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Simulation of sand particles detection inside a pipeline by photon radiography

Transmission pipelines are vital arteries in the petroleum industry, as the survival of this system depends on maintaining the capability of transferring fluid through the pipelines. In petroleum industry, transfer system faults lead to significant economic and social consequences and sometimes may produce critical situations. Transmission pipelines connect all systems, and any defect in their functions adversely affects other systems directly or indirectly. Small quantities of sand particles in transmission pipelines in petroleum industries can cause significant damage to pipes or installations such as valves. Therefore, the detection of these solid particles in oil or gas pipelines is essential. To prevent the costly consequences of passing sand particles through pipelines, early detection of these particles has a crucial impact on equipment lifetime and availability. There are some techniques for the detection of sand particles in the pipelines. Among applicable methods, photon radiography can be applied as an inspection technique along with other methods, or in some cases, where conventional inspection tools cannot be used. The high velocity of solid particles inside the pipeline leads to the destruction of any measuring device that is placed inside it. In addition, the pressure drop due to the placement of the measuring devices inside the pipeline has negative effects on the fluid transfer capacity of the pipe, which ultimately leading adverse economic consequences. In this paper, photon radiography as an in-situ, non-destructive, and online method for detecting sand particles flowing through the pipelines with oil, gas, or brine was studied. Simulation based on the Monte Carlo calculation was applied to evaluate the impact of this technique on sand particle detection in a pipeline. Obtained results showed that radiography, as a reliable, fast, and non-destructive method, could detect solid particles in transmitting pipelines.

Enhanced electron beam and X-ray beam therapy by applying nanoparticle heterojunctions: A Monte Carlo simulation

Cancer has become one of the major diseases that seriously threaten human health. In order to improve the therapeutic gain ratio (TGF) of conventional X-ray and electron beams, we studied the dose enhancement effect and secondary electrons emission of Au-Fe nanoparticle heterostructures by Monte Carlo method. Under the irradiation of 6 MeV photon and 6 MeV electron beams, the Au-Fe mixture has a dose enhancement effect. For this reason, we explored the secondary electrons production that leads to dose enhancement. For 6 MeV electron beam irradiation, Au-Fe nanoparticle heterojunctions have an higher electrons emission than Au and Fe nanoparticles. When cubic, spherical and cylindrical heterogeneous structures are considered, the electron emission of the columnar Au-Fe nanoparticles is the highest, with a maximum value of 0.00024. For 6 MV X-ray beam irradiation, Au nanoparticle and Au-Fe nanoparticle heterojunction have similar electrons emission, while Fe nanoparticle has the lowest one. When cubic, spherical and cylindrical heterogeneous structures are considered, the electron emission of the columnar Au-Fe nanoparticles is the highest, with a maximum value of 0.000118. This study contributes to improve the tumor-killing effect of conventional X-ray radiotherapy treatment and has guiding significance for the research of new nanoparticles.

Evaluation treatment planning system for oropharyngeal cancer patient using machine learning

Oropharyngeal cancer (OPC) comprises a group of various malignant tumours that grow in the throat, larynx, mouth, sinuses, and nose. THE RESEARCH AIMS: to investigate the performance of the OPC VMAT model by comparison to clinical plans in terms of dosimetric parameters and normal tissue complication probabilities.Tune the model which at least matches the performance of clinical created photon treatment plans and analyse and find the most appropriate strategic plan scheme for OPC.The machine learning (ML) plans are compared to the reference plans (clinical plans) based on dose constraints and target coverage. VMAT oropharynx ML model of Raystation development 11B version (non-clinical) was used. A model was trained by using different modalities. A different strategy of machine learning and clinical plans was performed for five patients. The dose Prescribed for OPC is 70 Gy, 2 Gy per fraction (2Gy/Fx). The PTV was derived for the primary tumour and secondary tumour, PTV+7000 cGy and PTV_5425 cGy volumetric modulated arc therapy (VMAT) were used with beams performing a full 360° rotation around the single isocenter.Organs at risk were observed that the volume of L-Eye in clinical plan (AF) for the case1 treatment planning could be successfully used ensuring efficiency and lower than MLVMAT and MLVMAT-org plans were 372 cGy, 697 cGy and 667 cGy respectively, while showed case2, case3, case4 and case5 are better to protect the critical organs in ML plan compare with a clinical plan. DHI for the PTV-7000 and PTV-5425 is between 1 and 1.34, While DCI for PTV-7000 and PTV-5425 is between 0.98 and 1.

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