Estimating contamination monitor efficiency for beta radiation by means of PENELOPE-2008 Monte Carlo simulation

Merk, Rainer; Mielcarek, J.; Döring, J.; Lange, B.; Lucks, Chr.

doi:10.1016/j.apradiso.2017.05.015

Cited by 2 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have used DOE for different applications such as, investigation of tribo-mechanical properties of CrAlCN coatings (Tillmann et al , 2016), optimization of the Candida Antarctica lipase B mediated epoxidation of monoterpenes (Ranganathan et al , 2016), optimization of the electrophoretic deposition process parameters for peeking base coatings (Atiq Ur Rehman et al , 2017) and optimization of design configurations of channels in plastic injection molds (Jahan et al , 2017). Similarly, many researchers have contributed to MCS and used it for different applications such as chemotactic bacteria on the basis of the kinetic model (Yasuda, 2017), beta radiation transport within radioactively-contaminated food samples (Merk et al , 2017), optimization of a sintering process (Matsuda et al , 2017) and building a time-dependent diffusion equation (Dumonteil et al , 2017). This section gives the roadmap to investigate and optimize the critical parameters.…”

Section: Literature Surveymentioning

confidence: 99%

Data-driven fleet management using MOORA: a perspective of risk management

Rane¹,

Potdar²,

Rane

2020

JM2

View full text Add to dashboard Cite

Purpose The purpose of this study is to investigate the best fleet for a new purchase based on multi-objective optimization on the basis of ratio (MOORA), reference point and multi-MOORA methods. This study further identifies critical parameters for fleet performance monitoring and exploring optimum range of critical parameters using Monte Carlo simulation. At the end of this study, fleet maintenance management and operations have been discussed in the perspectives of risk management. Design/methodology/approach Fleet categories and fleet performance monitoring parameters have been identified using the literature survey and Delphi method. Further, real-time data has been analyzed using MOORA, reference point and multi-MOORA methods. Taguchi and full factorial design of experiment (DOE) are used to investigate critical parameters for fleet performance monitoring. Findings Fleet performance monitoring is done based on fuel consumption (FC), CO2 emission (CE), coolant temperature (CT), fleet rating, revenue generation (RG), fleet utilization, total weight and ambient temperature. MOORA, reference point and multi-MOORA methods suggested the common best alternative for a particular category of the fleet (compact, hatchback and sedan). FC and RG are the critical parameters for monitoring the fleet performance. Research limitations/implications The geographical aspects have not been considered for this study. Practical implications A pilot run of 300 fleets shows saving of Rs. 2,611,013/- (US$36,264.065), which comprises total maintenance cost [Rs. 1,749,033/- (US$24,292.125)] and FC cost [Rs. 861,980/- (US$11,971.94)] annually. Social implications Reduction in CE (4.83%) creates a positive impact on human health. The reduction in the breakdown maintenance of fleet improves the reliability of fleet services. Originality/value This study investigates the most useful parameters for fleet management are FC, CE, CT. Taguchi DOE and full factorial DOE have identified FC and RG as a most critical parameters for fleet health/performance monitoring.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Data-driven fleet management using MOORA: a perspective of risk management

Rane¹,

Potdar²,

Rane

2020

JM2

View full text Add to dashboard Cite

show abstract

“…It has extensive information on various applications for radiotherapy and radiodiagnosis. [ 4 29 30 31 32 33 34 ] Since its first version launched in 1996, the MCS PENELOPE code has become a flexible and reliable tool to describe the coupled transport of photons and electrons in complex material structures,[ 35 36 ] presenting simplicity and versatility to be used in the two most used programming platforms such as Windows and Linux without the necessity of the usage of an intermediary interface. Moreover, the results obtained are presented in .dat extension which is easy to read in any code for statistical analysis such as Origin, Matlab, and Gnuplot.…”

Section: Introductionmentioning

confidence: 99%

Monte carlo calculation of the energy spectrum of a 6 MeV electron beam using penetration and energy loss of positrons and electrons code

et al. 2020

View full text Add to dashboard Cite

Background: The limited bibliographic existence of research works on the use of Monte Carlo simulation to determine the energy spectra of electron beams compared to the information available regarding photon beams is a scientific task that should be resolved. Aims: In this work, Monte Carlo simulation was performed through the PENELOPE code of the Sinergy Elekta accelerator head to obtain the spectrum of a 6 MeV electron beam and its characteristic dosimetric parameters. Materials and Methods: The central-axis energy spectrum and the percentage depth dose curve of a 6 MeV electron beam of an Elekta Synergy linear accelerator were obtained by using Monte Carlo PENELOPE code v2014. For this, the linear accelerator head geometry, electron applicators, and water phantom were simplified. Subsequently, the interaction process between the electron beam and head components was simulated in a time of 86.4x10 4 s. Results: From this simulation, the energy spectrum at the linear accelerator exit window and the surface of the phantom was obtained, as well as the associated percentage depth dose curves. The validation of the Monte Carlo simulation was performed by comparing the simulated and the measured percentage depth dose curves via the gamma index criterion. Measured percentage depth- dose was determined by using a Markus electron ionization chamber, type T23343. Characteristic parameters of the beam related with the PDD curves such as the maximum dose depth (R 100 ), 90% dose depth (R 90 ), 90% dose depth or therapeutic range (R 85 ), half dose depth (R 50 ), practical range (R p ), maximum range (R max ), surface dose (D s ), normalized dose gradient (G 0 ) and photon contamination dose (D x ) were determined. Parameters related with the energy spectrum, namely, the most probable energy of electrons at the surface (E p,0 ) and electron average energy ( E – 0 ) were also determined. Conclusion: It was demonstrated that PENELOPE is an attractive and accurate tool for the obtaining of dosimetric parameters of a medical linear accelerator since it can reliably reproduce important clinical data such as the energy spectrum, depth dose, and dose profile.

show abstract

Estimating contamination monitor efficiency for beta radiation by means of PENELOPE-2008 Monte Carlo simulation

Cited by 2 publications

References 14 publications

Data-driven fleet management using MOORA: a perspective of risk management

Data-driven fleet management using MOORA: a perspective of risk management

Monte carlo calculation of the energy spectrum of a 6 MeV electron beam using penetration and energy loss of positrons and electrons code

Contact Info

Product

Resources

About