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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Analytic Sensitivity Coefficients for General Multigroup Infinite Medium k-Eigenvalue Problems

This work presents a general set of equations that can be used to rapidly generate new benchmarks to verify nuclear data sensitivity calculations. The general multigroup infinite medium k-eigenvalue neutron transport equation is used to derive analytic expressions for the infinite medium k-eigenvalue, the scalar neutron flux and adjoint flux, and the sensitivity of k∞ to perturbations in the multigroup nuclear data of a single species, isotropic and elastic scattering, material. The multigroup nuclear data for U-235 and U-238 is presented along with their corresponding k-eigenvalues, forward flux, adjoint flux, and sensitivity profiles, which include the sensitivity of k∞ to the total, fission, capture, and scattering macroscopic cross sections as well as to the group-to-group scattering cross section matrix, group-wise fission neutron production, and the unconstrained and constrained fission neutron energy distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Linear and Nonlinear Solvers for Simulating Multiphase Flow within Large-Scale Engineered Subsurface Systems

Simulation of multiphase flow in the subsurface is well-known to be computationally challenging. While there have been many studies that have explored approaches to overcoming these challenges, they often utilize relatively simple case studies. In this paper, we focus on the unique numerical challenges posed by modeling large-scale engineered subsurface systems, characterized by discrete features embedded in a heterogeneous natural subsurface setting. The man-made features such as shafts, tunnels, and barriers often cause multiple challenges in modeling the domain for multiphase porous media flow. This flow scenario can have a wide range of applications such as nuclear waste repositories, enhanced recovery of a petroleum reservoir, geothermal engineering, and carbon sequestration. An example of these severe numerical challenges is the case of performance assessment (PA) for Waste Isolation Pilot Plant (WIPP), the only operating deep geological repository in the US, which simulates extreme material properties of bedded salt rock formation and extreme contrast due to open excavation next to the formation. The models have extremes not only of permeability and porosity but also of the constitutive models needed for multiphase flow; additionally, they have process models like salt creep closure reducing porosity over time, fracturing in clay and anhydrite interbeds of the bedded salt, gas generation from the waste materials, and unintentional human borehole intrusions in some scenarios. Numerical simulations require the solution of coupled systems of nonlinear PDEs; in our work, we use the open-source simulator PFLOTRAN which is based on Finite Volume discretization. The solution of the nonlinear equations requires use of the Newton-Raphson iteration at each time step, which entails the solution of the linearized Jacobian system at each iteration. The effects of all the processes (i.e., large number of unknowns, highly nonlinear constitutive relations, large contrasts in material properties in short distances) lead to an ill-conditioned Jacobian matrix that severely challenges traditional linear solver, i.e., stabilized biconjugate gradient with block Jacobi incomplete LU preconditioner (BCGS-ILU) leading to non-convergence for traditional Newton-Raphson nonlinear solver causing unacceptably long computation time for each model. This paper presents linear solvers such as constrained pressure residual (CPR) two-stage preconditioner with alternate-block-factorization (ABF) and quasi- implicit pressure and explicit saturation (QIMPES) decouplers and flexible generalized residual solver (FGMRES). The new general-purpose nonlinear solver, Newton trust-region dogleg Cauchy (NTRDC), is also introduced to resolve extreme nonlinearities in the models. We demonstrate the effectiveness of each method relative to the default BCGS-Newton solver. The two best cases had nearly 50 times speed-up and achieved completion of a simulation in 14 hours that never completed due to non-convergence with the default solver. We also investigate the strong scalability of each method and discuss some of the deficiencies found for Block Jacobi preconditioner using parallel domain decomposition, and node packing effects of modern processor architecture.

Preconditioner, Nonlinear, Porous media, Multiphas↗
DOE OSTI2025

Bayesian modeling of traffic-related air pollutants: A case study of urban transportation and air quality dynamics in Columbia, South Carolina

Traffic emissions significantly impact near-road air quality and public health. This research applies a Bayesian modeling framework to investigate these impacts using high-resolution traffic and air pollutant data from an urban corridor in Columbia, South Carolina. Despite a data collection period truncated by the COVID-19 lockdown, the Bayesian approach successfully identified significant predictors and quantified model uncertainty. Employing Bayesian Model Selection and Averaging enhanced prediction accuracy and evaluated model uncertainty. Findings indicate that higher temperatures and increased moisture levels elevate particulate matter (PM 1.0 , PM 2.5 , PM 10 ) concentrations, while traffic speed significantly affects nitrogen dioxide (NO 2 ) levels. Specifically, higher average traffic speeds (indicative of smoother flow) correspond to lower NO 2 concentrations, suggesting that less congested conditions reduce NO 2 emissions. This study highlights the robustness of Bayesian methods for generating reliable air quality insights even under data-constrained conditions. The findings underscore the importance of traffic flow management (e.g., reducing congestion) for mitigating near-road NO 2 exposure and provide a basis for developing targeted public health strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Analytical solution and parameter estimation for heat of wetting and vapor adsorption during spontaneous imbibition in tuff

An analytical expression is derived for the thermal response observed during spontaneous imbibition of water into a dry core of zeolitic tuff. Sample tortuosity, thermal conductivity, and thermal source strength are estimated from fitting an analytical solution to temperature observations during a single laboratory test. Here, the closed-form analytical solution is derived using Green's functions for heat conduction in the limit of “slow” water movement; that is, when advection of thermal energy with the wetting front is negligible. The solution has four free fitting parameters and is efficient for parameter estimation. Laboratory imbibition data used to constrain the model include a time series of the mass of water imbibed, visual location of the wetting front through time, and temperature time series at six locations. The thermal front reached the end of the core hours before the visible wetting front. Thus, the predominant form of heating during imbibition in this zeolitic tuff is due to vapor adsorption in dry zeolitic rock ahead of the wetting front. The separation of the wetting front and thermal front in this zeolitic tuff is significant, compared to wetting front behavior of most materials reported in the literature. This work is the first interpretation of a thermal imbibition response to estimate transport (tortuosity) and thermal properties (including thermal conductivity) from a single laboratory test.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Unified Framework to Reconcile Different Approaches of Modeling Transpiration Response to Water Stress: Plant Hydraulics, Supply Demand Balance, and Empirical Soil Water Stress Function

Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply-demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi-arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.

The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Freezing water at constant volume and under confinement

Water expands upon freezing. What happens when water is cooled below 0 °C in an undeformable, constant-volume container? This is a fundamental question in materials thermodynamics, and is also relevant in biological, geological, and technological applications in which ice forms under nano-, meso-, or macroscale confinement. Here, we analyze the phase-equilibria and kinetic behaviors of water and ice-1h in an isochoric (constant-volume) system. By making use of the Helmholtz potential F (temperature, volume), in contrast to the Gibbs potential G (temperature, pressure), we demonstrate significant changes in phase behavior when the specific volume of the container is constrained below that of ice-1h. We construct a T–V (temperature–volume) phase diagram for water and ice that features a broad two-phase equilibrium region, and we further derive an isochoric nucleation theory that reveals the existence of a critical confinement volume, on the order of microns, below which ice-1h is kinetically prohibited from forming.

42 ENGINEERING↗
DOE OSTI2023

Prediction of alkaline earth metal ion adsorption on goethite for various background electrolytes with the CD-MUSIC model

As water scarcity drives the use of more saline water sources, contaminant fate and transport models must capture the impact of high concentrations of alkaline earth metal ions (AEMs) and background electrolytes in these more complex waters. By utilizing macroscopic adsorption data from various electrolyte systems, a Charge Distribution – Multisite Complexation (CD-MUSIC) model, capable of incorporating electrolyte adsorption, was able to accurately simulate the adsorption behavior of alkaline earth metal ions onto goethite. The modeling effort was guided by previous spectroscopic and surface complexation modeling of alkaline earth metal adsorption and built on previous CD-MUSIC modeling that accounted for changes in crystal face contributions to the surface site density as a function of specific surface area. Further, the model was constrained to consider only two dominant surface complex species for each metal ion adsorption reaction. These two species were selected from 44 possible species through objective curve fitting of single-solute macroscopic adsorption data. While most of the alkaline earth metal surface complexes formed outer-sphere complexes at the goethite surface, an inner-sphere species was utilized for Mg 2+ . With the surface complex species and equilibrium constants obtained from this study, the calibrated model successfully predicted alkaline earth metal ion adsorption over a wide range of solution and surface conditions; the model predictions encompassed a wide range of pH (5–11), solute/solid ratio (1.37 × 10 -5 – 8.33 × 10 -4 mol -solute /g -solid ), ionic strengths (0.01 M – 0.7 M), and background electrolytes (Na + , Cs + , Rb + , Cl - , and NO 3 - ) using the same crystal face contribution methodology for site density, capacitance values, and surface acidity constants adopted for proton and cadmium adsorption in previous work (Han and Katz, 2019). Model simulations for a range of background water chemistries demonstrated the potential for Mg 2+ to reduce Cd 2+ adsorption to goethite in model seawater and oil- and gas-produced waters.

42 ENGINEERING↗
DOE OSTI2020

Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil

Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve because of the high degrees of freedom as well as the coupled nature of the conservation laws. While these issues pose a bottleneck for the use of such models in aerospace design, computational costs can be significantly minimized by constructing special, structure-preserving surrogate models called reduced-order models. In this work, we propose a machine learning method to construct reduced-order models via deep neural networks and we demonstrate its ability to preserve accuracy with a significantly lower computational cost. In addition, our machine learning methodology is physics-informed and constrained through the utilization of an interpretable encoding by way of proper orthogonal decomposition. Application to the inviscid transonic flow past the RAE2822 airfoil under varying freestream Mach numbers and angles of attack, as well as airfoil shape parameters with a deforming mesh, shows that the proposed approach adapts to high-dimensional parameter variation well. Notably, the proposed framework precludes the knowledge of numerical operators utilized in the data generation phase, thereby demonstrating its potential utility in the fast exploration of design space for diverse engineering applications. Comparison against a projection-based nonintrusive model order reduction method demonstrates that the proposed approach produces comparable accuracy and yet is orders of magnitude computationally cheap to evaluate, despite being agnostic to the physics of the problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Layers Can Be Deceiving: A Hopping Model for Small Molecule Diffusion in TATB Crystal

Sorption of small molecule gases in materials can play a significant role in their long‐term stability and compatibility within multi‐material assemblies. While many material properties of the insensitive high explosive TATB (1,3,5‐triamino‐2,4,6‐trinitrobenzene) are well understood, very little is known regarding its permeability to gases. TATB crystal exhibits a graphitic‐like layered packing structure that evokes a mental schema in which the layers form nanoscopic channels, but it is unclear whether this structure promotes gas transport. Here, we use molecular dynamics (MD) simulations to predict transport of small molecules through TATB single crystal. An approach to fit classical force fields is developed to model TATB interactions with H 2 O, He, Ne, and Ar, which is then combined with steered MD to probe gas transport along selected directions in the crystal. We find that small molecule transport occurs via a hopping mechanism that exhibits distinct jumps between interstitial sites and is substantially faster normal to the layers as compared to through them. This result stems from the finding that intralayer junctions between adjacent TATB molecules are the most stable interstitial sites and that energetic barriers are lower for hopping between adjacent layers. An empirical model for diffusion rate based on the MD data shows that the rate decays exponentially with increasing molecular radius and is negligibly small for all molecules larger than He, including common atmospheric gases. These findings have implications for the interpretation of experiments that measure surface area, material response to extreme conditions, and are expected to help constrain models for material aging.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optimization of leaf morphology in relation to leaf water status: A theory

The leaf economic traits such as leaf area, maximum carbon assimilation rate, and venation are all correlated and related to water availability. Furthermore, leaves are often broad and large in humid areas and narrower in arid/semiarid and hot and cold areas. We use optimization theory to explain these patterns. We have created a constrained optimization leaf model linking leaf shape to vein structure that is integrated into coupled transpiration and carbon assimilation processes. The model maximizes net leaf carbon gain (NPPleaf) over the loss of xylem water potential. Modeled relations between leaf traits are consistent with empirically observed patterns. As the results of the leaf shape-venation relation, our model further predicts that a broadleaf has overall higher NPPleaf compared to a narrowleaf. In addition, a broadleaf has a lower stomatal resistance compared to a narrowleaf under the same level of constraint. With the same leaf area, a broadleaf will have, on average, larger conduits and lower total leaf xylem resistance and thus be more efficient in water transportation but less resistant to cavitation. By linking venation structure to leaf shape and using water potential as the constraint, our model provides a physical explanation for the general pattern of the covariance of leaf traits through the safety-efficiency trade-off of leaf hydraulic design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Forward Modeling of Gamma Reaction History Signatures From Anticipated Deuterium-Tritium Filled MagLIF Implosions on Sandia’s Z-Machine

Nuclear reaction history measurements provide a bang time and burn width of Inertial Confinement Fusion (ICF) implosions and are essential for understanding implosion performance to constrain ICF capsule design. When fusion fuel contains Deuterium (D) and Tritium (T) gas, reaction history is informed by measuring the 16.75 MeV gamma rays generated from the D(T,γ) 5 He reaction. Such DT based reaction history measurements have not been made on the Magnetized Laser Inertial Fusion (MagLIF) platform on Sandia’s Z-Machine due to the lack of Tritium being used. The recent development of ICF implosions with tritiated fuel will open the possibility of measuring the gamma reaction history on the Z-Machine. A forward model of the Gamma Reaction History diagnostic on Z (GRH-Z) has been developed using the MCNP6.3 (Monte-Carlo N-Particle) radiation transport code. The model included the Z-Machine geometry of interest to characterize the impact of neutron induced gamma rays on the DT reaction history measurements. In addition, the impulse response functions of the GRH-Z diagnostic to understand the temporal response of the detector, and the minimum yields required to make a reaction history measurement were calculated. This approach also predicted that with T 2 gas doping of MagLIF implosions a reaction history may be made for high performance shots >8e12-2.4e13 depending on the chosen threshold for the detector, with a maximum signal to background ratio of 25%. It was found that for long duration ICF implosions that additional collimation will be needed to prevent the neutron induced gamma rays from modifying the shape of the measured DT reaction history curve.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Probe nuclear structure using the anisotropic flow at the Large Hadron Collider

Abstract Recent studies have shown that the shape and radial profile of the colliding nuclei have strong influences on the initial condition of the heavy ion collisions and the subsequent development of the anisotropic flow. Using A Multi-Phase Transport model (AMPT) model, we investigated the impact of nuclear quadrupole deformation $$\beta _2$$ β 2 and nuclear diffuseness $$a_0$$ a 0 of $$^{129}$$ 129 Xe on various of flow observables in Xe–Xe collisions at $$\sqrt{s_{\textrm{NN}}}=$$ s NN = 5.44 TeV. We found that $$\beta _2$$ β 2 has a strong influence on central collisions while $$a_0$$ a 0 mostly influences the mid-central collisions. The relative change of flow observables induced by a change in $$\beta _2$$ β 2 and $$a_0$$ a 0 are also found to be insensitive to the values of parameters controlling the strength of the interaction among final state particles. Our study demonstrates the potential for constraining the initial condition of heavy ion collisions using future system scans at the LHC.

Physics↗
DOE OSTI2026

Characterization of Organosulfates and Organonitrates in Vertically Resolved Aerosols over the Southern Great Plains Field Campaign Report

Aerosol is the largest individual source of uncertainty in assessing the Earth’s radiative balance due to poorly understood aerosol processes and aerosol-cloud interactions. Thus, to constrain aerosol’s climate impacts, it is critical to understand aerosol compositions at high elevations. Organic material accounts for about 20-80% of total aerosol mass in the atmosphere and is known to play a key role in aerosol processes and cloud formation. In prior research, characterization of aerosol molecular composition has been mostly carried out at ground level. In these ground-level measurements, total speciated organosulfates and organonitrates may contribute significantly to organic aerosol mass on the order of 10-40%, most of which are from abundant biogenic volatile organic compounds such as isoprene and monoterpenes. Organosulfates and organonitrates have also been shown to be present in substantial amounts in cloud water, suggesting that they can be transported and/or directly formed at high elevations. On the other hand, the less common and more expensive aircraft studies have performed high-altitude measurements, but were often limited in the number of different altitudes sampled and in sample collection under consistent conditions. Therefore, the vertical distributions of key aerosol components such as organosulfates and organonitrates are not well understood and have not been extensively studied. Nevertheless, it is important to understand how organosulfates and organonitrates in aerosols are distributed, transformed, participate in aqueous-phase processes, and interact with high-altitude clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Optimization of a Secondary Air Injector for a Rich-Quench-Lean (RQL) Ammonia Combustor Using Computational Fluid Dynamics

Ammonia has emerged as a promising medium for moving hydrogen around the globe due to its energy density, existing infrastructure for production, transportation, and storage, and multiple applications from fertilizer to power generation. However, one of the most significant challenges with ammonia combustion for power generation is the formation of nitrogen oxides (NOx) during combustion. Several combustion strategies have been developed to minimize the formation of NOx. One such strategy, known as rich-quench-lean (RQL), is a method that combusts NH3 in a fuel-rich environment, followed by a quick mix section and a lean burnout section. Rapid mixing of secondary air before lean burnout is thought to be important to minimize the formation of NOx. A genetic algorithm (GA) is used to parametrically vary the secondary air injector design, including the diameter, count, and angles over a constrained design space. The designs are evaluated using a non-reacting OpenFOAM model, with the objective function being the uniformity index of the secondary air (modeled as a scalar). Optimization campaigns show that traditional correlation-based designs might not be adequate. After running 414 OpenFOAM models, the optimal design is 32, 1 mm diameter, angled air injectors, increasing the uniformity index at the outlet of the quick mix zone by 36.8%. The most promising designs will be manufactured and tested in a small RQL burner setup, which is expected to lead to validation and insight into the practical usage of NH3 combustion for power generation.

ammonia combustion↗
DOE OSTI2024

The ultralow viscosity of volatile-rich kimberlite magma: Implications for the water content of primitive kimberlite melts

The eruption of deeply sourced kimberlite magma offers the fastest route to bring deep-seated volatiles back to the Earth’s surface. However, the viscosity of kimberlite magma, a factor governing its migration and eruption dynamics within Earth, remains poorly constrained. We conducted synchrotron in situ falling sphere viscometry experiments to examine kimberlite magma with different volatile contents (0 to 5 wt % H 2 O and 2 to 8 wt % CO 2 ) under high pressure-temperature conditions. The results reveal that the viscosity of volatile-rich kimberlite magma is ~1 to 2 orders lower than that of mid-ocean ridge basalt (MORB) and comparable to the ultramobile pure carbonate melt. Using the measured viscosity values, we simulated the ascent and eruption process of kimberlite magma. We found that a minimum content of ~0.5 wt % water in the primitive magma is necessary to allow the ultrafast eruption process of kimberlite, thereby enabling the preservation of diamonds and high-pressure mineral inclusions transported by the magma.

58 GEOSCIENCES↗
DOE OSTI2021

Experimental investigation of elemental and isotopic evaporation processes by laser heating in an aerodynamic levitation furnace

In this report we carried out evaporation experiments on a B-type calcium–aluminium-rich inclusion (CAI) melt in a gas-mixing aerodynamic levitation laser furnace, at 1873 K and an oxygen partial pressure of 10 -9.1 atm, for durations ranging from 60 to 600 s. Evaporation of SiO 2 and MgO follow the same trend as those observed in vacuum furnace experiments at the same temperature and starting composition, showing that their evaporation relative to one another from the melt is independent of pressure, oxygen fugacity, and hydrodynamical regime specific to the furnace. Isotopic ratios of Mg and Si in evaporation residues are used to derive fractionation factors of α 26/24 Mg vap-liq = 0.9906± 0.0004 and α 30/28 Si vap-lip = 0.9943±0.0003, which are both significantly closer to unity than those found for evaporation in a vacuum, which translates to less isotope fractionation. The residues are also less isotopically fractionated than expected for cases in which transport of the gas species away from the melt is diffusion-controlled at 1-atm. By analysing the flow regimes in our furnace, we find that advection by the levitating gas is the primary mode of mass transport away from the melt surface, as opposed to diffusion-limited transport in a vacuum or 1-atm tube furnace. A modified Hertz–Knudsen–Langmuir formulation accounts for this process, and shows that isotopic fractionation of both Si and Mg reflect a saturation factor (ratio of the pressure of the evaporating species to vapour saturation pressure) equal to 0.75. This is in perfect accord with recent measurements of Cu isotopic fractionation using a similar furnace. The fact that three elements (Mg, Si, Cu) with varying equilibrium vapour pressures, activity coefficients in the liquid, and diffusion coefficients in the gas have the same scaling behaviour to saturation pressure is a strong indication that the mechanism controlling evaporation is driven by the hydrodynamical regime imposed in the furnace. Therefore, this class of experiments can be used to constrain processes in which advection dominates over diffusion, such as (but not limited to) planetary ejecta, tektites, giant impacts, nebular condensation in a turbulent flow, or nuclear fallout material. Finally, the possibility to reach high temperatures (in excess of 3500 K) in this furnace allows it to be used to evaluate the activity coefficients of melt components in extreme conditions relevant to molten planetary interiors (i.e., magma oceans), with a specific focus on refractory elements.

58 GEOSCIENCES↗
DOE OSTI2025

Mid-latitude clouds contribute to Arctic amplification via interactions with other climate feedbacks

Abstract Traditional feedback analyses, which assume that individual climate feedback mechanisms act independently and add linearly, suggest that clouds do not contribute to Arctic amplification. However, feedback locking experiments, in which the cloud feedback is disabled, suggest that clouds, particularly outside of the Arctic, do contribute to Arctic amplification. Here, we reconcile these two perspectives by introducing a framework that quantifies the interactions between radiative feedbacks, radiative forcing, ocean heat uptake, and atmospheric heat transport. We show that including the cloud feedback in a comprehensive climate model can result in Arctic amplification because of interactions with other radiative feedbacks. The surface temperature change associated with including the cloud feedback is amplified in the Arctic by the surface-albedo, Planck, and lapse-rate feedbacks. A moist energy balance model with a locked cloud feedback exhibits similar behavior as the comprehensive climate model with a disabled cloud feedback and further indicates that the mid-latitude cloud feedback contributes to Arctic amplification via feedback interactions. Feedback locking in the moist energy balance model also suggests that the mid-latitude cloud feedback contributes substantially to the intermodel spread in Arctic amplification across comprehensive climate models. These results imply that constraining the mid-latitude cloud feedback will greatly reduce the intermodel spread in Arctic amplification. Furthermore, these results highlight a previously unrecognized non-local pathway for Arctic amplification.

58 GEOSCIENCES↗