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Contains data from the model validation in the 1D Heat Loss Models to Predict the Aquifer Temperature Profile during Hot/Cold Water Injection Project. The data include two COMSOL models (2D axisymmetric benchmark model and 2D Vinsome model), one python code (1D Vinsome based FEM numerical simulation), one matlab main code (1D Newton analytical solution and all results comparison visualization), and output files generated from the above models.
A common route for the synthesis of molybdenum nitrides is through the temperature-programmed reaction of molybdenum oxides with NH3, or ammonolysis. In this work, the role of precursor phase, gas phase chemistry (impact of H2O), and temperature profile on the reaction outcome (700 °C) was examined, which resulted in varying amounts of MoO2, H2MoO5, and the nitride phases—cubic γ (nominally Mo2N) and hexagonal δ (nominally MoN). The phase fraction of the δ phase increased with precursor in the sequence MoO2 > MoO3 > H2MoO5. Steam in the reaction gas also favored the production of δ over γ, but with too much steam, MoO2 was obtained in the product. Synthesis conditions for obtaining nearly phase-pure δ were identified: MoO2 as the precursor, 2% H2O in the gas stream, and a moderate heating rate (3 °C/min). In situ X-ray diffraction provided insights into the reaction pathway. Extensive physico-chemical analysis of the δ phase, including synchrotron X-ray and neutron diffraction, electron microscopy, thermogravimetric analysis, X-ray photoelectron spectroscopy, and prompt gamma activation analysis, revealed its stoichiometry to be MoO0.108(8)N0.892(8)H0.012(5), indicating non-trivial oxygen incorporation. The presence of N/O ordering and an impurity phase Mo5N6 were also revealed, detectable only by neutron diffraction. Notably, a computationally predicted MoON phase (doi: 10.1103/PhysRevLett.123.236402), of interest due to its potential to display a metal-insulator transition, did not appear under any reaction condition examined.
We present global 3D radiation magnetohydrodynamic simulations of accretion onto a 6.62 solar-mass black hole, with quasi-steady-state accretion rates reaching 0.016–0.9 times the critical accretion rate, which is defined as the accretion rate for powering the Eddington luminosity, assuming a 10% radiative efficiency, in three different runs. The simulations show no sign of thermal instability over hundreds of thermal timescales at 10 r g . The energy dissipation occurs close to the mid-plane in the near-critical runs and near the disk surface in the low–accretion rate run. The total radiative luminosity inside ~20 r g is about 1%–30% of the Eddington limit, with radiative efficiencies of about 6% and 3%, respectively, in the sub- and near-critical accretion regimes. In both cases, self-consistent turbulence generated by the magnetorotational instability leads to angular momentum transfer, and the disk is supported by magnetic pressure. Outflows from the central low-density funnel, with a terminal velocity of ~0.1c, are seen only in the near-critical runs. We conclude that these magnetic pressure–dominated disks are thermally stable and thicker than the α disk, and that the effective temperature profiles are much flatter than those in the α disks. The magnetic pressures of these disks are comparable within an order of magnitude to the previous analytical magnetic pressure–dominated disk model.
Changing-look active galactic nuclei (CLAGN) feature order-of-magnitude variability in both the continuum and broad line luminosities on months-to-years-long timescales, and are currently unexplained. Simulations have demonstrated that rotating black holes (BHs) sometimes tear apart tilted accretion disks. These tearing events violently restructure the disk on timescales much shorter than a viscous timescale, hinting at a connection to CLAGN. Here, we show that disk tearing can power changing-look events. We report synthetic observations of an extremely high-resolution, three-dimensional, general-relativistic magnetohydrodynamic simulation of a geometrically thin, tilted accretion disk around a rapidly rotating, 10 8 M ⊙ BH. We perform ray-tracing calculations that follow the thermal disk emission to both a line-of-sight camera and to a distribution of cameras in a prescribed torus-like broad-line region (BLR). The continuum photoionizes the BLR, and we calculate the resulting spectrum. Both the continuum and line luminosities undergo order-of-magnitude swings on months-to-years-long timescales. We find shorter, week-long variability driven by the geometric precession of the inner disk and an intraday quasiperiodic oscillation driven by radial breathing of the inner disk. When the torn disk precesses, it causes asymmetric illumination of the BLR, driving time-evolving red-to-blue asymmetries of the broad emission lines that may be a smoking gun for disk tearing. We also make predictions for future photometric observations from ULTRASAT and Vera Rubin Observatory, given a Shakura–Sunyaev–like effective temperature profile, both of which may play an important role in detecting future changing-look events. Future work should consider nonthermal emission, especially from the time-variable corona.
Abstract Binary black hole (BBH) mergers, particularly those with component masses in the pair-instability gap, may be produced by hierarchical mergers in the disks surrounding Active Galactic Nuclei (AGNs). While the interaction of an embedded BBH with an AGN disk is typically assumed to facilitate a merger, recent high-resolution hydrodynamical simulations challenge this assumption. However, these simulations often have simplified treatments for gas thermodynamics. In this work, we model the possible consequence of various feedback from an embedded BBH with a simple model that maintains an enhanced temperature profile around each binary component. We show that when the minidisks around each BH become hotter than the background by a factor of three, the BBH orbital evolution switches from expansion to contraction. By analyzing the gravitational torque profile, we find that this change in direction is driven by a weakening of the minidisk spirals and their positive torque on the binary. Our results highlight the important role of thermodynamics around BBHs and its effect on their orbital evolution, suggesting that AGN disks could be efficient factories for BBH mergers.
Abstract. The states of coupling between clouds and surface or boundary layer have been investigated much more extensively for marine stratocumulus clouds than for continental low clouds, partly due to more complex thermodynamic structures over land. A manifestation is a lack of robust remote sensing methods to identify coupled and decoupled clouds over land. Following the idea for determining cloud coupling over the ocean, we have generalized the concept of coupling and decoupling to low clouds over land, based on potential temperature profiles. Furthermore, by using ample measurements from lidar and a suite of surface meteorological instruments at the U.S. Department of Energy's Atmospheric Radiation Measurement Program's Southern Great Plains site from 1998 to 2019, we have developed a method to simultaneously retrieve the planetary boundary layer (PBL) height (PBLH) and coupled states under cloudy conditions during the daytime. The new lidar-based method relies on the PBLH, the lifted condensation level, and the cloud base to diagnose the cloud coupling. The coupled states derived from this method are highly consistent with those derived from radiosondes. Retrieving the PBLH under cloudy conditions, which has been a persistent problem in lidar remote sensing, is resolved in this study. Our method can lead to high-quality retrievals of the PBLH under cloudy conditions and the determination of cloud coupling states. With the new method, we find that coupled clouds are sensitive to changes in the PBL with a strong diurnal cycle, whereas decoupled clouds and the PBL are weakly related. Since coupled and decoupled clouds have distinct features, our new method offers an advanced tool to separately investigate them in climate systems.
The Arctic has warmed more rapidly than the global mean during the past few decades. The lapse rate feedback (LRF) has been identified as being a large contributor to the Arctic amplification (AA) of climate change. This particular feedback arises from the vertically non-uniform warming of the troposphere, which in the Arctic emerges as strong near-surface and muted free-tropospheric warming. Stable stratification and meridional energy transport are two characteristic processes that are evoked as causes for this vertical warming structure. Our aim is to constrain these governing processes by making use of detailed observations in combination with the large climate model ensemble of the sixth Coupled Model Intercomparison Project (CMIP6). We build on the result that CMIP6 models show a large spread in AA and Arctic LRF, which are positively correlated for the historical period of 1951–2014. Thus, we present process-oriented constraints by linking characteristics of the current climate to historical climate simulations. In particular, we compare a large consortium of present-day observations to co-located model data from subsets that show a weak and strong simulated AA and Arctic LRF in the past. Our analyses suggest that the vertical temperature structure of the Arctic boundary layer is more realistically depicted in climate models with weak (w) AA and Arctic LRF (CMIP6/w) in the past. In particular, CMIP6/w models show stronger inversions in the present climate for boreal autumn and winter and over sea ice, which is more consistent with the observations. These results are based on observations from the year-long Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic, long-term measurements at the Utqiagvik site in Alaska, USA, and dropsonde temperature profiling from aircraft campaigns in the Fram Strait. In addition, the atmospheric energy transport from lower latitudes that can further mediate the warming structure in the free troposphere is more realistically represented by CMIP6/w models. In particular, CMIP6/w models systemically simulate a weaker Arctic atmospheric energy transport convergence in the present climate for boreal autumn and winter, which is more consistent with fifth generation reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5). We further show a positive relationship between the magnitude of the present-day transport convergence and the strength of past AA. With respect to the Arctic LRF, we find links between the changes in transport pathways that drive vertical warming structures and local differences in the LRF. This highlights the mediating influence of advection on the Arctic LRF and motivates deeper studies to explicitly link spatial patterns of Arctic feedbacks to changes in the large-scale circulation.
Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.
Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions.
The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.
A systematic evaluation of gyrokinetic and gyrofluid model predictions of ion temperature gradient (ITG) stability and transport using parameters from DIII-D high confinement mode (H-mode) plasmas has been performed. The nonlinear CGYRO code is used to make the gyrokinetic predictions, and the quasilinear TGLF model for the corresponding gyrofluid predictions. The assessments are made at three radii (normalized toroidal flux ρtor = 0.4, 0.55, and 0.7) in three different plasma scenarios with varying levels of neutral beam heating and torque. For each of the nine cases (3 radii × 3 scenarios) considered, ITG turbulence is found to be the dominant long-wavelength instability and transport mechanism. The inclusions of both transverse magnetic fluctuations and dynamic fast beam ions are stabilizing for all cases considered, with strongest effects seen at ρor = 0.4 where the fast ion population and normalized plasma pressure β = 2μ0nT/B2 are highest. The further inclusion of parallel magnetic fluctuations does not have a meaningful impact on the ITG turbulence in these scenarios, but does destabilize (in combination with fast ions) new high-frequency instabilities at ρtor = 0.4 in the high power scenarios. In each case the linear and nonlinear ITG critical gradients are predicted to be lower than the measured ITG scale lengths and their associated uncertainties. Inclusion of equilibrium flow shear in the transport predictions generally leads to an upshift in effective critical gradient rather than a qualitative change in the predicted stiffness, with stronger responses typically seen in the gyrokinetic predictions than in the gyrofluid results. However, in most cases these upshifted gradients still remain below the measured values and their uncertainties. Although the predicted critical gradients are below the measured gradients, both models predicted flux-matching gradients consistent with measured values in six of the nine cases considered, with no clear systematic over- or underprediction. Thus, while the experimental ion temperature profiles do not appear to be closely pinned to the ITG critical gradient, both gyrokinetic and gyrofluid models are able to accurately match the measured gradients reasonably well in most cases.
Outline AGR-3/4 experiment and objectives Description of AGR-3/4 modeling approach and inputs Source term (release from particles) Temperature profiles Diffusion through concentric rings Adsorption across gaps between rings Simplified Source Model and updated model results Reconstruction of fission product distributions from gamma tomography data
The formulation and solution of a model that properly described the temperature profiles of uranium in fire conditions was prepared, verified and validated, to support a-posteriori benchmarking of historical experiments. Data from the multi-physics model combined with data regression from the experiments provides a useful tool for exploring sources of potential bias in historical experiments. The approaches used are augmented by visual observations and photographic evidence allowing for correlations between various phenomena, independent of whether or not that specific phenomena are part of the validated model. In this manner, the validated model is a tool for a-posteriori benchmarking, and not a model designed to replicate the experimental output exactly for any specific experiment. For experiments like the Clark (2015) experiments, this integrated model (including chemistry) demonstrated that the largest bias was associated in the metallurgy of the coupons. The model developed does serve as a basis for defending which of the historical alloys tested are the most appropriate for any other alloy being considered (e.g., one that has not yet been tested). The model identifies key reactions that impact uranium chemistry and can significantly bias experimental results. Through use of the model and energy balances, data from historical experiments by Elder and Tinkle (1980) were regressed and benchmarked. This study identifies that all uranium alloys are not equal and that there are distinct differences between the dominant alloys representing distinct metallurgical phases.
We investigate and model the cook-off behavior of LX-17 to understand the response of explosive systems in abnormal thermal environments. Decomposition has been explored via conventional ODTX (One-Dimensional Time-to-eXplosion), PODTX (ODTX with pressure-measurement), TGA (Thermo-Gravimetric Analysis), and DSC (Differential Scanning Calorimetry) experiments under isothermal and ramped temperature profiles. The data were used to fit reaction rate parameters for proposed schemes in an ALE3D computational model. This model includes chemical reactions, thermo- and hydro-dynamics, and material properties, including thermal expansion, compressibility, and strength. These parameterizations were carried out utilizing a Python evolutionary optimization method on LLNL’s high-performance computing clusters. Additional experiments are being developed to further characterize and monitor decomposition intermediates to improve the model. Once experimentally validated, this model will be scalable to several applications involving LX-17. Furthermore, the optimization methodology developed herein should be applicable to other high explosive materials.
To understand the potential and limitations for recovering thermal and mechanical energy from closed-loop geothermal systems a collaborative study is underway that will investigate an array of system configurations, working fluids, geothermal reservoir characteristics, operational periods, and heat transfer enhancements. Closed-loop geothermal systems are distinguished from hydrothermal or enhanced geothermal systems (EGS) in that the working fluid only circulates through drilled boreholes. Principal objectives of this study are to determine upper limits for thermal and mechanical energy recovery and optimal operational and configuration parameters for each scenario. Teams of scientists and engineers are applying a suite of numerical simulation and analytical tools to model the heat recovery from closed-loop geothermal systems, and then optimizing operational and configuration parameters to maximize the thermal and mechanical energy recovery. Results from the suite of numerical simulators and analytical tools, such as outlet and inlet states and temperature profiles in the geothermal reservoir over time are intercompared to increase confidence in the analysis. This paper documents the study findings for closed-loop systems in hot-dry-rock and hot-wet-rock reservoirs, where water is the working fluid. The characteristics of the hot-dry-rock reservoir were based on the U.S. Department of Energy's Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site, near Milford Utah. Two objective functions are defined to optimize the operational and configuration parameters of the system, one each for the recovery of mechanical and thermal energy over the period of operation. For both objective functions, a surface plant thermal to mechanical energy conversion factor and an energy drilling cost is required. In keeping with the study objectives the surface plant conversion factor is determined from a second-law of thermodynamics analysis of a generic binary plant, and drilling costs are based on those from the Utah FORGE site and current national electrical costs.
To understand the potential and limitations for recovering thermal and mechanical energy from closed-loop geothermal systems a collaborative study is underway that will investigate an array of system configurations, working fluids, geothermal reservoir characteristics, operational periods, and heat transfer enhancements. Closed-loop geothermal systems are distinguished from hydrothermal or enhanced geothermal systems (EGS) in that the working fluid only circulates through drilled boreholes. Principal objectives of this study are to determine upper limits for thermal and mechanical energy recovery and optimal operational and configuration parameters for each scenario. Teams of scientists and engineers are applying a suite of numerical simulation and analytical tools to model the heat recovery from closed-loop geothermal systems, and then optimizing operational and configuration parameters to maximize the thermal and mechanical energy recovery. Results from the suite of numerical simulators and analytical tools, such as outlet and inlet states and temperature profiles in the geothermal reservoir over time are intercompared to increase confidence in the analysis. This paper documents the study findings for closed-loop systems in hot-dry-rock and hot-wet-rock reservoirs, where water is the working fluid. The characteristics of the hot-dry-rock reservoir were based on the U.S. Department of Energy’s Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site, near Milford Utah. Two objective functions are defined to optimize the operational and configuration parameters of the system, one each for the recovery of mechanical and thermal energy over the period of operation. For both objective functions, a surface plant thermal to mechanical energy conversion factor and an energy drilling cost is required. In keeping with the study objectives the surface plant conversion factor is determined from a second-law of thermodynamics analysis of a generic binary plant, and drilling costs are based on those from the Utah FORGE site and current national electrical costs.
One pathway for road-ready and final disposition packaging configurations for the aluminum-clad spent nuclear fuel (ANSF) fuel is storage within helium backfilled sealed Department of Energy (DOE) standard canisters. The typical packaging configuration for the 15-foot DOE standard canisters places 10 advance test reactor (ATR) elements a basket, and three baskets are loaded within each DOE canister. During in-reactor operations and cooling pond storage conditions, oxyhydroxide layers form on the surface of the aluminum clad fuel. These layers produce hydrogen gas over time due to the fuel’s radiation field. As part of the packing procedure, the ATR fuel should be dried to remove any residual physio-/chemi- sorbed water bound to the surface. A 50-year CFD model of the DOE canister packaged with fuel was developed to provide a temperature profile for coupled chemical modeling of the conditions within the canister. The results of this modeling include results at fully saturated and fully dried fuel cladding conditions. In the associated experimental work, radiolysis experiments tests were completed in a helium environment, and G-values for the radiolytic production of hydrogen from the oxyhydroxide layers were provided. That reaction was coupled with the thermal profiles and gas-phase reactions to develop a 50-year model of the conditions within a sealed DOE canister with ATR fuel. For a nominal scenario of stored ATR fuel, after 50 years the model results give a 1.36 atm total pressure, 7% mole percent hydrogen, for the upper decay heat, 1.51 atm total pressure, 16% mole percent hydrogen, and for upper decay heat with undried fuel 2.6 atm total pressure, 15% mole percent hydrogen. No case modeled yields significant oxygen, and for the lower decay heat case that is modeled, hydrogen concentrations are under the 4% flammability limit after 50 years of storage. The modeled pressures for all cases modeled are below the pressure limit for the DOE standard sealed canister.
An integrally-geared (IG) compressor-expander (compander) for use in a nominal 10 MW-scale concentrated solar power (CSP) supercritical carbon dioxide (sCO2) plant application was designed, manufactured, and tested. The integrally-geared compander (IGC) developed for this application comprises multiple pinion shafts interacting through a single bull gear to create a compact package and utilize a lowcost, low-speed driver. The present work will detail the design of the high-pressure high-temperature expander casing on an integrally geared frame, and illustrate how the project successfully mitigated the risk of fatigue and creep while allowing for rapid thermal transients in the design. Furthermore, test data from the test campaign will be presented supporting the analysis. Test results also show temperature profiles during operation exceeding 720° Celsius.