mitigate the risk of manufacturing defects and improve the efficiency of the autoclave-processed thick composite component curing process, parameter sensitivity analysis and optimization of the curing profile were conducted using a finite element model, Sobol sensitivity analysis, and the multi-objective optimization method. The FE model based on the heat transfer and cure kinetics modules was developed by the user subroutine in ABAQUS and validated by experimental data. The effects of thickness, stacking sequence, and mold material on the maximum temperature (Tmax), temperature gradient (ΔT), and degree of curing (DoC) were discussed. Next, parameter sensitivity was tested to identify critical curing process parameters that have significant effects on Tmax, DoC, and curing time cycle (tcycle). A multi-objective optimization strategy was developed by combining the optimal Latin hypercube sampling, radial basis function (RBF), and non-dominated sorting genetic algorithm-II (NSGA-II) methods. The results showed that the established FE model could predict the temperature profile and DoC profile accurately. Tmax always occurred in the mid-point regardless of laminate thickness; the Tmax and ΔT increased non-linearly with the increasing laminate thickness; but the DoC was affected slightly by the laminate thickness. The stacking sequence has little influence on the Tmax, ΔT, and DoC of laminate. The mold material mainly affected the uniformity of the temperature field. The ΔT of aluminum mold was the highest, followed by copper mold and invar steel mold. Tmax and tcycle were mainly affected by the dwell temperature T2, and DoC was mainly affected by dwell time dt1 and dwell temperature T1. The multi-objective optimized curing profile could reduce the Tmax and tcycle by 2.2% and 16.1%, respectively, and maintain the maximum DoC at 0.91. This work provides guidance on the practical design of cure profiles for thick composite parts.

Shimmy analysis of straddle-type monorail vehicle with single-axle bogies based on factor model

Shimmy is one of the important factors affecting the ride comfort and lateral stability of straddle-type monorail vehicles with single-axle bogies (SMVS). To obtain the main parameters affecting SMVS shimmy, a global sensitivity analysis strategy for SMVS shimmy is proposed. The shimmy response surface model is constructed, and the sensitivity analysis of the shimmy response surface model is carried out by the Sobol method. Based on this, the shimmy factor model of SMVS is established by factor analysis method. A novel SMVS shimmy analysis method based on factor model is proposed. The shimmy factor model is verified by the dynamic model of SMVS. The results show that 11 main factors affecting the shimmy are obtained by sensitivity analysis. Through the dimension reduction technology of factor analysis, four main factors affecting SMVS shimmy are extracted. The complex coupling relationship between the shimmy factors of SMVS is revealed. The established shimmy factor model can effectively analyse the shimmy state of SMVS, and the dynamic simulation results also show consistency. This study not only proposes a new perspective to understand the SMVS shimmy mechanism, but also provides the foundation for further optimal design of monorail vehicle stability.

Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction.

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a transcription factor-gene regulatory network (TRN), which is modeled through a Bayesian network (BN). In this article, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide optimal experimental design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. We have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

Vibroacoustic Transfer Characteristics of Underwater Cylindrical Shells Containing Complex Internal Elastic Coupled Systems

Cylindrical shells containing complex elastic coupling systems are the main structural form of underwater vehicles. Therefore, in this paper, the vibroacoustic radiation problem of underwater cylindrical shells containing complex internal elastic coupling systems is studied. Firstly, the dynamics model of the complex elastic coupled system is established through the method of integrated conductivity. The sound pressure distribution law and the general magnitude relationship between the performance index of hydroacoustic radiation and vibration isolation are investigated through numerical simulation. A strategy of global sensitivity analysis and related parameter optimization is carried out, by applying the Sobol’ method to the dynamics model. It could be concluded that the main flap of sound pressure at low and medium frequencies appears in the direction of the excitation force or the perpendicular to the excitation force, the magnitudes correspondence between the vibration level drop—power flow—hydroacoustic radiation at low frequencies can be expressed as a relatively simple function, and the vibroacoustic transmission of the system at lower order resonance frequencies is dominated by the parameter configuration of the vibration isolation device, while at higher frequencies is more influenced by the modalities of the base structure. The transfer power flow and the level drop are used as objective functions to optimise the acoustic radiation index of the coupled system, with the best results obtained when the transfer power flow and the level drop are used together as objective functions.

Using deep generative adversarial network to explore novel airfoil designs for vertical-axis wind turbines

Wind energy has emerged as an attractive alternative to the current fossil fuel-based energy mix. In this context, small-scale H-Darrieus vertical-axis wind turbines (VAWTs) combine interesting characteristics for harvesting wind energy in urban-like conditions. Still, H-Darrieus turbines are reported to experience relatively low aerodynamic efficiency. Even though several devices have been proposed to increase the aerodynamic performance of H-Darrieus turbines, the literature seems to overlook the potential of specifically designed airfoil shapes. In part, this is a by-product of different shortcomings related to the most common airfoil parameterization methods, such as restricted shape variability, high dimensionality, discontinuous spaces, and/or non-orthogonal parameters. Seeking to overcome these drawbacks altogether, we investigate here the benefits of the Bézier-GAN as an airfoil parameterization method for H-Darrieus turbines. For that, we use computational fluid dynamics (CFD) simulations along with sensitivity analysis, metamodeling, and optimization strategies. The results show that the Bézier-GAN integrates nicely with the proposed framework, substantially reducing the total computational cost of the experiment. By expanding the bounds of the latent design space, we can easily explore novel airfoil designs. The sensitivity analysis clearly indicates a lack of two-way interactions between the latent variables, which further simplifies both the metamodeling and the optimization processes. The optimal geometry increased the turbine performance by 20.5% relative to a NACA 0015 and by 9.1% relative to a NACA 0021—two common airfoil shapes used in H-Darrieus turbines. Interestingly, the optimal geometry was found outside the original bounds of the design space, further confirming that the search for novel airfoil designs may open the way for better aerodynamic performance of small-scale H-Darrieus turbines.