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Irradiation of refractory uranium dioxide and uranium carbide fuels at high specific powers to high burnups
Irradiated fuel capsules containing uranium dioxide and uranium carbide
A FORTRAN-4 computer program for handling fuel pin data is described. Its main features include standardized output, easy access for data manipulation, and tabulation of important material property data. An additional feature allows simplified preparation of input decks for a fuel swelling computer code (CYGRO-2). Data from over 300 high temperature nitride and carbide based fuel pin irradiations are listed.
The scope of this testing includes the Surface Acoustic Wave Sensor System delivered to KSC: two interrogator (transceiver) systems, four temperature sensors, with wooden mounting blocks, two antennas, two power supplies, network cables, and analysis software. Also included are a number of additional temperature sensors and newly-developed hydrogen sensors
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To enable better implementation of uranium monocarbide (UC) as an advanced nuclear fuel for future high-temperature reactors, it is essential to have a thorough knowledge of its thermal and thermodynamic properties under reactor operational conditions. In this work, we studied thermal bulk oxidation of UC by simultaneous thermal analysis consisting of thermogravimetric analysis – differential scanning calorimetry coupled with evolved gas mass spectrometry (TGA-DSC-MS), and we examined the thermodynamic stability of UC using high temperature oxide melt drop solution calorimetry. Further, in air, our studied UC sample (which contains ~5 mol% UO 2 ) was found to undergo a step-wise thermal oxidation process consisting of consecutive oxidations and thermal decomposition reactions: 0.95UC·0.05UO 2 → UO 3 ·0.29(C x O y ) + 0.66CO 2 → UO 3 ·0.20(C x O y ) + 0.09CO 2 → UO 3 ·0.03(C x O y ) + 0.17CO 2 → U 3 O 8 + 0.03CO 2 + 0.166O 2 . DSC was further used to determine the enthalpies of reactions associated with this series of oxidation reactions. Synchrotron X-ray diffraction (XRD) and extended X-ray absorption spectroscopy (EXAFS) were performed to characterize both the long- and short-range structures of UC. The standard enthalpy of formation (ΔH° f ) of UC was determined to be –50.7 ± 10.8 kJ/mol·atom, in good agreement with previous values measured by bomb calorimetry. Lastly, the enthalpic landscape of U-C compounds, including UC, U 2 C 3 , and α-UC 1.94 , were established based on the enthalpy normalized per mole atom, which suggests that U-C phases are thermodynamically stable at lower C/U ratios.
The synthesis of UC using carbothermic reduction of UO 2 and C mixtures has been well studied at high temperatures. However, the product phase behavior of carbothermic reduction at low temperatures (≤1773 K) is not well studied. Such a study is important as low temperatures permit single phase UC synthesis without forming secondary higher carbides, and it further supports the knowledge base of the process that needs to be used for transuranic elements such as plutonium that have high vapor pressures at elevated temperatures. Therefore, a low temperature carbothermic reduction of two different C/UO 2 molar ratios under inert and reducing environments have been studied here. Two different sample holding crucibles, alumina (Al 2 O 3 ) and graphite, were also used here to differentiate the hypostoichiometric (UC 1-a ) and oxygen dissolved (UC 1-x O x ) uranium monocarbide phases adding more details on the two systems. Also, the reaction kinetics involved in the formation of UC via the carbothermic reduction of UO 2 +C using product phases instead of evolved gases such as carbon monoxide is reported here. Under inert atmospheres but with significant oxygen partial pressures, the low temperature carbothermic reduction of UO 2 +C produced up to 90 wt.% UC 1-x O x type oxycarbides as was confirmed by Xray powder diffraction. Reducing Ar-4%H 2 environments at these temperatures were not successful in synthesizing UC as it reduces the amount of C required for the carbothermic reduction, leaving UC phase at a non-equilibrium state. Inert atmospheres with low or negligible oxygen partial pressures on the other hand produced near stoichiometric UC at high phase purity, especially at 1673 – 1773 K temperature range. An activation energy of 377±75 kJmol -1 was also calculated using product phase concentrations of the carbothermic reduction of UO 2 +C under these inert Ar (g) atmospheres.
We investigate the structural and magnetic properties of La 0.7 Sr 0.3 CrO 3 /La 0.7 Sr 0.3 MnO 3 (LSMO) heterostructures grown on (001)-oriented MgO by molecular beam epitaxy. Due to the large film-substrate lattice mismatch, strain relaxation is found to occur within the first 2–3 unit cells (uc) of the film as evidenced by reflection high energy electron diffraction and high-resolution synchrotron x-ray reciprocal space mapping. Furthermore, we find that the presence of the LSCO spacer and capping layers leads to ferromagnetism in ultra-thin LSMO layers with thicknesses of the order of 2 uc with the magnetic easy axis oriented in the film plane. Net magnetic moments of 1.4 and 2.4 μ B /Mn are measured for (2 uc LSCO/2 uc LSMO) and (2 uc LSCO/4 uc LSMO) superlattices, respectively, by superconducting quantum interference device magnetometry. The effective magnetic anisotropy of the relaxed (2 uc LSCO/4 uc LSMO) heterostructure is found to be an order of magnitude higher than bulk LSMO highlighting the critical role of interfacial interactions in tuning magnetic anisotropy at complex oxide interfaces.
Uranium carbides (UCs) are prevalent inclusions in U metal that form during melting operations from interactions with crucible walls and the casting chamber atmosphere. Although UCs have been studied extensively since the beginning of U metal foundry operations, there are still unknowns regarding the effects of thermomechanical processing on their sizes and morphology. Here, we present the results of a series of controlled cooling experiments with molten uranium to elucidate the effect of cooling rate on inclusion morphology in a-U. Samples were melted using a vacuum induction melter and manually cooled at rates of 2.5, 1.7, 1.1, 0.8, 0.3 (± 1%) K/s from ~1600 K to <700 K in under 1 hour. Subsequent scanning electron microscopy (SEM) was performed on cross-sections of the samples, revealing a complex mixture of UC morphologies that are indicative of diffusion and growth influenced by the thermal processing of the U matrix. Image analysis using the Morphological Analysis of Materials (MAMA) software showed that UC sizes generally grew larger with slower cooling rates, and the two slowest cooling rates noticeably impacted the inclusion circularity and ellipse aspect ratio. These results indicate that UC morphology is sensitive to short cooling rates (<1 hour) and could therefore be controlled in the production of metallic nuclear fuels. Additionally, inclusion speciation and morphologies could potentially provide forensic clues about processing history of unknown metal samples. Understanding the driving forces involved in UC morphology evolution is beneficial for evaluating metal fuels for next generation nuclear reactors and for identifying signatures for nuclear forensics.
Refractory carbide nuclear fuel has been one of the most promising fuel candidates for space nuclear propulsion due to its high melting point, temperature stability, and compatibility in a hot hydrogen environment. In this study, U x Zr 1-x C y fuel was produced by means of a carbothermic reduction process in different UC compositions including 5,10, 20, and 30 at.% UC in the fuel compound. The powder feedstock was consolidated via direct current sintering with densities up to 97% of the theoretical density. The samples with different U x Zr 1-x C y compositions were exposed to hot hydrogen at 2600 K for a cumulative time of 300 min. The samples were characterized by SEM, XRD, density, and measured for mass losses. The high-density samples displayed improved performance in hot hydrogen by minimizing porous sites and reducing areas of direct contact with hydrogen gas, leading to reduced mass losses. Variations in sample density proved to induce large changes in mass loss rates, increasing them up to 90%. The compositions with higher UC content reported the largest mass losses in the study. The loss of uranium occurred primarily at the surfaces exposed to the hot hydrogen where changes in the lattice constant confirmed losses exceeding 50% of the initial UC content in higher compositions, specifically to U 0.3 Zr 0.7 C y . XRD analyses revealed the presence of UH 3 in U 0.3 Zr 0.7 C y suggesting that metallic uranium formed inside the sample as a product of carbon losses. High-density U x Zr x-1 C y fuel with UC concentrations at or below 20 at.% UC exhibited stability and negligible density changes in a high temperature hydrogen environment.
Perovskites have gained popularity both as the active material in photovoltaics and as bulk triplet sensitizers for solid-state triplet–triplet annihilation upconversion (TTA-UC). Prior to widespread implementation into commercial photovoltaics, an in-depth understanding of the environmental influences on device performance is required. To this point, the temperature-dependent structure–function properties of TTA-UC within methylammonium formamidinium lead triiodide (MAFA)/rubrene UC devices are explored. A strong temperature dependence of the underlying UC dynamics is observed, where the maximum UC efficiency is achieved at 170 K, reflecting the competition between triplet diffusion length, diffusion rate, and triplet–triplet encounter events. Furthermore, a combination of spectroscopic and structural methods and theoretical modelling illustrates that despite the significantly increased carrier lifetime of the perovskite at low temperatures, the TTA-UC dynamics are not governed by the underlying sensitizer properties but rather limited by the underlying triplet diffusion.
This project aims to develop efficient and robust computational methods for solving the security-constrained unit commitment and alternating current optimal power flow problem (SC-UC-ACOPF). The SC-UC-ACOPF problem is at the center of the short-term operation of the U.S. Power Grid. It is solved every week, every day, and every 10 minutes to plan for the optimal action of electricity generation and consumption by minimizing the generation cost and maintaining power system reliability against potential disruptions of equipment failures. In mathematical terms, SC-UC-ACOPF is a challenging large-scale mixed-integer nonlinear optimization model. This means that the decisions involve both discrete variables, e.g. the turning on and off of generators and switching of transmission lines and transformers, and continuous decisions, e.g. the amount of energy generated by each generator and the power flows in the power grid. The physics of the power flow is described by nonlinear equations involving real and reactive power and bus voltages. Another key feature is the large number of contingencies, i.e. the system needs to stay reliable in face of failure of any one equipment, such as transmission lines and generators. The U.S. power grids are extremely complicated and large scale with more than 5,000 generators, 50,000 buses, and 100,000 high-voltage transmission lines, making the SC-UC-ACOPF a very large-scale computation challenge. The research developed in this project aims to solve the SC-UC-ACOPF problems in the three timescales, i.e. weekly, daily, and every 10-min. The proposed computational methods are built on a principled algorithmic approach of decomposition and penalization. More specifically, the algorithm develops spatial and temporal decomposition by exploiting the strong temporal coupling and weak spatial coupling of the UC problem and the complementary feature, i.e. weak temporal coupling and strong spatial coupling of the ACOPF problem. The algorithm also leverages recent progresses in strong convex relaxation of ACOPF. A unique feature of the proposed approach is that it generates a valid, global upper bound on the optimal maximum profit. In this way, a global optimality gap is available to measure the quality of the solution. To further speed up computation, the research team has developed a plethora of effective heuristics to strengthen the iterative penalty-based decomposition framework. For instance, a heuristic is developed to construct inner approximations of the time coupling constraints within the time decoupled problems. Contingencies are pre-screened and low-rank matrix computation is exploited to find the almost unique solution to each contingency. A novel heuristic for line switching is proposed and tested with positive impacts on instances where line switching is beneficial. Taking a systematic approach and carefully handling every detail of the problem pays off. The TIM-GO’s performance throughout the trials and the final event was stellar. TIM-GO garnered the second highest total prize money and is ranked in the top three positions across all categories of comparison.
Uranium carbide (UC) is a leading candidate fuel for Generation IV reactors due to its high uranium density and thermal conductivity. However, its irradiation performance—particularly gas bubble swelling and defect dynamics—remains poorly characterized. Using in-situ transmission electron microscopy (TEM), we irradiated UC with 300 keV Xe + and 1 MeV Kr 2+ ions at temperatures up to 900 °C to quantify swelling behavior and dislocation loop evolution. The swelling remained below 0.6 % across all temperatures, suggesting the dimensional stability of UC under irradiation at these temperatures. Dislocation loops grew faster in UC than in UO 2 or UN, correlating with its lower homologous temperature. Notably, nanograin structures emerged in thin regions of the lamellar, mirroring phenomena previously observed in UO 2 and ZrC. These results address critical knowledge gaps in the radiation tolerance of UC and provide insight into its suitability for advanced reactor systems.
Perovskite-sensitized upconversion (UC) has resulted in near-infrared-to-visible UC at solar-relevant fluxes. However, the successful implementation of UC devices into operating solar cells will result in exposure to similar environmental stressors as for the commercial photovoltaics (PVs), mainly elevated temperatures, and continuous irradiation. In this article, we investigated the effects of these two stressors, heat and light, on the triplet generation process at the perovskite/rubrene interface. Following exposure to both stressors, local discrepancies across the upconversion device were discovered. Here, the first region showed changes to the morphology, and no detectable upconverted emission was observed. Through the combination of optical microscopy and spectroscopy, crystallization of the organic semiconductor layer, degradation of dibenzotetraphenylperiflanthene, and concurrent degradation of the perovskite sensitizer were found. These effects culminate in a reduction in both triplet generation and triplet–triplet annihilation. In the second region, no changes to the morphology were present and visible UC emission was observed following exposure to both stressors. To probe the triplet sensitization process at elevated temperatures, transient absorption spectroscopy was performed. The presence of the excited spin-triplet state of rubrene at 60 °C highlighted successful triplet generation even at elevated temperatures. This work emphasizes the challenges and continued potential for the integration of perovskite-sensitized UC into commercial photovoltaic devices.
Day after day, system operators are faced with the challenge of taking unit commitment (UC) decisions under uncertain net load conditions. The standard operating procedure for taking UC decisions begins by leveraging auxiliary data on covariates (such as the day of the week or latest weather information) to generate a point prediction for net load, which is used in solving a deterministic UC problem. Such an approach, however, is known to deliver a notoriously poor out-of-sample (OOS) performance, as it completely disregards the stochastic nature of net load. While stochastic programming models explicitly represent uncertainty, they mostly do so using a generic set of scenarios that neglect covariate observations, squandering useful auxiliary data that could be harnessed to glean insights into uncertainty. In this article, we discuss a contextual stochastic optimization approach to UC, which effectively exploits covariate observations while explicitly assessing uncertainty so as to boost the OOS performance of UC decisions. The key thrust of our approach is to leverage regression models, along with their empirical residuals, to set up and solve sample average approximation problems. Not only do we prove that our approach satisfies the requisite conditions for asymptotic optimality and consistency laid out in (Kannan et al., 2022), but we also assess its performance on several case studies conducted using real-world data collected in California ISO and New York ISO grids. In conclusion, results show that the proposed approach can significantly improve OOS performance compared to alternative methods proposed in the literature under varying dataset sizes.