Improved sensitivity through Morris extension

Juan, Santiago; Corre, B.; Claeys‐Bruno, Magalie; Sergent, Michelle

doi:10.1016/j.chemolab.2011.10.006

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…Given the relatively high cost of DFT simulations for the deformed nanotubes, it is infeasible to simulate nearly all possible combinations in the input space. Purely random sampling of the input space is not desirable either, since it may require a large number of sampling points to learn the pattern in the data accurately [99][100][101]. To address this challenge, we generate sequences of quasi-random sampling points in the input space to reduce the number of simulations required for training an accurate ML model.…”

Section: A Design Of Experiments To Explore the Input Spacementioning

confidence: 99%

“…Hypercube sampling [107]. These methods are often evaluated based on their measure of uniformity [100,108,109], and such criteria suggest that Optimal Latin hypercube sampling [110] and Sobol sequences [104,111] offer a great balance between uniform and random sampling. In this work, we have chosen Sobol sequences (low discrepancy quasirandom sequences), to sample the input space.…”

Section: Systemmentioning

confidence: 99%

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Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

Pathrudkar,

Yu,

Ghosh

et al. 2022

Preprint

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We present a machine learning based model that can predict the electronic structure of quasione-dimensional materials while they are subjected to deformation modes such as torsion and extension/compression. The technique described here applies to important classes of materials systems such as nanotubes, nanoribbons, nanowires, miscellaneous chiral structures and nanoassemblies, for all of which, tuning the interplay of mechanical deformations and electronic fields -i.e., strain engineering -is an active area of investigation in the literature. Our model incorporates global structural symmetries and atomic relaxation effects, benefits from the use of helical coordinates to specify the electronic fields, and makes use of a specialized data generation process that solves the symmetry-adapted equations of Kohn-Sham Density Functional Theory in these coordinates. Using armchair single wall carbon nanotubes as a prototypical example, we demonstrate the use of the model to predict the fields associated with the ground state electron density and the nuclear pseudocharges, when three parameters -namely, the radius of the nanotube, its axial stretch, and the twist per unit length -are specified as inputs. Other electronic properties of interest, including the ground state electronic free energy, can be evaluated from these predicted fields with low-overhead post-processing, typically to chemical accuracy. Additionally, we show how the nuclear coordinates can be reliably determined from the predicted pseudocharge field using a clustering based technique. Remarkably, only about 120 data points are found to be enough to predict the three dimensional electronic fields accurately, which we ascribe to the constraints imposed by symmetry in the problem setup, the use of low-discrepancy sequences for sampling, and efficient representation of the intrinsic low-dimensional features of the electronic fields. We comment on the interpretability of our machine learning model and anticipate that our framework will find utility in the automated discovery of low-dimensional materials, as well as the multi-scale modeling of such systems.

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Section: A Design Of Experiments To Explore the Input Spacementioning

confidence: 99%

Section: Systemmentioning

confidence: 99%

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

Pathrudkar,

Yu,

Ghosh

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Suppose there is an input parameter with n trajectories. The sensitivity mean (d * (x i , t)) and standard deviation (S(x i , t)) of the i th input parameter the observed elementary effect at time t can be expressed as [46]…”

Section: Sensitivity In the Time Intervalmentioning

confidence: 99%

Reliability and Safety Modelling of the Electrical Control System of the Subsea Control Module Based on Markov and Multiple Beta Factor Model

et al. 2019

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Replacement of the subsea control module (SCM) and halting production caused by the failure of the SCM are costly, and the electrical control system plays a large role in the failure modes of the SCM. Hence, the analysis of its reliability and safety is important. Markov processes and the multiple beta factor model are used to model the reliability and safety for the electrical control system of the SCM that consider the effects of multiple factors, including the failure detection rate, common-cause failure, and the failure rate of each module, for evaluating system reliability and safety. The Morris screening method that is based on a large amount of random data with equal probability is applied to reliability and safety models that vary with time for quantifying the effect levels of each factor on the system reliability and safety in the time interval. The effect of each factor on the system and its interaction degree with other factors are evaluated in the time interval. Finally, the system model is simulated in MATLAB. The simulation demonstrates that the system reliability and safety gradually decrease and the effect of each factor on them increases over time; the reliability is most sensitive to the failure rates of the input, central processing unit, and output modules, whereas the safety is most sensitive to the failure detection rate. The common-cause failure and the comparator module have some effects on the reliability and safety of the system.

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“…Based on the values of µ i * and σ i, the Morris method identifies factors having: negligible effects, linear and additive effects, or nonlinear or interactions effects (Santiago et al, 2012). Fig.…”

Section: 𝑟𝑟mentioning

confidence: 99%

Numerical modelling of microalgae systems for wastewater treatment

Solimeno

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Reactions and processes that occur in microalgae and bacteria systems are difficult to understand because most of them take place simultaneously and depend on many parameters such as temperature, solar radiation, nutrients availability (e.g. carbon and nitrogen) as well on certain inhibitory conditions (e.g excess of oxygen in the culture medium). In comparison with conventional wastewater treatment technologies, less is known about the physical, chemical and biochemical reactions and processes that occur in microalgae-bacteria treatment systems. The main outcome of the present PhD thesis was to develop a new integrated mechanistic model, named BIO_ALGAE, which includes crucial physical and biokinetic processes to simulate microalgae growth in different type of cultures, and most particularly in wastewater. The model was used to advance the understanding the inherent complexity of microalgae and bacteria interactions that occur in high rate algal ponds (HRAP) and photobioreactors.BIO_ALGAE model was mainly built by coupling the River Water Quality Model 1 (RWQM1) formulation and the modified ASM3 model, and was implemented in COMSOL MultiphysicsTM simulation platform. Inorganic carbon, as a limiting substrate for the growth of microalgae, is one of the major innovative features of BIO_ALGAE. Carbon is an essential resource for microalgae production. Moreover, temperature, photorespiration, pH dynamics, solar radiation, light attenuation and transfer of gases to the atmosphere are considered main limiting factors for microalgae growth. In a pragmatic approach to reduce the model¿s complexity in the initial stages of its development, it was decided to start by studying physical, chemical and biokinetic processes of microalgae alone, hence neglecting bacterial processes. Once calibrated the most uncertain parameters of the model, bacteria processes were added, and this gave place to the integral model BIO_ALGAE. This model was calibrated and validated with high quality experimental data from pilot raceway ponds over short-time scale and for long-term operation.The BIO_ALGAE model has proved to be an efficient tool to understand microalgae and bacteria interactions in wastewater treatment and to simulate the dynamics of different components in the ponds. The model was used to investigate the effect of environmental conditions and nutrients availability on microalgae growth and the different hydraulic retention time (HRT) operating strategies on the relative proportion of microalgae and bacteria and biomass production. Moreover, thanks to the model it was possible to optimize the performance of both HRAP and photobioreactor. Las reacciones y los procesos que ocurren en sistemas mixtos de microalgas y bacterias son difíciles de entender ya que la mayoría de ellos tienen lugar simultáneamente y dependen de muchos parámetros tales como temperatura, radiación solar, disponibilidad de nutrientes (e.g. carbono y nitrógeno) así como ciertas condiciones inhibitorias (e.g. exceso de oxígeno en el medio de cultivo). En comparación con las tecnologías convencionales de tratamiento de aguas residuales, actualmente hay poco conocimiento de las reacciones físicas, químicas y bioquímicas y de los procesos que se producen en los sistemas de tratamiento de microalgas y bacterias. El objetivo principal de la presente tesis doctoral fue desarrollar un nuevo modelo mecanístico integrado, denominado BIO_ALGAE, que incluye procesos físicos y bioquinéticos cruciales para simular el crecimiento de microalgas en diferentes tipos de cultivos, principalmente en aguas residuales. El modelo se utilizó para comprender de una mejor forma las interacciones que se llevan a cabo entre microalgas y bacterias en lagunas de alta carga (LAC) y fotobiorreactores. El modelo BIO_ALGAE se construyó mediante el acoplamiento del River Water Quality Model 1 (RWQM1) y del modelo ASM3 modificado, y se implementó en la plataforma de simulación COMSOL MultiphysicsTM. El carbono inorgánico, utilizado como sustrato limitante para el crecimiento de microalgas, es una de las principales características innovadoras de BIO_ALGAE. Además, la temperatura, la fotorespiración, la dinámica del pH, la radiación solar, la atenuación de la luz y la transferencia de gases a la atmósfera se consideraron los principales factores limitantes del crecimiento de las microalgas. Para reducir la complejidad del modelo en las etapas iniciales de su desarrollo, se decidió empezar por estudiar los procesos físicos, químicos y bioquinéticos sólo de las microalgas, dejando de lado los procesos bacterianos. Una vez calibrados los parámetros más sensibles del modelo, se añadieron los procesos bacterianos, lo que dio lugar al modelo integral BIO_ALGAE. Este modelo fue calibrado y validado con datos experimentales de alta calidad procedentes de LAC operadas a corto y largo plazo. El modelo BIO_ALGAE ha demostrado ser una herramienta eficaz para entender las interacciones de microalgas y bacterias en el tratamiento de aguas residuales y simular la dinámica de diferentes componentes en las LAC. El modelo se utilizó para investigar el efecto de las condiciones ambientales y la disponibilidad de nutrientes en el crecimiento de microalgas. También se estudió el efecto del tiempo de retención hidráulica sobre la proporción relativa de microalgas-bacterias y la producción de biomasa. Gracias al modelo fue posible optimizar el rendimiento tanto de las lagunas de alta carga como del fotobiorreactor.

show abstract

Improved sensitivity through Morris extension

Cited by 14 publications

References 10 publications

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

Reliability and Safety Modelling of the Electrical Control System of the Subsea Control Module Based on Markov and Multiple Beta Factor Model

Numerical modelling of microalgae systems for wastewater treatment

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