Engineering topics

Degtiarenko, Pavel

Publications and source records attributed to Degtiarenko, Pavel.

2 records

DOE OSTI2023

Photon Activation Analysis in Gallium, Nickel, and Vanadium

Here this research explored the development of the photonuclear production method of 67 Cu from 71 Ga as well as 47 Sc from 51 V. Both products serve as high-demand research medical radioisotopes. Furthermore, an understanding of these reactions is significant to fundamental nuclear physics and astrophysics. Bremsstrahlung flux was induced by an electron linac and a 1-mm tungsten radiator. Irradiation of gallium oxide powder, 98.78% pure 71 Ga, and a natural vanadium foil at 30.9 MeV and 100 W for 1 h produced 64.4 ± 0.4 Bq/W·s·kg of 67 Cu and 164 ± 3.1 Bq/W·s·kg of 47 Sc. A secondary irradiation with 99.6% pure 71 Ga and natural vanadium at 31.5 MeV and 100 W for 1.1 h produced 79.8 ± 0.9 Bq/W·s·kg of 67 Cu and 136 ± 7.2 Bq/W·s·kg of 47 Sc. The photoinduced activation is promising; however, further research into optimal geometry and power is required to maximize specific activity. Natural nickel was also irradiated to serve as a benchmark comparison. Effective cross sections for each reaction were inferred.

47Sc↗

DOE OSTI2022

An LSTM Deep Learning Network for ¿Background Radiation Prediction

Determination of appropriate background radiation is important in any measurement application. Environmental radiation monitors and monitors used to assess dose to individuals outside of controlled areas are particularly susceptible to changes in readings due to fluctuations in the environmental conditions. These fluctuations (e.g. radon progeny concentrations) lead to changes in the detector response in the actual radiation environment, and they need to be taken into account when extracting the net operational doses. Work has been ongoing to apply advances in Deep Learning and Artificial Intelligence to account for changes in detector responses based on environmental parameters; in particular, a Long-Short Term Memory (LSTM) Deep Learning architecture has been utilized to incorporate time-series data into a prediction model. In this work, we present the current status of the project to predict radiation measurements based on meteorological conditions and air packet trajectories extracted using the National Oceanic and Atmospheric Administration's (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) model.

Degtiarenko, Pavel↗