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DOE OSTI2020

Supo Thermal Model Development IV

This report describes the continuation of the Computational Fluid Dynamics (CFD) model of the Supo cooling system described in the reports, Supo Thermal Model Development, II, and III. In the previous reports, the solution natural convection of Supo was simulated using a 2D, axisymmetric model that altered the actual geometry. A 3D geometry of Supo will more accurately estimate the natural convection heat transfer coefficient (HTC) of the system since Supo contains several non-symmetric internal components that can only be modeled in 3D. This, in turn, will improve the natural convection HTC results for comparison purposes with past operational data and previous 2D CFD results. Unfortunately, the complexities of performing multiphase 3D CFD simulations has proven too large of a task for our current capabilities. Instead, this report will focus on the 3D model development for the 2.39 kW/L (31.3 kW) case and limitations and challenges associated with the 3D simulations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Heat Exchanger Design for Waste Heat Recovery using Additive Manufacturing

With anticipated increases in global energy use and their associated emissions, there are many efforts to improve the emissions and efficiency of many industrial processes through waste heat recovery (WHR). To maximize improvements, the heat exchangers in WHR power cycles must be designed and optimized for a given heat source. Meanwhile, additive manufacturing (AM) is a single part manufacturing technique that provides the opportunity to develop unique and complex heat exchanger designs. Consequently, this work centers on the modeling, design, optimization, and testing of AM shell and tube heat exchangers for WHR. Specifically, a modified one-dimensional (1+1-D) and three-dimensional (3-D) heat exchanger models were developed and validated, with the 1+1-D model serving as the basis for design optimization. After modifications to the design to allow for AM, heat exchanger geometry optimizations were completed, targeting increased effectiveness across a range of physical parameters. Of the designs considered, nine were additively manufactured using Ti5553, and then tested using an external heat source and working fluid. Experimentally, the nineteen-tube, zero-baffle heat exchanger displayed the best overall performance, with the effects of dimensional accuracy, surface roughness, and baffles in the other designs also indicated. Next, the experimental results were used as inputs for the 1+1-D model. Although the original model was able to predict fluid outlet temperatures within 5% differences, larger variations in the heat exchanger performance parameters, temperature differences, and pressure drop necessitated optimizing the pressure drop and heat transfer calculations within the model. Thus, the resulting improved model was able to predict the heat transfer rates, heat source temperature difference, and effectiveness within 15% differences, while showing improvements in the pressure drops and working fluid temperature differences. The improved model serves as the foundation for future work to improve the design and performance of AM heat exchangers.

Spickler, Bailey Ann↗

DOE OSTI2020

Development of Methods for In-line Monitoring of Yield Stress During the Transfer of Radioactive Waste - 20435

Radioactive waste slurries at the Savannah River Site (SRS) and Hanford often behave as non-Newtonian fluids with a yield stress. The yield stress increases the size of equipment needed to transport or mix these slurries and can increase the risk of process upsets. This property is often measured by collection of samples and transportation to a laboratory, which can increase the risk of personnel exposure and even change the rheology. In addition, slurry rheology has been observed to change with time. Real-time in-line monitoring (RTIM) of rheological properties of slurries can have substantial benefits to critical processes which are currently in operation or will be part of future plans in Department of Energy (DOE) complex processes. The need for an appropriate RTIM technology that can potentially minimize or eliminate numerous sampling operations was recently identified. This research at Florida International University focuses on addressing the need for a reliable RTIM technology at US Department of Energy sites. The objective of this work is to investigate and develop appropriate and reliable technologies that can perform accurate and easy measurements of yield stress with minimal changes to the flow conditions at US Department of Energy sites. Use of ultrasound in longitudinal and shear modes was considered as a method for monitoring of yield stress variations with no disturbance to the flow. Measurement of shear wave velocity (in shear mode) and monitoring of sound velocity variations with start of flow from rest (longitudinal mode) are the underlying principles. Measurement of pressure loss and liquid rise (due to static differential pressure) during flow of slurries were two other methods that were considered and investigated. In addition, analysis of stability depth of a penetrometer in slurry medium (penetrometer method) revealed a potential suitability. This paper presents efforts associated with investigation of different methods mentioned earlier for kaolin-water mixtures at different concentrations. Theoretical analysis showed that all methods under investigation were capable of monitoring yield stress in a desirable range of 0 to 70 pascal. A bench scale test setup was constructed for investigation of pressure loss and liquid rise methods. Simulants at different concentrations were created for initial testing campaigns. For ultrasonic testing, effects of yield stress on sound propagation speed in longitudinal and shear modes were investigated for simulants placed inside PVC pipes. Parameters such as simulant concentration and pipe diameter were varied to find optimal values. It was found that spacing (or medium thickness) of 1/8 inch or less was necessary to observe echoes with sufficient strength. In addition, a significant effect of yield stress on sound propagation velocity was observed. For the liquid rise method, kaolin-water simulant at 1.17 specific gravity was pumped through a 1/4 inch pipe and liquid height was measured in a vertical riser branching off a three-way connection. Static gauge pressure was varied within [0.3 to 1.1] psi range by increase of pumping pressure and flow. Results showed consistent yield stress data within 10% deviation from values reported by a rheometer. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Simultaneous imaging of two-phase velocities in particle-laden flows by two-color optical phase discrimination

In this Letter, we present a particle image/tracking velocimetry (PIV/PTV) technique for simultaneous velocity measurement of both fluid and particle phases, adopting newly developed optical phase discrimination methods and novel optical particles. Spherical acrylic (PMMA) particles of diameter ∼ <#comment/> O ( 100 µ <#comment/> m ) were used as the particle phase, while fine B A M : E u 2 + phosphors of diameter ∼ <#comment/> O ( 1 µ <#comment/> m ) were used as the fluid tracer. Under Nd:YAG 355 nm laser excitation, both the laser-induced fluorescence (LIF) from PMMA and laser-induced phosphorescence (LIP) from B A M : E u 2 + provided sufficiently strong signals for PIV imaging with two non-intensified cameras and were clearly separable for phase discrimination using spectral filters and temporal profiles. The advantages of the PIV/PTV method include the relatively low cost of B A M : E u 2 + phosphors, high sphericity and narrow size distribution of PMMA particles with LIF emission, and direct optical discrimination eliminating artifacts, while requiring much less computational capacity for PIV/PTV processing of complex particle-laden flows.

Bi, Xiaopeng (ORCID:0000000190847325)↗

DOE OSTI2021

High pressure, high flow rate batch mixing apparatus for high throughput experiments

An automated, high pressure, high flow rate batch mixing apparatus has been designed and constructed for rapid, stable, and repeatable mixing of multiple gases and vapors. The apparatus operates as an intermittent batch mixer with cycles of topping off fresh mixture to maintain pressure in an accumulator tank until consumed in an experimental apparatus. At high duty cycles, the apparatus can also function at steady state. This style of mixing is suitable for experiments such as high repetition rate shock tubes and other devices with intermittent flow demands. It is compact and portable, facilitating use in locations such as synchrotron light sources. The entire apparatus is heated to permit the mixing of vapors from species with low volatilities. The apparatus is fully automated and runs for extended periods with the only intervention being to refresh reagent supplies. Here, the accuracy and repeatability of the apparatus were verified by periodic gas sampling and analysis with gas chromatography. Multi-component mixtures spanning a wide range of complexity, dilution, and volatility of constituents have been prepared. The compositions of the majority of the mixture were found to be stable over several filling cycles, repeatable, and with the proper calibration of set-point conditions, accurate. Challenges were encountered preparing a mixture from multi-component liquids, and potential solutions are discussed.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Large Eddy Simulation of Convective Heat Transfer in a Random Pebble Bed Using the Spectral Element Method

The development of fluoride-cooled high-temperature reactors has drastically increased the demand for an in-depth understanding of the heat transfer (HT) in packed beds cooled by liquid salts. The complex flow fields and space-dependent porosity found in a pebble bed require a detailed understanding to ensure the proper cooling of the reactor core during normal and accident conditions. As detailed experimental data are complicated to obtain for these configurations, high-fidelity simulation such as large eddy simulation and direct numerical simulation (DNS) can be employed to create a high-resolution heat transfer numerical database that can assist in addressing industrial-driven issues associated with the heat transfer behavior of fluoride-cooled high-temperature reactors. In this paper, we performed a series of large eddy simulation using computational fluid dynamics (CFD) code NekRS to investigate the heat transfer for a bed of 1741 pebbles. Further, the characteristics of the flow, such as average, rms, and time series of velocity and temperature, have been analyzed. Porous media averages have also been performed. The simulation results show a good agreement between non-conjugate heat transfer and conjugate heat transfer. The generated data will be used to benchmark heat transfer modeling methods and local maxima/minima of heat transfer parameters. It will also be used for supporting convective heat transfer quantification for Kairos Power and benchmarking lower fidelity models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Host-Directed, Bioelectronic Immunomodulation for Protection Against Emerging Pathogens

Acute care of patients with severe infections often relies on systemic administration of pharmaceuticals and monitoring of complex physiological symptoms to identify immune system dysfunction, which can lead to increased mortality. Furthermore, determining disease-specific treatment plans often leads to a delay in patient care. To address this, we proposed an immune modulation system that electrically detects and responds to a patient’s immune system status, creating an agnostic means of treating illness and infection. Two pieces of hardware were developed for this task: a minimally-invasive sensor and a vagus nerve stimulator. Stimulation of the vagus nerve is known to modulate the immune system. The sensor is a microfabricated, silicon-based microneedle array capable of interfacing with interstitial fluid to detect small molecules such as inflammatory proteins (cytokines) and pharmaceuticals (vancomycin). Process optimization to manufacture the needles refined the silicon etch process, creating needle patches long enough to penetrate skin and reach interstitial fluid. The needles were tested for mechanical strength and stability, and did not shatter when inserted into skin models. The needles are coated with a thin film metal, turning them into electrodes for electrochemical sensing of our target molecules. We hybridized aptamers to the surface of the electrode to act as the sensing layer and were able to detect changes in the conformation of the aptamer electrochemically in the presence of the target molecule. The stimulator was a cuff electrode that encircled the vagus nerve. Rodent studies were conducted in which rodents were exposed to an inflammatory event and vagus nerve stimulation (VNS) was applied. It was demonstrated that optimized electrical stimulation of the vagus nerve created measurably different levels of cytokines in blood samples, and certain cytokines released during the inflammatory event were either upregulated or downregulated. In sum, this project successfully developed new platforms and technologies that can, with further development, enable better temporal insight into biomarker changes in the body, letting healthcare providers know of possible immune system dysfunction before they are detected physiologically. We also demonstrated the value of VNS and its possible use in treating immune system response to inflammation and illness.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network

The study of solute transport in porous media is of interest in many chemical engineering systems. Some example applications include packed bed catalytic reactors, filtration devices, and batteries. The pore scale modeling of these systems is time consuming and may require large computing resources, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of flow simulations and then predict fields orders of magnitude faster, and with less computational resources, in new domains. However, it is crucial to provide the neural network with an effective description of the domain and the undergoing operating conditions to be able to train models that generalize accurately in unseen samples. Therefore, research is needed to employ neural networks in new complex systems. The appropriate training of a network for predicting coupled flow and solute transport processes is an outstanding problem due to the complex interplay between geometry and operating conditions. In this work, we train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). Further, we train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Simulation of Microcapsule Transport in Fractured Media Using Coupled CFD‐DEM

Geothermal energy is sustainable and gaining momentum as a solution to energy crises and environmental issues. However, challenges like production temperature and thermal breakthrough can impact geothermal project efficiency. One innovative solution to alleviate the thermal breakthrough is to inject polymer-based materials that are encapsulated in microcapsules into fractures to modify fracture permeability and prevent preferential flow. In our study, we utilized a coupled computational fluid dynamics and discrete element method to simulate the transport of microcapsules under various scenarios controlled by microcapsule size, microcapsule concentration, and fracture roughness. For a smooth fracture, the results indicate that small microcapsules can travel through a smooth fracture regardless of their concentrations. Large microcapsules can transport through a smooth fracture when present in lower concentrations. However, medium and mixed-size microcapsules tend to cause the sealing of a smooth fracture, irrespective of their concentrations. For a rough fracture, the transport of microcapsules is complicated by their interactions with the rough fracture walls. The presence of two sealing positions in a rough fracture adds further complexity to this transport phenomenon. The size and concentration of microcapsules control one sealing location, while the rough fracture walls determine the other sealing location. The rough walls substantially affect microcapsule transport, rendering the role of microcapsule size and concentration less significant. The simulation results suggest that complex fracture surfaces significantly elevate the occurrence of sealing behavior. To mitigate sealing behavior within more complex fractures, it would be beneficial to use smaller and lower concentrations of microcapsules.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

A massively parallel and scalable multi-CPU material point method

Harnessing the power of modern multi-GPU architectures, we present a massively parallel simulation system based on the Material Point Method (MPM) for simulating physical behaviors of materials undergoing complex topological changes, self-collision, and large deformations. Our system makes three critical contributions. First, we introduce a new particle data structure that promotes coalesced memory access patterns on the GPU and eliminates the need for complex atomic operations on the memory hierarchy when writing particle data to the grid. Second, we propose a kernel fusion approach using a new Grid-to-Particles-to-Grid (G2P2G) scheme, which efficiently reduces GPU kernel launches, improves latency, and significantly reduces the amount of global memory needed to store particle data. Finally, we introduce optimized algorithmic designs that allow for efficient sparse grids in a shared memory context, enabling us to best utilize modern multi-GPU computational platforms for hybrid Lagrangian-Eulerian computational patterns. We demonstrate the effectiveness of our method with extensive benchmarks, evaluations, and dynamic simulations with elastoplasticity, granular media, and fluid dynamics. In comparisons against an open-source and heavily optimized CPU-based MPM codebase [Fang et al. 2019] on an elastic sphere colliding scene with particle counts ranging from 5 to 40 million, our GPU MPM achieves over 100x per-time-step speedup on a workstation with an Intel 8086K CPU and a single Quadro P6000 GPU, exposing exciting possibilities for future MPM simulations in computer graphics and computational science. Moreover, compared to the state-of-the-art GPU MPM method [Hu et al. 2019a], we not only achieve 2x acceleration on a single GPU but our kernel fusion strategy and Array-of-Structs-of-Array (AoSoA) data structure design also generalizes to multi-GPU systems. Our multi-GPU MPM exhibits near-perfect weak and strong scaling with 4 GPUs, enabling performant and large-scale simulations on a 10243 grid with close to 100 million particles with less than 4 minutes per frame on a single 4-GPU workstation and 134 million particles with less than 1 minute per frame on an 8-GPU workstation.

Wang, Xinlei↗

DOE OSTI2024

Symbolic diagnostics to interpret and analyze neural network models

Embedded machine-learned models (EMLMs) have the promise to improve the predictive accuracy of engineering simulators in environments of national interest. EMLMs often comprise complex input-output maps (e.g., neural networks), which make them unamenable to rigorous analysis and generally difficult to interpret. In the face of decades of theory, this lack of interpretability is a significant barrier to building confidence in these models. This work outlines an approach to interpret EMLMs using sparse polynomial regression for comparison with theoretical understanding. To do so, we build on the concept of Locally Interpretable Model-agnostic Explanations (LIME) using physics-informed clustering, prototype selection, and library construction. While general, we demonstrate our method on tensor-basis neural networks used in Reynolds-Averaged Navier-Stokes simulations of hypersonic fluid flows. Results are presented for a simulated toy model and for direct numerical simulations (DNS) of turbulent flows over a flat plate.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Multiplexed Anodic Stripping Voltammetry Detection of Heavy Metals in Water Using Nanocomposites Modified Screen-Printed Electrodes Integrated With a 3D-Printed Flow Cell

In this study, we present multiplexed anodic stripping voltammetry (ASV) detection of heavy metal ions (HMIs)—As(III), Cd(II), and Pb(II)—using a homemade electrochemical cell consisting of dual working, reference and counter screen-printed electrodes (SPE) on polyimide substrate integrated with a 3D-printed flow cell. Working and counter electrodes were fabricated by the screen-printing of graphite paste while the Ag/AgCl paste was screen-printed as a reference electrode (Ag/AgCl quasi-reference electrode). The working electrodes were modified with (BiO) 2 CO 3 -reduced graphene oxide (rGO)-Nafion [(BiO) 2 CO 3 -rGO-Nafion] and Fe 3 O 4 magnetic nanoparticles (Fe 3 O 4 MNPs) decorated Au nanoparticles (AuNPs)-ionic liquid (IL) (Fe 3 O 4 -Au-IL) nanocomposites separately to enhance HMIs sensing. Electrochemical detection was achieved using square wave ASV technique. The desired structure of the flow electrochemical cell was optimized by the computational fluid dynamic (CFD). Different experimental parameters for stripping analysis of HMIs were optimized including deposition time, deposition potential and flow rate. The linear range of calibration curves with the sensing nanocomposites modified SPE for the three metal ions was from 0–50 μg/L. The limits of detection (S/N = 3) were estimated to be 2.4 μg/L for As(III), 1.2 μg/L for Pb(II) and 0.8 μg/L for Cd(II). Furthermore, the homemade flow anodic stripping sensor platform was used to detect HMIs in simulated river water with a 95–101% recovery, indicating high selectivity and accuracy and great potential for applicability even in complex matrices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Compliant heat exchangers, heat pipes and methods for making same

A method for fabricating heat exchangers using additive manufacturing technologies. Additive manufacturing enables the manufacture of heat exchangers with complex geometries and/or with internal and external integral surface features. Additive manufacture also facilitates the manufacture of heat exchangers with regional variations, such as changes in size, shape and surface features. In one embodiment, the present invention provides a heat exchanger with a helicoidal shape that provides axial elastic compliance. In one embodiment, the internal channel of the heat exchanger varies along its length. The internal channel may have a cross-sectional area that increases progressively from one end to the other. In one embodiment, the external shape of the tubular structure may be non-circular to optimize heat transfer with an external heat transfer fluid. In one embodiment, the present invention provides a heat pipe with an internal wicking structure formed as an integral part of the additive manufacturing process.

Lara-Curzio, Edgar↗

DOE OSTI2020

Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach

Reynolds-Averaged Navier-Stoke (RANS) models offer an alternative avenue in predicting flow characteristics when the corresponding experiments are difficult to achieve due to geometry complexity, limited budget, or knowledge. RANS models require the knowledge of subgrid scale physics to solve conservation equations for mass, energy, and momentum. Mechanistic turbulence models, such as k-epsilon, are generally evaluated and calibrated for specific flow conditions with various degrees of uncertainty. These models have limited capability to assimilate a substantial amount of data due to model form constraints. Meanwhile, deep learning (DL) has been proven to be universal approximators with the potential to assimilate available, relevant, and adequately evaluated data. Moreover, deep neural networks (DNNs) can create surrogate models without knowing function forms. Such a data-driven approach can be used in updating fluid models based on observations as opposed to hard-wiring models with precalibrated correlations. The paper presents progress in applying DNNs to model Reynolds stress using two machine learning (ML) frameworks. A novel flow feature coverage mapping is proposed to quantify the physics coverage of DL-based closures. It can be used to examine the sufficiency of training data and input flow features for data-driven turbulence models. The case of a backward-facing step is formulated to demonstrate that not only can DNNs discover underlying correlation behind fluid data but also they can be implemented in RANS to predict flow characteristics without numerical stability issues. Finally, the presented research is a crucial stepping-stone toward the data-driven turbulence modeling, which potentially benefits the design of data-driven experiments that can be used to validate fluid models with ML-based fluid closures.

42 ENGINEERING↗

DOE OSTI2020

Transcritical diffuse-interface hydrodynamics of propellants in high-pressure combustors of chemical propulsion systems

Rocket engines and high-power new generations of gas-turbine jet engines and diesel engines oftentimes involve the injection of one or more reactants at subcritical temperatures into combustor environments at high pressures, and more particularly at pressures higher than those corresponding to the critical points of the individual components of the mixture, which typically range from 13 to 50 bars for most propellants. This class of trajectories in the thermodynamic space has been traditionally referred to as transcritical. However, the fundamental understanding of fuel atomization, vaporization, mixing, and combustion processes at such high pressures remains elusive. In particular, whereas fuel sprays are relatively well characterized at normal pressures, analyses of dispersion of fuel in high-pressure combustors are hindered by the limited experimental diagnostics and theoretical formulations available. The description of the thermodynamics of hydrocarbon-fueled mixtures employed in chemical propulsion systems is complex and involves mixing-induced phenomena, including an elevation of the critical point whereby the coexistence region of the mixture extends up to pressures much larger than the critical pressures of the individual components. As a result, interfaces subject to surface-tension forces may persist in multicomponent systems despite the high pressures, and may give rise to unexpected spray-like atomization dynamics that are otherwise absent in monocomponent systems above their critical point. In this article, the current understanding of this phenomenon is reviewed within the context of propulsion systems fueled by heavy hydrocarbons. Emphasis is made on analytical descriptions at mesoscopic scales of interest for computational fluid dynamics. In particular, a set of modifications of the constitutive laws in the Navier–Stokes equations for multicomponent flows, supplemented with a high-pressure equation of state and appropriate redefinitions of the thermodynamic potentials, are introduced in this work based on an extended version of the diffuse-interface theory of van der Waals. The resulting formulation involves revisited forms of the stress tensor and transport fluxes of heat and species, and enables a description of the mesoscopic volumetric effects induced by transcritical interfaces consistently with the thermodynamic phase diagram of the mixture at high pressures. Additionally, applications of the theory are illustrated in canonical problems, including dodecane/nitrogen transcritical interfaces in non-isothermal systems. The results indicate that a transcritical interface is formed between the propellant streams that persists downstream of the injection orifice over distances of the same order as the characteristic thermal-entrance length of the fuel stream. The transcritical interface vanishes at an edge that gives rise to a fully supercritical mixing layer.

02 PETROLEUM↗

DOE OSTI2024

In-Situ Species Concentration Measurements In Ammonia-Mix Flames Using Ftir Spectroscopy

Hydrogen and ammonia represent two carbon-free fuel sources that could be used in place of current fossil energy sources in combustion systems. To develop optimized ammonia combustion systems, validated modeling tools are needed. In the open literature, it has been shown that the complex chemistry associated with fuel-bound nitrogen contained in ammonia differs greatly from natural gas or hydrogen combustion. As a result, several new chemical kinetic mechanisms have been developed. Many of these mechanisms have been validated experimentally, however this has primarily focused on bulk parameters such as laminar flame speed and ignition delay time. Critically, high quality measurements of species concentrations are needed under controlled conditions which are easily represented by simple models. In this paper, direct, in-situ measurements of species concentrations and gas temperature are performed in a laminar flat-flame burner. This arrangement enables comparison with 1D model predictions, better isolating chemical kinetics from the fluid dynamics. Quantitative species concentrations are determined by absorption spectroscopy using an FTIR spectrometer. Fuel compositions representative of cracked ammonia (NH 3 /H 2 ) and ammonia-natural gas (NH 3 /CH 4 ) are considered for rich and lean equivalence ratios. A major focus of the paper is on the selection of spectral features for nitric oxide and ammonia and correcting for large amounts of baseline H 2 O absorption.

36 MATERIALS SCIENCE↗

DOE OSTI2024

In-Situ Species Concentration Measurements in Ammonia-Mix Flames Using FTIR Spectroscopy

Hydrogen and ammonia represent two carbon-free fuel sources that could be used in place of current fossil energy sources in combustion systems. To develop optimized ammonia combustion systems, validated modeling tools are needed. In the open literature, it has been shown that the complex chemistry associated with fuel-bound nitrogen contained in ammonia differs greatly from natural gas or hydrogen combustion. As a result, several new chemical kinetic mechanisms have been developed. Many of these mechanisms have been validated experimentally, however this has primarily focused on bulk parameters such as laminar flame speed and ignition delay time. Critically, high quality measurements of species concentrations are needed under controlled conditions which are easily represented by simple models. In this paper, direct, in-situ measurements of species concentrations and gas temperature are performed in a laminar flat-flame burner. This arrangement enables comparison with 1D model predictions, better isolating chemical kinetics from the fluid dynamics. Quantitative species concentrations are determined by absorption spectroscopy using an FTIR spectrometer. Fuel compositions representative of cracked ammonia (NH3/H2) and ammonia-natural gas (NH3/CH4) are considered for rich and lean equivalence ratios. A major focus of the paper is on the selection of spectral features for nitric oxide and ammonia and correcting for large amounts of baseline H2O absorption.

Bedick, Clinton↗

DOE OSTI2026

FunDiff: diffusion models over function spaces for physics-informed generative modeling

Recent advances in generative modeling-particularly diffusion models and flow matching-have been widely used for synthesizing discrete data such as images and videos. However, adapting these models to physical applications remains challenging, as the quantities of interest are continuous functions governed by complex physical laws. To address this, we introduce FunDiff, an efficient and robust framework for generative modeling in function spaces. FunDiff combines a latent diffusion process with a function autoencoder architecture to handle input functions with varying discretizations, generates continuous functions that can be evaluated at arbitrary locations, and seamlessly incorporate physical priors. These priors are enforced through architectural constraints or physics-informed loss functions, ensuring that generated samples satisfy fundamental physical laws. We theoretically establish minimax optimality guarantees for density estimation in function spaces, demonstrating that diffusion-based estimators achieve optimal convergence rates under suitable regularity conditions. We further demonstrate the practical effectiveness of FunDiff across diverse applications in fluid dynamics and solid mechanics. Empirical results indicate that our method can generate physically consistent samples with high fidelity to the target distribution, and exhibit robustness to noisy and low-resolution data.

Wang, Sifan [Yale University, New Haven, CT (Unite↗