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DOE OSTI2023

Direct Numerical Simulation of Transitional and Turbulent Flows Over Multi-Scale Surface Roughness—Part I: Methodology and Challenges

Abstract High-fidelity simulation of transitional and turbulent flows over multi-scale surface roughness presents several challenges. For instance, the complex and irregular geometrical nature of surface roughness makes it impractical to employ conforming structured grids, commonly adopted in large-scale numerical simulations due to their high computational efficiency. One possible solution to overcome this problem is offered by immersed boundary methods, which allow wall boundary conditions to be enforced on grids that do not conform to the geometry of the solid boundary. To this end, a three-dimensional, second-order accurate boundary data immersion method (BDIM) is adopted. A novel mapping algorithm that can be applied to general three-dimensional surfaces is presented, together with a newly developed data-capturing methodology to extract and analyze on-surface flow quantities of interest. A rigorous procedure to compute gradient quantities such as the wall shear stress and the heat flux on complex non-conforming geometries is also introduced. The new framework is validated by performing a direct numerical simulation (DNS) of fully developed turbulent channel flow over sinusoidal egg-carton roughness in a minimal-span domain. For this canonical case, the averaged streamwise velocity profiles are compared against results from the literature obtained with a body-fitted grid. General guidelines on the BDIM resolution requirements for multi-scale roughness simulation are given. Momentum and energy balance methods are used to validate the calculation of the overall skin friction and heat transfer at the wall. The BDIM is then employed to investigate the effect of irregular homogeneous surface roughness on the performance of an LS-89 high-pressure turbine blade at engine-relevant conditions using DNS. This is the first application of the BDIM to realize multi-scale roughness for transitional flow in transonic conditions in the context of high-pressure turbines. The methodology adopted to generate the desired roughness distribution and to apply it to the reference blade geometry is introduced. The results are compared to the case of an equivalent smooth blade.

Engineering↗

DOE OSTI2022

Regulation of the central safety factor and normalized beta under low NBI torque in DIII-D

An algorithm has been designed to simultaneously control the central safety factor (q 0 ) and normalized beta (β N ) while ensuring near-zero torque from the neutral beam injection in DIII-D. Feedback control of q 0 and β N in tokamaks can be beneficial due to the close relationship that these variables have with plasma performance and magneto-hydrodynamic stability. In addition, low neutral-beam-torque conditions are of special interest in present devices because future burning-plasma tokamaks such as ITER will most likely operate at very low plasma rotation. The control synthesis of the algorithm presented in this work is based on a linearized, one-dimensional (1D) model of the current-profile dynamics coupled with a zero-dimensional (0D) plasma-energy balance. The actuators considered are neutral beam injection and electron-cyclotron heating and current drive, and discrete logic determines the neutral-beam injection powers that deliver near-zero torque. Here, the algorithm has been tested in nonlinear, 1D simulations using COTSIM (Control-Oriented Transport SIMulator) and in DIII-D experiments, demonstrating satisfactory performance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Data-driven Quasi-static Surrogate Model for Nuclear-powered Integrated Energy Systems

The integration of nuclear power into energy systems presents a promising avenue to address the growing global energy demands while minimizing greenhouse gas emissions. In this paper, we introduce a data-driven quasi-static surrogate model for nuclear-powered Integrated Energy Systems (IES) that comprises various components, including a small modular reactor (SMR), energy manifold (EM), balance of plant (BOP), high-temperature steam electrolysis (HTSE), and district heating (DH) system. Traditional physics-based models for these components often entail significant computational overhead and time consumption, necessitating the development of efficient surrogate models. The development of a complete surrogate model for the IES involves the creation of individual surrogate models for each component, leveraging machine learning techniques and simulated data. These isolated surrogate models are subsequently integrated, enabling a holistic view of the IES and reducing the computational burden associated with detailed physics-based simulations. This paper outlines the development process, validation, and the performance evaluation of the surrogate models. The findings shed light on the accuracy and applicability of the surrogate models in practical scenarios, demonstrating their potential to expedite the analysis of nuclear-powered IES and inform future research and development efforts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Data analytic methods are being developed to address the problem of how to assign a sensor set in a nuclear facility such that a requisite level of process monitoring capability is realized and that the sensor set is sufficiently rich to determine the status of the individual sensors with respect to need for calibration. There is an awareness in the nuclear industry that data analytics combined with rich sensor sets represent a means to improve operations and reduce costs. In the industry the calibration problem has been previously approached as an empirical data-driven problem with several methods having been developed. However, the experience of the utilities over the past ten years with these methods indicates that the absence of physics-based information renders the data-driven approach less reliable. Complicating factors such as the inherent variability of operation (both equipment alignment and operating condition) can confound a pure data-driven approach while there are no rigorous guidelines for determining what constitutes an adequate sensor set. The solution under development to overcome these shortcomings supplements the data analytic method with process information in a so-called process-constrained data-analytic approach. Simple balance equations are written for generic components (e.g., mechanical pump, valve, and heat exchanger). These do not require a priori knowledge of process parameters, such as heat transfer coefficients or friction factors. All that is needed on the part of the utility user is to identify the components and how they are connected. This report describes the development of a parallel computing capability for determining the optimal sensor set. The optimal sensor set problem suffers from the curse of dimensionality. Computation time increases exponentially as the size of the system grows. To overcome this difficulty a pre-conditioner algorithm is developed to find an approximate solution close the actual solution. This serves as a seed for the full-blown algorithm and acts to constrain the space that must searched. The optimization algorithms are described and the implementation on a parallel computing platform is described. The application of the method to a use case we are solving in collaboration with our utility partner served to illustrate how the default sensor set in a nuclear plant may not provide sufficient coverage to infer sensor calibration status.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Modeling Molten Salt Spreading and Heat Transfer using MELTSPREAD – An Uncertainty Analysis

This report summarizes spreading and heat transfer results for molten salt pouring onto a flat stainless steel substrate that were calculated using MELTSPREAD to gain insight into bulk salt behavior during a molten salt spill accident. MELTSPREAD was developed at Argonne to model the one-dimensional flow and freezing behavior of molten corium and is being applied to model molten salts for the first time as part of this work. An uncertainty analysis of thermophysical properties and spill conditions was performed using MELTSPREAD to determine which properties and conditions have the greatest impact on model outcome. The model was run with and without the inclusion of decay heat. Eutectic FLiNaK was used as the salt composition because it is well-characterized and appropriate for model development. Small salt volumes were used for the initial model runs described in this report so that the model results can be compared to the results from ongoing experiments on molten salt spreading and heat transfer currently being conducted at Argonne at a benchtop scale. The spreading of a small volume of FLiNaK (50 mL) was found to be limited by the balance between the molten salt surface tension and gravity and not by freezing for the pour conditions and assumed mechanisms of heat transfer in the model. Heat transfer from the salt was highly inefficient to due to its low thermal conductivity, high heat capacity, and high heat of fusion and varying these properties had no effect on the spreading behavior. Changing the viscosity and density of the salt slightly affected the spreading behavior. Setting the salt surface tension to approximately zero to simulate the salt wetting the substrate had the greatest effect on the spreading behavior and this increased both the spreading velocity and overall spreading area. Including decay heat in the model had a significant effect on the long-term cooling behavior of the salt but had no effect on the overall spreading behavior. Future effort should involve modeling a larger volume of molten salt to represent a reactor case spill scenario because the spreading behavior of large volumes of salt may differ from the small volumes that were the focus of this document.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Increasing melt efficiency and secondary alloy usage in aluminum die casting

In this project, the focus was to develop thermodynamic and kinetic models for the aluminum melting process involving secondary alloys, refractory materials and flux materials. The major research outcome of this study is 1) the refractory composition of CaO (8-12%), microsilica (1-3%), BaSO 4 and/or BaO (no more than 5%), B 2 O 3 (5%) and Al 2 O 3 (balance) effectively reduced the reactions with liquid Al and alloying/impurity elements and heat transfer during melting and holding process. 2) a flux composition of NaCl-KCl-(1-10%)KAlF 4 exhibited an effective covering effect and prevented the dross formation during melting of Al process. 3) A high Fe (0.44%) content recycled Al alloy with compatible mechanical properties was developed based on the impurities control methods, which provides great potential to replace the commercial structural alloys from primary Al and increase the usage of secondary alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Thermal Integration of Advanced Nuclear Reactors with a Reference Refinery, Methanol Synthesis, and a Wood Pulp Plant (Rev.1)

The present report is intended to provide process flow diagrams (PFDs) and energy and mass balance data sheets for a U.S. industrial sector subset with which nuclear heat and power could be integrated—a subset that includes the oil refining, methanol and pulp and paper industries. Coupling options for integrating nuclear energy into these industries are quantitatively outlined for reference systems, and future work will extend this analysis in greater detail. Opportunities for integrating small modular nuclear reactors (SMNRs) were investigated for each of the industrial process configurations. Aspen HYSYS and Cycle-Tempo models for a high-temperature gas-cooled reactor were developed to evaluate the proposed integration. This introductory evaluation provides a general description and assessment of the operating principles, reactor coolant core outlet temperature, and reactor size to be integrated with industry. The industrial processes of oil refining and the production of methanol, pulp and paper were simulated by using Aspen HYSYS, Aspen Plus, and the PRELIM (Petroleum Refinery Life Cycle Inventory Model) tool to develop process details. Cycle-Tempo models then extend the process modeling results to obtain net energy demands (e.g., heat, steam, and electricity) when accounting for process steam and waste heat recovery. This information is intended to foster the analysis of integrating an SMNR to decarbonize industrial facilities. The SMNR would provide reliable, competitive, and sustainable clean energy while reducing carbon emissions and other environmental impacts, such as water withdrawals, consumption, and contamination. The refining industry, exhibited in Figure ES1, is a leading consumer of fossil -fuel-based heat, power, and hydrogen in the U.S. industrial sector, generating over 164 million metric tons (MMT) of CO 2 emissions in 2023. The overall mass and energy pertaining to a generalized complex refinery in the United States is reflected in Figure ES1, along with energy metrics regarding integration with a nuclear power plant (NPP). Data sheets were developed to indicate the energy requirements for the overall refinery and each refinery process. The data sheet for the overall refinery is shown in Table ES2.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Record Efficiency InGaAs Thermophotovoltaic Cells For Energy Storage Applications

Utility-scale energy storage is a significant technical challenge and a key barrier to widespread adoption of renewable energy. An efficient, modular, and scalable solution can be achieved by storing energy in the form of high-temperature heat and then extracting energy, on demand, using thermophotovoltaic (TPV) cells to convert the heat back to electricity. The ideal thermal emitter temperature is determined by balancing the desire for higher power densities, high-temperature compliant materials, and efficient, cost-effective thermophotovoltaic cells, among other considerations. 0.74-eV InGaAs lattice-matched to InP is a good choice for a cell absorber, and here we describe our development of InGaAs TPV cells with efficiencies of 38.8% from an ~1850 degrees celsius source temperature. This is the highest efficiency yet measured and reported for a single-junction TPV device. This efficiency was achieved by carefully quantifying and then reducing the parasitic absorption in the semiconductor layers, and by significantly reducing the series resistance to minimize the power loss.

ENERGY STORAGE,ENGINEERING,SOLAR ENERGY↗

DOE OSTI2024

Data-driven Quasi-static Surrogate Models for Nuclear-powered Integrated Energy Systems

The integration of nuclear power into energy systems presents a promising avenue to address the growing global energy demands while minimizing greenhouse gas emissions. In this paper, we introduce a data-driven quasi-static surrogate model for nuclear-powered Integrated Energy Systems (IES) that comprises various components, including a small modular reactor (SMR), steam manifold, balance of plant (BOP), high-temperature steam electrolysis (HTSE), and district heating (DH) system. Traditional physics-based models for these components often entail significant computational resource and time consumption, necessitating the development of efficient surrogate models. The development of a complete surrogate model for the IES involves the creation of individual surrogate models for each component, leveraging machine learning techniques and simulated data. These isolated surrogate models are subsequently integrated, enabling a holistic view of the IES and reducing the computational burden associated with detailed physics-based simulations. This paper outlines the development process, validation, and the performance evaluation of the surrogate models. The exceptional performance, with low root-mean-squared errors and R-squared scores of at least 99.8% across all individual surrogate models, underscores their accuracy and practical applicability. These results demonstrate the potential of these models to expedite the analysis of nuclear-powered IES, offering insights that can shape future research and development efforts.

08 HYDROGEN↗

DOE OSTI2020

Enlarged Nonclosure of Surface Energy Balance With Increasing Atmospheric Instabilities Linked to Changes in Coherent Structures

The nonclosure of surface energy balance remains an outstanding problem in eddy covariance (EC) measurements of land-surface fluxes of heat, water vapor, and CO 2 . Our data collected from an EC tower over a semiarid sagebrush ecosystem indicate that under unstable atmospheric conditions, the nonclosure becomes increased with increasing instability, consistent with many other studies. It is demonstrated that the increased nonclosure is not caused by the inadequate sampling of large-scale turbulent motions using a 30-min averaging interval, though the scales of turbulent motions dominating sensible and latent heat fluxes become enlarged with increasing instability. Quadrant analysis is then used to reveal that the flux contributions from ejections remain nearly constant with increasing instability, whereas the flux contributions from sweeps are reduced and their time fractions increase. Our results imply that the increased nonclosure of surface energy balance is associated with changes in turbulent structures including their dominant time scales and flux contributions of ejections and sweeps as the atmospheric instability increases, which require further studies using vertically distributed observations and/or large eddy simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage

The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.

Li, He↗

DOE OSTI2022

Understanding the E balance for water management in hydroxide exchange membrane fuel cells

As a key challenge for hydroxide exchange membrane fuel cells (HEMFCs), understanding water management deeply is critical for performance and durability improvement. Here, in this work, we show that the balance between water generation and phase-change-induced (PCI) flow is the main factor controlling cell voltage over a wide range of conditions where the cells are vulnerable to severe flooding. The regulation of flooding by heat generation can be reflected by a stable voltage with increase of current density in the polarization curve. This PCI balance voltage (E balance ), which determines the shape of polarization curve, is modeled as a function of cell operating conditions and gas diffusion layer (GDL) properties. Temperature, pressure, and thermal conductivity of GDL were found important for tuning the level of E balance , both in HEMFCs and proton exchange membrane fuel cells. The understanding of E balance will help to generate strategies for resolving flooding in HEMFCs, and it offers a new perspective for polarization curve interpretation.

25 ENERGY STORAGE↗

DOE OSTI2026

Primary Heat Transport System Design Considerations for Xcimer Energy’s Athena Fusion Pilot Plant

Fusion energy promises a reliable, carbon-free source of power; however, significant challenges remain before it can be deployed as an economical energy source. In addition to achieving fusion conditions, power plants must operate under extreme temperatures, radiation, and mechanical loads while maintaining high efficiency and availability. These requirements place strong demands on engineering design and plant operation. This work focuses on the engineering challenges associated with balance of plant analysis for inertial fusion energy systems. In particular, this paper examines the design considerations for primary heat transfer systems in fusion pilot plants employing molten fluoride salt coolants, with particular emphasis on system layout optimization and the balance between competing design objectives using the Xcimer Energy Athena inertial pilot plant design as a case study. Through systematic analysis of candidate system configurations and parametric sensitivity studies, we identify key engineering trade-offs governing salt inventory, pumping power requirements, and operational flexibility. The analysis employs system-level modeling tools to explore the design space and establish relationships between geometric parameters and system performance metrics.

Greenwood, Scott [ORNL] (ORCID:0000000333480736)↗

DOE OSTI2026

Eddy Covariance Theory: A Review

Eddy covariance (EC), the gold standard for measuring ecosystem scale gas and heat exchanges, has transformed our understanding of the breathing of the biosphere, and thus global change biology. Despite numerous methodological improvements and insights gained from the technique, the community faces persistent challenges that have been present since the first EC measurements. Here, we review the theoretical developments underpinning EC. We present theoretical developments in four important areas that have relevance to EC measurements of the net ecosystem exchanges (NEE) of gases and heat from a single tower: (i) measuring the total vertical flux density, (ii) flux attenuation, (iii) coordinate rotations, and (iv) energy balance closure. Persistent problems with EC measurements, such as the inability to close the energy budget, led us to identify two priorities for revisiting the theory underlying: (i) sensible heat flux calculations, and (ii) constraining the mean vertical wind velocity. We present a framework for improved calculation of sensible heat flux derived from first principles of fluid mechanics and thermodynamics that considers coupled heat and mass transfer so that conservation of both is obeyed. These refinements are motivated by the need for unbiased measurements of energy and mass transfer between the land surface and atmosphere for ecosystem research and to validate satellite observations and land surface models.

ecosystem fluxes↗

DOE OSTI2023

Load balancing for multi-beam additive manufacturing systems

As powder bed fusion (PBF) additive manufacturing (AM) becomes a more mature field, system configurations are gradually moving away from the classic single heat source, layer-by-layer system configurations towards unconventional system configurations that offer higher throughput. Higher throughput systems allow PBF systems to be considered for a larger variety of industrial applications. However, the inclusion of multiple heat sources, or beams, also increases the complexity of the control schemes needed. For multi-beam systems with overlapping fields of view, the distribution of workload, or load balancing, across these beams directly affects the total print time for a build. Additionally, the probability of any beam failing in a multi-beam system increases with the number of beams. While manual methods of load balancing and dealing with beam failures are reasonable for current generation multi-beam systems, as system configurations become more complex, manual methods will become prohibitively inefficient. Here, this paper introduces two different ways to load balance multi-beam systems of various configuration types, regardless of their complexity, which are highly performant. A consequence of this performance is the enablement of on-the-fly load balancing in the event a beam fails, thus improving system robustness.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Observations of Subduction, Downward Heat Flux and Dense Filament Collapse in the Northern Gulf of Mexico

Submesoscale processes are important contributors to the global heat budget and generally support upward heat transport through restratification. However, in salinity‐stratified regions, such as the northern Gulf of Mexico with its influx of freshwater from the Mississippi‐Atchafalaya river system, temperature can act like a passive tracer and submesoscale processes can contribute to downward heat transport. Oceanic heat content is a factor in many environmental risks the region faces, for example, hurricane intensification, and marine heatwaves. During the 2022 field campaign of the Submesoscales Under Near‐Resonant Inertial Shear Experiment, a sampling plan was developed to study such submesoscale processes in high resolution. Over 31 hr, four assets (two research ships and two remotely controlled boats) drove in parallel across a dense filament, capturing its evolution in time and space. The observations show that surface waters, warmed by daytime solar radiation, were subducted and that the associated overturning circulation transported heat below the surface layer where it was later irreversibly mixed away. The estimated downward heat flux was as strong as the concurrent net air‐sea heat flux into the ocean. The filament was then observed to rapidly collapse which we attribute to boundary layer turbulence and the breakdown of geostrophic balance. The collapsing fronts display behaviors indicative of gravity currents. These observations highlight how in salinity‐stratified regions, frontal dynamics can be associated with downward heat flux and how the submesoscale can play an important role in the oceanic heat budget.

58 GEOSCIENCES↗

DOE OSTI2023

Pedestal particle balance studies in JET-ILW H-mode plasmas

JET-ILW type I ELMy H-modes at 2.5 MA/2.8 T with constant NBI heating (23 MW) and gas fuelling rate were performed, utilising edge localised mode (ELM) pacing by vertical kicks and plasma shaping (triangularity, δ) as tools to disentangle the effects of ELMs, inter-ELM transport and edge stability on the pedestal particle balance. In agreement with previous studies, the pedestal confinement improves with increasing δ, mostly due to a significant increase in pedestal density while the ELM frequency ($f_{\mathrm{ELM}}$) is decreased. Improved pedestal confinement with increasing δ was observed even when the pedestal MHD stability was degraded artificially by vertical kicks, implying that increased triangularity may favourably affect the inter-ELM pedestal recovery. The workflow developed to quantify the pedestal particle balance uses high time-resolution profile reflectometry to characterise the inter-ELM evolution of the plasma particle content ($\mathrm{d} N/\mathrm{d}t$), the NEO drift-kinetic solver to evaluate the neoclassical fluxes and interpretative EDGE2D-EIRENE simulations to estimate the edge particle source. The edge particle source is then constrained by deuterium Balmer-α line intensity measurements in the main chamber, which are, however, strongly affected by reflections from the metal walls. The reflections are accounted for by the CHERAB code taking the divertor emission (the brightest light source in the torus) distribution from imaging spectroscopy measurements as input. Our analysis shows that in the second half of the ELM cycle, the volume-integrated particle source is larger than $\mathrm{d} N/\mathrm{d}t$, indicating that transport plays a key role in the inter-ELM pedestal recovery.

fuelling↗

DOE OSTI2021

Design and performance evaluation of a dual-circuit thermal energy storage module for air conditioners

We present experimental results and a validated numerical model of a dual-circuit phase-change thermal energy storage module for air conditioners. The module incorporates a phase-change material encapsulated in compressed expanded natural graphite foam. We used n-tetradecane as the PCM with a transition temperature (~4.5 °C) suitable for air-conditioning applications. Heat exchange to and from the module is accomplished through two fluid loops operating as a heat source and sink embedded inside multiple slabs of the composite material. This dual-circuit design enables easier integration with air-conditioning equipment and provides enhanced flexibility in system operation as compared to the state-of-the-art thermal storage systems. When integrated with an air-conditioner, this design will enable peak-load shaving and enhances operational efficiency. The thermal storage device was designed for a nominal storage capacity of ~ 3.5 kWh. We evaluated the heat transfer and energy storage performance of this device using standalone heat transfer experiments to estimate key thermal resistances and identify design improvements before integration with an air conditioner. The numerical model of the heat exchanger uses a combination of discretized and lumped parameter approaches to maintain a balance between accuracy and computational expense. Our analyses show that the geometric features and integration of fluid tubes are key contributors to the thermal contact resistance between the fluid and the thermal storage material, and consequently, to the overall performance of the thermal storage module. Our standalone experiments also identified important operating scenarios in which this thermal storage module can be used for air-conditioning in buildings.

25 ENERGY STORAGE↗