The Multi-Isotope Process Monitor is being developed at Pacific Northwest National Laboratory as a method to verify the process conditions within a nuclear fuel reprocessing facility using the gamma spectra of various process streams. The technique uses multivariate analysis techniques such as principal component analysis and partial least squares regression applied to gamma spectra collected of a process stream in order to classify the contents as belonging to a normal versus off-normal chemistry process. This approach to process monitoring is designed to function automatically, nondestructively, and in near real-time. To extend the Multi-Isotope Process Monitor, an analysis method to char- acterize spent nuclear fuel based on the reactor of origin, either pressurized or boiling water reactor, and burnup of the fuel using nuclide concentrations as input data has been developed. While the Multi-Isotope Process Monitor uses gamma spectra as input data, nuclide activities were used in this work as an initial step before Nuclide composition information was generated using ORIGEN-ARP for different fuel assembly types, initial 235U enrichments, burnup values, and cooling times. This data was used to train, tune, and test several multivariate analysis algorithms in order to compare their performance and identify the technique most suited for the analysis. To perform the classification based on reactor type, four methods were considered: k-nearest neighbors, linear and quadratic discriminant analysis, and support vector machines. Each method was optimized, and its performance on a validation set was used to determine the best method for classifying the fuel reactor class. Partial least squares was used to make burnup predictions. Three models were generated and tested: one trained on all the data, one trained for just pressurized water reactors, and one trained for boiling water reactors. Quadratic discriminant analysis was chosen as the best classifier of reactor class because of its simplicity and its potential to be extended to classify spent nuclear fuel’s fuel assembly type, i.e, more specific classes, using nuclide concentrations as input data. In the case of predicting the burnup of spent fuel using partial least squares, it was determined that making reactor-specific partial least squares models, one trained for pressurized water reactors and one trained for boiling water reactors, performed better than a single, general model that was trained for all light water reactors. Thus, the the classifier, regression algorithm, and all the necessary intermediate data processing steps were combined into a single analysis method and implemented as a Matlab function called “burnup.” This function was used to test the analysis routine on an additional set of data generated in ORIGEN-ARP. This dataset included samples with parameters that were not represented in the development data in order to ascertain the analysis method’s ability to analyze data for which it has not been explicitly trained. The algorithm was able to achieve perfect binary classification of the reactor as being a pressurized or boiling water reactor on the dataset and made burnup predictions with an average error of 0.0297%. / text

Several alternative strategies are being considered as spent nuclear fuel (SNF) management options. Transuranic nuclides (TRU) are responsible for the SNF long-term radiotoxicity beyond the first 500 years. One of the most viable approaches suggests creating new transmutation fuels containing TRUs for use in thermal and fast nuclear reactors. Irradiation of TRUs results in their transmutation and ultimate incineration by fission. The objective of this thesis is to analyze the impact of conventional PWR spent fuel variations on TRU-fueled Very High Temperature Reactor (VHTR) systems. This effort was focused on the prismatic core configuration. The 3D core models were created for use in calculations with the SCALE 5.1 code system. As part of the research effort, basic nuclear characteristics of TRUs were taken into consideration. The potential variations of PWR spent fuel compositions were modeled with the International Atomic Energy Agency (IAEA) Nuclear Fuel Cycle Simulation System, VISTA. The VHTR configurations with varying TRU compositions were analyzed assuming a single-batch core operation. Their performance was compared to the VHTR cases with low enriched uranium (LEU). The analysis shows that TRUs can be effectively utilized in the VHTR systems. The TRU-fueled VHTRs exhibit favorable performance characteristics.

Several alternative strategies are being considered as spent nuclear fuel (SNF) management options. Transuranic nuclides (TRU) are responsible for the SNF long-term radiotoxicity beyond the first 500 years. One of the most viable approaches suggests creating new transmutation fuels containing TRUs for use in thermal and fast nuclear reactors. Irradiation of TRUs results in their transmutation and ultimate incineration by fission. The objective of this thesis is to analyze the impact of conventional PWR spent fuel variations on TRU-fueled Very High Temperature Reactor (VHTR) systems. This effort was focused on the prismatic core configuration. The 3D core models were created for use in calculations with the SCALE 5.1 code system. As part of the research effort, basic nuclear characteristics of TRUs were taken into consideration. The potential variations of PWR spent fuel compositions were modeled with the International Atomic Energy Agency (IAEA) Nuclear Fuel Cycle Simulation System, VISTA. The VHTR configurations with varying TRU compositions were analyzed assuming a single-batch core operation. Their performance was compared to the VHTR cases with low enriched uranium (LEU). The analysis shows that TRUs can be effectively utilized in the VHTR systems. The TRU-fueled VHTRs exhibit favorable performance characteristics.

A process for recycling spent nuclear fuel cladding, a zirconium alloy (Zircaloy), into a metal powder that may be used for advanced nuclear fuel applications, was investigated to determine if it is a viable strategy. The process begins with hydriding the Zircaloy cladding hulls after the spent nuclear fuel has been dissolved from the cladding. The addition of hydrogen atoms to the zirconium matrix stresses the lattice and forms brittle zirconium hydride, which is easily pulverized into a powder. The dehydriding process removes hydrogen by heating the powder in a vacuum, resulting in a zirconium metal powder. The two main objectives of this research are to investigate the dehydriding process and to design, build and demonstrate a specialized piece of equipment to process the zirconium from cladding hulls to metal powder without intermediate handling. The hydriding process (known from literature) took place in a 95 percent argon - 5 percent hydrogen atmosphere at 500 degrees C while the dehydriding process conditions were researched with a Thermogavimetric Analyzer (TGA). Data from the TGA showed the dehydriding process requires vacuum conditions (~0.001 bar) and 800 degrees C environment to decompose the zirconium hydride. Zirconium metal powder was created in two separate experiments with different milling times, 45 minutes (coarse powder) and 12 hours (fine powder). Both powders were analyzed by three separate analytical methods, X-Ray Diffraction (XRD), size characterization and digital micrographs. XRD analysis proved that the process produced a zirconium metal. Additionally, visual observations of the samples silvery color confirmed the presence of zirconium metal. The presence on zirconium metal in the two samples confirmed the operation of the hydriding / milling / hydriding machine. Further refining of the hydride / milling / dehydride machine could make this process commercially favorable when compared to the high cost of storing nuclear waste and its components. An additional important point is that this process can easily be used on other metals that are subject to hydrogen embrittlement, knowing the relevant temperatures and pressures associated with the hydriding / dehydriding of that particular metal.

The traditional paradigm declares tetravalent uranium to be immobile under reducing conditions – an assumption widely employed for nuclear waste management strategies. In contrast, experiments presented here demonstrate this assumption, although valid for low temperatures, can be erroneous for high temperature natural systems. This project focuses on the ability of sulfate-bearing solutions to transport uranium at reduced conditions and elevated temperatures, identifies the new species U(OH)2SO4, derives thermodynamic constants necessary for modeling, and expands the quantifiable range of U4+ mobility to more neutral pH conditions. The data obtained enable more accurate assessment of uranium mobility by updating the existing uranium thermodynamic databases and is applicable to uranium fluid transport in oreorming systems and nuclear waste repositories.

No description available.

Fuel burnup calculations provide material concentrations and intrinsic neutron and gamma source strengths as a function of irradiation and cooling time. Detailed, full-core 3D burnup calculations are critical for nuclear fuel management studies, including core design and spent fuel storage safety and safeguards analysis. For core design, specifically during refueling, full- core pin-wise, axially-dependent burnup distributions are necessary to determine assembly positioning to efficiently utilize fuel resources. In spent fuel storage criticality safety analysis, detailed burnup distributions enable best-estimate analysis which allows for more effective utilization of storage space. Additionally, detailed knowledge of neutron and gamma source distributions provide the ability to ensure nuclear material safeguards. The need for accurate and efficient burnup calculations has become more urgent for the simulation of advanced reactors and monitoring and safeguards of spent fuel pools. To this end, the Virginia Tech Transport Theory Group (VT3G) has been working on advanced computational tools for accurate modeling and simulation of nuclear systems in real-time. These tools are based on the Multi-stage Response-function Transport (MRT) methodology. For monitoring and safety evaluation of spent fuel pools and casks, the RAPID (Real-time Analysis for Particle transport and In-situ Detection) code system has been developed. This dissertation presents a novel methodology and algorithm for performing 3D fuel bur- nup calculations, referred to as bRAPID- Burnup with RAPID . bRAPID utilizes the existing RAPID code system for accurate calculation of 3D fission source distributions as the trans- port calculation tool to drive the 3D burnup calculation. bRAPID is capable of accurately and efficiently calculating assembly-wise axially-dependent fission source and burnup dis- tributions, and irradiated-fuel properties including material compositions, neutron source, gamma source, spontaneous fission source, and activities. bRAPID performs 3D burnup calculations in a fraction of the time required by state-of-the-art methodologies because it utilizes a pre-calculated database of response functions. The bRAPID database pre-calculation procedure, and its automation, is presented. The ex- isting RAPID code is then benchmarked against the MCNP and Serpent Monte Carlo codes for a spent fuel pool and the U.S. Naval Academy Subcritical Reactor facility. RAPID is shown to accurately calculate eigenvalue, subcritical multiplication, and 3D fission source dis- tributions. Finally, bRAPID is compared to traditional, state-of-the art Serpent Monte Carlo burnup calculations and its performance will be evaluated. It is important to note that the automated pre-calculation proceedure is required for evaluating the performance of bRAPID. Additionally, benchmarking of the RAPID code is necessary to understand RAPID's ability to solve problems with variable burnups distributions and to asses its accuracy. / Ph. D. / In a nuclear reactor, the energy released from a fission reaction, the splitting of an atomic nucleus into smaller parts, is harnessed to generate electricity. Nuclear reactors rely on fuel, typically comprised of uranium oxide (UO₂). While the reactor is operating and the fuel is being used, or “burned”, for power production it is undergoing numerous nuclear reactions, including fission, and radioactive decays which alter the material composition. Knowing the time evolution of fuel as it is burned in the reactor, i.e., concentration of isotopes and sources of radiation, is critical. Nuclear reactor designers and operators use this information to optimize power production and perform safety analysis of used nuclear fuel. By performing fuel burnup calculations, material concentrations and radiation source strengths are obtained as a function of time in an operating nuclear reactor. Using traditional computational techniques, these calculations are extremely time consuming and, for certain problems, can be difficult to obtain an accurate solution. Ideally, a reactor designer would like to know the three-dimensional (3D) distribution of material compositions and sources; however this level of detail would require an excessive amount of calculation time, therefore simplified models and assumptions are used. For the design of the new generation of nuclear reactors, and monitoring and safeguards analysis, this level of detail will be required in lieu of the availability of experimental facilities which do not currently exist. This dissertation presents a novel methodology and algorithm for performing accurate 3D fuel burnup calculations in real-time, referred to as bRAPID (Burnup with RAPID). bRAPID utilizes an existing nuclear software, RAPID (Real-time Analysis for Particle transport and In-situ Detection), developed in the Virginia Tech Transport Theory Group (VT3G), which has been shown to accurately solve time-independent nuclear calculations in significantly less time than traditional approaches. bRAPID is capable of accurately calculating 3D material and source distributions as a function of time in an operating nuclear reactor, and requires significantly less time and computational resources than traditional approaches. To ensure that bRAPID is relatively easy to use, a number of automated routines have been developed and are presented. RAPID is benchmarked against the traditional code systems MCNP (Monte Carlo N-Particle) and Serpent, both of which are widely used in the nuclear community, for a spent fuel storage pool and the U.S. Naval Academy subcritical nuclear reactor facility. RAPID is shown to accurately calculate system parameters (eigenvalue and subcritical multiplication factor) and 3D fission source distributions. Finally, bRAPID is compared to the traditional burnup approach, using the Serpent code system. bRAPID is shown to accurately calculate system parameters and 3D material and source distributions in significantly less time than the traditional approach.

Förbrukat kärnbränsle är mycket radioaktivt och behöver därför lagras i djupa geologiska förvar i tusentals år innan det säkert kan återföras till naturen. På grund av de långa lagringsperioderna görs säkerhetsanalyser av de djupa geologiska förvaren. Under säkerthetsanalyserna görs upplösningsexperiment på förbrukat kärnsbränsle för att utvärdera konsekvenserna av att grundvatten läcker in i bränslet vid barriärbrott. Dessa experiment är både dyra och tidskrävande, varför beräkningsmodeller som kan förutsäga förburkat kärnbränsles upplösningsbeteende är önskvärda. Denna avhandling fokuserar på att samla in tillgängliga experimentella data från upplösningsexperiment för att uppdatera och utöka en databas. Med hjälp av databasen har upplösningsbeteendet för varje radionuklid utvärderats och jämförts med tidigare kunskap från befintlig litteratur. Även om det var svårt att vara avgörande om beteendet hos element där en begränsad mängd data fanns tillgänglig, motsvarar de upplösningsbeteenden som hittats för olika radionuklider i denna avhandling inte bara tidigare studier utan ger också ett verktyg för att hantera och jämföra förbrukat kärnbränsles upplösningsdata från olika utgångsmaterial, bestrålningshistorik och betingeleser under upplösning. Dessutom gjorde sammanställningen av en så stor mängd experimentella data det möjligt att förstå var framtida experimentella ansträngningar bör fokuseras, exempelvis finns det en brist på data under reducerande förhållanden. Dessutom utvecklades och kördes maskininlärningsmodeller med hjälp av Artificial Neural Network (ANN), Random Forest (RF) och XGBoost-algoritmer med hjälp av databasen, varefter prestandan utvärderades. Prestanda för varje algoritm jämfördes för att få en förståelse för vilken modell som presterade bäst, men också för att förstå om dessa typer av modeller är lämpliga verktyg för att förutspå förbrukat kärnbränsles upplösningsbeteende. Den bäst presterande modellen, med träning och test R2 resultat nära 1, var XGBoost-modellen. Även om XGBoost hade en hög prestanda, drogs slutsatsen att mer experimentell data behövs innan maskininlärningsmodeller kan användas i verkliga situationer. / Spent nuclear fuel (SNF) is highly radioactive and therefore needs to be stored in deep geological repositories for thousands of years before it can be safely returned to nature. Due to the long storage times, performance assessments (PA) of the deep geological repositories are made. During PA dissolution experiments of SNF are made to evaluate the consequences of groundwater leaking into the fuel canister in case of barrier failure. These experiments are both expensive and time consuming, which is why computational models that can predict SNF dissolution behaviour are desirable.  This thesis focuses on gathering available experimental data of dissolution experiments to update and expand a database. Using the database, the dissolution behaviour of each radionuclide (RN) has been evaluated and compared to previous knowledge from existing literature. While it was difficult to be conclusive on the behaviour of elements where a limited amount of data was available, the dissolution behaviours found of different radionuclides in this thesis not only correspond to previous studies but also provide a tool to manage and compare SNF leaching data from different starting materials, irradiation history and leaching conditions. Moreover, the compilation of such a large amount of experimental data made it possible to understand where future experimental efforts should be focused, i.e. there is a lack of data during reducing conditions. In addition, machine learning models using Artificial Neural Network (ANN), Random Forest (RF) and XGBoost algorithms were developed and run using the database after which the performances were evaluated. The performances of each algorithm were compared to get an understanding of which model performed best, but also to understand whether these kinds of models are suitable tools for SNF dissolution behaviour predictions. The best performing model, with training and test R2 scores close to 1, was the XGBoost model. Although XGBoost, had a high performance, it was concluded that more experimental data is needed before machine learning models can be used in real situations.

No description available.

In this thesis, the response of uranium minerals and poorly crystalline phases to changes in geochemical conditions through geological time has been assessed in order to understand the mobility of natural uranium in the fracture network of a proposed site for a spent nuclear fuel repository in Forsmark, Sweden. Identification and characterization of solid phase uranium have been performed through electron microprobe analysis and optical petrography (Article I). The identified uraninite, haiweeite, and uranophane crystals have been dated using U-Pb and Pb-Pb isotope ratios obtained from secondary ion mass spectrometry and laser ablation-inductively coupled plasma-mass spectrometry (Article II). The mobility of uranium in current Forsmark groundwaters and fracture system has been modelled using the PHREEQC geochemical program and Ra and Rn isotope systematics (Article IV). The rate of submarine groundwater discharge (SGD) from the Forsmark coast to Öregrundsgrepen has also been modelled using Ra isotopes (Article V). Results from these studies support a geologically early (~1200 Ma) oxidation of U(IV) to U(VI). It is further suggested that the old U(VI) minerals present in the bedrock are soluble at the pe values and alkalinities observed in the Forsmark groundwaters. At pe &lt; −4.6 and alkalinity &lt; 60 mg/L, U(VI) can be reduced to U(IV) and deposited in the fracture network. Although a non-negligible rate of SGD has been observed, this cannot be attributed to the discharge of deep (&gt;200 m.b.s.l.) Forsmark groundwaters on the basis of current data. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: In press. Paper 4: Manuscript. Paper 5: Manuscript.</p>