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DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗

DOE OSTI2025

The effect of blue and infrared laser melting frequency on oxide morphology in 304L

Continuous Wave Laser Beam (LB) melting offers control over the localised heating and cooling of melt pools for welding, brazing, additive manufacturing and solidification. Research into laserliquid metal interactions has primarily focused on the heat and mass transport under large thermal gradients imposed by the localised melting conditions. Recent commercial availability of blue (450 nm) wavelength lasers has enabled the printing of material with poor IR (1064) nm attenuation like Cu. However, little research has been conducted into varying input laser frequency and scan rate at fixed absorption to understand the effects of laser light on surface oxide formation and the underlying liquid metal thermodynamic response. Here, this article conducts laser melting of 304L under Blue (450 nm) and Infrared (IR, 1064 nm) laser frequencies and examines their impact on laser oxide thickness, chemistry and coloration. We find that laser frequencies induce changes in the oxide layer thickness and chemistry that cannot be explained using conventional thermal absorption shifts and fluid dynamics. We suggest that light coupling may have thermodynamic implications for the chemical potential of the liquid metal, which may drive the observed phase behaviour. Identifying mechanisms for the tuning of the chemical potential via laser frequency control could enable the ability to control solidification microstructure and atomic ordering in laser welding and additive manufacturing processes.

304L↗

DOE OSTI2025

Design Overview of a High-Pressure Helium Flow Visualization Apparatus for Blanket Cooling Studies

Cooling of the fusion blanket first wall remains a significant challenge given the adverse conditions of heat and particle flux encountered near the plasma. Helium emerges as an attractive cooling candidate because of its chemical and neutronic inertness and separability from hydrogenic species (e.g. tritium). Because of the low thermal mass of helium, optimization of these coolant channels is warranted to provide high heat transfer performance at low pumping costs. Increasingly, computational fluid dynamics (CFD) simulations are employed to model and optimize these flow channels, and accompanying experimental data are needed to validate the predictions of these models. To provide the aforementioned experimental data, a high-pressure helium flow visualization upgrade has been designed for the Helium Flow Loop Experiment facility. This apparatus was built to American Society of Mechanical Engineers boiler and pressure vessel standards to withstand operating pressure of 4 MPa and mated to high-pressure glass windows. Seedless flow visualization is performed via high-speed background oriented schlieren (BOS), with image correlation used for time-resolved two-dimensional velocimetry at frequencies in excess of 60 kHz. Rectangular flow channel test articles are additively manufactured via laser powder bed fusion and installed into this visualization apparatus, with one-sided heating supplied by resistive heaters. In conclusion, the chosen test geometries were informed by prior CFD simulations, and the helium flow structures observed via BOS (detachment, recirculation, etc.) will be used for the validation of these accompanying models, in support of the design and optimization of blanket cooling channel configurations.

Helium flow↗

DOE OSTI2025

Design and Analysis of an Integrated Additively Manufactured Test Article for Plasma-Facing Components

Current plasma-facing components (PFCs) used in helium-cooled divertor modules are complex structures with tungsten tile, steel sleeve components, and cartridges, all assembled in a helium-cooled multiple jet (HEMJ) structure. The goal of this project is to simplify the complex PFC design using additive manufacturing techniques to create a single integrated tungsten test article. Apart from the flexibility this opens up in exploring a wide array of geometries for the article, having a single integrated article significantly reduces the number of joints and parts in the article, thus reducing chances of leaks. A process called electron beam melting has shown to produce very high-density samples and unique geometries, enabling HEMJ or similar designs. To validate and optimize this novel design, the model underwent a series of computational fluid dynamics and finite element analysis simulations to replicate steady-state heat flux in the divertors. The simulations presented in this study consider a steady-state base heat flux of 5 MW/m 2 , with water serving as the coolant. Future research will explore the use of helium as a coolant, simulate edge-localized-mode conditions, and include experimental validation. Since 3D-printed tungsten is anisotropic, the build direction versus build plane of the article are taken into consideration for the test article strength. Because of the high operating temperatures and low ductility of tungsten, thermal creep and brittle fracture are important failure mechanisms to consider. In conclusion, the cap is evaluated with various flow velocities and nozzle diameters, and an optimal design choice is made for which this cap will survive the divertor conditions with a conservative safety margin.

Divertor↗

DOE OSTI2026

Simulation of Channel Flow with Square Ribs for Blanket First-Wall Cooling: Geometry-Specific Tuning of k-ω Model Using Adjoint Method

Cooling of the plasma-facing first wall is challenging in the design of blanket components because of the high heat flux (on the order of 𝑀𝑊/𝑚2) from the plasma, especially when a low thermal mass medium like helium is chosen as the coolant. Therefore, heat transfer enhancement in which the convective heat transfer rate is augmented by the addition of turbulence-promoting structures becomes a key initiative for providing sufficient cooling capability with helium. Previously, computational fluid dynamics simulations had been performed on pipe flows with different transverse and longitudinal ribbed geometries at Oak Ridge National Laboratory to compare the enhancement performance among different ribbed geometries. Rib shape morphing had been conducted to obtain an optimized rib profile. In the work presented here, the adjoint method is adopted in the ANSYS Fluent solver for turbulence model augmentation, and the Generalized k-ω (GEKO) turbulence model is employed because of its ability of tuning the turbulence model. The Nusselt number and pressure drop obtained from the channel flow with bottom ribbed wall experiments are used as the targets. Sensitivity analysis provides information as guidance to improve the turbulence model accuracy. The augmented GEKO model is tuned for the studied ribbed channel geometry and flow conditions, providing improved predictive accuracy within this context. Extension to other configurations offers potential but may require additional tuning and validation.

Xu, Tracy [ORNL] (ORCID:0009000193700887)↗

DOE OSTI2026

Impact of high-fidelity temperature feedback modeling on fusion blanket neutronics

Accurate modeling and simulation of neutron and gamma transport and interactions within fusion tokamaks represents a critical area of research for fusion energy development. This paper presents a method to couple computational fluid dynamics (CFD) results to high-fidelity neutronics analyses, to explore the fusion neutronics’ feedback to blanket temperature and density. This method is demonstrated on a sector model of a tokamak with immersion molten salt blanket, and the relative differences in tritium breeding ratio (TBR) with spatially-resolved temperature modeling are explored. Additionally, the influence of higher fidelity neutronics results on the subsequent CFD analysis is explored. Results show that global TBR changes $<$0.1% due to temperature spatial distribution modeling. However, local TBR in areas with high temperature variability can have up to ∼4% difference.

blanket fusion↗

DOE OSTI2026

Mesh-based multiphysics coupling acceleration for fusion neutronics through clustering for fusion blanket applications

Accurate modeling of particle transport within fusion blankets is essential for predicting performance metrics such as heat deposition and the tritium breeding ratio (TBR). However, high-fidelity coupling of thermal fluids from computational fluid dynamics (CFD) to neutronics simulations often incurs significant computational costs due to the complexity of surface intersection calculations in Monte Carlo codes. This paper presents an accelerated multiphysics coupling method for neutronics that utilizes hierarchical agglomerative clustering to map complex material property distributions to a neutronics model. Implemented within the fusion reactor design and assessment (FREDA) framework, the method leverages existing Python packages to automate the creation of clustered geometries for OpenMC. The approach is demonstrated on a sector model of an ARC-class tokamak with an immersion molten salt blanket, and an simple geometry with varying isotopic concentrations. Results show that the clustering method significantly reduces computational burden without compromising fidelity, providing a foundation for agile iteration of neutronics simulations involving multiple coupled material properties.

Bae, Jin Whan [ORNL] (ORCID:0000000326548907)↗

DOE OSTI2024

Improvements to the Blade Element Momentum Formulation of OpenFAST for Skewed Inflows

In this work, we modify the blade element momentum algorithm of OpenFAST to improve its predictions under large skewed inflow conditions. We use the well-known Glauert's skew correction and introduce continuous extension of the model for high-thrust conditions. We present the rationale behind Glauert's empirical model and discuss the different conventions possible for the axial induction factor. We verify the model against the higher-fidelity lifting-line vortex method and blade-resolved computational fluid dynamics, and we observe that the new implementation enhances the accuracy and reliability of OpenFAST's aerodynamic modeling capabilities in conditions involving large skew angles. For the parametric studies run using the different codes, we find that the power changes with the skew angle as cos 1.7 (θ skew ) and the thrust as cos 0.65 (θ skew ). An analysis of the azimuthal variation of the induced velocities in the rotor plane reveals that current redistribution models used in blade element momentum codes may need to be refined.

17 WIND ENERGY↗

DOE OSTI2024

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

Current wind resources within the United States (US) indicate a potential to profitably install nearly 1,400 gigawatts of distributed wind (DW) capacity. This amount is equivalent to over half of the United States’ current energy demand from electricity, making it enough to power millions of homes and businesses and replace countless fossil fuel-based generating plants. Despite the potential growth of DW in the US, deployments are presently hindered by a lack of confidence in resource estimation methods. One potential challenge is that smaller-scale turbines, with hub heights of 40 meters or less, are disproportionately impacted by obstacles such as buildings and vegetation. These obstacles may produce complex wake effects, best modeled with high-fidelity complex fluid dynamics (CFD) models that are too computationally expensive to use for routine siting and resource assessment. Thus, installers today make use of heuristics and simple equations to approximate the impact of obstacles while also leveraging long-term resource data from commercial or publicly available atmospheric models. This study evaluates these historical and commonly used methods alongside new lower-order obstacle models produced from CFD simulations and measurement-based bias correction. The preliminary results from this study show the importance of taking care in the choice and application of mesoscale atmospheric models and the significant value of bias correction using measurements from nearby meteorological towers. Detailed obstacle modeling provides only modest additional gains in performance and, in some cases, can add error, especially at sites where turbines have already been located to avoid obvious impact from upwind obstacles. These findings reinforce the importance of collecting in situ measurements and suggest that obstacle models may be better applied in practice to automated or computer-aided siting, rather than in economic wind resource assessments.

17 WIND ENERGY↗

DOE OSTI2025

Flow and thermal modelling of the argon volume in the DarkSide-20k TPC

The DarkSide-20k dark matter experiment, currently under construction at LNGS, features a dual-phase time projection chamber (TPC) with a ∼ 50 t argon target from an underground well. At this scale, it is crucial to optimise the argon flow pattern for efficient target purification and for fast distribution of internal gaseous calibration sources with lifetimes of the order of hours. To this end, we have performed computational fluid dynamics simulations and heat transfer calculations. The residence time distribution shows that the detector is well-mixed on time-scales of the turnover time (∼ 40 d). Notably, simulations show that despite a two-order-of-magnitude difference between the turnover time and the half-life of 83m Kr of 1.83 h, source atoms have the highest probability to reach the centre of the TPC 13 min after their injection, allowing for a homogeneous distribution before undergoing radioactive decay. We further analyse the thermal aspects of dual-phase operation and define the requirements for the formation of a stable gas pocket on top of the liquid. We find a best-estimate value for the heat transfer rate at the liquid-gas interface of 62 W with an upper limit of 144 W and a minimum gas pocket inlet temperature of 89 K to avoid condensation on the acrylic anode. This study also informs the placement of liquid inlets and outlets in the TPC. The presented techniques are widely applicable to other large-scale, noble-liquid detectors.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Spatiotemporal predictions of toxic urban plumes using deep learning

Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets. In emergency response situations, alternative methods are needed that can run quickly and adequately capture important spatiotemporal features. Here, we present a novel deep learning model called ST-GasNet inspired by the mathematical equations that govern the behavior of plumes as they disperse through the atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set of temporal sequences of ground-level toxic urban plumes generated by a high-resolution large eddy simulation model. On independent sequences, ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the early-time behavior as an input, even when a building splits a large plume into smaller plumes. By incorporating large-scale wind boundary condition information, ST-GasNet achieves a prediction accuracy of at least 90% on test data for the entire prediction period.

Civil and Environmental Engineering↗

DOE OSTI2026

Emergence of vorticity and viscous stress in finite-scale quantum hydrodynamics

The Madelung equations offer a hydrodynamic description of quantum systems, from single particles to quantum fluids. In this formulation, the probability density is mapped onto the fluid density and the phase is treated as a scalar potential generating the velocity field. As examples of potential flows, quantum fluids described in this way are inherently irrotational, but quantum vortices may arise at discrete points where the phase is undefined. In this paper, starting from this irrotational description of a quantum fluid, a coarse-graining procedure is applied to arrive at a macroscopic description of the quantum fluid in terms of a hierarchy of moments in which the role of velocity is played by a Favre average of the microscopic velocity field. This hierarchy is truncated using an explicit closure derived from an expansion in a finite length scale. The resulting coarse-grained fields are shown to allow for finite vorticity at any point in the fluid. Additionally, it is shown that this vorticity obeys a similar equation to the vorticity equation in classical hydrodynamics and includes a vortex-stretching term. The particular closure employed here also gives rise to a novel stress term in the fluid equations, which in the appropriate limit appears analogous to an artificial viscous stress from computational fluid dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Ray-tracing image simulations of transparent objects with complex shape and inhomogeneous refractive index

Optical images of transparent three-dimensional objects can be different from a replica of the object’s cross section in the image plane, due to refraction at the surface or in the body of the object. Simulations of the object’s image are thus needed for the visualization and validation of physical models. We report ray tracing image simulations that achieved high physical fidelity, reproducing optical behaviors and image features not rendered in previous studies. We replicated brightfield microscopy images of drops with complex shapes, and images of pressure and shock waves traveling inside them. For high physical fidelity, the simulations must replicate the spatial and angular distribution of illumination rays, and both the experiment and the simulation must be designed for accurate optical modeling. The simulations are highly sensitive to the properties of the drops and can be used to diagnose and refine fluid dynamics models. The simulated images can also be optimized to extract multiple 3D properties from experimental images. Compared to specialized single-shot 3D imaging methods, this approach has the advantage that it preserves the experimental simplicity, the high resolution, and the visual interpretability characteristic to basic optical imaging. The techniques introduced here are directly applicable to optical microscopy, so they can be used in other fields, such as microfluidics and biology, to expand the type and the accuracy of three-dimensional information that can be extracted from basic optical images.

Cavitation↗

DOE OSTI2025

Optimal observables for the chiral magnetic effect from machine learning

The detection of the chiral magnetic effect (CME) in relativistic heavy-ion collisions remains challenging due to substantial background contributions that obscure the expected signal. In this Letter, we present a novel machine learning approach for constructing optimized observables that significantly enhance CME detection capabilities. By parametrizing generic observables constructed from flow harmonics and optimizing them to maximize the signal-to-background ratio, we systematically develop CME-sensitive measures that outperform conventional methods. Using simulated data from the anomalous viscous fluid dynamics framework, our machine learning observables demonstrate up to 90% higher sensitivity to CME signals compared to traditional 𝛾 and 𝛿 correlators, while maintaining minimal background contamination. The constructed observables provide physical insight into optimal CME detection strategies and offer a promising path forward for experimental searches of the CME at the BNL Relativistic Heavy Ion Collider and the CERN Large Hadron Collider.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Hard jet substructure in a multistage approach

We present predictions and postdictions for a wide variety of hard jet-substructure observables using a multistage model within the framework. The details of the multistage model and the various parameter choices are described in []. A novel feature of this model is the presence of two stages of jet modification: a high-virtuality phase [modeled using the modular all twist transverse-scattering elastic-drag and radiation model ()], where modified coherence effects diminish medium-induced radiation, and a lower virtuality phase [modeled using the linear Boltzmann transport model ()], where parton splits are fully resolved by the medium as they endure multiple scattering induced energy loss. Energy-loss calculations are carried out on event-by-event viscous fluid dynamic backgrounds constrained by experimental data. The uniform and consistent descriptions of multiple experimental observables demonstrate the essential role of modified coherence effects and the multistage modeling of jet evolution. Using the best choice of parameters from [], and with no further tuning, we present calculations for the medium modified jet fragmentation function, the groomed jet momentum fraction z g and angular separation r g distributions, as well as the nuclear modification factor of groomed jets. These calculations provide accurate descriptions of published data from experiments at the Large Hadron Collider. Furthermore, we provide predictions from the multistage model for future measurements at the BNL Relativistic Heavy Ion Collider. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Effect of causality constraints on Bayesian analyses of heavy-ion collisions

There have long been questions about the limits to the validity of relativistic fluid dynamics and whether it is being used outside its regime of validity in modern simulations of relativistic heavy-ion collisions. An important new tool for answering this question is a causality analysis in the nonlinear regime—if the solutions of the evolution equations do not respect relativistic causality, then they are not a faithful representation of the underlying relativistic theory (in this case, quantum chromodynamics). Using this nonlinear criterion, it has recently been shown that hydrodynamics is indeed being used outside its regime of validity in simulations, at least sometimes. Here we explore the phenomenological implications, particularly the quantitative effects of demanding limits on acausality in modern Bayesian parameter estimation. We find that, while typically only a small fraction of the system's energy is initially in an acausal regime, placing strict limits on the allowed energy fraction significantly changes the preferred properties of the initial condition, which in turn alters the extracted medium properties such as bulk viscosity, where large values are no longer favored. Furthermore, these findings highlight the importance of developing better theoretical descriptions of the early-time, out-of-equilibrium dynamics of relativistic heavy-ion collisions.

Bayesian methods↗

DOE OSTI2025

QCD jets in a hot and dense medium: A study of shower formation time and collision kernels

With the use of , a model which considers evolving QCD jets against a fluid dynamical background, it is shown that the introduction of formation time to the parton shower after the initial hard scattering is essential for a simultaneous description of charged hadron and jet R A A . This inclusion also improves jet shape ratios at small angle and jet fragmentation function ratios of leading charged hadrons. The framework is then aimed at a study of the leading-order, next-to-leading-order, and nonperturbative collision kernels. Sizable differences in the modification of jet substructure observables, i.e., jet shape and fragmentation functions, are observed. Such differences are caused by the difference in the radiation rates of relatively soft gluons that survive in the evolution in medium. Published by the American Physical Society 2025

Modarresi-Yazdi, Rouzbeh (ORCID:0000000290377447)↗

DOE OSTI2025

Dielectron production in central Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02TeV

The first measurement of the 𝑒 + ⁢𝑒 − pair production at midrapidity and low invariant mass in central Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV at the Large Hadron Collider is presented. The yield of 𝑒 + ⁢𝑒 − pairs is compared with a cocktail of expected hadronic decay contributions in the invariant mass (𝑚𝑒⁢𝑒) and pair transverse momentum (𝑝 T,𝑒⁢𝑒 ) ranges 𝑚 𝑒⁢𝑒 < 3.5 GeV/𝑐 2 and 𝑝 T,𝑒⁢𝑒 < 8 GeV/𝑐. For 0.18 < 𝑚 𝑒⁢𝑒 < 0.5 GeV/𝑐 2 the ratio of data to the cocktail of hadronic contributions amounts to 1.40 ± 0.11⁢(stat.) ± 0.23 ⁢(syst.) ± 0.16⁢ (cocktail) and 1.42 ± 0.11⁢ (stat.) ± 0.23 (syst.)$^{+0.24}_{−0.29}$ ⁢(cocktail), including or not including medium effects in the estimation of the heavy-flavor background, respectively. It is consistent with predictions from two different models for an additional contribution of thermal 𝑒+⁢𝑒− pairs from the hadronic and partonic phases. In the intermediate-mass range (1.2 < 𝑚 𝑒⁢𝑒 < 2.6 GeV/𝑐 2 ), the pair transverse impact parameter of the 𝑒 + ⁢𝑒 − pairs (DCA𝑒⁢𝑒, where “DCA” denotes “distance of closest approach”) is used for the first time in Pb-Pb collisions to separate displaced dielectrons from heavy-flavor hadron decays from a possible (thermal) contribution produced at the interaction point. The data are consistent with a suppression of 𝑒 + ⁢𝑒 − pairs from $𝑐\bar{𝑐}$ and an additional prompt component. Finally, the first direct-photon measurement in the 10% most central Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV is reported via the study of virtual direct photons in the transverse momentum range 1 < 𝑝 T < 5 GeV/𝑐. A model including prompt photons, as well as photons from the preequilibrium and fluid-dynamic phases, can reproduce the result, while being at the upper edge of the data uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗