Determination of worst case loading margin using Genetic Algorithm

2009 ◽  
Author(s):  
A. R. Phadke ◽  
Manoj Fozdar ◽  
K. R. Niazi
Keyword(s):  
Genetic Algorithm ◽  
Worst Case

FIDDLE. Simultaneous Indexing and Structure Solution from Powder Diffraction Data using a Genetic Algorithm and Correlation Functions

2019 ◽  
Author(s):  
Carmen Guguta ◽  
Jan M.M. Smits ◽  
Rene de Gelder
Keyword(s):  
Genetic Algorithm ◽  
Powder Diffraction ◽  
Diffraction Data ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Simultaneous Optimization ◽  
Powder Diffraction Data ◽  
Structure Solution ◽  
Matching Technique

A method for the determination of crystal structures from powder diffraction data is presented that circumvents the difficulties associated with separate indexing. For the simultaneous optimization of the parameters that describe a crystal structure a genetic algorithm is used together with a pattern matching technique based on auto and cross correlation functions.<br>

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

Technical Analysis of Ocean Towing Evolutions

2021 ◽  
Author(s):  
Bartley Eckhardt ◽  
Daniel Fridline ◽  
Richard Burke
Keyword(s):  
Failure Modes ◽  
Human Life ◽  
Single Point ◽  
Forensic Analysis ◽  
Sea State ◽  
Worst Case ◽  
Strain Monitoring ◽  
Load Limit ◽  
Offshore Oil

Ocean towing in general, and non-routine tows in particular, present unique technical challenges to towing vessel owners/operators, salvors, the offshore oil/gas and wind industries, and others. When such tows “go wrong”, the harm to human life, property and/or the environment can be significant. The authors have drawn from their work on the Towing Safety Advisory Committee’s investigation of the grounding of the MODU Kulluk to present methods and considerations in analyzing ocean towing evolutions, both “routine” and “non-routine”. (TASK 14-01) The methods and considerations presented should be employed in advance of a towing evolution, but can be used in accident reconstruction and forensic analysis when an evolution has failed. The methods presented are iterative, and consider 2 x 6 degree freedom of motion (of the towing vessel(s) and towed vessel respectively) and characteristics of the towline, and facilitate determination of: Worst Case Conditions. Extreme Towline Tension (ETT) as a function of sea state and speed. Limits of the Tow (Go-No Go Criteria). Recommended Catenary Length as a function of sea state and speed. Size and Selection of the Towing Vessel and Gear, including: Required Bollard Pull. Required Strength, Characteristics and Condition of the Towline. Limits and Set Points of the Towing Winch, Automatic or Manual. Required Strength and Characteristics of the Synthetic Emergency Towline and its methods of deployment and connection. Working Load Limit (WLL) of the Shackles, Delta Plate and Attachment Points. Required Strength and Characteristics of Bridles, Pendant and Surge Gear/Shock Lines. The authors further explore the implications of single point failure modes, redundancy in gear and towing vessel(s), high cycle fatigue, and strain monitoring.

Statistical Safe Braking Analysis

2011 ◽  
Author(s):  
David F. Thurston
Keyword(s):  
Control Systems ◽  
System Capacity ◽  
Worst Case ◽  
Stopping Distance ◽  
Train Control ◽  
Classic Theory ◽  
Braking Distance ◽  
Prime Determinant ◽  
Worst Case Approach

The main objective in optimizing train control is to eliminate the waist associated with classical design where train separation is determined through the use of “worst case” assumptions that are invariant to the system. In fact, the worst case approach has been in place since the beginning of train control systems. Worst case takes the most conservative approach to the determination of train stopping distance, which is the basis for design of virtually all train control. This leads to stopping distances that could be far more that actually required under the circumstances at the time the train is attempting to brake. Modern train control systems are designed to separate trains in order to provide safety of operation while increasing throughput. Calculations for the minimum distance that separates trains have traditionally been based on the sum of a series of worst case scenarios. The implication was that no train could ever exceed this distance in stopping. This distance is called Safe Braking Distance (SBD). SBD has always been calculated by static parameters that were assumed to be invariant. This is, however, not the case. Parameters such as adhesion, acceleration, weight, and reaction vary over time, location or velocity. Since the worst case is always used in the calculation, inefficiencies result in this methodology which causes degradation in capacity and throughput. This is also true when mixed traffic with different stopping characteristics are present at the same time. The classic theory in train control utilizes a SBD model to describe the characteristics of a stopping train. Since knowledge of these conditions is not known, poor conditions are assumed. A new concept in train control utilizes statistical analysis and estimation to provide knowledge of the conditions. Trains operating along the line utilize these techniques to understand inputs into their SBD calculation. This provides for a SBD calculation on board the train that is the shortest possible that maintains the required level of safety. The new SBD is a prime determinant in systems capacity. Therefore by optimizing SBD as describes, system capacity is also optimized. The system continuously adjusts to changing conditions.

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