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Most published Xplore authors for Uranium

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Xplore Articles related to Uranium

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Weight optimized centroid localization algorithm on radioactive pollution monitoring by WSN for uranium tailings

2016 6th International Conference on Electronics Information and Emergency Communication (ICEIEC), 2016

Aiming at the problem of high cost of cable layout and poor maintenance in traditional radioactive pollution monitoring for uranium tailings, considering banded dam and little node connectivity, a weight optimized centroid localization algorithm is proposed. Under different index, by comparing the positioning accuracy and the influence of the external disturbance, new algorithm changes the index of weight and measured ...


Direct Alpha Spectrometry of Irradiated Nuclear Fuel

2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2017

Alpha-particle spectrometry was performed on irradiated nuclear fuel using a single-crystal (sc) diamond semiconductor particle detector. The detector was manufactured using chemical vapor deposition (CVD) methods, square in shape measuring 4.6-mm per side, and 0.5-mm thick. Square aluminum electrodes deposited on each side of the detector, 4.0-mm per side, defined the semiconductor's sensitive area while a smaller, 1-mm diameter round ...


Discrimination of high-Z materials using muon scattering tomography

2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD), 2016

Legacy nuclear waste can contain anything from parts of fuel rods to coveralls worn by the workers, stored inside large concrete containers. It is important to identify the materials present in these containers with techniques that are non-invasive and scalable, in order to be applied for a considerable amount of large volumes. Using muon scattering tomography, we show that it ...


Developing Support Vector Machine Prediction Capabilities of Uranium Enrichment Based on Gamma-Gamma Coincidence Signatures

2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2017

The primary difficulty of obtaining useful information from gamma-gamma coincidence spectra is the process of determining which spectral features are useful for drawing a conclusion. This is especially true for dense and complicated coincidence spectra, such as those of fission products from irradiated uranium samples. A set of binary classification support vector machine algorithms was trained with supervised learning on ...


Radioactive waste analysis of CNPP-1 (PWR) spent nuclear fuel

2015 Power Generation System and Renewable Energy Technologies (PGSRET), 2015

Nuclear power is safe, reliable, environment friendly and large scale energy generating technology. But it is also a fact that the public serious concern about the management of radioactive waste is associated with this technology. The spent nuclear fuel is being safely stored at nuclear power facilities for about 50 years. It has not yet decided whether the used fuel ...


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Educational Resources on Uranium

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IEEE-USA E-Books

  • Weight optimized centroid localization algorithm on radioactive pollution monitoring by WSN for uranium tailings

    Aiming at the problem of high cost of cable layout and poor maintenance in traditional radioactive pollution monitoring for uranium tailings, considering banded dam and little node connectivity, a weight optimized centroid localization algorithm is proposed. Under different index, by comparing the positioning accuracy and the influence of the external disturbance, new algorithm changes the index of weight and measured distance -1 to -1.5, resulting in an accuracy improvement in long distance. Simulation demonstrated that compared with centroid localization algorithm and maximum likelihood estimation, average positioning error of the new algorithm was reduced by 37.5% and 30.7%, and the maximum positioning error was reduced by 1/5 and by 2/5. With high reliability and stability, the new algorithm can meet the requirements of position in radioactive pollution monitoring for uranium tailings.

  • Direct Alpha Spectrometry of Irradiated Nuclear Fuel

    Alpha-particle spectrometry was performed on irradiated nuclear fuel using a single-crystal (sc) diamond semiconductor particle detector. The detector was manufactured using chemical vapor deposition (CVD) methods, square in shape measuring 4.6-mm per side, and 0.5-mm thick. Square aluminum electrodes deposited on each side of the detector, 4.0-mm per side, defined the semiconductor's sensitive area while a smaller, 1-mm diameter round aperture defined the region of the detector exposed to the fuel. This aperture was approximately 3 mm from the substrate face. The fuel sample consisted of a rectangular piece of irradiated nuclear fuel measuring approximately 2.2 mm x 0.5 mm in area and 0.15-mm thick. The fuel was made as a compact of metallic fuel powder in an aluminum matrix. The powder was an alloy of 93% uranium and 7% molybdenum (U7Mo), with a235U starting enrichment of 19.75%. Fuel powder particles ranged in size from 10 μm to 100 μm in diameter. The pre-irradiation uranium density in the fuel was 8 g U cm-3. The sample's burn-up was exceptionally high at 42% FIMA (fissions per initial metal atom). The on- contact photon dose rate from the sample was measured to be ~1 R hr-1(0.01 Gy hr-1), the oncontact beta dose rate from the sample was measured at ~30 R hr-1(0.3 Gy hr-1). Data was collected in air for 8,572 s with the detector suspended over the fuel sample by approximately 10 mm. The spectrum collected during the measurement showed the presence of significant quantities of higher-order transmutation actinides including238Pu,241Pu, and244Cm, in agreement with radiochemical assay data for the sample. The beta-particle event rate in the detector exceeded the alpha-particle rate by a factor of -5×105. Despite this high count rate, the extremely fast signals from the scCVD diamond semiconductor allowed operation with a dead-time of 10.62% during the measurement.

  • Discrimination of high-Z materials using muon scattering tomography

    Legacy nuclear waste can contain anything from parts of fuel rods to coveralls worn by the workers, stored inside large concrete containers. It is important to identify the materials present in these containers with techniques that are non-invasive and scalable, in order to be applied for a considerable amount of large volumes. Using muon scattering tomography, we show that it is possible to discriminate uranium blocks from lead, tungsten and plutonium, inside a concrete filled cylinder, by selecting the muon tracks that pass through the volumes of interest. There is very good discrimination between uranium and lead or tungsten for block sizes upwards of 2 × 2 × 2 cm3. We even show that we can discriminate between lumps of uranium and plutonium.

  • Developing Support Vector Machine Prediction Capabilities of Uranium Enrichment Based on Gamma-Gamma Coincidence Signatures

    The primary difficulty of obtaining useful information from gamma-gamma coincidence spectra is the process of determining which spectral features are useful for drawing a conclusion. This is especially true for dense and complicated coincidence spectra, such as those of fission products from irradiated uranium samples. A set of binary classification support vector machine algorithms was trained with supervised learning on gamma-gamma coincidence data from irradiated uranium samples of natural, low, and high- enriched uranium. The resulting model will be capable of classifying uranium samples into the correct enrichment regime (natural, low, or high) based on their gamma-gamma coincidence spectra. Current results with the binary classification support vector machines indicate a 92% classification accuracy. This eliminates the need for a spectral analyst to parse through the data and manually determine which spectral features are indicative of enrichment. The basis functions used by the model to make classification decisions can also inform the analyst of less obvious spectral features that are indicative of enrichment. Ongoing work to improve classification accuracy is discussed.

  • Radioactive waste analysis of CNPP-1 (PWR) spent nuclear fuel

    Nuclear power is safe, reliable, environment friendly and large scale energy generating technology. But it is also a fact that the public serious concern about the management of radioactive waste is associated with this technology. The spent nuclear fuel is being safely stored at nuclear power facilities for about 50 years. It has not yet decided whether the used fuel is treated as a resource or waste. About 96% of Uranium is still present in the fuel when it is removed from the reactor. The spent fuel composition depends on the initial amount of fuel, irradiation time and power of the reactor. This paper focuses to simulate the material composition, radioactivity and its corresponding decay heat profiles of the CNPP-I using depletion computer code ORIGEN-2. For this purpose irradiation model of the single PWR fuel assembly (with 3% U-235 fuel enrichment) is developed for the period of 365 days. The simulated values of radioactivity of actinides, fission products and activation products and their decay heat are compared with the analytical results obtained from the Malbrain et al. Model. This study shows that, nuclei heavier than uranium are obtained when uranium captures one or several neutrons without fission. Thus, one finds almost 1% of plutonium. Actinides other than plutonium (neptunium, americium, and curium) are less abundant. About 3% of the mass consists of fission products of 235 239 U and Pu. The spent nuclear fuel decay heat comes mainly from unstable fission products, unstable actinides, and activated structural and cladding materials. The main source of decay heat production in spent nuclear fuel is the beta decay of fission products.

  • 4S reactor: An educational model for universities a gen IV reactor — Super safe small and simple

    An educational model of a small compact 4th-generation nuclear reactor is presented for use as an educational model in universities. For the full-scale power reactor of 10 MWe, a Monte Carlo simulation was carried out to determine the fluxes in the core. The reactor is at the stage of commercialization in the near future and presents a promising option for countries like Pakistan where substantial off-grid populations are deprived of electricity from the national grid. It is also a safe nuclear power system free form the past problems of waste handling or proliferation worries.

  • Remote detection of uranium with filament ablation spectroscopy

    We demonstrate that femtosecond filaments can efficiently excite metallic uranium over distances on the order of 10 meters, and that characteristic uranium atomic and molecular signatures can be simultaneously detected in seconds.

  • Two-dimensional fluorescence spectroscopy for measuring uranium isotopes in femtosecond laser ablation

    We present the first two-dimensional fluorescence spectroscopy measurements of uranium isotopes in femtosecond laser ablation plasmas. A new method of signal normalization is presented to reduce noise in absorption-based measurements of laser ablation.

  • Integrated X-ray detection system for determination of nuclear material concentration

    An integrated system of X-ray Absorption Spectrometry (XAS), also called L-edge densitometry for uranium (LED) and X-ray Fluorescence spectrometry (XRF) is designed using Monte Carlo simulation for determining concentration of uranium and minor actinides for safeguards. The equipment is compact compared to the K-edge densitometer with high purity germanium detector with liquid nitrogen cooling due to using a low energy X-ray source and a heavy shielding system. The system has been validated from simulation and analysis of the LED/XRF spectrum from this feasibility study.

  • Comparative analysis of classification algorithms

    Machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and underfitting. Neural Network, k-Nearest Neighbors and Naïve Bayes were chosen for this experiment, since their results in previous experiments were reasonable for the log data. Also this paper contains a comparative analysis of those recognition algorithms applied to the log data of Inkai uranium deposits in Kazakhstan.



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