Optimal design and operation of batch reactors. 1. Theoretical framework

1993 ◽  
Vol 32 (5) ◽  
pp. 866-881 ◽  
Author(s):  
Masoud Soroush ◽  
Costas Kravaris
Keyword(s):  
Optimal Design ◽  
Theoretical Framework ◽  
Batch Reactors

Thermal risk in batch reactors: Theoretical framework for runaway and accident

2016 ◽  
Vol 43 ◽  
pp. 75-82 ◽  
Author(s):  
Lei Ni ◽  
Ahmed Mebarki ◽  
Juncheng Jiang ◽  
Mingguang Zhang ◽  
Vincent Pensee ◽  
...  
Keyword(s):  
Theoretical Framework ◽  
Batch Reactors ◽  
Thermal Risk

scholarly journals LiSBOA: LiDAR Statistical Barnes Objective Analysis for optimal design of LiDAR scans and retrieval of wind statistics. Part I: Theoretical framework

2020 ◽  
Author(s):  
Stefano Letizia ◽  
Lu Zhan ◽  
Giacomo Valerio Iungo
Keyword(s):  
Optimal Design ◽  
Velocity Field ◽  
Scalar Fields ◽  
Theoretical Framework ◽  
Function Optimization ◽  
Objective Analysis ◽  
Lidar Measurements ◽  
Wind Statistics ◽  
Suitable Technique ◽  
Selection Of

Abstract. A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for optimal design of LiDAR scans and retrieval of the velocity statistical moments is proposed. The LiSBOA represents an adaptation of the classical Barnes scheme for the statistical analysis of unstructured experimental data in N-dimensional spaces and it is a suitable technique for the evaluation over a structured Cartesian grid of the statistics of scalar fields sampled through scanning LiDARs. The LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. This revisited theoretical framework for the Barnes objective analysis enables the formulation of guidelines for optimal design of LiDAR experiments and efficient application of the LiSBOA for the post-processing of LiDAR measurements. The optimal design of LiDAR scans is formulated as a two cost-function optimization problem including the minimization of the percentage of the measurement volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field. The optimal design of the LiDAR scans also guides the selection of the smoothing parameter and the total number of iterations to use for the Barnes scheme.

Optimal Design of Self-Adaptive Fingers for Proprioceptive Tactile Sensing

2017 ◽  
Vol 9 (5) ◽  
Author(s):  
Bruno Belzile ◽  
Lionel Birglen
Keyword(s):  
Optimal Design ◽  
Proximal Phalanx ◽  
Optimization Procedure ◽  
Theoretical Framework ◽  
Alternative Methods ◽  
Tactile Sensing ◽  
Sense Of Touch ◽  
Novel Design ◽  
Range Of Values ◽  
Self Adaptive

The sense of touch has always been challenging to replicate in robotics, but it can provide critical information when grasping objects. Nowadays, tactile sensing in artificial hands is usually limited to using external sensors which are typically costly, sensitive to disturbances, and impractical in certain applications. Alternative methods based on proprioceptive measurements exist to circumvent these issues but they are designed for fully actuated systems. Investigating this issue, the authors previously proposed a tactile sensing technique dedicated to underactuated, also known as self-adaptive, fingers based on measuring the stiffness of the mechanism as seen from the actuator. In this paper, a procedure to optimize the design of underactuated fingers in order to obtain the most accurate proprioceptive tactile data is presented. Since this tactile sensing algorithm is based on a one-to-one relationship between the contact location and the stiffness measured at the actuator, the accuracy of the former is optimized by maximizing the range of values of the latter, thereby minimizing the effect of an error on the stiffness estimation. The theoretical framework of the analysis is first presented, followed by the tactile sensing algorithm, and the optimization procedure itself. Finally, a novel design is proposed which includes a hidden proximal phalanx to overcome shortcomings in the sensing capabilities of the proposed method. This paper demonstrates that relatively simple modifications in the design of underactuated fingers allow to perform accurate tactile sensing without conventional external sensors.

scholarly journals LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework

2021 ◽  
Vol 14 (3) ◽  
pp. 2065-2093 ◽  
Author(s):  
Stefano Letizia ◽  
Lu Zhan ◽  
Giacomo Valerio Iungo
Keyword(s):  
Optimal Design ◽  
Velocity Field ◽  
Dimensional Space ◽  
Numerical Data ◽  
Scalar Fields ◽  
Theoretical Framework ◽  
Function Optimization ◽  
Objective Analysis ◽  
Mean Velocity ◽  
Data Set

Abstract. A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of the velocity statistical moments is proposed. LiSBOA represents an adaptation of the classical Barnes scheme for the statistical analysis of unstructured experimental data in N-dimensional space, and it is a suitable technique for the evaluation over a structured Cartesian grid of the statistics of scalar fields sampled through scanning lidars. LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. This revisited theoretical framework for the Barnes objective analysis enables the formulation of guidelines for the optimal design of lidar experiments and efficient application of LiSBOA for the postprocessing of lidar measurements. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the percentage of the measurement volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field. The optimal design of the lidar scans also guides the selection of the smoothing parameter and the total number of iterations to use for the Barnes scheme. LiSBOA is assessed against a numerical data set generated using the virtual lidar technique applied to the data obtained from a large eddy simulation (LES). The optimal sampling parameters for a scanning Doppler pulsed wind lidar are retrieved through LiSBOA, and then the estimated statistics are compared with those of the original LES data set, showing a maximum error of about 4 % for both mean velocity and turbulence intensity.

Optimal design and operation of batch reactors. 2. A case study

1993 ◽  
Vol 32 (5) ◽  
pp. 882-893 ◽  
Author(s):  
Masoud Soroush ◽  
Costas Kravaris
Keyword(s):  
Optimal Design ◽  
Batch Reactors

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Adaptive and Maladaptive Neural Plasticity Due to Facial Nerve Palsy

2016 ◽  
Vol 224 (2) ◽  
pp. 102-111 ◽  
Author(s):  
Carsten M. Klingner ◽  
Stefan Brodoehl ◽  
Gerd F. Volk ◽  
Orlando Guntinas-Lichius ◽  
Otto W. Witte
Keyword(s):  
Facial Nerve ◽  
Neural Plasticity ◽  
Cortical Plasticity ◽  
Brain Plasticity ◽  
Motor Nerve ◽  
Predictive Coding ◽  
Theoretical Framework ◽  
Cortical Reorganization ◽  
Nerve Lesion ◽  
Peripheral Facial Palsy

Abstract. This paper reviews adaptive and maladaptive mechanisms of cortical plasticity in patients suffering from peripheral facial palsy. As the peripheral facial nerve is a pure motor nerve, a facial nerve lesion is causing an exclusive deefferentation without deafferentation. We focus on the question of how the investigation of pure deefferentation adds to our current understanding of brain plasticity which derives from studies on learning and studies on brain lesions. The importance of efference and afference as drivers for cortical plasticity is discussed in addition to the crossmodal influence of different competitive sensory inputs. We make the attempt to integrate the experimental findings of the effects of pure deefferentation within the theoretical framework of cortical responses and predictive coding. We show that the available experimental data can be explained within this theoretical framework which also clarifies the necessity for maladaptive plasticity. Finally, we propose rehabilitation approaches for directing cortical reorganization in the appropriate direction and highlight some challenging questions that are yet unexplored in the field.

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