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The Waste Isolation Pilot Plant (WIPP) was opened in 1999 as a solution to the long-term disposal of the nation's nuclear defense-generated transuranic (TRU) waste. The salt beds were recommended as the ideal location because they are free from flowing fresh water, easily mined, impermeable, and geologically stable. However, for performance assessment modeling, release scenarios must be considered. The most likely release scenario was determined to be human intrusion leading to direct and/or long-term brine release (US DOE 1995, US DOE 1996, Perkins et al., 1999). The focus of this research is the Rustler formation because it is located above the WIPP and is the most transmissive layer (Perkins et al., 1999). Objective: To determine the effect of chelating ligands ( e.g. EDTA) on dissolution of dolomite in high ionic strength systems. Do brines impact dolomite dissolution and how is it affected by EDTA binding with cations? By varying the ionic strengths, different brine conditions can be simulated similar to those in and around the Culebra member and evaluate trends of dissolved Ca and Mg in the aqueous phase from the quantified data. Conclusions: <2% of dolomite dissolved over one week at pH 8.5. Greater dissolution occurred at higher ionic strength and in the presence of NaCl with approximately 0.4 vs. 0.9% of Mg removed from dolomite in 0.1 and 1.0 M Na. Further, 0.3 vs. 0.9% of Mg was removed from dolomite in 1.0 M IS CaCl{sub 2} versus NaCl. EDTA increased dissolution of dolomite at low ionic strength due to its strong complexation affinity for Ca/Mg with slightly greater removal of Ca than Mg. Significant differences were not observed at high ionic strength due to the presence of large concentrations of competing cations (Na, Ca, Mg). Slightly higher concentrations of Ca were removed from dolomite than Mg after one week, suggesting either incongruent dissolution or release through ion exchange of Ca from trace mineral impurities. Further, when EDTA was present, it may have increased Ca removal due to its greater complexation for Ca than Mg. Environmental Implications: High ionic strength brines and low ionic strength solutions with organic ligands increase dissolution of dolomite. Due to the long half-lives of radionuclides in the TRU waste, it may be important to consider the long term impact of dolomite dissolution for potential release scenarios.
The Advanced Test Reactor (ATR) at INL measures reactor power through two methods, thermal and Nitrogen-16 (N-16) activity. Water power calculator (WPC) is a thermal power system that measures flow and temperature to determine the thermal quadrant powers. The N-16 system utilizes a beta chamber detector that outputs reactivity levels to calculate lobe power through an algorithm called lobe power calculation and indication system (LPCIS). The LPCIS utilizes the N-16 system and the WPC system to determine reactor core power levels. The WPC provides accurate calculations of quadrant and total reactor thermal power. With the use of WPC measurements, thermal-to-N-16 (T2N) power ratios are produced to determine if the two indication systems agree on core power. Relative magnitude equations are used to utilize N-16 coefficients and multipliers to improve the indications of the LPCIS. This is crucial for maintaining safety limits. Currently, the LPCIS system uses linear equations and matrices to calculate lobe power through multipliers and coefficients. Advancements in technology and system upgrades have assisted system engineers at INL with the objective to reach a more dynamic system. In return the system demonstrates an increase in the accuracy of power reading while maintaining safety margins. The new proposed coefficient and multiple method implements a cycle specific, and a physics-based model intended for changing coefficients during operation. This calibration experiment focuses on power splitting, outer shim and neck shim, as well as fuel burning into the RDAS weighting factors. Results show that the Physics learning method yields a smaller error margin inside of the desired power range for the data set 166-A. This proved true for both constrained and unconstrained testing. This is most likely due to the physics data fitting approach that resulted in favorable coefficients and multipliers for 166-A. Continuing to improve the physics-based model will help improve the power accuracy of the LPCIS system.
Pressurized oxy-combustion (POC) is a promising candidate to reduce carbon emissions in power generation. Designing an effective burner plays a vital role in developing new combustion technologies. Because of high pressure, the volumetric fraction of particles in a pressurized oxy-combustor could be higher than that in the conventional combustor, consequently, the particle dynamics in the pressurized vessel need to be evaluated accordingly. Specifically, the present computational work analyzes the pattern of the particle dispersion by extracting the particle mass flow rate and the particle size distribution in the pressurized combustor. A large eddy simulation (LES) with the environment pressure 15-bar and 100 kWth thermal input of fuel particle has been performed. The particle mass flow rates sampled in several cross-section planes strongly fluctuate, indicating the impacts of the flame as well as the gas-phase flow on the particle movement. Animations of the particle-gas flow show that the flow recirculation plays a major role in producing the pulsing phenomenon of the particle mass flow rate.
A method of adding physical layer security to a class of spread spectrum systems has been recently proposed. In this paper, we look into the rate at which an eavesdropper may gain information about the system to decipher the data symbols. The Shannon mutual information is used to measure the rate of information that may be gained by an eavesdropper. The k-nearest neighbors (k-NN) method is used to obtain estimates of relevant entropy values, which will then be used to quantify the rate of information recovery as more data is transmitted. It turns out that such information recovery requires the adoption of special methods that avoid any destructive bias in the estimates. Details of these methods are also presented.
Through Net Zero World, the National Renewable Energy Laboratory (NREL), the National Energy Secretariat of Argentina, La Pampa's Energy and Mining Secretariat, Pampetrol, and the Administracion Provincial de Energia de La Pampa (APELP) conducted an integrated analysis of the power system of the La Pampa province in Argentina. The study assessed the impact that solar and battery energy storage systems could have on the region grid's reliability and electricity costs, providing insights into long-term decarbonization strategies across Argentina. The analysis demonstrated that these systems are expected not only to help with decarbonization efforts but also to bolster grid resiliency, reduce line losses, and improve voltage regulation while lowering electricity costs. The paper offers a replicable framework for similar studies across other Argentine provinces, which could inform a national grid reliability plan and promote regional coordination. Such coordination will play a key role in supporting a nationwide clean energy transition as Argentina increases its share of renewable energy. By coordinating subnational energy plans with national strategies, Argentina can secure a resilient clean energy future, leveraging the substantial renewable resources available across its provinces.
This work presents a projection-based multiphysics Model Order Reduction (MOR) framework for the analysis of nuclear systems and its application to parametric simulations of Molten Salt Reactors (MSR). The framework, named GeN-ROM, is developed using OpenFOAM® and employs a Proper Orthogonal Decomposition aided Reduced-Basis technique (POD-RB). It can be used to reduce steady-state and transient multiphysics problems involving parametric fluid dynamics, heat exchange, and neutronics phenomena. For the treatment of structural elements in the hydraulic systems, a porous medium approach has been adopted. The reduction process is data-driven and snapshot information is extracted via POD to learn the solution manifold and to build global spatial basis functions. At the data collection phase, GeN-ROM makes use of the solvers available in GeN-Foam, a similarly OpenFOAM®-based multiphysics framework developed for the analysis of nuclear reactors. The global bases are used both to approximate the solution fields and to project the full-order equations onto lower-dimensional subspaces, thus considerably reducing the number of unknowns in a numerical system. This reduction leads to significant computational speedups, which is ideal for multi-query applications such as uncertainty quantification or design optimization. The developed tool has been tested using a 2D multiphysics model of the Molten Salt Fast Reactor (MSFR) with steady-state and transient scenarios, with speedups on the order of 10 – 10 5 .
The first order reversal curve (FORC) method is a magnetometry based technique used to capture nanoscale magnetic phase separation and interactions with macroscopic measurements using minor hysteresis loop analysis. This makes the FORC technique a powerful tool in the analysis of complex systems which cannot be effectively probed using localized techniques. However, recovering quantitative details about the identified phases which can be compared to traditionally measured metrics remains an enigmatic challenge. We demonstrate a technique to reconstruct phase-resolved magnetic hysteresis loops by selectively integrating the measured FORC distribution. From these minor loops, the traditional metrics—including the coercivity and saturation field, and the remanent and saturation magnetization—can be determined. In order to perform this analysis, special consideration must be paid to the accurate quantitative management of the so-called reversible features. This technique is demonstrated on three representative materials systems, high anisotropy FeCuPt thin-films, Fe nanodots, and SmCo/Fe exchange spring magnet films, and shows excellent agreement with the direct measured major loop, as well as the phase separated loops.
The report presents results from the development of a cloud-based, Big Data analysis framework for power systems. The computational pipeline uses the Apache Spark framework running in an OpenStack cloud infrastructure. A real-world phasor measurement unit (PMU) dataset has been used to carry out the analysis. Several Machine Learning (ML) methods have been developed and implemented for event and anomaly detection and classification. Actual examples of power system events detection and analysis using synchrophasor data are presented. It has been shown that applications of the cloud-based computing environment and the Apache Spark framework enable a significant increase in the computational efficiency of large-scale PMU data analysis.
A variety of US electric power sector capacity expansion models are used by decision makers and analysts to evaluate competition among generation, transmission, and storage technologies to meeting the demands of the system. CEMs use least-cost optimization to identify optimal portfolios of investments capable of satisfying all specified requirements. While CEMs are a useful tool to inform pathways to meet future needs, projections can differ significantly between tools for apparently similar scenario assumptions. Differences in model structure, scope, and input assumption contribute to this issue. This work compares model response with harmonization between four modeling teams on issues significant to the representation and development of nuclear energy.
The 3013-container-package consists of a convenience, inner and outer container and is used for long-term storage of plutonium-bearing materials. A destructive examination (DE) protocol has been developed to examine the container package visually and with microscopic aid to find any corrosion conditions that could result in the loss of the integrity of the container package over its lifetime. The DE protocol contains three main steps: initial container examination, helium (He) leak testing, and detailed imaging and analysis of the inner container closure weld region (ICCWR). The ICCWR has been determined to be bounding, defined as exhibiting worst case conditions for stress corrosion cracking (SCC) of the inner container. To assess SCC in the ICCWR, the inner container lid is cut into quarters and the weld and He-leak testing gasket are removed. Then a citric acid wash is performed to remove adherent chlorides from the ICCWR. The wash is then sent for analysis to determine the concentration of chlorides in the ICCWR. While the analysis for chloride concentration is being performed, the quarter sections are further sectioned into 1/8th subsections by cutting each section in half. These subsections are washed using nitric acid to remove corrosion products. Then each subsection is imaged using a Wide Angle 3-D Measurement System (WAMS). After analysis of microscope images for potential SCC, additional imaging can be performed, including subsurface imaging. After review, a determination is made of whether the container integrity may potentially affect the safe storage of the material.
Novel concepts enabling a resilient future power system and their subsequent experimental evaluation are experiencing a steadily growing challenge: large scale complexity and questionable scalability. The requirements on a research infrastructure (RI) to cope with the trends of such a dynamic system therefore grow in size, diversity and costs, making the feasibility of rigorous advancements questionable by a single RI. Analysis of large scale system complexity has been made possible by the real-time coupling of geographically separated RIs undertaking geographically distributed simulations (GDS), the concept of which brings the equipment, models and expertise of independent RIs, in combination, to optimally address the challenge. This article presents the outputs of IEEE PES Task Force on Interfacing Techniques for Simulation Tools towards standardization of GDS as a concept. First, the taxonomy for setups utilized for GDS is established followed by a comprehensive overview of the advancements in real-time couplings reported in literature. The overview encompasses fundamental technological design considerations for GDS. The article further presents four application oriented case studies (real-world implementations) where GDS setups have been utilized, demonstrating their practicality and potential in enabling the analysis of future complex power systems.
Human effects on ecosystems date back thousands of years, and anthropogenic biomes—anthromes—broadly incorporate the effects of human population density and land use on ecosystems. Forests are integral to the global carbon cycle, containing large biomass carbon stocks, yet their responses to land use and climate change are uncertain but critical to informing climate change mitigation strategies, ecosystem management, and Earth system modeling. Using an anthromes perspective and the site locations from the Global Forest Carbon (ForC) Database, we compare intensively used, cultured, and wildland forest lands in tropical and extratropical regions. We summarize recent past (1900-present) patterns of land use intensification, and we use a feedback analysis of Earth system models from the Coupled Model Intercomparison Project Phase 6 to estimate the sensitivity of forest carbon stocks to CO 2 and temperature change for different anthromes among regions. Modeled global forest carbon stock responses are positive for CO 2 increase but neutral to negative for temperature increase. Across anthromes (intensively used, cultured, and wildland forest areas), modeled forest carbon stock responses of temperate and boreal forests are less variable than those of tropical forests. Tropical wildland forest areas appear especially sensitive to CO 2 and temperature change, with the negative temperature response highlighting the potential vulnerability of the globally significant carbon stock in tropical forests. The net effect of anthropogenic activities—including land-use intensification and environmental change and their interactions with natural forest dynamics—will shape future forest carbon stock changes. These interactive effects will likely be strongest in tropical wildlands.
We carried out a systematic study of full-orbit phase curves for known transiting systems in the northern ecliptic sky that were observed during Year 2 of the TESS primary mission. We applied the same methodology for target selection, data processing, and light-curve fitting as we did in our Year 1 study. Out of the 15 transiting systems selected for analysis, seven—HAT-P-7, KELT-1, KELT-9, KELT-16, KELT-20, Kepler-13A, and WASP-12—show statistically significant secondary eclipses and day–night atmospheric brightness modulations. Small eastward dayside hot-spot offsets were measured for KELT-9b and WASP-12b. KELT-1, Kepler-13A, and WASP-12 show additional phase-curve variability attributed to the tidal distortion of the host star; the amplitudes of these signals are consistent with theoretical predictions. We combined occultation measurements from TESS and Spitzer to compute dayside brightness temperatures, TESS-band geometric albedos, Bond albedos, and phase integrals for several systems. The new albedo values solidify the previously reported trend between dayside temperature and geometric albedo for planets with 1500 K < T {sub day} < 3000 K. For Kepler-13Ab, we carried out an atmospheric retrieval of the full secondary eclipse spectrum, which revealed a noninverted temperature–pressure profile, significant H{sub 2}O and K absorption in the near-infrared, evidence for strong optical atmospheric opacity due to sodium, and a confirmation of the high geometric albedo inferred from our simpler analysis. We explore the implications of the phase integrals (ratios of Bond to geometric albedos) for understanding exoplanet clouds. We also report updated transit ephemerides for all of the systems studied in this work.