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  1. Deep learning-assisted modeling for χ(2) nonlinear optics

    Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present a long short-term memory-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation formore » data-integrated modeling frameworks and digital twins of laser systems.« less
  2. Stellarator Design Exploration Using Symbolic-Regression Neutronics Surrogates

    Systems codes require fast, simplified models to rapidly evaluate fusion power plant concepts, but neutronics analyses are often a computational bottleneck. Here, to address this, surrogate models for key neutronics responses have been developed using 3-D neutronics-ready models built with the open-source code ParaStell from a database of stellarator equilibria. Neutronics responses such as tritium breeding ratio (TBR), nuclear heating, and neutron-induced radiation damage displacements per atom (dpa) were simulated using OpenMC. Through sensitivity analysis and symbolic regression (SR), simple power-law formulas were derived connecting these neutronics responses to global stellarator parameters, including fusion power, plasma surface area, and plasmamore » elongation. Validation shows these formulas can predict the simulation results with low error, enabling quick and accurate assessment of neutronics requirements in stellarator design exploration activities with systems codes.« less
  3. Model-based economic analysis under uncertainty for PFAS treatment by granular activated carbon and ion exchange technologies

    Recent drinking water regulations have imposed the need for per- and polyfluoroalkyl substances (PFAS) remediation. In response, treatment facilities may be required to retrofit existing treatment schemes to treat PFAS below maximum contaminant levels (MCLs). Adsorption technologies such as granular activated carbon (GAC) and ion exchange (IX) have been demonstrated to be effective; however, there are limited techno-economic metrics available which provide guidance on technology selection and design for diverse PFAS-containing source water conditions. Process systems engineering (PSE) tools which can traditionally perform these analyses are hindered by the data availability, model validity, and understanding of treatment phenomena for emergingmore » contaminants. This work employs published data regressions, statistical models, process models, techno-economic analyses, and other process systems tools in a model-based uncertainty framework to consider the limitations of emerging contaminant research. Through this analysis framework, economic results are provided as probabilistic distributions based on the uncertainty of the models and diverse conditions that treatment facilities experience.« less
  4. Subpolar North Atlantic Water Mass Transformation and Overturning in Eddying and Non‐Eddying Simulations

    Buoyancy forcing in the subpolar North Atlantic Ocean (SPNA) is an important driver of the Atlantic Meridional Overturning Circulation (AMOC). To advance understanding of the mechanisms connecting the two processes and their relative importance in sub-basins within the SPNA, we apply the Water Mass Transformation Framework to a matched-pair of forced, ocean-sea ice simulations configured at eddying and non-eddying resolution. Within the first decade of simulation, the non-eddying simulation produces a weak AMOC, ~11 Sv at 26.5 ° N, while the AMOC in the eddying simulation is more realistic and is thus analyzed for comparison. Surface water mass transformation andmore » boundary transport are calculated in both density and temperature–salinity coordinates in three separate deep water formation regions, the Iceland Basin, the Irminger Sea and Labrador Sea, during the first decade of both simulations. We identify strong surface freshening in the Irminger and Labrador seas in the non-eddying simulation during the AMOC decline. This freshening significantly reduces the density of the surface outcrops where surface water mass formation occurs, essentially removing this contribution to deep water formation. Concurrently, boundary transports in these two regions are warmer and saltier in the non-eddying simulation compared to the eddying simulation where the density of surface water mass formation is stable. The warming and salinification of boundary transports in the non-eddying simulation is interpreted as an obstacle to deep water formation. Labrador Sea surface water mass transformation is important, despite its small contribution to buoyancy overturning.« less
  5. Evaluating ecosystem water use efficiency and recovery dynamics during flash droughts: insights from observations and model simulations

    Flash droughts (FD), rapidly emerging in a warming future, disrupt ecosystems, agriculture, and water security. Ecosystem water use efficiency (WUE), the ratio of gross primary production (GPP) to actual evapotranspiration (AET), balances carbon assimilation and water loss. FD rapidly disrupts this balance, making WUE critical for assessing plant stress and recovery. Here, this study investigates the dynamics of landscape-scale WUE, and the components of GPP and AET under FD utilizing both observed data from the Missouri Ozark AmeriFlux site (US-MOz) and version 2 of the U.S. Department of Energy’s Earth, Energy, Exascale System Model (E3SM) Land Model (ELMv2). Observations andmore » simulations reveal GPP as dominant for WUE during earlier FD events (2005, 2007, 2012), shifting to AET in recent events (2014, 2018). This agreement indicates that the ELM can capture the shifting dynamics of GPP and AET in regulating WUE under FD conditions. However, the ELM systematically underestimates both GPP and AET and does so in a manner that does not preserve their ratio. As a result, WUE is also underestimated, suggesting that GPP is more strongly underestimated than AET. Furthermore, the ELM also underestimates the speed of GPP recovery, producing an artificially prolonged GPP recovery time following FD events. Observed environmental drivers such as vapor pressure deficit (VPD), soil moisture (SM), and predawn leaf water potential (PLWP) effectively predict WUE, but ELM primarily highlights SM, underestimating VPD’s role. This study demonstrates that relying solely on soil moisture fails to capture the rapid hydraulic recovery observed in PLWP, underscoring the necessity of integrating plant hydraulics into land surface models to improve flash drought predictability.« less
  6. A comprehensive modeling of falling film evaporators subject to vapor flow, pass arrangements, and refrigerants

    Improving the heat transfer performance of falling film evaporators is a crucial step for improving the energy efficiency of the heat pump or refrigeration systems. This study conducts a numerical investigation for the practical-scale falling film evaporator based on the epsilon–NTU method with an updated heat transfer correlation. The algorithm was validated with lab-scale and real-scale falling film evaporator experimental results, and the prediction reaches a mean absolute deviation of 12.5%. Here, the parametric study encompasses eight refrigerants: R-134a, R-410A, R-600a, R-717, R-1270, R-152a, R-1234yf, and R-1234ze(E). The results indicate that vaporization enthalpy of refrigerant is a key property inmore » selecting an appropriate working fluid because it helps minimize severe heat transfer degradation caused by dry-out. Additionally, the vapor-flow–induced heat transfer degradation can be predicted using the critical Weber number. Furthermore, the trade-off between extending the tube length and increasing the number of tubes for heat transfer improvement is discussed. Finally, different two-pass arrangements show deviations of less than 4 %.« less
  7. Dynamic and static corrosion of chromium-alumina refractory in simulated nuclear waste glass

    Monofrax® K-3, a chromium–alumina refractory, is widely used in nuclear waste glass melters due to its high durability. However, aggressive vitrification conditions still lead to corrosion that limits melter lifespan. Here, this study investigates two key corrosion modes, subsurface and melt-line corrosion, under static and dynamic conditions, using a custom setup enabling precise control of melt velocity and temperature. Experimental results, interpreted using diffusion-limited dissolution models, show that (i) subsurface corrosion increases by approximately 10% per mm/s increase in melt velocity, while melt-line corrosion remains virtually unaffected, (ii) corrosion rate dependence on temperature is inversely proportional to melt viscosity, andmore » (iii) glass composition affects both corrosion modes similarly, based on Cr2O3, Al2O3, and alkali contents. The experimental data also allowed the determination of diffusion coefficients of major dissolving species, which were found to be consistent between the melt-line and subsurface corrosion models, indicating their suitability for future CFD studies.« less
  8. Chemical Reactor Network Modeling of Ammonia Rich-Quench-Lean Combustion Using a Partially Stirred Reactor Approach

    Ammonia is a promising alternative to hydrogen with high energy density and favorable storage and transport characteristics. However, low flammability and a propensity for high nitrogen oxide (NOx) emissions make direct utilization challenging. Recently, two-stage rich-quench-lean (RQL) combustion strategies have shown promise in achieving low NOx emissions with ammonia. In this approach, the rich stage serves to oxidize a portion of the fuel while thermally decomposing as much of the remaining ammonia as possible, generating hydrogen. In the second (lean) stage, air is rapidly introduced, burning out the hydrogen and residual ammonia. Two-stage RQL combustion of ammonia has been investigatedmore » in the open literature both experimentally and numerically. In general, idealized chemical reactor network (CRN) models predict NOx concentrations below those of 2D/3D computational fluid dynamics models and experiments. The primary drivers of these discrepancies may be largely attributed to finite rate mixing nonadiabatic operation. The typical CRN model is comprised of a perfectly-stirred-reactor (PSR), followed by a plug-flow-reactor (PFR), meant to represent the flame, and postflame zones, respectively. In the two-stage RQL approach two PSR-PFR networks are arranged sequentially, corresponding to the rich and lean stages, with secondary air injection in between. In the authors' past work, this arrangement has demonstrated the significant sensitivity of exit NOx to the rich stage equivalence ratio, while the amount of secondary air injection was shown to be less critical. In this paper, the CRN model is extended to (1) include the impacts of heat loss and (2) utilize a partially-stirred-reactor (PaSR) approach to study the impacts of mixing on emissions performance. Varying amounts of heat loss are applied to the rich relaxation zone to understand emissions performance and changes to optimization of equivalence ratio and residence time. Premixed and nonpremixed configurations are considered in the rich stage PaSR, with varying degrees of mixing intensity to study the interaction between mixing, transport, and kinetic timescales. Critically, the impact of mixing between hot products and secondary air injection is studied to understand practical injector needs. Results show unburnt ammonia leaving the rich stage as a primary contributor to NOx emissions – driven both by increased heat loss and reduced mixing rates. Furthermore, heat losses have been shown to create conditions that are conducive to increased N2O formation in the lean stage. In conclusion, the results of this study will be considered in the context of developing optimized two-stage RQL combustors for ammonia.« less
  9. Computational Analysis of Anode and Cathode Structuring Effects on Charge and Discharge in Graphite|LiNi0.6Mn0.2Co0.2O2 Batteries

    Structured electrodes (SEs) improve the rate capability of Lithium-ion batteries by engineering micrometer-scale electrolyte regions into the electrode, promoting rapid ionic transport. Prior research has focused on structuring one electrode (anode or cathode) with an analysis on either the charge or discharge performance. We present a holistic study using three-dimensional models to investigate the isolated effects of structuring either electrode and the combined effects of structuring both electrodes on the charge and discharge capacity of single-layer cells at 4 C and 6 C. Volumetric and gravimetric discharge energy density (Wh/Lstack and Wh/kgstack) and charge capacity (Ah/kgstack and Ah/Lstack) are evaluatedmore » for multi-layer pouch cell stacks. Pairing SE anodes with SE cathodes demonstrated improvements up to 15% in discharge Wh/kgstack and up to 33% in charge Ah/kgstack over a conventional cell; Energy required to charge per Ah/kgstack was improved by 13%–14%. SE cathodes paired with a conventional anode exhibited improvements of 0.3%–22% across all performance metrics evaluated. Conversely, pairing a SE anode with a conventional cathode demonstrated improved charge capacity up to 13% but showed a 2%–23% lower discharge energy density. The importance of aligning SEs in a cell from a performance and manufacturing perspective is also analyzed.« less
  10. 4.0 MOOSE: Enabling massively parallel Multiphysics simulation

    Approaching 18 years of existence, MOOSE—the Multiphysics Object-Oriented Simulation Environment—is being developed at a higher pace than ever before. With significant support from four research institutions across the globe, and dozens of new contributors, the capabilities of the framework are being expanded to meet modeling challenges in a wide variety of fields from nuclear system design, to geomechanics, to material science. This includes new development in equation discretization techniques, solver methods, meshing capabilities, application deployment, and user interface improvements. Applications built on MOOSE benefit from all these improvements.
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