Regional Mechanical Performance and the Effects of Surface Defects in AM Al-10Si-Mg.
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Globally, many solar power plants and other types of renewable energy are being located in water-scarce regions. Many projects rely on groundwater resources whose sustainability is uncertain. In the Chuckwalla Basin in California, quantification of recharge and trans-valley underflow is needed to estimate the impacts of solar project withdrawals on the water table. However, such estimates are highly challenging due to data scarcity, heterogeneous soils and long residence times. Conventional assessment employs isolated groundwater models configured with crude and uniform estimates of recharge. Here, we employ a data-constrained surface subsurface processes model, PAWS+CLM, to provide an ensemble of recharges and underflows with perturbed parameters. Then, the Parameter Estimation (PEST) package is used to calibrate MODFLOW aquifer conductivity and filter out implausible recharges. The novel dual-model approach, potentially applicable in other arid regions, can effectively assimilate groundwater head observations, reject unrealistic parameters, and narrow the range of estimated drawdowns. Simulated recharge concentrates along alluvial fans at the mountain fronts and ephemeral washes where run-off water infiltrates. If an evenly distributed recharge was assumed, it resulted in under-estimated drawdown and larger uncertainty bounds. The withdrawals are approaching total inflow, suggesting the system will be nearing, if not exceeding, its sustainable groundwater production capacity, and a boom of such projects will not be sustainable. Especially, the cost/benefit of pumped-storage projects is called into question as the initial-fill phase depletes entire area’s recharge. Our study highlights the stress on groundwater resources of solar development, and that the speed of groundwater recovery does not indicate sustainability. Main point 1: A novel dual model approach, involving an integrated surface/subsurface model and a groundwater parameter-estimation model, was able to better constrain the model. Main point 2: The groundwater system may be nearing, if not exceeding, its sustainable groundwater production capacity and the speed of recovery is not indicative of sustainability. Main point 3: Results from using conventionally-assumed uniform recharge distort calibrated K fields and impacts assessment
The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein–protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein–protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein–protein interactions, further validating their relevance. Two protein–protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.
In the identification of differential equations from data, significant progresses have been made with the weak/integral formulation. In this paper, we explore the direction of finding more efficient and robust test functions adaptively given the observed data. While this is a difficult task, we propose weighting a collection of localized test functions for better identification of differential equations from a single trajectory of noisy observations on the differential equation. We find that using high dynamic regions is effective in finding the equation as well as the coefficients, and propose a dynamics indicator per differential term and weight the weak form accordingly. For stable identification against noise, we further introduce a voting strategy to identify the active features from an ensemble of recovered results by selecting the features that frequently occur in different weighting of test functions. Systematic numerical experiments are provided to demonstrate the robustness of our method.
Mixed-phase clouds at high latitudes contribute to the uncertainty in predicting cloud feedbacks and climate sensitivity, mainly due to the complexity of microphysical processes that influence the partitioning between the supercooled liquid and ice phases, and hence, cloud radiative effects on regional scales. Particularly in Arctic mixed-phase clouds, the activation of ice-nucleating particles (INPs) from various aerosol populations remains a leading source of uncertainty. We developed an aerosol-cloud one-dimensional (AC-1D) model, which provides a novel framework to prognostically treat INP and ice crystal budgets while explicitly accounting for polydisperse and multicomponent aerosol that activate INPs following different freezing parameterizations. The AC-1D model is informed by large-eddy simulations to probe the impact of INP representation on predicted ice crystal number concentrations (N i ) and ice crystal budgets in mixed-phase Arctic stratus. We apply three immersion freezing (IMF) parameterizations, two time-independent (singular) and one time-dependent (classical nucleation theory), to predict the evolution of the INP reservoir and resulting ice crystal budget from polydisperse mineral dust, organic (humic-like substances), and sea spray aerosol particle size distributions. Our analysis focuses on how variations in aerosol number concentration and cloud system parameters such as cloud cooling rate, cloud-top entrainment rate, and ice crystal fall speed influence the INP reservoir and ice crystal budgets. Furthermore, this study investigates the competitive ice nucleation dynamics in mixed aerosol environments and provides a process-level quantification of the INP budget terms, which directly controls ice crystal budgets. For all studied case scenarios, the aerosol types and associated particle size distributions significantly impact INP and N i , and the choice between a time-dependent and a singular freezing description yields orders-of-magnitude differences in the predicted INP and N i over the 10 h simulation time, reflecting typical cloud lifetimes. Our results show that the influence of cloud cooling, INP entrainment, and sedimentation varies significantly depending on the chosen freezing parameterization. These findings underscore the critical need for robust IMF parameterizations and precise cloud system observations to enhance the accuracy of models in predicting mixed-phase cloud structure and evolution.
Potential economic and environmental benefits of increasing nitrogen-use efficiency (NUE) are widely recognized but scarcely quantified. This study quantifies the effects of increased NUE on 1) the national agricultural economy using a simulation model of US agriculture and 2) regional water quality effects using a biogeochemical model for the Arkansas-White-Red river basin. Here, national economic effects are reported for NUE improvement scenarios of 10%, 20%, 50%, and 100%, whereas regional water quality effects are estimated for a 20% NUE improvement scenario in the Arkansas-White-Red river basin. Simulating a 20% increase in NUE in row crops is shown to reduce N requirements by 1.4 million tonnes y-1 and increase farmer net profits by 1.6% ($743 million) per year by 2026 over the baseline simulation for the same period. For each 10% increase in NUE, annual farm revenues for commodity crops increased over the baseline by approximately $350 million per year by 2026. Changes in crop prices and land-use relative to the baseline were less than 2%. This suggests a net benefit even though fertilizer cost savings can result in increased cultivation of land, i.e., ‘Jevon's paradox’. Results from the biogeochemical model of the Arkansas-White-Red river basin suggest that a 20% increase in NUE corresponds to a 5.72% reduction in nitrate loadings to freshwaters, with higher reductions in agricultural watersheds. The value of these reductions was estimated as $43 ha-1, for a total of $15.3 to 136.7 million yr-1 in avoided water treatment costs. After estimating the social value of increased NUE, we conclude with a discussion of potential strategies to increase efficiency and the research needed to achieve this goal. These include perennialization of the agricultural landscape, genetic crop improvement, targeted fertilizer application, and manipulation of the plant-root microbiome.
A window for quiescent H-mode (QH-mode) operation in DIII-D plasmas in the diagram of pedestal magnetohydrodynamic (MHD) stability was identified through linear MHD stability analysis, accounting for the effects of plasma rotation and ion diamagnetic drift. The operation window lies between the stability boundaries of the kink/peeling mode (K/PM) identified with and without the inclusion of plasma rotation effects. In this region, the mode remains unstable unless rotation effects are considered alongside the ion diamagnetic drift, which is consistently included in the analysis. The stabilization of the MHD mode, facilitated by the coupled effects of plasma rotation and ion diamagnetic drift, plays a crucial role in widening the window, enabling the attainment of the QH-mode state. Furthermore, the suppression of edge localized modes (ELM) can be achieved by controlling the pedestal structure to ensure the plasma state remains within the operation window. The location of the operation window in the stability diagram depends on the K/PM stability properties. Therefore, optimizing conditions for QH-mode requires adjustments based on changes in stability characteristics. A pressure pedestal and its associated bootstrap current density near the last closed flux surface are advantageous for situating the plasma state within the window. However, excessive current density can trigger ELMs. This trend was confirmed through comparisons of MHD stability diagrams between QH-mode and ELMy H-mode plasmas.
The 2D scrape-off-layer turbulence code (nSOLT), which includes 1D kinetic neutral–plasma interactions, is applied to study effects of neutral recycling on plasma turbulence for parameters illustrative of the MAST-U divertor region. Neutral recycling is modeled by injecting a fraction of the parallel plasma flux to the divertor back into the simulation domain as a source of Franck–Condon-distributed neutrals. Stationary sources, concentrated at the magnetic separatrix, model plasma streaming into the divertor region from the upstream scrape-off-layer and sustain plasma turbulence absent neutral recycling. Starting from one such no-neutrals equilibrium, we initiate recycling in a numerical experiment designed to diagnose and identify the effects of various neutral–plasma interactions on the divertor plasma, divertor turbulence, and plasma exhaust. The onset of recycling triggers an initial burst of enhanced cross field plasma transport that is quelled by ionization cooling and charge–exchange (CX) friction, with growing neutral pressure, leading to a quiescent, turbulence-free state. Diagnosis of this transient burst reveals that (1) the sudden increase in plasma density due to ionization dominates the onset of the burst, (2) electron cooling due to ionization increases collisionality and disconnects blob filaments from the sheath, and (3) CX friction drives tripole polarization of a blob that can dominate the curvature-driven dipole polarization, leading to the stagnation of blob propagation and reduced radial turbulent transport. It is shown that CX friction is negligible compared to sheath physics in determining equilibrium mean flow shearing rates, for parameters considered herein (specifically a short connection length to the divertor target), while it can significantly reduce interchange-instability growth rates.
Non-technical summary A substantial increase in wind energy deployment worldwide is required to help achieve international targets for decreasing global carbon emissions and limiting the impacts of climate change. In response to global concerns regarding the environmental effects of wind energy, the International Energy Agency Wind Technical Collaborative Program initiated Task 34 – Working Together to Resolve Environmental Effects of Wind Energy or WREN. As part of WREN, this study performed an international assessment with the global wind energy and environmental community to determine priority environmental issues over the next 5‒10 years and help support collaborative interactions among researchers, developers, regulators, and stakeholders. Technical summary A systematic assessment was performed using feedback from the international community to identify priority environmental issues for land-based and offshore wind energy development. Given the global nature of wind energy development, feedback was of interest from all countries where such development is underway or planned to help meet United Nations Intergovernmental Panel on Climate Change targets. The assessment prioritized environmental issues over the next 5–10 years associated with wind energy development and received a total of 294 responses from 28 countries. For land-based wind, the highest-ranked issues included turbine collision risk for volant species (birds and bats), cumulative effects on species and ecosystems, and indirect effects such as avoidance and displacement. For offshore wind, the highest-ranked issues included cumulative effects, turbine collision risk, underwater noise (e.g. marine mammals and fish), and displacement. Emerging considerations for these priorities include potential application to future technologies (e.g. larger turbines and floating turbines), new stressors and species in frontier regions, and cumulative effects for multiple projects at a regional scale. For both land-based and offshore wind, effectiveness of minimization measures (e.g. detection and deterrence technologies) and costs for monitoring, minimization, and mitigation were identified as overarching challenges. Social media summary Turbine collisions and cumulative effects among the international environmental priorities for wind energy development.
Periodic dilation bands are shown to occur when cylinder-shaped saturated cement specimens are subjected to rapid decompression, with the spacing between the bands scaling as a power law of the decompression time. This behavior is predicted by theory, which is specified to this scenario based on prior work that was more general and applied to compaction. Specifically, the formation of the bands is shown to coincide with periodicity that naturally arises in the effective stress when the material follows a Terzaghi-type consolidation law, although in reverse for dilation and with material deformation described by a rate-dependent viscoplastic law. When strain rate is proportional to effective stress to a power that is greater than one, periodic regions of tensile effective stress arise through the poromechanical fluid-solid coupling. The theoretically-predicted exponent of the resulting power-law relationship between dilation band spacing and unloading time successfully brackets the experimental results, for which it is observed that the exponent is slightly stronger than a square-root relationship (0.28 to 0.67) at testing temperatures of 20 °C and 90 °C. Further, these results demonstrate that rapid-depressurization leads to periodic fracturing that could, on the one hand, be detrimental to the isolation provided by cement used to seal wellbores in the petroleum industry. On the other hand, the dilation bands could also be favorable to production if generated in low-permeability reservoir rocks such as shales that are targeted for petroleum production or granites that are targeted for geothermal energy.
Climate change is expected to have far-reaching effects at both the global and regional scale, but local effects are difficult to determine from coarse-resolution climate studies. Dynamical downscaling can provide insight into future climate projections on local scales. Here, we present a new dynamically downscaled dataset for Indiana and the surrounding regions. Output from the Community Earth System Model (CESM) version 1 is downscaled using the Weather Research and Forecasting model (WRF). Simulations are run with a 24-hr reinitialization strategy and a 12-hr spin-up window. WRF output is bias corrected to the National Centers for Environmental Protection/National Center for Atmospheric Research 40-year Reanalysis project (NCEP) using a modified quantile mapping method. Bias-corrected 2-m air temperature and accumulated precipitation are the initial focus, with additional variables planned for future releases. Regional climate change signals agree well with larger global studies, and local fine-scaled features are visible in the resulting dataset, such as urban heat islands, frontal passages, and orographic temperature gradients. This high-resolution climate dataset could be used for down-stream applications focused on impacts across the domain, such as urban planning, energy usage, water resources, agriculture and public health.
We propose and demonstrate a microstructurally-based experimental method to quantitively determine the depth regions in self-ion-irradiated metals that are affected by the injected interstitial effect and various surface effects, focusing on the choice of safe analysis zones to minimize the impact of these phenomena. The goal is to define the depth ranges where extracted data can be confidently applied to ion-neutron correlations for reactor application. Since ion energies in the range of 1–5 MeV are most frequently employed by the radiation effects community, irradiations were conducted at four energies in this range, all proceeding at 475 °C. The experiment was conducted on relatively pure single crystal iron to focus only on physical phenomena, avoiding the influence of possibly confounding chemical or segregation processes. Care was also taken to minimize the influence of other physical factors such as crystalline orientation. It was shown that, at 475 °C, ion energies of ≤1 MeV were too shallow in penetration and did not yield a safe depth range, but irradiations at 2.5 MeV and above yielded useful safe zones with predicted swelling behavior becoming independent of ion energy. The surface-affected zone width was found to be roughly twice that of the void-denuded zone width and to be independent of accumulated displacement dose. The largest injected-interstitial effect arises from the injected interstitial depression of void nucleation and growth. The interstitial-affected region starts at about one half of the projected range and does not show any “spreading” of its influence in depth as the peak damage level increases from 50 to 100 dpa. This study provides some confidence that enhances the credibility of ion simulation when applied to prediction of void swelling in neutron environments.
This report provides the results on microstructural characterization of the first commercial heat of Alloy 709 plate material undergone additional age hardening treatment. In FY19, through thermodynamic-kinetic modeling and microstructural characterization, it is determined that the 775°C/10 h age-hardening treatment can promote desired precipitation on dislocations and has the potential to further improve the mechanical performance of the material. In FY20, the treatment protocol was applied to the ESR-1150°C condition, and the microstructure was then compared with the ESR-1100°C condition. It was found that the 1150°C solution annealing (SA) dramatically reduced the dislocation density compared to the 1100°C SA, resulting a smaller number density of Nb-rich fine precipitates which grow on dislocations during the age hardening treatment. Other features are very similar between the two. The effect of age hardening on creep performance is discussed. In addition, TEM characterization of a creep-ruptured sample (600°C/330 MPa) was performed. Specimens from the grip region and the gauge region was studied. The grip region specimen was effectively aged at 600°C for extended period, while the gauge region specimen was mechanically deformed at 600°C. The microstructures are very different, implying the importance of a dynamical effect on precipitation.
Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.
We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO 2 ) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO 2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO 2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.
Strong gradient regions in tokamaks such as the pedestal or internal transport barriers are regions of reduced turbulence where neoclassical transport can play a dominant role. In pedestals, gradient lengths comparable to the ion poloidal gyroradius have been measured. Standard neoclassical theory can miss important strong gradient effects in these regions because it assumes that the gradient length scales of density, temperature and potential are larger than the ion poloidal gyroradius. We extend plateau regime neoclassical theory into regions of gradients of the order of the ion poloidal gyroradius to capture strong gradient effects on transport processes in the pedestal and internal transport barriers. The fundamental idea behind our new framework is to keep a scale separation between the orbit widths and the gradient length scales by performing a large aspect ratio expansion. In the plateau regime, strong gradients cause poloidal variation that is in–out as well as up–down asymmetric. We study two different test cases assuming either radial force balance or the absence of turbulence and show that strong gradient effects can enhance or reduce standard neoclassical theory predictions in the plateau regime in strong gradient regions.
Abstract The Colorado River Basin is an important natural resource for the semi‐arid southwestern United States (US), where it provides water to more than 40 million people. While nearly 1.5°C of anthropogenic warming has occurred across this region from the 1880s to 2021, climate models show little agreement in the precipitation change during the same historical period, with no trend in the mean of the latest (sixth) generation of Global Climate Models. As such, here we focus on how the CO 2 increase and associated anthropogenic warming over the historical period has impacted runoff across the Colorado Basin. We find that the Colorado Basin's runoff over the historical period has decreased by 8.1% per degree Celsius of warming (°C −1 ). However, the magnitude of this sensitivity is reduced to 6.8% °C −1 when considering vegetation response to historical CO 2 . For present‐day conditions, this translates to runoff reductions of 10.3% due to anthropogenic increases in both temperature and CO 2 since 1880. We demonstrate that Colorado Basin's natural flow has been decreased by roughly the storage of Lake Mead during the 2000–2021 megadrought due to this long term anthropogenic influence, suggesting the basin's first shortage in 2021 would likely not have occurred without anthropogenic warming. We further show warming has led to disproportionate aridification in snowpack regions, causing runoff to decline at double the rate relative to non‐snowpack regions. Thus, despite only making up ∼30% of the basin's drainage area, 86% of runoff decreases in the Colorado Basin is driven by water loss in snowpack regions.
Cloud feedback is the largest uncertainty in estimating Equilibrium Climate Sensitivity. In this study we focus on the equatorial Pacific, where CMIP6 model cloud feedback spread is notably large. Cloud radiative effects in this region are relevant for the global climate. Our findings show that models predict a consistent shift towards the ascent regime in response to El Nino-like sea surface warming. Models diverge in terms of the radiative impact due to differences in cloud characteristics in ascent and subsidence regimes. Using the observed relationship between circulation regime and cloud radiative effect, we find a reduction in the regional mean cloud feedback estimate from 0.77 to 0.22 W m -2 K -1 , though this does not substantially lessen the model spread in total feedback. Pathways to reduce this spread include: improving confidence in the regional ocean warming pattern, and using observations and models to understand cloud type and circulation interactions.