Stochastic simulation of power systems with integrated intermittent renewable resources
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This paper presents a general overview of nuclear data evaluation and its applications as developed at NRG, Petten. Based on concepts such as robustness, reproducibility and automation, modern calculation tools are exploited to produce original nuclear data libraries that meet the current demands on quality and completeness. This requires a system which comprises differential measurements, theory development, nuclear model codes, resonance analysis, evaluation, ENDF formatting, data processing and integral validation in one integrated approach. Software, built around the TALYS code, will be presented in which all these essential nuclear data components are seamlessly integrated. Besides the quality of the basic data and its extensive format testing, a second goal lies in the diversity of processing for different type of users. The implications of this scheme are unprecedented. The most important are: 1. Complete ENDF-6 nuclear data files, in the form of the TENDL library, including covariance matrices, for many isotopes, particles, energies, reaction channels and derived quantities. All isotopic data files are mutually consistent and are supposed to rival those of the major world libraries. 2. More exact uncertainty propagation from basic nuclear physics to applied (reactor) calculations based on a Monte Carlo approach: 'Total' Monte Carlo (TMC), using random nuclear data libraries. 3. Automatic optimization in the form of systematic feedback from integral measurements back to the basic data. This method of work also opens a new way of approaching the analysis of nuclear applications, with consequences in both applied nuclear physics and safety of nuclear installations, and several examples are given here. This applied experience and feedback is integrated in a final step to improve the quality of the nuclear data, to change the users vision and finally to orchestrate their integration into simulation codes.
A lack of coherent plans to analyze, manage, and understand data threatens the various opportunities offered by new neuro-technologies. High-performance computing will allow exploratory analysis of massive datasets stored in standardized formats, hosted in open repositories, and integrated with simulations.
GammaTracker is a portable handheld radioisotope identifier using position sensitive CdZnTe detectors. High confidence isotope identification is possible on GammaTracker owing to the system's relatively high energy resolution and count rate sensitivity. A study was undertaken to evaluate the isotope identification performance of a prototype unit. Background and source spectra for various nuclides were measured and then randomly sampled to simulate various integration times and source intensities. The resulting spectral data sets were then run through the isotope identification algorithm to determine the probability of detection and the false alarm rate for each nuclide. The process was repeated for various isotope identification input parameters until an optimized set was achieved. This paper presents results from the injection study.
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This study aims to support the prioritization of research and development (R&D) pathways of an anaerobic technology leveraging hydrogel-encapsulated biomass to treat high-strength organic industrial wastewaters, enabling decentralized energy recovery and treatment to reduce organic loading on centralized treatment facilities. To characterize the sustainability implications of early-stage design decisions and to delineate R&D targets, an encapsulated anaerobic process model was developed and coupled with design algorithms for integrated process simulation, techno-economic analysis, and life cycle assessment under uncertainty. Across the design space, a single-stage configuration with passive biogas collection was found to have the greatest potential for financial viability and the lowest life cycle carbon emission. Through robust uncertainty and sensitivity analyses, we found technology performance was driven by a handful of design and technological factors despite uncertainty surrounding many others. Hydraulic retention time and encapsulant volume were identified as the most impactful design decisions for the levelized cost and carbon intensity of chemical oxygen demand (COD) removal. Encapsulant longevity, a technological parameter, was the dominant driver of system sustainability and thus a clear R&D priority. Ultimately, we found encapsulated anaerobic systems with optimized fluidized bed design have significant potential to provide affordable, carbon-negative, and distributed COD removal from high strength organic wastewaters if encapsulant longevity can be maintained at 5 years or above.
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With likely increases in probable maximum precipitation (PMP) in a changing environment, critical infrastructures such as major reservoirs and nuclear power plants are subject to elevated risk. To understand how factors such as PMP variability, climate change, land use land cover (LULC) change, antecedent soil moisture conditions, and reservoir storage may individually or jointly affect the magnitude of probable maximum flood (PMF), we conducted integrated hydrometeorological simulations involving both the Weather Research Forecasting model and the distributed hydrologic model (DHSVM) over the Alabama–Coosa–Tallapoosa (ACT) River Basin in the southeastern United States. A total of 120 relative humidity–maximized PMP storms under historic and projected future climate conditions were used to drive DHSVM in current and projected future LULC conditions. Overall, PMP and PMF are projected to increase significantly over the ACT River Basin. Sources of meteorological forcing data sets and climate change were found to be the most sensitive factors affecting PMF, followed by antecedent soil moisture, reservoir storage, and then LULC change. In conclusion, the ensemble of PMP and PMF simulations, along with their sensitivity, allows us to better quantify the potential risks associated with hydroclimatic extreme events to critical infrastructures for energy–water security.
Abstract Reconstructed historical streamflow time series can supplement limited streamflow gauge observations. However, there are common challenges of typical modeling approaches: process‐based hydrologic models can be data/computation‐intensive, and statistics‐based models can be region/stream‐specific. Here we present a nationally scalable modeling framework integrating the simulated runoff from the Variable Infiltration Capacity (VIC) model with the Routing Application for Parallel computatIon of Discharge (RAPID) routing model leveraging high‐performance computing. We demonstrate an efficient method of assimilating streamflow at US Geological Survey (USGS) streamflow monitoring sites using a simple hierarchical approach in the VIC‐RAPID framework. The result is a reconstructed 36‐year (1980–2015) daily and monthly streamflow dataset (Dayflow) at ∼2.7 million NHDPlusV2 stream reaches in the conterminous US (CONUS). We perform a comprehensive evaluation at 7,526 USGS sites and characterize their error statistics. The results demonstrate that 49% of the USGS sites demonstrate Kling–Gupta Efficiency (KGE) > 0.5 and 58% of the sites show percentage bias within ±20% for the daily naturalized streamflow. Streamflow data assimilation across CONUS shows an overall improvement over naturalized streamflow, notably in the western semiarid‐to‐arid regions. Comparison to other national and global streamflow reanalysis datasets such as the National Water Model and Global Reach‐scale A priori Discharge Estimates for SWOT demonstrates improved KGE, reduced bias, and directions for Dayflow improvements. Investigations of error statistics with key hydrologic, hydroclimatic, and geomorphologic basin characteristics reveal region‐specific patterns which may help improve future framework applications. Overall, Dayflow may enable a better understanding of hydrologic conditions in a changing environment, especially in locations currently not represented by streamflow monitoring networks.
Fractured rocks play a crucial role in myriad natural and engineered systems, including Earth's critical zone, oil/gas/geothermal reservoirs, and geological CO 2 /H 2 /waste storage systems. While complex electrical conductivity is extensively used to estimate the pore and grain sizes of conventional porous rocks and soils, it is rarely used to predict the aperture size of fractured rocks and this remains poorly understood. Here, integrating theory, simulations, and experiments, we show that under external fields, fractured rocks follow the fracture-and-matrix coupling to make the bulk complex conductivity non-linear with respect to water conductivity. We find that the relaxation time and quadrature conductivity for porous media do not apply to fractured rocks, but, instead, reasonably accurate predictions of aperture size can be made based on the true formation factor. This study unravels the fundamental mechanism governing conduction and polarization of fractured rocks and paves the way for the non-invasive investigation of global fractured rocks.
Clean, safe and economical nuclear energy requires new materials capable of withstanding severe radiation damage. One strategy of imparting radiation resistance to solids is to incorporate into them a high density of solid-phase interfaces capable of absorbing and annihilating radiation-induced defects. Here we show that elastic interactions between point defects and semicoherent interfaces lead to a marked enhancement in interface sink strength. Our conclusions stem from simulations that integrate first principles, object kinetic Monte Carlo and anisotropic elasticity calculations. Surprisingly, the enhancement in sink strength is not due primarily to increased thermodynamic driving forces, but rather to reduced defect migration barriers, which induce a preferential drift of defects towards interfaces. The sink strength enhancement is highly sensitive to the detailed character of interfacial stresses, suggesting that ‘super-sink’ interfaces may be designed by optimizing interface stress fields. Lastly, such interfaces may be used to create materials with unprecedented resistance to radiation-induced damage.
Photodynamic therapy (PDT) offers several advantages for treating cancers, but its efficacy is highly dependent on light delivery to activate a photosensitizer. Advances in wireless technologies enable remote delivery of light to tumors, but suffer from key limitations, including low levels of tissue penetration and photosensitizer activation. Here, we introduce DeepLabCut (DLC)-informed low-power wireless telemetry with an integrated thermal/light simulation platform that overcomes the above constraints. The simulator produces an optimized combination of wavelengths and light sources, and DLC-assisted wireless telemetry uses the parameters from the simulator to enable adequate illumination of tumors through high-throughput (<20 mice) and multi-wavelength operation. Together, they establish a range of guidelines for effective PDT regimen design. In vivo Hypericin and Foscan mediated PDT, using cancer xenograft models, demonstrates substantial suppression of tumor growth, warranting further investigation in research and/or clinical settings.
Abstract The relationship between electron–phonon ( e -ph) interactions and charge-density-wave (CDW) order in the bismuthate family of high-temperature superconductors remains unresolved. We address this question using nonperturbative hybrid Monte Carlo calculations for the parent compound BaBiO 3 . Our model includes the Bi 6 s and O 2 p σ orbitals and coupling to the Bi-O bond-stretching branch of optical phonons via modulations of the Bi-O hopping integral. We simulate three-dimensional clusters of up to 4000 orbitals, with input model parameters taken from ab initio electronic structure calculations and a phonon energy ℏΩ 0 = 60 meV. Our results demonstrate that the coupling to the bond-stretching modes is sufficient to reproduce the CDW transition in this system, despite a relatively small dimensionless coupling. We also find that the transition deviates from the weak-coupling Peierls’ picture. This work demonstrates that off-diagonal e -ph interactions in orbital space are vital in establishing the bismuthate phase diagram.
Understanding cesium (Cs) transport in TRistructural ISOtropic (TRISO) particle fuel is crucial for predicting fission product release in high-temperature reactors. However, current challenges include significant scatter in diffusivity data and unexplained temperature-dependent diffusion regimes in the silicon carbide layer. This study addresses these challenges by developing a multiscale, mechanistic Cs transport model integrating atomistic simulations and phase field modeling. Our model quantifies temperature and grain size effects on Cs diffusivity, attributing experimentally observed regimes to a transition from bulk-dominated diffusivity at high temperatures to grain boundary-dominated diffusivity at lower temperatures. The model, validated against diffusion measurements and advanced gas reactor (AGR)-1 and AGR-2 post-irradiation fission product release data, enhances the predictive capability of the BISON fuel performance code. This study advances our understanding of Cs release from TRISO particles and its dependence on temperature and silicon carbide grain size, with implications for the safety and efficiency of high-temperature nuclear reactors.
By 2050, shale gas production is expected to exceed three-quarters of total US natural gas production. However, current unconventional hydrocarbon gas recovery rates are only around 20%. Maximizing production of this natural resource thus necessitates improved understanding of the fundamental mechanisms underlying hydrocarbon retention within the nanoporous shale matrix. In this study, we integrated molecular simulation with high-pressure small-angle neutron scattering (SANS), an experimental technique uniquely capable of characterizing methane behavior in situ within shale nanopores at elevated pressures. Samples were created using Marcellus shale, a gas-generative formation comprising the largest natural gas field in the United States. Our results demonstrate that, contrary to the conventional wisdom that elevated drawdown pressure increases methane recovery, a higher peak pressure led to the trapping of dense, liquid-like methane in sub-2 nm radius nanopores, which comprise more than 90% of the measured nanopore volume, due to irreversible deformation of the kerogen matrix. These findings have critical implications for pressure management strategies to maximize hydrocarbon recovery, as well as broad implications for fluid behavior under confinement.
New activation function is examined for its ability to accelerate the performance of doing logic programming in Hopfield network. This method has a higher capacity and upgrades the neuro symbolic integration. Computer simulations are carried out to validate the effectiveness of the new activation function. Empirical results obtained support our theory.
Recent progress in integrating the latest LHCD model based on ray-tracing into the Integrated Plasma Simulator (IPS) is reported. IPS, a python based framework for time dependent tokamak simulation, was expanded recently to incorporate LHCD simulation using GENRAY/CQL3D (ray-tracing/3D Fokker-Planck package). Using GENRAY/CQL3D in the IPS framework, it becomes possible to include parasitic LHCD power loss near the plasma edge, which was found to be important in experiments particularly at high density as expected on reactors. Moreover, it allows for evolving the velocity distribution function in 4 D (ν{sub ∥}, ν⊥, r/a, t) space self-consistently. In order to validate the code, IPS is applied to LHCD experiments on Alctor C-Mod. In this paper, a LHCD experiment performed at the density of n{sub e}∼0.5×10{sup 20}m{sup −3} where good LHCD efficiency and the development of internal transport barrier (ITB) was reported, is modelled in a predictive mode and the result is compared with experiment.