Multi-parameter Sensitivity Analysis Research Articles

A computational analysis of the role of integrins and Rho-GTPases in the emergence and disruption of apical-basal polarization in renal epithelial cells.

Apical-basal polarization in renal epithelial cells is crucial to renal function and an important trigger for tubule formation in kidney development. Loss of polarity can induce epithelial-to-mesenchymal transition (EMT), which can lead to kidney pathologies. Understanding the relative and combined roles of the involved proteins and their interactions that govern epithelial polarity may provide insights for controlling the process of polarization via chemical or mechanical manipulations in an in vitro or in vivo setting. Here, we developed a computational framework that integrates several known interactions between integrins, Rho-GTPases Rho, Rac and Cdc42, and polarity complexes Par and Scribble, to study their mutual roles in the emergence of polarization. The modeled protein interactions were shown to induce the emergence of polarized distributions of Rho-GTPases, which in turn led to the accumulation of apical and basal polarity complexes Par and Scribble at their respective poles, effectively recapitulating polarization. Our multiparametric sensitivity analysis suggested that polarization depends foremost on the mutual inhibition between Rac and Rho. Next, we used the computational framework to investigate the role of integrins and GTPases in the generation and disruption of polarization. We found that a minimum concentration of integrins is required to catalyze the process of polarization. Furthermore, loss of polarization was found to be only inducible via complete degradation of the Rho-GTPases Rho and Cdc42, suggesting that polarization is fairly stable once it is established. Comparison of our computational predictions against data from in vitro experiments in which we induced EMT in renal epithelial cells while quantifying the relative Rho-GTPase levels, displayed that EMT coincides with a large reduction in the Rho-GTPase Rho. Collectively, these results demonstrate the essential roles of integrins and Rho-GTPases in the establishment and disruption of apical-basal polarity and thereby provide handles for the in vitro or in vivo regulation of polarity.

Parameter sensitivity analysis for CO-mediated sickle cell de-polymerization

This study investigates the impact of melting/binding rates (referred to hereafter as the parameters) over the polymers and monomers on the dynamics of carbon-monoxide-mediated sickle cell hemoglobin (HbS) de-polymerization. Two approaches, namely the traditional sensitivity analysis (TSA) and the multi-parameter sensitivity analysis (MPSA), have been developed and applied to the mathematical model system to quantify the sensitivities of polymers and monomers to the parameters. The Runge-Kutta method and the Monte-Carlo simulation are employed for the implementation of the sensitivity analyses. The TSA utilizes the traditional sensitivity functions (TSFs). The MPSA enumerates the overall effect of the model input parameters on the output by perturbing the model input parameters simultaneously within large ranges. All four concentrations (namely, de-oxy HbS monomers, CO-bound HbS monomers, de-oxy Hbs polymer and CO-bound HbS polymer) as model outputs, and all four binding/melting rates (namely, the CO binding and melting rates for polymers and monomers) as input parameters are considered in this study. The sensitivity results suggest that TSA and MPSA are essentially consistent.

Application of cell mapping to control optimization for an antenna servo system on a disturbed carrier

Abstract. The cell-mapping method, due to its global optimality, has been applied to solve multi-objective optimization problems (MOPs) and optimal control problems. However, the curse of dimensionality limits its application in high-dimensional systems. In this paper, the multi-parameter sensitivity analysis is investigated to reduce the parameter space dimension, which broadens the application of cell mapping to MOPs in high-dimensional parameter space. A post-processing algorithm for MOPs is introduced to help choose proper control parameters from the Pareto set. The proposed scheme is applied successfully in the control parameter optimization of an adaptive nonsingular terminal sliding-mode control for an antenna servo system on a disturbed carrier. Moreover, as the existing global optimal tracking control with an adjoining cell-mapping method may generate tracking-phase differences, an optimal-sliding-mode combined-control strategy is proposed. By using the combined-control strategy, the azimuth and pitch angles of the antenna system are controlled to catch up to a target trajectory with the minimum cost function and to keep high-precision tracking after that.

Feasible domain of cycling operating conditions and model parameters for Holby–Morgan model of platinum catalyst degradation in PEMFC

In the paper, electrochemical behavior of platinum based cathode catalyst in a polymer electrolyte membrane fuel cell is studied under non-symmetric square-wave electric potential cycling applied in accelerated stress test. Following the modeling by Holby and Morgan, degradation due to Pt dissolution, ion diffusion, and oxide coverage is performed with respect to cycling operating conditions and model parameters. The computer simulation demonstrates impact of the electrochemical surface area loss rate under variation of the temperature, pH, platinum particle diameter, loading, Pt to Carbon volume fraction, and their simultaneous effect. From physical consistency and multi-parametric sensitivity analysis, a statement on feasible domain of the parameters for the PEMFC operation is concluded.

Can population models be a useful tool for evaluating the status of data-deficient species?

Population and life history data remains sparse for many species, with low prospects for improvement due to the costs of research and difficulties associated with gathering field data. However, data-deficient species may nevertheless face substantial threats. Effective approaches are required to quantitatively assess threats and the effects of data uncertainty in the assessments. We explore the use of population models to assess the impact of uncertain threatening processes in the data-deficient Tasmanian masked owl Tyto novaehollandiae castanops. We addressed data-deficiency with a multi-parameter sensitivity analysis. This showed that reproductive output, adult mortality and juvenile mortality had the greatest impact on population size and growth. Using literature on a related but better studied species, the barn owl (Tyto alba), we then used population models to simulate the impacts of rodenticide exposure by increasing stochastic variation in reproductive output, adult, and juvenile mortality. We investigated rodenticide exposure because, although there is evidence of secondary poisoning of Tasmanian masked owls through consumption of contaminated prey, the effects of potentially widespread exposure on the population are unclear. The effect on population size and growth from increased stochasticity in the rodenticide sensitive parameters was negative. However, the magnitude of impacts depended on the baseline juvenile mortality rate used in each model, highlighting the importance of this uncertain parameter on our assessment of risk. Our study serves as an example of the use of population models for exploring hypothetical scenarios for data-deficient populations. Population models are an important tool for setting priorities for research and sounding a precautionary alarm where potential threats may be concealed by uncertainty.

A simplified ANN-based evaluation method for creep sensitive girder bridge identification at preliminary design stage

Excessive deflection which exceeded expectations has been identified as one of the main problems contributing to long-term performance degradation of long-span girder bridges. Although the 3D refined finite element model of the full bridge can improve the prediction accuracy of long-term creep deformation, there is still a lack of efficient creep-sensitive structural judgment methods in the preliminary design stage. This study was thus intended to develop a new evaluation method and model to identify the possibility of excessive deflection will occur by utilizing simple structural design parameters, which contributed to new understandings of the creep deformation of bridge structural level. A finite element model database established by 20 long-span prestressed concrete girder bridges with actual project backgrounds was used for providing training, testing, and validation sample datasets for the artificial neural networks (ANN). The Back Propagation (BP) model was chosen for creating mappings among variables and results and solving complex problems with a fairly accurate prediction even without sufficient information. The indicator of deflection-span ratio difference between creep models was proposed as the key analysis factor. A case study was conducted to validate the effectiveness of the ANN model. The mean absolute error (MAE) between the results of the ANN model and FE analysis was 6.25%, which indicated a high consistency. The analysis of bridge design parameters using the ANN model revealed that it had a high risk of excessive deformation, which was consistent with the conclusion of the actual situation. Multiparameter sensitivity analysis results from the model also suggest that the side-span ratio, the main span, and the deflection caused by the pavement are the most three significant impacts compared to different input variables in this model. Based on the results, it can be concluded that the performance of the ANN-based technique can predict possible excessive creep problems in advance.

Molten salt reactor system dynamics in simulink and modelica, a code to code comparison

Understanding reactor dynamics is essential for the development of MSRs. Modern computational software allows rapid development and simulation of multiphysics systems. As such, MSRs can be simulated in various programming languages to study their behavior. Meanwhile, employing various software environments and methods can help ensure that simulation results are accurate and openly accessible to the broader research community. In this publication, two variations of software implementation for an MSR dynamic modeling tool in MATLAB Simulink and OpenModelica are compared. A code-to-code comparison is conducted, and both implementations are benchmarked for accuracy and simulation run time. In addition to the benchmark, a pilot quality assurance program is introduced for the implementation of the OpenModelica model. Finally, a multi-parameter sensitivity analysis is performed to demonstrate possible applications of the Modelica model.

Pore-scale parametric sensitivity analysis of liquid water transport in the gas diffusion layer of polymer electrolyte membrane fuel cell

Water management in the gas diffusion layer (GDL) is crucial for improving the performance of polymer electrolyte membrane fuel cells (PEMFCs). In this study, a modified stochastic method that fully took into account the fiber deflection and layered structure in the thickness direction was proposed to reconstruct the realistic porous structures of three-dimensional (3D) GDL. The liquid water transport within the GDL was then numerically studied using the 3D Lattice Boltzmann method (LBM). Subsequently, the contributions of different parameters (porosity, fiber diameter, thickness, wettability, and pressure difference) to the transport process of liquid water were elucidated by implementing a multi-parametric sensitivity analysis (MPSA). The numerical results indicate that the liquid water penetration process can be divided into initial injection, selective percolation, and diffusion stages. With the decreases in porosity or increase in contact angle, the flow is inclined to transform from gentle flow to accumulation and secondary breakthrough owing to the increased percolation resistance. Further accumulation of the liquid within GDL can be identified when the evolution time progresses from the breakthrough point to the creep point. Moreover, for GDL, the degree of influence of the structural parameters on the breakthrough velocity is ranked in the order of GDL porosity, fiber diameter, GDL thickness, pressure difference, and GDL wettability. The total liquid saturation is dominated by GDL wettability and is insensitive to GDL porosity and fiber diameter owing to the conflict between the percolation resistance of the pores and the percolation time within GDL. The current work consolidates the understanding of the influencing mechanisms of different structural parameters on liquid water transport characteristics, contributing to the optimal design of the gas diffusion layer.

Thermal management research for a 2.5 MWh energy storage power station on airflow organization optimization and heat transfer influential characteristics

Most of the thermal management for the battery energy storage system (BESS) adopts air cooling with the air conditioning. However, the air-supply distance impacts the temperature uniformity. To improve the BESS temperature uniformity, this study analyzes a 2.5 MWh energy storage power station (ESPS) thermal management performance. It optimizes airflow organization with louver fins and simulates its heat transfer behavior. To improve the flow rate distribution along the airflow passage, the air-supply organization of louver fins for the overhead air conditioner is optimized. It indicates that the air-supply angle (A) for louver fins and the battery spacing (D) impacts greatly on BESS thermal management performance. The air supply optimal organization with the A of 90° and the D of 18mm was determined, of which the average temperature (TA) rise of the battery can be reduced by 18%, and the temperature difference (TD) is decreased by 23%. Furthermore, a multi-parametric sensitivity analysis is conducted, and the different heat-flow parameter influential sequence for the TA and TD are revealed. The air supply volume flow rate (L) and the air supply temperature (AST) has a significant effect on the TA reduction, and the AST shows significance on the TD decreasing. When AST is reduced to 290 K, the TA rise of batteries decreased by 71%. When the L is increased 10 times, the TA rise of batteries decreased by 227%. When the AST is decreased to 290 K, the TD is reduced by 39%. When the L for air supply is increased by 10 times, the TD will increase by 28%. In this research, the louver fins are firstly adopted for orifices air supply organization to improve thermal field uniformity for BESS.

Impact of nickel–titanium super-elastic material properties on the mechanical performance of self-expandable transcatheter aortic valves

Self-expandable transcatheter aortic valves (TAVs) elastically resume their initial shape when implanted without the need for balloon inflation by virtue of the nickel–titanium (NiTi) frame super-elastic properties. Experimental findings suggest that NiTi mechanical properties can vary markedly because of a strong dependence on the chemical composition and processing operations. In this context, this study presents a computational framework to investigate the impact of the NiTi super-elastic material properties on the TAV mechanical performance. Finite element (FE) analyses of TAV implantation were performed considering two different TAV frames and three idealized aortic root anatomies, evaluating the device mechanical response in terms of pullout force magnitude exerted by the TAV frame and peak maximum principal stress within the aortic root. The widely adopted NiTi constitute model by Auricchio and Taylor (1997) was used. A multi-parametric sensitivity analysis and a multi-objective optimization of the TAV mechanical performance were conducted in relation to the parameters of the NiTi constitutive model. The results highlighted that: five NiTi material model parameters (EA, σtLS, σtUS, σtUE and σcLS) are significantly correlated with the FE outputs; the TAV frame geometry and aortic root anatomy have a marginal effect on the level of influence of each NiTi material parameter; NiTi alloy candidates with pareto-optimal characteristics in terms of TAV mechanical performance can be successfully identified. In conclusion, the proposed computational framework supports the TAV design phase, providing information on the relationship between the super-elastic behavior of the supplied NiTi alloys and the device mechanical response.

Conjugate heat transfer study of various cooling structures and sensitivity analysis of overall cooling effectiveness

The conjugate heat transfer of a turbine blade is influenced by several factors. To analyze the influence of each factor, the published one-dimensional conjugate heat transfer model was improved through theoretical analysis in this study. An overall cooling effectiveness equation containing three dimensionless parameters (adiabatic film cooling effectiveness η, Biot number on the mainstream side Big, and ratio between the heat transfer coefficients of the external and internal walls hg/hi) was obtained. The sensitivity of the overall cooling effectiveness ϕ to these three parameters was obtained through a multi-parameter sensitivity analysis. The results showed that increasing η could improve ϕ the most effectively. The interactions between the dimensionless parameters were analyzed by developing sensitivity charts. The results showed that increasing η from 0.4 to 0.5 could reduce the sensitivity of ϕ to the other two parameters by approximately 15%, whereas increasing Big had little effect on the sensitivity of ϕ to each dimensionless parameter. Increasing hg/hi could improve the sensitivity to η. The above conclusions could also be applied to the plate film hole and plate impingement effusion structures. The effects of different internal cooling structures and film hole structures on the three dimensionless parameters were studied by performing numerical simulations, which verified the accuracy of the one-dimensional conjugate heat transfer model in this study. The results showed that the internal cooling structures had little effect on the distribution of η and Big. The heat transfer coefficient on the coolant side could be effectively improved by installing film holes. The film hole structures mainly affected ϕ by influencing the distribution of η.

An investigation into the multi-parameter identification and model optimization strategy of the PCT curves of hydrogen storage alloys by multiple intelligent algorithms

A multi-parameter sensitivity analysis method was proposed based on Latin hypercube sampling and the rank correlation method. The general genetic algorithm was improved by incorporating a niche parallel algorithm, suspicious peak point judgment, and a local search strategy, and both the improved genetic algorithm and multi-parameter sensitivity analysis method were implemented through C++ programming. The hydrogen pressure-composition isotherm (PCT curve) was found to play an important role in the study of hydrogen storage properties of metallic materials, and its reaction enthalpy change term was widely replaced by a power series form of smoothing. Based on the improved multi-parameter sensitivity analysis method, the effects of the range of parameter values and the number of samples on sensitivity were investigated, where the sensitivity ranking of the 10 parameters in the classical model was obtained. In addition, the equilibrium hydrogen pressure expression models of the highest order i of the power series from 3 to 9 were developed. The influence of the highest order on the optimization of multi-parameter identification was compared using an improved genetic algorithm, and we concluded that the eighth order polynomial model had the optimal convergence and fitting effect. Based on the inspiration of the function trend shape, two innovative forms of the PCT model power series were proposed in this work. Combined with typical examples, three high-order models were fully evolved, and a better power series form was obtained compared to the traditional model.

Energy Optimization for Fenestration Design: Evidence-Based Retrofitting Solution for Office Buildings in the UAE

With the prevalent use of large glazings, particularly in office buildings, offices receive an abundance of light and are among the largest consumers of electricity. Moreover, in an extreme hot arid climate such as in the UAE, achieving comfortable daylighting levels without increasing solar heat gain is a challenge, in which the window or fenestration design plays an essential role. This research adopts a case study of a higher education (HE) office building on the United Arab Emirates University (UAEU) campus, selected to investigate an evidence-based retrofitting solution for the west façade that can be applied in existing office buildings in the UAE in order to reduce cooling energy load as well as enhance indoor environmental quality. To achieve an evidence-based retrofitting solution, the research design built upon a comprehensive exploratory investigation that included indoor environmental quality physical monitoring and occupant satisfaction surveying. Model simulation was performed by means of DesignBuilder software to perform a single- and multiparameter sensitivity analysis for three key passive window design parameters, i.e., window-to-wall ratio, glazing type, and external shading, aimed towards minimizing annual cooling load and solar heat gain, while maintaining appropriate indoor daylight illuminance levels. The results highlight the importance of the window-to-wall ratio (WWR), as it is the single most significant parameter effecting total energy consumption and daylighting levels. The results recommend 20–30% WWR as the optimum range in the west façade. However, by utilizing high performance glazing types and external shading, equal energy savings can be achieved with a larger WWR. Double Low E tinted glazing and 0.4 projection shading overhang and side fin revealed a noteworthy reduction of energy use intensity of 14%. The study concludes with final retrofitting solutions and design recommendations that aim to contribute validated knowledge towards enhancing window performance in a hot arid climate to guide architects and stakeholders to apply a range of passive parameters towards reducing energy consumption and improving occupant comfort in office buildings.

Computational analysis of cortical neuronal excitotoxicity in a large animal model of neonatal brain injury

BackgroundNeonatal hypoxic brain injury is a major cause of intellectual and developmental disability. Hypoxia causes neuronal dysfunction and death in the developing cerebral cortex due to excitotoxic Ca2+-influx. In the translational piglet model of hypoxic encephalopathy, we have previously shown that hypoxia overactivates Ca2+/Calmodulin (CaM) signaling via Sarcoma (Src) kinase in cortical neurons, resulting in overexpression of proapoptotic genes. However, identifying the exact relationship between alterations in neuronal Ca2+-influx, molecular determinants of cell death, and the degree of hypoxia in a dynamic system represents a significant challenge.MethodsWe used experimental and computational methods to identify molecular events critical to the onset of excitotoxicity-induced apoptosis in the cerebral cortex of newborn piglets. We used 2–3-day-old piglets (normoxic [Nx], hypoxic [Hx], and hypoxic + Src-inhibitor-treatment [Hx+PP2] groups) for biochemical analysis of ATP production, Ca2+-influx, and Ca2+/CaM-dependent protein kinase kinase 2 (CaMKK2) expression. We then used SimBiology to build a computational model of the Ca2+/CaM-Src-kinase signaling cascade, simulating Nx, Hx, and Hx+PP2 conditions. To evaluate our model, we used Sobol variance decomposition, multiparametric global sensitivity analysis, and parameter scanning.ResultsOur model captures important molecular trends caused by hypoxia in the piglet brain. Incorporating the action of Src kinase inhibitor PP2 further validated our model and enabled predictive analysis of the effect of hypoxia on CaMKK2. We determined the impact of a feedback loop related to Src phosphorylation of NMDA receptors and activation kinetics of CaMKII. We also identified distinct modes of signaling wherein Ca2+ level alterations following Src kinase inhibition may not be a linear predictor of changes in Bax expression. Importantly, our model indicates that while pharmacological pre-treatment significantly reduces the onset of abnormal Ca2+-influx, there exists a window of intervention after hypoxia during which targeted modulation of Src-NMDAR interaction kinetics in combination with PP2 administration can reduce Ca2+-influx and Bax expression to similar levels as pre-treatment.ConclusionsOur model identifies new dynamics of critical components in the Ca2+/CaM-Src signaling pathway leading to neuronal injury and provides a feasible framework for drug efficacy studies in translational models of neonatal brain injury for the prevention of intellectual and developmental disabilities.

Multi-parameter seismic fragility and sensitivity analysis of long-span cable-stayed bridge based on multi-task lasso regression

The accuracy and efficiency of the seismic fragility assessment of bridges encounter challenges when the span increases and the structure becomes more complex, which is significantly affected by various uncertain parameters and has to be studied. Multi-parameter seismic fragility analysis (MP-SFA) is a powerful tool that explicitly connects fragilities and multiple variables, including structural parameters and seismic intensity measures (IM). This paper presents an efficient generation method for MP-SFA based on multi-task Lasso regression and is verified using an example of a long-span cable-stayed bridge. Based on the framework of MP-SFA, the mean fragility curves of various bridge components are established, which averages the influence of all considered random parameters. A sensitivity analysis method is also established to identify significant variables for fragility function. The analysis and numerical results show the necessity of MP-SFA to overcome the dispersion of bridge fragility due to the incapability of a single parameter. PGV is shown as the most significant parameter for the case. However, the other IMs also have a non-negligible impact on fragility.

Interval-Valued and Circular Intuitionistic Fuzzy Present Worth Analyses

Present worth (PW) analysis is an important technique in engineering economics for investment analysis. The values of PW analysis parameters such as interest rate, first cost, salvage value and annual cash flow are generally estimated including some degree of uncertainty. In order to capture the vagueness in these parameters, fuzzy sets are often used in the literature. In this study, we introduce interval-valued intuitionistic fuzzy PW analysis and circular intuitionistic fuzzy PW analysis in order to handle the impreciseness in the estimation of PW analysis parameters. Circular intuitionistic fuzzy sets are the latest extension of intuitionistic fuzzy sets defining the uncertainty of membership and non-membership degrees through a circle whose radius is r. Thus, we develop new fuzzy extensions of PW analysis including the uncertainty of membership functions. The methods are given step by step and an application for water treatment device purchasing at a local municipality is illustrated in order to show their applicability. In addition, a multi-parameter sensitivity analysis is given. Finally, discussions and suggestions for future research are given in conclusion section.

Improved model-free adaptive predictive control method for direct data-driven control of a wastewater treatment process with high performance

In this paper, an improved model-free adaptive predictive control (IMFAPC) method with controller parameters sensitivity discrimination and optimization is proposed, to solve the problem that using conventional methods is difficult to effectively control the nitrate concentration and dissolved oxygen concentration of wastewater treatment process (WWTP). First, aiming at the nonstationary dynamic characteristics of WWTP and the limitation of control accuracy of conventional MFAPC method in the nonlinear dynamic system control, a novel improved MFAPC method is proposed considering the effect of the WWTP output tracking error variation on the control of nitrate concentration and dissolved oxygen concentration. Meanwhile, in order to obtain the optimum control performance, the sensitivity discrimination and optimization for the multi-parameter set of the proposed IMFAPC method are performed by combining the Monte Carlo experiment based multi-parameter sensitivity analysis and the hybrid intelligent optimization. Then, the stability of the proposed IMFAPC scheme is analyzed and proved by the BIBO method. Finally, various data experiments are carried out to verify the proposed method. The experimental results demonstrate that the proposed method can control the nitrate nitrogen and dissolved oxygen concentration rapidly and accurately under different operating conditions.

Neural network assisted multi-parameter global sensitivity analysis for nanostructure scatterometry

Optical scatterometry has been widely used for measuring periodic thin-film structures in a fast and non-invasive way. However, shrinking structure dimensions, along with increasing structural complexity, give rise to challenging such nanoscale scatterometry. For in-line nano-fabrication process control, it is critical to enhance the measurability of small feature change. Accordingly, there is a strong need to evaluate the global sensitivity performance of different measurement strategies for identifying the best scheme that exhibits a remarkable change of optical responses under small dimension fluctuation. Such analysis requires not only an appropriate sensitivity indicator but also time-intensive computation.This paper presents an efficient approach to multi-parameter global sensitivity analysis (GSA) for scatterometry. A neural network (NN) assisted, moment-independent, and adaptive GSA method is proposed. A computationally cost-effective deep neural network is developed as a surrogate model for circumventing the simulation-based forward modeling. A scatterometry configuration problem for typical grating nanostructures is studied to demonstrate the effectiveness and the generalization potential of our approach. Our results show that the proposed approach is a powerful tool for not only examining sensitivity performance but also expediting the GSA optimization process for fast scatterometry.

Principles underlying efficient exciton transport unveiled by information-geometric analysis

Adapting techniques from the field of information geometry, we show that open quantum system models of Frenkel exciton transport, a prevalent process in photosynthetic networks, belong to a class of mathematical models known as “sloppy.” Performing a Fisher-information-based multiparameter sensitivity analysis to investigate the full dynamical evolution of the system and reveal this sloppiness, we establish which features of a transport network lie at the heart of efficient performance. We find that fine tuning the excitation energies in the network is generally far more important than optimizing the network geometry and that these conclusions hold for different measures of efficiency and when model parameters are subject to disorder within parameter regimes typical of molecular complexes involved in photosynthesis. Our approach and insights are equally applicable to other physical implementations of quantum transport.Received 8 May 2020Revised 31 May 2021Accepted 8 June 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.L032001Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasExcitonsOpen quantum systems & decoherenceQuantum transportTechniquesGeometryInformation theoryMaster equationCondensed Matter, Materials & Applied PhysicsAtomic, Molecular & Optical

Wall thinning behaviors of high strength 0Cr21Ni6Mn9N tube in numerical control bending considering variation of elastic modulus

Wall thinning, as one of the key defects in tube bending determined the forming quality and limit, is more easily to occur due to the specific properties of high strength 0Cr21Ni6Mn9N stainless steel tube (0Cr21Ni6Mn9N-HS tube). To achieve tube accuracy numerical control (NC) bending forming, the wall thinning characteristics of the 0Cr21Ni6Mn9N-HS tube should be clarified. An analytical model was proposed to reveal the essential relation between tube parameters and wall thickness distribution. Considering the varied elastic modulus, a finite element (FE) model was applied to explore the wall thinning under different geometrical and process parameters. Using the modified multi-parameter sensitivity analysis method combined with FE simulation, the sensitivity of the wall thinning to geometrical and process parameters was carried out. The experiments of NC tube bending were conducted to validate the analytical and simulate results. The results show that the varied elastic modulus can enhance the wall thinning degree, but has no obvious effect on wall thinning characteristics. The wall thinning characteristics under different geometrical and process parameters are revealed and the reasonable parameters ranges for the 0Cr21Ni6Mn9N-HS tube in NC bending are obtained. The most sensitive parameter on wall thinning is the relative bending radius, while the bending angle is the least one.