Monte Carlo Simulations Research Articles

Importance of magnetic shape anisotropy in determining triaxial magnetic anisotropy of ferromagnetic semiconductor CrSCl monolayer

Abstract Magnetic anisotropy (MA) is pivotal for stabilizing long-range magnetic order in two-dimensional (2D) systems against thermal fluctuations. Here, we conduct a comprehensive investigation of the electronic and magnetic properties of CrSCl monolayer using first-principles methods and Monte Carlo (MC) simulations. Our results reveal that CrSCl monolayer exhibit a direct band gap ferromagnetic semiconductor (FMS) with a high Curie temperature (TC, 143 K). Notably, we identify triaxial magnetic anisotropy in this monolayer, characterized by the easy magnetization axis along the y-axis, intermediate axis along the x-axis, and hard axis along the z-axis. This anisotropy arises from a combination of magnetocrystalline anisotropy and shape anisotropy, in which shape anisotropy dominating over weak magnetocrystalline anisotropy. Orbital projection analysis shows that the major contribution of magnetic anisotropy energy comes from the d orbital of Cr atom. These findings provide some insights into the strain response of MA and suggest that studies of other FM monolayers may uncover future contenders for strain-switchable and ultra-compact spintronics devices.

High Volumetric and Gravimetric Acetylene Uptakes in a Cu-MOF with Tailored Cages.

NTUniv-80 exhibits record-high volumetric and gravimetric acetylene (C2H2) uptakes of 210 cm3 (STP) g-1 and 227 cm3 (STP) cm-3 at 295 K and 1 atm, respectively. Grand canonical Monte Carlo (GCMC) simulation indicates that those remarkable C2H2 uptakes are attributed to the open Cu2+ sites and tailored cages.

Within-chain parallelization-Giving Stan Jet Fuel for population modeling in pharmacometrics.

Stan is a powerful probabilistic programming language designed mainly for Bayesian data analysis. Torsten is a collection of Stan functions that handles the events (e.g., dosing events) and solves the ODE systems that are frequently present in pharmacometric models. To perform a Bayesian data analysis, most models in pharmacometrics require Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution. However, MCMC is computationally expensive and can be time-consuming, enough so that people will often forgo Bayesian methods for a more traditional approach. This paper shows how to speed up the sampling process in Stan by within-chain parallelization through both multi-threading using Stan's reduce_sum() function and multi-processing using Torsten's group ODE solver. Both methods show substantial reductions in the time necessary to sufficiently sample from the posterior distribution compared with a basic approach with no within-chain parallelization.

Onset of global instability in a premixed annular V-flame

We investigate self-excited axisymmetric oscillations of a lean premixed methane–air V-flame in a laminar annular jet. The flame is anchored near the rim of the centrebody, forming an inverted cone, while the strongest vorticity is concentrated along the outer shear layer of the annular jet. Consequently, the reaction and vorticity dynamics are largely separated, except where they coalesce near the flame tip. The global eigenmodes corresponding to the linearised reacting flow equations around the steady base state are computed in an axisymmetric setting. We identify an arc branch of eigenmodes exhibiting strong oscillations at the flame tip. The associated eigenvalues are robust with respect to domain truncation and numerical discretisation, and they become destabilised as the Reynolds number increases. The frequency of the leading eigenmode is found to correspond to the Lagrangian disturbance advection time from the nozzle outlet to the flame tip. The essential role of this convective mechanism is also supported by resolvent analysis, which finds that the same flame-tip disturbance structure and frequency are optimally amplified when the flame is subjected to external white noise forcing. Strong non-modal effects in the form of pseudo-resonance are not found. Nonlinear time-resolved simulation further reveals notable hysteresis phenomena in the subcritical regime prior to instability. Hence, even when the flame is linearly stable, perturbations of sufficient amplitude can trigger limit-cycle oscillations and higher-dimensional dynamics sustained by nonlinear feedback. A Monte Carlo simulation of passive tracers in the unsteady flame suggests a nonlinear non-local instability mechanism. Notably, linear analysis of the subcritical time-averaged limit-cycle state yields eigenvalues that do not match the nonlinear periodic oscillation frequencies. This mismatch is attributed to the fundamentally nonlinear dynamics of the subcritical V-flame instability, where the dichromatic, non-local interaction between the heat release rate along the flame surface and the vortex dynamics in the jet shear layer cannot be approximated as a simple distortion of the mean flow.

Greenness and mortality in an industrialized area in Southern Italy: a health impact assessment study

Abstract Background In Taranto the effects of industrial installations have been studied, but less attention has been placed on the impacts of green-oriented policies. The “3-30-300” rule states that every citizen should have 30% tree-canopy-cover in their neighborhood and not live more than 300m away from the nearest green space. In this Health Impact Assessment (HIA) study, we aimed to quantify the deaths that could be prevented in population aged ≥20 years by implementing this policy in Taranto. Methods Population weighted exposures (PWE) to green spaces (10m) were estimated using the 2022 greenest season NDVI from Sentinel-2 and the 2021 ESA WorldCover within a 300m (WHO 2016) buffer. We set a tree-cover optimal target of 30% and interim targets from 10% to 25%. For each tree-cover target, we set the counterfactual exposure (CE) as the corresponding PWE to NDVI. Age-specific preventable deaths (PD) were calculated by multiplying the baseline deaths (ISTAT) by the preventable fractions (PF). Preventable years of life lost (PYLL) were calculated by multiplying the PD by the specific life expectancy. To compute the 95%CI, we ran 100,000 Monte Carlo simulations to propagate the uncertainties of both CE and ERF to the final estimates. Results The PWEs to NDVI and tree-cover-proportion were 0.19 and 8.0%, respectively. For the 30% tree-cover target (currently achieved by the 1.5% of population), the CE (NDVI) and PF (%) were 0.33 (0.32-0.33) and 5.4% (4.1%-6.2%). A total of 126 (97-146) PD and 1270 (976-1474) PYLL were estimated. Three-cover, shrubland and grassland were associated to higher NDVI values, while built-up and deprivation-index were associated to lower greenness. NDVI and summer temperature showed an inverse relationship. Conclusions This study as a pioneering effort in Southern Italy. Providing more green space could prevent a substantial number of premature deaths in Taranto, potentially up to 5.4% by achieving a 30% tree-cover. Key messages • Providing more green space could prevent a substantial number of premature deaths in Taranto, potentially up to 5.4% by achieving a 30% tree-cover. • Built-up and deprivation-index were associated to lower greenness. NDVI and summer temperature showed an inverse relationship.

A New Generalized Family of Weibull-Exponentiated Half Logistic-G Distribution with Applications

We propose a new extension of the Weibull-exponentiated half logistic-G (WEHL-G) distribution, called the Marshall-Olkin-Weibull-exponentiated half logistic-G (MO-WEHL-G) family of distributions. The properties of this family of distributions including quantile function, distribution of the order statistics, hazard rate function, Rényi entropy and moments of residual life are presented. To estimate the parameters of the MO-WEHL-G family of distributions, six different estimation approaches are used, namely, maximum likelihood, Anderson-Darling, Ordinary Least Squares, Weighted Least Squares, Cramér-von Mises and Maximum Product of Spacing. The consistency properties of the six estimation methods were assessed using Monte Carlo simulations for a special case of the MO-WEHL-G family of distributions. The flexibility and importance of the proposed model was assessed using numerous goodness-of-fit statistics on three different data sets.

Examining the local Universe isotropy with galaxy cluster velocity dispersion scaling relations

In standard cosmology, the Universe is assumed to be statistically homogeneous and isotropic. This assumption suggests that the expansion rate of the Universe, as measured by the Hubble parameter, should be the same in all directions. However, our recent study based on galaxy clusters finds an apparent angular variation of approximately $9<!PCT!>$ in the Hubble constant, $H_0$, across the sky. In the study, the authors utilised galaxy cluster scaling relations between various cosmology-dependent cluster properties and a cosmology-independent property, i.e. the temperature of the intracluster gas ($T$). A position-dependent systematic bias of $T$ measurements can, in principle, result in an overestimation of apparent $H_0$ variations. Therefore, it is crucial to confirm or exclude this possibility. In this work, we search for directional $T$ measurement biases by examining the relationship between the member galaxy velocity dispersion and gas temperature ($ v -T$) of galaxy clusters. Both measurements are independent of any cosmological assumptions and do not suffer from the same potential systematic biases. Additionally, we search for apparent $H_0$ angular variations independently of $T$ by analysing the relations between the X-ray luminosity and Sunyaev-Zeldovich signal with the velocity dispersion, $L_ X v $ and $Y_ SZ v To study the angular variation of scaling relation parameters, we determined the latter for different sky patches across the extra-galactic sky. We constrained the possible directional $T$ bias using the $ v -T$ relation, as well as the apparent $H_0$ variations using the $L_ X v $ and $Y_ SZ v $ relations. We utilised Monte Carlo simulations of isotropic cluster samples to quantify the statistical significance of any observed anisotropies. We calculated and rigorously took into account a correlation of $L_ X $ and $Y_ SZ $ residuals. No significant directional $T$ measurement biases are found from the $ v -T$ anisotropy study. The probability that the previously observed $H_0$ anisotropy is caused by a directional $T$ bias is only $0.002<!PCT!>$. On the other hand, from the joint analysis of the $L_ X v $ and $Y_ SZ v $ relations, the maximum variation of $H_0$ is found in the direction of $(295^ with a statistical significance of $3.64 fully consistent with our previous results. Our findings, based on the analysis of new scaling relations utilising a completely independent cluster property, $ v $, strongly corroborate the previously detected anisotropy of galaxy cluster scaling relations. The underlying cause, for example, $H_0$ angular variation or large-scale bulk flows of matter, remains to be identified.

Extraction of Spectra in the Shell Model Monte Carlo Method Using Imaginary-Time Correlation Matrices

Extraction of Spectra in the Shell Model Monte Carlo Method Using Imaginary-Time Correlation Matrices

Dependence of Thermally Activated Relaxation of Crystalline Stems on the Molecular Topology at Crystalline/Amorphous Interfaces in Polyethylene.

We investigate the relaxation dynamics of crystalline stems in relation to the molecular topology of the crystalline/amorphous interface, employing coarse-grained molecular dynamics. To efficiently generate model semicrystalline systems of linear polyethylene with a realistic interphase morphology, we simplified the Monte Carlo method by introducing molecular dynamics for faster relaxation. The structural properties of the generated systems are validated against experimental measurements, theoretical predictions, and existing simulation data. The models suggest that the probability distribution of loop-entry sites on the lamellar surface can be described by a power law in terms of the distance between the entry sites. By considering realistic interphase morphology, we are able to improve the prediction of the overall activation energy for the relaxation of crystalline stems, aligning it closely with experimental measurements. The largest model predicts that crystalline stems connected via large loops, i.e., those that exceed the entanglement length, and long tails are associated with increased activation energy; whereas stems connected to shorter tails show the lowest activation energy. These predictions can guide the future development of tougher semicrystalline polymers by providing insights into how amorphous chain morphology contributes to the activation energy and the relaxation dynamics of crystalline chains.

Cloud droplet size distributions governed by condensation, collision–coalescence, and sedimentation. Part 2: Analytical expressions for size distribution shape and asymptotic behavior for large-droplet tails

Abstract Droplet growth via condensation and collision-coalescence has been investigated in the limit of very weak coalescence growth: a collector-mode approximation emerges for coalescence growth that consists solely of droplets in the tail collecting droplets near the main mode of the cloud droplet size distribution, and that does not deplete the mode. In this second part, the approximation is formalized, leading to a simplified form of the collision-coalescence term in the equation governing the evolution of the droplet size distribution. This form allows for analytical solutions for transient growth and for steady-state distributions representing a balance between condensation, collision-coalescence growth, and removal by sedimentation. The solutions lead to an expression for the drizzle growth time, formally, the finite time in which a droplet approaches infinite size. The drizzle growth time is related to a transition radius at which droplet growth switches from condensation-dominated to coalescence-dominated. The solutions also provide a dimensionless group that governs the steady-state distribution shape, which is referred to as the drizzle number. The solutions are checked by comparing to a Monte Carlo model developed in Part 1, and it is found that the analytical solution is excellent when the drizzle number is much greater than unity, which is the typical situation for the microphysical conditions achievable in a convection-cloud chamber. The steady-state size distribution has the form of a modified gamma distribution, with a transition to power-law tail at sufficiently large radius. The solutions are formally valid for a convection-cloud chamber, but can also be extended to shallow, mixed-layer clouds such as mixing fogs.

Preparation and characterization of a new Gd2O3-epoxy composite for neutron shielding applications

The current study aims to introduce a new polymeric composite consisting of epoxy resin as the matrix and gadolinium oxide (Gd2O3) as the neutron adsorption ingredient. The shielding performance of the composite was assessed by neutron attenuation experiments with an Am-Be source and polyethylene moderator. The results of these experiments showed an appreciable agreement with the Monte Carlo simulations. Other characteristics of the composite, including mechanical strength, thermal stability, microtexture, and its chemical compositions, were examined using standard tensile test, thermogravimetric analysis, X-ray diffraction, scanning electron microscopy, static light scattering analyses, and Fourier-transform infrared spectroscopy (FTIR). The results indicated that the new composites offer appreciable neutron absorption properties so that samples with 0.5%, 2%, 5%, and 10% Gd2O3 content could reduce the neutron beam intensity by 54%, 63%, 66%, and 70% at a thickness of 4 cm.

Defining a maximum heat release rate probability distribution function for design fires in sprinkler‐protected residential buildings

AbstractIn fire safety engineering analysis of sprinkler‐protected residential buildings, the maximum heat release rate is a key parameter requiring consideration. Several documents provide advice for estimating the heat release rate of a sprinkler‐controlled fire, with a prevailing suggestion that it is fixed upon activation of the first sprinkler. When carrying out deterministic analysis, this requires the engineer to assume fixed fire parameters and consider that sprinklers limit fire growth. To explore these assumptions, the study uses three deterministic models to estimate a sprinkler‐controlled maximum heat release rate for a representative apartment layout. The models include Alpert's correlation, a B‐RISK zone model and a computational fluid dynamics model in the Fire Dynamics Simulator. These deterministic models are compared to a probabilistic model in B‐RISK, where Monte Carlo simulations are used to generate a range of maximum heat release rates from distribution functions for fire and sprinkler properties. An output distribution function is generated with a mean of 296.6 kW and a standard deviation of 503.8 kW, with a lognormal distribution (μ = 5.014, σ = 1.165) estimated as a best‐fit. The deterministic models are estimated to sit in the 92–98 percentile range of this function, indicating that common deterministic assumptions are reasonably conservative. The article concludes with suggesting that, for deterministic analysis, a percentile between the 80th and 99th (340–2640 kW) could be qualitatively selected based on the design objectives, building situation and relative consequence of a fire. Further research is needed to establish guidelines for selecting appropriate percentiles across various building scenarios.

Polarization study of the P-wave charmonium radiative decay into a light vector meson at e+e- collider experiment

Abstract In this work, a formalism is presented for the helicity amplitude analysis of the decays ψ(2S) →γ1χcJ, χcJ→γ2V (V=ρ0, Φ, ω) (the subscript 1,2 is used to distinguish the two radiative photons), and the polarization expressions of the P-wave charmonia χcJ and the vector mesons ρ0, Φ, ω for experimental measurements at Electron-Positron Collider. In addition, we derive the formulae of the angular distributions of the χc1,2→γV to extract the degree of transverse polarization PT of e+e- pairs with symmetric beam energy as well as the ratios of two helicity amplitudes x (in χc1 decays) and x, y (in χc2 decays) representing the relative magnitudes of transverse to longitudinal polarization amplitude, and validate it by performing the Monte Carlo simulation. Finally, the statistical sensitivity of PT, x and y are estimated based on the large ψ(2S) data samples collected at the current and proposed future e+e- collider experiment.Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must main tain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Sciences and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd

Screening Na-Excess Cation-Disordered Rocksalt Cathodes with High Performance.

The practical application of Na-ion cathode materials is currently restricted by their low energy density and sluggish dynamics, while the cation-disordered rocksalt (DRX) structures offer a possible solution to the challenge. In this study, among the 24 candidates containing d0 elements, we use mixing temperature as a descriptor to screen the synthesizable Na-excess DRX, and we have identified Na1.2Mn0.4Mo0.4O2 as the most promising candidate that exhibits a Na percolating fraction of 53%, which is higher than that of Li1.2Mn0.4Ti0.4O2 (35%) proposed in the previous study due to the larger lattice constant in Na-excess DRX cathodes. More importantly, Na1.2Mn0.4Mo0.4O2 is predicted to have a capacity of 228 mAh/g with an energy density of 552 Wh/kg derived from percolation theory and cluster-expansion Monte Carlo simulations, which is higher than that of Na1.3Nb0.3Mn0.4O2 and Na1.14Mn0.57Ti0.29O2 synthesized recently. For a better understanding, the redox mechanism is explored, which involves Mo4+/Mo6+, Mn3+/Mn4+, and O2-/On- (0 < n < 2), indicating the participation of anionic redox. Meanwhile, the Na+ diffusion prefers a divacancy mechanism via an o-t-o diffusion channel with a low diffusion barrier of 0.29 eV. This study expands the family of DRX for the cathode of Na-ion batteries with enhanced performance.

Stochastic error propagation with independent probability distributions in LCA does not preserve mass balances and leads to unusable product compositions—a first quantification

Abstract Purpose To mitigate the effects of the triple planetary crisis of climate change, pollution and biodiversity loss, a system-based approach to estimating environmental impacts—such as life cycle assessment (LCA)—is critical. International standards recommend using uncertainty analysis to improve the reliability of LCA, but there has been debate about how to do this for many years. In particular, in order to characterise uncertainty in the inputs and outputs of each unit process in an LCA, a prevalent approach is to represent each one by an independent probability distribution. Thus, any physical relationships between inputs and outputs are ignored, which causes two potential errors during Monte Carlo simulation (a popular method for propagating uncertainty through an LCA model). First, the sum of the inputs to a unit process may not equal the sum of the outputs (i.e. there may be a mass imbalance), and second, the proportions of each input and output may be unrealistic (e.g. too much cement in a concrete production unit process). However, while some literature has discussed the problem, it has not yet been quantified. Methods Therefore, this paper investigates the extent to which existing uncertainty characterisation approaches, where there is a lack of parameterisation or correlations in databases, lead to mass imbalances and unrealistic variations in unit process compositions when performing uncertainty analysis. The matrix-based structure of LCA and the standard uncertainty analysis procedure using Monte Carlo (MC) simulation to propagate uncertainty are described. We apply the procedure to a concrete production process. Two uncertainty characterisation approaches are also explored to assess the effect of data quality scoring on mass imbalances and the mass contribution of each exchange (i.e. production compositions). Results and discussion For median data quality scores and using a typical (basic + additional uncertainty) uncertainty characterisation approach, the 1000-iteration MC simulation leads to mass imbalances ranging from − 49 to + 30% of the original mass and found that the mass imbalance exceeded existing prescribed plausibility limits on 62.7% of MC runs. On average across all exchanges, the exchange mass exceeded the 5% plausible variation limit on 77.7% of MC runs. This means that the final concrete product compositions are unlikely to be realistic or functionally equivalent to one another. We discuss the appropriateness of using universal variances for the underlying normal distribution for data quality scores (“additional uncertainty”) when input exchange quantities are of different scales. Additionally, we discuss potential solutions to the mass imbalance problem and their suitability for implementation at a database scale. Conclusions We have quantified, for the first time, the significant impact that uncertainty characterisation via independent probability distributions has on maintaining mass balances and plausible product compositions in unit processes. To overcome these challenges, databases would need to be parameterised and have the ability to sum quantities to perform mass balance checks during uncertainty analysis.

Impact of Skidding and Slope on Grapple Skidder Productivity and Costs: A Monte Carlo Simulation in Eucalyptus Plantations

Background: In the context of mechanized timber harvesting, alterations in technical parameters, such as skidding distance and terrain slope, have the potential to influence the productivity and production costs associated with the self-propelled grapple skidder. Furthermore, these variables are inherently uncertain, which could potentially cause forest managers to make inaccurate decisions. The objective was to analyze whether four skidding distances and two slope classes influence the productivity and production costs of the grapple skidder in Eucalyptus planted forests from a stochastic perspective using the Monte Carlo method. Methods: Productivity was estimated using the time study protocol. To calculate the cost per scheduled hour of the grapple skidder, both fixed and variable costs were considered, and subsequently, the production cost was determined. Results: The mean productivity of the grapple skidder on flat slopes was 114.35 m3 h−1, while on wavy to strong wavy slopes it was 80.43 m3 h−1. In flat slopes, considering all skid distance ranges, the mean production cost was 0.82 USD m−3, while in wavy to strong wavy slopes it was 1.48 USD m−3. The mean values for operator labor costs and fuel account for 58.1% of the cost per scheduled hour of the grapple skidder. Conclusions: The mean productivity of the grapple skidder in Eucalyptus planted forests decreased with increasing skidding distance in both slope classes but was 29.7% lower on wavy to strong wavy slopes compared to flat slopes. The mean production cost of the grapple skidder during timber skidding on flat slopes is 80.0% lower than on wavy to strong wavy slopes. For future investigations, the impact of other slope classes, skid distances, and silvicultural aspects on productivity and production costs can be considered from a stochastic perspective using the Monte Carlo method.

Magnetic ordering and dynamics in monolayers and bilayers of chromium trihalides: atomistic simulations approach

We analyze magnetic properties of monolayers and bilayers of chromium iodide, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CrI}_3$$\\end{document}, in two different stacking configurations: AA and rhombohedral ones. Our main focus is on the corresponding Curie temperatures, hysteresis curves, equilibrium spin structures, and spin wave excitations. To obtain all these magnetic characteristic, we employ the atomistic spin dynamics and Monte Carlo simulation techniques. The model Hamiltonian includes isotropic exchange coupling, magnetic anisotropy, and Dzyaloshinskii-Moriya interaction. As the latter interaction is relatively weak in pristine \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CrI}_3$$\\end{document}, we consider a more general case, when the Dzyaloshinskii-Moriya interaction is enhanced externally (e.g. due to gate voltage, mechanical strain, or proximity effects). An important issue of the analysis is the correlation between hysteresis curves and spin configurations in the system, as well as formation of the skyrmion textures.

AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response.

Computational models of tumor growth are valuable for simulating the dynamics of cancer progression and treatment responses. In particular, agent-based models (ABMs) tracking individual agents and their interactions are useful for their flexibility and ability to model complex behaviors. However, ABMs have often been confined to small domains or, when scaled up, have neglected crucial aspects like vasculature. Additionally, the integration into tumor ABMs of precise radiation dose calculations using gold-standard Monte Carlo (MC) methods, crucial in contemporary radiotherapy, has been lacking. Here, we introduce AMBER, an Agent-based fraMework for radioBiological Effects in Radiotherapy that computationally models tumor growth and radiation responses. AMBER is based on a voxelized geometry, enabling realistic simulations at relevant pre-clinical scales by tracking temporally discrete states stepwise. Its hybrid approach, combining traditional ABM techniques with continuous spatiotemporal fields of key microenvironmental factors such as oxygen and vascular endothelial growth factor, facilitates the generation of realistic tortuous vascular trees. Moreover, AMBER is integrated with TOPAS, an MC-based particle transport algorithm that simulates heterogeneous radiation doses. The impact of radiation on tumor dynamics considers the microenvironmental factors that alter radiosensitivity, such as oxygen availability, providing a full coupling between the biological and physical aspects. Our results show that simulations with AMBER yield accurate tumor evolution and radiation treatment outcomes, consistent with established volumetric growth laws and radiobiological understanding. Thus, AMBER emerges as a promising tool for replicating essential features of tumor growth and radiation response, offering a modular design for future expansions to incorporate specific biological traits.

Robust data-driven model-free adaptive control to uncertainties and disturbances for NARMAX systems

This paper introduces an enhancement to the model-free adaptive control (MFAC) method based on a new control input cost function for full-form dynamic linearization (FFDL) for NARMAX systems. The new control cost function includes the one-step-ahead tracking error, OER, and output error integral. The main feature of the approach is that it has a high ability to overcome disturbances in the control input and measured noise in the output. Analytical and extensive simulations have demonstrated that the novel control law exhibits superior response and robustness in comparison to the prototype MFAC. Moreover, it guarantees stability and convergence of the tracking error by enforcing bounded-input bounded-output (BIBO) criteria. In addition, 20 Monte Carlo simulations were conducted to test the initial conditions and random control parameters, and the results demonstrated that the proposed control outperformed the prototype MFAC.

Probabilistic Modeling Geometric Tolerance and Low Cycle Fatigue Life of Gas Turbine Compressor Blade

Abstract This paper presents a probabilistic low cycle fatigue (LCF) assessment for geometric tolerances on high load contact surfaces of gas turbine compressor blades. The typical patterns of the geometric deviations for the root contact flank of the compressor blades are identified and characterized according to coordinate measurement machine (CMM) measurements of the blade root. These typical patterns are closely related to the root form manufacture tools and process. Finite element (FE) models for the typical geometric deviation patterns are created based on nodal coordinates transformation of the surface nodes on the blade root contact flanks and radius. An optimized blade root profile tolerance is established, which enables significant cost saving. The elastic-plastic FE analysis with nonlinear contact model, material strain-life test, response surface, and constrained Monte Carlo simulation are used to create a probabilistic LCF life model for the optimized tolerance. The model quantifies the effect of the geometric deviations, blade mass, and material property on the blade LCF life. The result shows that with the optimized tolerance, the probability of blade LCF failure is very low and acceptable. It is also shown that the strain life material property is the most critical factor for the LCF failure. The root profile tolerance and blade mass are seen to have a much weaker effect on the blade LCF life.