Supplemental Material: Thermodynamic limits for assimilation of silicate crust in primitive magmas

ABSTRACTA calibration procedure for determining the model input parameters of standard a-Si:H layers, which comprise a single junction a-Si:H solar cell, is presented. The calibration procedure consists of: i) deposition of the separate layers, ii) measurement of the material properties, iii) fitting the model parameters to match the measured properties, iv) simulation of test devices and comparison with experimental results. The inverse modeling procedure was used to extract values of the most influential model parameters by fitting the simulated material properties to the measured ones. In case of doped layers the extracted values of the characteristic energies of exponentially decaying tail states are much higher than the values reported in literature. Using the extracted values of model parameters a good agreement between the measured and calculated characteristics of a reference solar cell was reached. The presented procedure could not solve directly an important issue concerning a value of the mobility gap in a-Si:H alloys.

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Principal Conditions and Geochemical Trends in Formation of High-grade Bauxite Deposits, Republic of Guinea

E3S Web of Conferences ◽

10.1051/e3sconf/20199801035 ◽

2019 ◽

Vol 98 ◽

pp. 01035 ◽

Cited By ~ 1

Author(s):

Vladimir Mamedov ◽

Alexey Chausov ◽

Marina Makarova

Keyword(s):

High Grade ◽

Model Input ◽

Republic Of Guinea ◽

Input And Output

Conditions and principal geochemical trends in the formation of bauxite-bearing lateritic mantles are considered on the example of Fouta Djalon-Mandingo Bauxite-bearing Province as the best natural model. Input and output dynamics of petrogenic components was calculated on the isovolumetric base and the obtained data were used to interpret the zoned structure of weathered profile as resulting from its hydrogeological and gas regimes. Although Al separates from Fe in the formation of bauxite horizon, three-valence iron is a typomorphic element of the lateritic landscape as a whole, with 50% Al leaching from lateritic mantle.

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Development of a Stochastic Approach for Vehicle FE Models

Transportation: Making Tracks for Tomorrow’s Transportation ◽

10.1115/imece2003-55107 ◽

2003 ◽

Cited By ~ 1

Author(s):

David Riha ◽

Joseph Hassan ◽

Marlon Forrest ◽

Ke Ding

Keyword(s):

Finite Element ◽

Reliability Analysis ◽

System Reliability ◽

Model Parameters ◽

Fe Model ◽

Original Design ◽

Acceptance Criteria ◽

Model Input ◽

Input Parameters ◽

Occupant Injury

This paper describes the development of a mathematical model capable of providing realistic simulations of vehicle crashes by accounting for uncertainty in the model input parameters. The approach taken was to couple advanced and efficient probabilistic and reliability analysis methods with well-established, high fidelity finite element and occupant modeling software. Southwest Research Institute has developed probabilistic analysis software called NESSUS. This code was used as the framework for a stochastic crashworthiness FE model. The LS-DYNA finite element model of vehicle frontal offset impact and the MADYMO model of a 50th percentile male Hybrid III dummy were integrated with NESSUS to comprise the crashworthiness characteristics. The system reliability of the vehicle is computed by defining ten acceptance criteria performance functions; four occupant injury criteria and six compartment intrusion criteria. The reliability for each acceptance criteria was computed using NESSUS to identify the dominant acceptance criteria of the original design. The femur axial load acceptance criteria event has the lowest reliability (46%) followed by the HIC event (58%) and the door aperture closure event (73%). One approach to improve the reliability is to change vehicle parameters to improve the reliability for the dominant criteria. However, a parameter change such as vehicle strength/stiffness may have a beneficial effect on certain acceptance criteria but be detrimental to others. A system reliability analysis was used to include the contribution of all acceptance criteria to correctly quantify the vehicle reliability and identify important parameters. A redesign analysis was performed using the computed probabilistic sensitivity factors. These sensitivities were used to identify the most effective changes in model parameters to improve the reliability. A redesign using 11 design modifications was performed that increased the original reliability from 23% to 86%. Several of the design changes include increasing the rail material yield strength and reducing its variation, reducing the variation of the bumper and rail installation tolerances, and increasing the rail weld stiffness and reducing its variation. The results show that major reliability improvements for occupant injury and compartment intrusion can be realized by certain specific modifications to the model input parameters. A traditional (deterministic) method of analysis would not have suggested these modifications.

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Inverse Modeling Techniques for Parameter Estimation in Engineering Simulation Models

Volume 2: Aircraft Engine; Ceramics; Coal, Biomass and Alternative Fuels; Controls, Diagnostics and Instrumentation; Environmental and Regulatory Affairs ◽

10.1115/gt2006-90058 ◽

2006 ◽

Cited By ~ 1

Author(s):

Srikanth Akkaram ◽

Don Beeson ◽

Harish Agarwal ◽

Gene Wiggs

Keyword(s):

Heat Transfer ◽

Parameter Estimation ◽

Inverse Modeling ◽

Simulation Models ◽

Posteriori Probability ◽

Design System ◽

Model Parameters ◽

Input And Output ◽

Ill Posed ◽

Well Posed

Computational simulation models are extensively used in the development, design and analysis of an aircraft engine and its components to represent the physics of an underlying phenomenon. The use of such a model-based simulation in engineering often necessitates the need to estimate model parameters based on physical experiments or field data. This class of problems, referred to as inverse problems [1] in the literature can be classified as well-posed or ill-posed dependent on the quality (uncertainty) and quantity (amount) of data that is available to the engineer. The development of a generic inverse modeling solver in a probabilistic design system [2] requires the ability to handle diverse characteristics in various models. These characteristics include (a) varying fidelity in model accuracy with simulation times from a couple of seconds to many hours (b) models being black-box with the engineer having access to only the input and output (c) non-linearity in the model (d) time-dependent model input and output. The paper demonstrates methods that have been implemented to handle these features with emphasis on applications in heat transfer and applied mechanics. A practical issue faced in the application of inverse modeling for parameter estimation is ill-posedness that is characterized by instability and non-uniqueness in the solution. Generic methods to deal with ill-posedness include (a) model development, (b) optimal experimental design and (c) regularization methods. The purpose of this paper is to communicate the development and implementation of an inverse method that provides a solution for both well-posed as well as ill-posed problems using regularization based on the prior values of the parameters. In the case of an ill-posed problem, the method provides two solution schemes — a most probable solution closest to the prior, based on the singular value decomposition (SVD) and a maximum a-posteriori probability solution (MAP). The inverse problem is solved as a finite dimensional non-linear optimization problem using the SVD and/or MAP techniques tailored to the specifics of the application. The paper concludes with numerical examples and applications demonstrating the scope as well as validating the developed method. Engineering applications include heat transfer coefficient estimation for disk quenching in process modeling, material model parameter estimation, sparse clearance data modeling, steady state and transient engine high-pressure compressor heat transfer estimation.

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Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-8021-2017 ◽

2017 ◽

Vol 17 (12) ◽

pp. 8021-8029 ◽

Cited By ~ 9

Author(s):

Thomas Berkemeier ◽

Markus Ammann ◽

Ulrich K. Krieger ◽

Thomas Peter ◽

Peter Spichtinger ◽

...

Keyword(s):

Experimental Data ◽

Genetic Algorithm ◽

Monte Carlo ◽

Kinetic Models ◽

Reaction Rate ◽

Rate Coefficients ◽

Model Parameters ◽

Data Sets ◽

Model Input ◽

Input Parameters

Abstract. We present a Monte Carlo genetic algorithm (MCGA) for efficient, automated, and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find and characterize the solution of an optimization problem. It addresses an issue inherent to complex models whose extensive input parameter sets may not be uniquely determined from limited input data. Such ambiguity in the derived parameter values can be reliably detected using this new set of tools, allowing users to design experiments that should be particularly useful for constraining model parameters. We show that the MCGA has been used successfully to constrain parameters such as chemical reaction rate coefficients, diffusion coefficients, and Henry's law solubility coefficients in kinetic models of gas uptake and chemical transformation of aerosol particles as well as multiphase chemistry at the atmosphere–biosphere interface. While this study focuses on the processes outlined above, the MCGA approach should be portable to any numerical process model with similar computational expense and extent of the fitting parameter space.

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Identification of Hydrologic Models, Optimized Parameters, and Rainfall Inputs Consistent with In Situ Streamflow and Rainfall and Remotely Sensed Soil Moisture

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0240.1 ◽

2018 ◽

Vol 19 (8) ◽

pp. 1305-1320 ◽

Cited By ~ 6

Author(s):

Ashley J. Wright ◽

Jeffrey P. Walker ◽

Valentijn R. N. Pauwels

Keyword(s):

Time Series ◽

Soil Moisture ◽

Input Data ◽

Remotely Sensed ◽

Model Parameters ◽

Rainfall Time Series ◽

Hydrological Models ◽

Rainfall Runoff ◽

Model Input ◽

Rainfall Estimates

Abstract An increased understanding of the uncertainties present in rainfall time series can lead to improved confidence in both short- and long-term streamflow forecasts. This study presents an analysis that considers errors arising from model input data, model structure, model parameters, and model states with the objective of finding a self-consistent set that includes hydrological models, model parameters, streamflow, remotely sensed (RS) soil moisture (SM), and rainfall. This methodology can be used by hydrologists to aid model and satellite selection. Taking advantage of model input data reduction and model inversion techniques, this study uses a previously developed methodology to estimate areal rainfall time series for the study catchment of Warwick, Australia, for multiple rainfall–runoff models. RS SM observations from the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) satellites were assimilated into three different rainfall–runoff models using an ensemble Kalman filter (EnKF). Innovations resulting from the observed and predicted SM were analyzed for Gaussianity. The findings demonstrate that consistency between hydrological models, model parameters, streamflow, RS SM, and rainfall can be found. Joint estimation of rainfall time series and model parameters consistently improved streamflow simulations. For all models rainfall estimates are less than the observed rainfall, and rainfall estimates obtained using the Sacramento Soil Moisture Accounting (SAC-SMA) model are the most consistent with gauge-based observations. The SAC-SMA model simulates streamflow that is most consistent with observations. EnKF innovations obtained when SMOS RS SM observations were assimilated into the SAC-SMA model demonstrate consistency between SM products.

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Observer-based control for active suspension system with time-varying delay and uncertainty

Advances in Mechanical Engineering ◽

10.1177/1687814019889505 ◽

2019 ◽

Vol 11 (11) ◽

pp. 168781401988950 ◽

Cited By ~ 1

Author(s):

Kuiyang Wang ◽

Ren He ◽

Heng Li ◽

Jinhua Tang ◽

Ruochen Liu ◽

...

Keyword(s):

Active Suspension ◽

State Observer ◽

Suspension System ◽

Linear Matrix ◽

Model Parameters ◽

Time Varying ◽

Gain Matrix ◽

Time Varying Delay ◽

Input And Output ◽

Varying Delay

Time-varying input delay of actuators, uncertainty of model parameters, and input and output disturbances are important issues in the research on active suspension system of vehicle. In this article, a design methodology involving state observer and observer-based dynamic output-feedback [Formula: see text] controller considering the above four factors simultaneously is put forward for active suspension system. First, the dynamics equations of active suspension system with time-varying delay are established according to its structure and principle, and its state equations, state observer, and observer-based controller considering time-varying delay, uncertainty of model parameters, and input and output disturbances are given separately. Second, the observer-based controller for quarter-vehicle active suspension system is designed in terms of the linear matrix inequality and the Lyapunov–Krasovskii functional, and the design problem of observer-based controller is converted into the solving problem of linear matrix inequalities. Finally, the gain matrix of observer and the gain matrix of controller are obtained by means of the developed controller and the model parameters of active suspension system; the MATLAB/Simulink model of this system is established; and three numerical simulation cases are given to show the effectiveness of the proposed scheme.

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