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DOE OSTI2024

Kink Instability of Flux Ropes in Partially Ionized Plasmas

In the solar atmosphere, flux ropes are subject to current-driven instabilities that are crucial in driving plasma eruptions, ejections, and heating. A typical ideal magnetohydrodynamics instability developing in flux ropes is the helical kink, which twists the flux rope axis. The growth of this instability can trigger magnetic reconnection, which can explain the formation of chromospheric jets and spicules, but its development has never been investigated in a partially ionized plasma (PIP). Here, we study the kink instability in PIP to understand how it develops in the solar chromosphere, where it is affected by charge-neutral interactions. Partial ionization speeds up the onset of the nonlinear phase of the instability, as the plasma β of the isolated plasma is smaller than the total plasma β of the bulk. The distribution of the released magnetic energy changes in fully ionized plasma and PIP, with a larger increase in internal energy associated with the PIP cases. The temperature in PIP increases faster also due to heating terms from the two-fluid dynamics. PIP effects trigger kink instability on shorter time scales, which is reflected in more explosive chromospheric flux rope dynamics. These results are crucial to understanding the dynamics of small-scale chromospheric structures—minifilament eruptions—that thus far have been largely neglected but could significantly contribute to chromospheric heating and jet formation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Optimal Operation of Residential High Performance Water Heater for Reduction of Electricity Cost and Peak Demand Through Field Validation

Water heating accounts for about 18% of a typical US home’s energy use. Modern water heaters have enabled control options through APIs, offering customers the opportunity to reduce their energy cost and peak demand by dynamically adjusting settings. A water heater’s capacity to store energy using its storage tank makes it an asset for peak demand reduction and energy cost savings. For this reason, a mixed-integer linear programming model is proposed to minimize the energy cost of a high-performance water heater while also reducing the peak demand of the residential household under a time-of-use utility rate by dynamically changing the water heater’s running mode. Specifically, a multi-objective optimization model is formulated to determine the mode settings of the water heater considering hot water use, time-of-use rate, and peak demand limit of the residential household. The mode settings are associated with different dead bands of water temperature for triggering on/off action of the heat pump and heating element. A 66-gal hybrid electric high performance water heater was used for numerical simulation and practical experiments. The simulation results were well aligned with measurements of practical experiments, validating the soundness of the thermodynamic model. In addition, reductions of energy cost, enabling affordability, and reducing peak demand are demonstrated. The research team also developed a software framework with dashboards to automatically and continuously monitor and manage devices.

Liu, Guodong [ORNL] (ORCID:0000000213498608)↗

DOE OSTI2026

Elucidating the Microscale Behavior and Phase Separation Kinetics of Thermally Responsive Ionic Liquid–Water Mixtures

Thermally responsive ionic liquids (ILs) exhibit liquid-liquid phase separation into a water-rich (WR) and ionic-liquid-rich (ILR) phase when heated above a lower critical solution temperature (LCST). This phase behavior has been leveraged for applications ranging from forward osmosis (FO) desalination, where the IL acts as a draw solute, to refrigeration and dehumidification cycles, where the IL acts as a liquid desiccant. While significant effort has been devoted to characterizing the thermodynamic and thermophysical properties of LCST ILs, their phase separation kinetics have not been investigated. In this work, we describe the macroscale phase separation kinetics (phase separation time) by gleaning insight into the microscale colloidal behavior of aqueous mixtures of four different materials, P 4444 TFA (tetrabutylphosphonium-2,4-trifluoroacetate), P 4444 DMBS (tetrabutylphosphonium-2,4-dimethyl-benzenesulfonate), N 4444 Sal (tetrabutylammonium salicylate), and P 4444 Sal (tetrabutylphosphonium salicylate) as a function of IL concentration at a separation temperature of 70 °C. We report the discontinuous microscale size distributions for each material and correlate their theoretical settling velocities to experimental phase separation times. The results indicate that a simple Stokes' law model can predict the phase separation time within reasonable accuracy. Overall, this work lays the foundation for understanding the micro- to macroscale phase separation behavior and kinetics of LCST ILs for various water-energy applications.

LCST↗

DOE OSTI2025

Transient Modeling and Simulation of a Generic Stable Salt Reactor

A SAM system-level model of a generic stable salt reactor has been developed to investigate thermal-hydraulic behavior and safety performance under steady and transient conditions. The model integrates information generated from a reactor physics analysis using PROTEUS and PERSENT, and a computation fluid dynamics (CFD) analysis using STAR-CCM+. A loose, iterative coupling scheme between PROTEUS and SAM is implemented to calculate the equilibrium power and temperature distributions in the steady-state critical core condition. The converged steady-state model is then used in PERSENT to calculate the four reactivity feedback temperature coefficients (Doppler, fuel density, coolant density, and core radial expansion) and kinetic parameters that are needed in SAM to model the temperature feedback effects in transient simulations. Within the fully enclosed liquid fuel pins, natural convection is the dominant heat transfer mechanism. The STAR-CCM+ model of the fuel pin considers conjugate heat transfer from the liquid fuel salt to the pin cladding and external reactor coolant. The CFD results of the axial and radial temperature profiles are used to empirically determine an effective fuel salt thermal conductivity in the SAM fuel pin model so that the temperatures predicted by the SAM model match as closely as possible the CFD results. In the central region of the fuel pin, the effective thermal conductivity is as high as similar to 60 times the physical fuel salt thermal conductivity. The whole-plant SAM model is then used to simulate an unprotected station blackout transient. The results of this simulation showed that the large negative fuel axial expansion reactivity feedback reduces fission power to similar to 2.4% nominal power. The core is cooled by natural circulation, which removes heat in the core to the emergency heat removal system, and ultimately, to the ambient. However, peak fuel salt and cladding temperatures can potentially reach as high as 1500 K, albeit briefly, if the shutdown mechanism fails to operate.

stable salt reactor; transient simulations; system↗

DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗

DOE OSTI2025

Dynamic Heat Flow and Current Distribution Analysis in the Bottom Anode of an Electric Arc Furnace Using Fiber-Optic Sensors

A reliable method for monitoring bottom anode wear during DC Electric Arc Furnace (DC-EAF) operation is of critical importance for safe and efficient steel production. Underestimation of bottom wear poses a serious safety risk that must be avoided, while overestimation of bottom wear also poses challenges, as premature anode replacement is expensive and affects EAF productivity. Previously, we demonstrated that fiber-optic sensors can be successfully deployed to create a spatially distributed temperature map to monitor the health of the anode. The present work explores the heat flow and current density distribution in bottom anode pins to predict bottom wear, steel penetration events, and monitor refractory erosion. Small dynamic variations in pin temperature induced by joule heating during arcing also provide a means to observe local current flows in each pin. When mapped, these measurements provide a real-time view of the non-uniform and dynamic current flow in the bottom anode during EAF operation that can affect bottom wear.

Bottom Anode↗

DOE OSTI2026

Effects of Gasoline Composition and Operating Parameters on the Response of End-Gas Autoignition to Nitric Oxide in a Lean-Burn DI-SI Engine

Lean operation of spark-ignition engines can lead to engine thermal efficiency gains and lower NOx emissions due to reduced combustion temperatures. Yet, lean operation could still face challenges in end-gas autoignition and knock generation due to higher intake pressures and trapped NO in the residual gas. Here, this study evaluates the impact of NO on end-gas autoignition for two gasoline fuels with similar octane rating but different composition: high cycloalkane fuel (HCA) and high olefin fuel (HO). Experiments were performed at stoichiometric and lean (λ = 2) conditions and at two engine speeds of 1400 rpm and 2000 rpm. Accompanying chemical kinetics simulations in CHEMKIN revealed that the mechanisms controlling the effect of NO on autoignition are similar λ = 2 and λ = 1, with NO + HO 2 = NO 2 + OH being the main pathway for enhancing reactivity by promoting low-temperature heat release (LTHR). The compositionally different fuels reacted differently to NO seeding and engine speed, and differences were augmented at λ = 2 compared to λ = 1 as the end-gas autoignition shifted to the low temperature regime. HO, which has inherent low temperature chemistry, was strongly impacted by engine speed at low NO seeding levels, with no noticeable peak of LTHR detected at 2000 rpm. On the other hand, LTHR of HCA was marginally affected by shortened residence time at higher engine speed as NO + HO 2 reaction was not greatly affected by shorter time scales, since HO 2 production was sustained even at 2000 rpm to support OH generation from NO + HO 2 . Contrary to HO, HCA exhibited greater sensitivity to NO seeding, as the increased OH production at higher NO concentrations offset the OH-quenching effect of cyclopentane, which accounts for 28.6% of HCA’s composition. Consequently, a sensitivity analysis revealed that fuels with weak inherent low-temperature chemistry, like HCA, are likely to be more sensitive to variations in NO concentration and charge temperature, whereas fuels with strong low temperature chemistry are more sensitive to variations in end-gas λ and intake pressure.

Energy↗

DOE OSTI2026

Detection of scintillation light in noble gases with wavelength-shifting optical fibers

Wavelength-shifting (WLS) techniques enable particle detectors based on noble gases, whose scintillation light is predominantly emitted in the vacuum-ultraviolet. We investigate WLS fibers coated with tetraphenyl butadiene (TPB) for scintillation light detection in gaseous xenon and argon at pressures up to 8.5 bar, motivated by future high-pressure xenon time-projection chambers of the NEXT program. Two detector configurations are studied: an elongated high-pressure vessel with four PTFE panels equipped with WLS fibers read by temperature-stabilized SiPMs, and a compact box-shaped detector operated at 1 bar Xe with WLS fibers read out by PMTs. Both operate with continuous gas purification. The detector response is characterized using cosmic muons and alpha particles from a $^{241}$Am source. With the SiPM setup, we measure a light collection efficiency (LCE) of ${1.18 \pm 0.01~\mathrm{(sta.)}~^{+0.07}_{-0.09}~\mathrm{(sys.)}~\%}$ for xenon and ${1.07 \pm 0.01~\mathrm{(sta.)}~^{+0.06}_{-0.08}~\mathrm{(sys.)}~\%}$ for argon. With PMT readout, we measure a LCE of ${0.45 \pm 0.01~\mathrm{(sta.)} \pm 0.05~\mathrm{(sys.)}~\%}$ in xenon, in agreement with the SiPM result once photon detection efficiency is accounted for. Average scintillation waveforms in xenon and argon are studied to assess the time structure of the emitted light. Cosmic-muon measurements yield a mean energy required to produce a scintillation photon $45\pm7~\mathrm{(sta.)}~^{+4}_{-5}~\mathrm{(sys.)}~\mathrm{eV}$ at 1.5 bar, in agreement with the literature. The results demonstrate that TPB-coated WLS fiber systems can reliably detect scintillation light in high-pressure gaseous noble detectors, with a LCE representing an upper limit for realistic large-scale TPCs, where additional photon losses from materials and fiber attenuation are expected.

Soleti, S. R. [Donostia Intl. Phys. Ctr., San Seba↗

DOE OSTI2025

Optimization of La 2 NiO 4+δ Electrolysis Cell Oxygen Electrode through Surfactant-Enabled LaCoO 3±δ Nanocatalyst Deposition

Lanthanum nickelate (LNO) has shown promise as a Cr-resistant air electrode material for SOECs but has suboptimal surface oxygen exchange properties. Nanocoating of the LNO surface with lanthanum cobaltite (LCO) was chosen to improve cell performance as a surface oxygen conductor. The work focused on the implementation of a two-step nano-LCO film deposition utilizing catechol molecules in a porous LNO electrode. The subgoals of the work were to maintain nanosized LCO particles/ grains to increase active surface area and to control the regularity/ homogeneity of the coating across the microstructure. To achieve these goals, a novel surfactant-enhanced liquid infiltration method was utilized, where nucleation sites were spread across the electrode structure to control the location and size of LCO particles. Various catechol surfactant compositions were evaluated for their ability to control the kinetics of nanoparticle deposition and the homogeneity of the coating. Chelated LCO was characterized by X-ray diffraction (XRD), which found a substantial improvement in LCO formation with surfactant addition and determined polymerized norepinephrine to be the best-performing surfactant, with 88.4% pure LCO formed at low temperature. X-ray photoelectron spectroscopy (XPS) confirmed LCO nanostructures formed by the two-step infiltration process, showing no impurities and a stable perovskite structure. Deposition kinetics were analyzed using atomic force microscopy (AFM), correlating infiltration times and solution molarity to nanoparticle size and distribution, the results of which were confirmed in symmetrical cell samples by scanning electron microscopy (SEM). Electrochemical impedance spectroscopy (EIS) testing demonstrated substantial improvements in polarization resistance, where the nanocoating reduced the resistance by ∼55% to 0.152 Ω·cm 2 at 700 °C and 0.039 Ω·cm 2 at 800 °C. Electrical conductivity relaxation (ECR) at this temperature confirmed an improved surface oxygen exchange coefficient of the LCO + LNO heterostructure predicted by the Bode data from EIS, alongside a reduction in activation energy by about 30%.

Deposition↗

DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗

DOE OSTI2025

Lifetime study of the ColdADC for the Deep Underground Neutrino Experiment

ColdADC is a custom ASIC digitizer implemented in 65 nm CMOS technology using specialized techniques for long-term reliability in cryogenic environments. ColdADC was developed for use in the DUNE Far Detector complex, which will consist of four liquid argon time projection chambers. Each contains 17 kilotons liquid argon as the target material in order to measure neutrino oscillations. Approximately 40,000 ColdADC ASICs will be installed for DUNE in the first two large detectors and will be operated at cryogenic temperatures during the experiment without replacement. The lifetime of the ColdADC is a critical parameter affecting the data quality and physics sensitivity of the experiment. A measurement of the lifetime of the ColdADC was carried out, and the results shown in this paper assure orders of magnitude longer lifetime of the ColdADC than the planned operation time of the detectors.

Front-end electronics for detector readout↗

DOE OSTI2025

Size-Resolved Chemical Composition of Particles Collected Using STAC at the Ground Site During the SAIL Campaign in Gunnison, Colorado

Aerosol particles were collected using a four-stage Size and Time-resolved Aerosol Collector (STAC) during the SAIL field campaign. Each stage of STAC separates particles into distinct aerodynamic size fractions with 50% cut-off diameters: Stage A: 2.27 µm Stage B: 0.615 µm Stage C: 0.421 µm Stage D: 0.119 µm Each stage provides both size- and time-resolved sampling, enabling investigation of particle composition across different atmospheric regimes. Only a subset of samples was selected for analysis based on prevailing meteorological conditions (e.g., temperature, humidity, and air-mass influence) to capture representative aerosol types under distinct weather patterns. Collected substrates were first examined under Scanning Electron Microscopy (SEM) to evaluate particle loading, morphology, and spatial distribution. Subsequently, Computer-Controlled Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (CCSEM/EDX) was performed to obtain size-resolved elemental composition of individual particles. A rule-based classification scheme was applied to categorize particles into major compositional groups (e.g., biological, carbonaceous, dust, sulfate, Na-rich, and mixed types). This dataset provides high-resolution morphological and chemical information on atmospheric particles collected during the SAIL campaign, offering insights into the influence of meteorology on aerosol composition and mixing state.

Size and Time-resolved Aerosol Collector↗

DOE OSTI2025

Origin of Stabilization of Ligand-Centered Mixed Valence Ruthenium Azopyridine Complexes: DFT Insights for Neuromorphic Applications

Redox-driven conductance changes are critical processes in molecular- and coordination-complex-based memristive thin films and devices that are envisioned for neuromorphic technologies, but fundamental mechanisms of conductance switching are not fully understood. Here, we explore charge disproportionation (CD) processes in [Ru II L 2 ](PF 6 ) 2 molecular systems that intrinsically involve interfragment charge transfer (IFCT). Using a combination of ab initio molecular dynamics simulation (AIMD), time-dependent density functional theory (TD-DFT), and density functional theory (DFT) calculations, we investigate the electron transfer mechanisms and the roles of temperature and cell volumetric expansion in facilitating the counterion movements and electronic transitions required for low-cost IFCT and charge redistribution. A detailed analysis of the density of states and TD-DFT calculations highlights that unpaired electrons play a crucial role in low-energy transitions, with the azo (N=N) groups of the ligand serving as the primary sites for electronic transport between molecular fragments, further stabilizing the asymmetric state. Localization of added electrons on azo ligands occurs with negligible change at the Ru centers, supported by atomic volume expansions up to +4.74 bohr 3 , and goes along with a progressive reduction of the HOMO−LUMO gap across redox states, suggesting enhanced conductivity. The TD-DFT analysis reveals a dominant IFCT excitation at 2082.76 nm in the doubly reduced (22) state, while a stabilization energy of 1.20 eV of the asymmetric (13) state relative to the symmetric (22) state is predicted by constrained DFT. Periodic DFT and AIMD simulations emulating a molecular film show that the stabilization of the asymmetric state, relative to a symmetric one, translates in net charge separation values (order of ∼0.33 e) that are strongly linked to increased counterion mobility (average counterion displacements exceeding 0.7 Å per atom during CD events) and the involvement of azo groups in electron redistribution. These findings, which align with previously reported experimental and computational data, provide key insights into the IFCT mechanisms and electronic transport facilitated by azo groups, with important implications for redox-driven memristive and neuromorphic technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Universal Magnetic Phases in Twisted Bilayer MoTe 2

Twisted bilayer MoTe 2 (tMoTe 2 ) has emerged as a robust platform for exploring correlated topological phases, yet the evolution of its magnetism and topology with twist angle remains an open question. Here, we systematically map the magnetic phase diagram of tMoTe 2 by using local optical spectroscopy and scanning nanoSQUID-on-tip magnetometry. We identify spontaneous ferromagnetism at filling factors ν = −1 and −3 across twist angles from 2.1° to 3.7°, revealing a universal, twist-angle-insensitive ferromagnetic phase. At 2.1°, we further observe robust ferromagnetism at ν = −5, absent at larger twist angles. Temperature-dependent measurements reveal a contrasting twist-angle dependence of the Curie temperatures between ν = −1 and −3, indicating a distinct interplay between the exchange interactions and bandwidth for the two Chern bands. Despite broken time-reversal symmetry, no topological gap is detected at ν = −3. Furthermore, our results establish a global framework for understanding and controlling magnetic order in tMoTe 2 .

Insulators↗

DOE OSTI2025

The GFDL‐CM4X Climate Model Hierarchy, Part I: Model Description and Thermal Properties

We present the GFDL‐CM4X (Geophysical Fluid Dynamics Laboratory Climate Model version 4X) coupled climate model hierarchy. The primary application for CM4X is to investigate ocean and sea ice physics as part of a realistic coupled Earth climate model. CM4X utilizes an updated MOM6 (Modular Ocean Model version 6) ocean physics package relative to CM4.0, and there are two members of the hierarchy: one that uses a horizontal grid spacing of 0.25° (referred to as CM4X‐p25) and the other that uses a grid 0.125° (CM4X‐p125). CM4X also refines its atmospheric grid from the nominally 100 km (cubed sphere C96) of CM4.0–50 km (C192). Finally, CM4X simplifies the land model to allow for a more focused study of the role of ocean changes to global mean climate. CM4X‐p125 reaches a global ocean area mean heat flux imbalance of -0.02 W m -2 within $\mathcal{O}$ (150) years in a pre‐industrial simulation, and retains that thermally equilibrated state over the subsequent centuries. This 1850 thermal equilibrium is characterized by roughly 400 ZJ less ocean heat than present‐day, which corresponds to estimates for anthropogenic ocean heat uptake between 1870 and present‐day. CM4X‐p25 approaches its thermal equilibrium only after more than 1000 years, at which time its ocean has roughly 1100 ZJ more heat than its early 21st century ocean initial state. Furthermore, the root‐mean‐square sea surface temperature bias for historical simulations is roughly 20% smaller in CM4X‐p125 relative to CM4X‐p25 (and CM4.0). We offer the mesoscale dominance hypothesis for why CM4X‐p125 shows such favorable thermal equilibration properties.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Spotlight: efficient automated global optimization in rietveld analysis of diffraction data

Performing reliable Rietveld analysis on tens or hundreds of powder diffraction datasets from parametric or time-resolved experiments often poses a bottleneck in extracting meaningful results from the data. While automated analysis of data has recently been demonstrated, high temperature annealing studies, during which phase transformations occur and lattice parameters may change due to repartitioning of elements, are prime examples where automation by a simple phase identification from a database of room temperature structures or automation by sequential refinements is likely to fail. To enable reliable, efficient, automated Rietveld analysis, we present a Python package named Spotlight , building on established Rietveld packages such as MAUD, GSAS , or GSAS-II , which extends the refinement of best fit parameters to a global optimization using an ensemble of optimizers leveraging hierarchical parallel execution on high-performance computing clusters. Spotlight further enables the efficient design of refinement plans through the iterative automated machine-learning of a surrogate for the refinement on which the global optimizations are performed until results from the surrogate converge to the response surface data. We demonstrate Spotlight with the analysis of uranium molybdenum and Ti–6Al–4V datasets, as well as in two open-source tutorials analyzing aluminium oxide and lead sulphate.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Multiphysics Running-In Simulations for Pebble-Bed Reactors with Griffin

Griffin, a Multiphysics Object-Oriented Simulation Environment (MOOSE)–based application targeting transient modeling of advanced reactors, has been used recently to model pebble-bed reactors (PBRs). The modeling effort has focused thus far on equilibrium core calculations. A new capability to simulate the running-in phase of PBR operation has been added to Griffin. This work demonstrates the new capability with a coupled multiphysics running-in simulation. Griffin computes power densities in the core at each time step of the running-in simulation and passes these to Pronghorn, which models fluid flow and heat transfer to calculate pebble surface temperatures. These surface temperatures are used along with the power densities in a heat conduction model to compute average fuel and moderator temperatures, which are passed back to Griffin and accounted for with temperature-dependent cross sections. This work also describes a novel methodology for determining appropriate pebble feed rates and control rod positioning during the running-in simulation. Furthermore, the RZ-geometry model used in this work requires minimal computational resources and can be used for optimization and uncertainty studies in future works.

Griffin↗

DOE OSTI2026

Requirements Description of DASSH-F

This report reviews the modeling and simulation capabilities of Argonne National Laboratory’s DASSH code that is used in present reactor analysis activities. These capabilities will be used to establish the set of verification tasks necessary to verify DASSH for use on commercial projects. A similar approach was taken for the PERSENT, REBUS and DIF3D software packages. The DASSH program is a thermal analysis code designed to rapidly allow a reactor design engineer to obtain flow rates requirements that satisfy peak temperature constraints in the domain. DASSH is a follow-on development to the SE2-ANL software and SUPERENERGY-2 software that it is based upon. DASSH was designed to account for both neutron and gamma heating and is inherently connected to the GAMSOR part of the ARC suite of fast reactor analysis software. SE2-ANL is a developed piece of software from the 1980s while DASSH is a modern implementation with notable improvements in geometry handling. The most important upgrade of DASSH relative to SE2-ANL is that it can analyze multiple time points in a single run where SE2-ANL can only treat a single time point. This allows the user to understand the impact of and search the flow distribution for the entire operational period of a reactor design considering pressure drop, peak coolant and fuel temperatures, and thermal striping. DASSH has three input paths that have to be verified. The first input path builds the geometry and power distribution based upon the DIF3D model but ignores the gamma heating aspects of the problem. The second input path also builds the geometry from the DIF3D model but it takes the neutron and gamma heating distributions from GAMSOR. The third input path is to take the geometry and power distribution directly from user input (i.e. not coupled to DIF3D or GAMSOR). DASSH also has many built in correlations for material properties along with a user defined specification of the fuel, structure, and coolant properties. There are correlations for flow split, mixing, pressure drop, and heat transfer coefficients (subchannel rather than a direct methodology). In total, verification of DASSH will require an extensive testing to cover all possible user features of the software.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗