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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Plasma assisted NH 3 /H 2 /air ignition in nanosecond discharges with non-equilibrium energy transfer

Ammonia (NH 3 ), with its high energy density and easiness to store and transport as a hydrogen carrier, has become a promising alternative green fuel. However, its adoption in power generation is hindered by challenges such as low burning velocity, slow low-temperature oxidation, high NO x emissions, and ignition difficulty. Here, this work computationally investigates the effects of non-equilibrium energy transfer by nanosecond discharges on NH 3 ignition and flame propagation in an NH 3 /H 2 /air flow at 700 K and 1 atm. The simulation results demonstrate that NH 3 /air mixtures require a large ignition energy due to their large critical ignition radius. It is shown that adding 30 % hydrogen significantly reduces the critical ignition radius and minimum ignition energy. Two-dimensional modeling further shows a non-monotonic dependence of ignition kernel volume on the applied voltage and reduced electric field. The optimum ignition enhancement occurs at 200 Td where the generation of electronically excited species and radicals including N 2 (B), O( 1 D) and OH becomes most efficient. Higher voltages divert electron energy toward ionization, which makes it less effective for NH 3 ignition. The study also identifies an optimal electrode gap size for a given pulse energy. Smaller gap sizes increase deposited energy density, raising temperature and radical concentrations. However, excessive reduction of the gap distance reduces flame propagation speed due to the flame stretch effect in rich mixtures with the effective Lewis number greater than unity. A nonlinear relationship between pulse repetition frequency and ignition kernel volume is observed in a nanosecond pulsed high frequency discharge (NPHFD). An optimal frequency range of 200 kHz to 2 MHz is found when two pulses are used. In addition, an optimal number of pulses exists for each pulse repetition frequency, with higher frequencies requiring more pulses to maximize the overlap region. These findings provide critical insights on developing controlled plasma discharge techniques for efficient NH 3 ignition in reactive flows within internal combustion engines and gas turbines.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

An economic analysis of the role of materials, system engineering, and performance in electrochemical carbon dioxide conversion to formate

The development of technologies that utilize carbon dioxide is important to mitigating climate change. The electrochemical reduction of carbon dioxide is one technology that can utilize greenhouse gasses that would be otherwise be emitted to the atmosphere by producing chemicals and fuels from carbon dioxide and electricity. Significant progress has been made in the experimental performance of carbon dioxide reduction systems with novel catalyst designs, new materials, and systems engineering; however, no work has linked such changes in stack design and materials to capital costs for the stack itself. In this study, we present an analysis that accounts for and analyzes the impacts of alternative materials and system architectures on manufactured costs of carbon dioxide reduction stacks, thus providing a framework to understand exactly how these advances impact the at-scale capital costs of these systems. Specifically, we consider the impact that the addition of a catholyte buffer layer has on an electrolyzer reducing carbon dioxide to formate, finding that the cost of manufacturing this part only increases stack costs by about $30/m 2 at high manufacturing rates, while previous work finds that this part improves system performance. This work shows that the links between system performance, materials, and costs are nonlinear, and that achieving low-cost scalability requires optimization of not just performance parameters but also the use of low-cost and highly scalable materials. These results bridge experimental and techno-economic analysis of processes for carbon dioxide reduction, informing researchers by providing a quantifiable estimate of the impact of advances in electrochemical carbon dioxide reduction technology on manufactured stack capital costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Evaluating the demand response potential of ammonia plants

Demand-side management/demand response (DSM/DR) are key strategies for mitigating the inherent variability in electricity generation rates by renewable sources. This article represents—to our knowledge—the first foray into assessing the DR potential of ammonia plants. Ammonia plants are interesting candidates for DR initiatives because of their significant electricity use (for operating compressors driving the synthesis loop) and the ability to store the ammonia product relatively easily and safely. Our approach is based on formulating and solving an optimal DR scheduling problem for an ammonia plant while accounting for the process dynamics. To this end, we introduce a new Hammerstein–Wiener-inspired modeling framework based on injecting linear dynamics in a first-principles static nonlinear model of the process. The results are encouraging; for the cases considered, peak-time power consumption decreases between 3.57% and 7.40%, coupled with 1.39% to 3.70% reductions in operating cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Time-periodic steady-state solution of fluid-structure interaction and cardiac flow problems through multigrid-reduction-in-time

In this study, a time-periodic MGRIT algorithm is proposed as a means to reduce the time-to-solution of numerical algorithms by exploiting the time periodicity inherent to many applications in science and engineering. The time-periodic MGRIT algorithm is applied to a variety of linear and nonlinear single- and multiphysics problems that are periodic-in-time. It is demonstrated that the proposed parallel-in-time algorithm can obtain the same time-periodic steady-state solution as sequential time-stepping. It is shown that the required number of MGRIT iterations can be estimated a priori and that the new MGRIT variant can significantly and consistently reduce the time-to-solution compared to sequential time-stepping, irrespective of the number of dimensions, linear or nonlinear PDE models, single-physics or coupled problems and the employed computing resources. The numerical experiments demonstrate that the time-periodic MGRIT algorithm enables a greater level of parallelism yielding faster turnaround, and thus, facilitating more complex and more realistic problems to be solved.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Proper orthogonal decomposition based reduced-order modeling of flux-Limited gray thermal radiation

Here, in this work, a proper orthogonal decomposition (POD) based reduced-order model (ROM) is developed to solve gray, flux-limited thermal radiation diffusion. We focus on the variable opacity radiation penetration benchmark posed by Olson, Auer, and Hall. The T -3 relationship for opacity in conjunction with high-temperature radiation penetrating an initially cold material produces a strong thermal radiation shock. This class of problems is particularly challenging for standard POD-based reduced-order modeling due to the nonlinearities presented by 1) the T 4 source term, and 2) flux-limited diffusion operator. To address these challenges and develop a cost competitive ROM, we employ a “hyper-reduction” technique through discrete empirical interpolation (DEIM) and allow for adaptive reduced-order projections through principal interval decomposition (PID). Performance of the proposed methodology is quantified by comparing the cost savings and accuracy relative to a full-order computation. Reference solutions and snapshot data are obtained through a full-order calculation performed by the University of Chicago maintained astrophysics code, FLASH. For consistency and potential extensibility, the developed ROM is also implemented in FLASH. We find that in the initialization regime, where the thermal radiation wave is initially created by the warming of the material, this class of problems is highly reducible and suitable for POD-based ROMs. However, the strong convective nature of the wave propagation regime is less reducible and more challenging to create an efficient ROM.

42 ENGINEERING↗
DOE OSTI2023

Vertical Hydrologic Exchange Flows Control Methane Emissions from Riverbed Sediments

CH 4 emissions from inland waters are highly uncertain in the current global CH 4 budget, especially for streams, rivers, and other lotic systems. Previous studies have attributed the strong spatiotemporal heterogeneity of riverine CH 4 to environmental factors such as sediment type, water level, temperature, or particulate organic carbon abundance through correlation analysis. However, a mechanistic understanding of the basis for such heterogeneity is lacking. Here, in this study, we combine sediment CH 4 data from the Hanford reach of the Columbia River with a biogeochemical-transport model to show that vertical hydrologic exchange flows (VHEFs), driven by the difference between river stage and groundwater level, determine CH 4 flux at the sediment–water interface. CH 4 fluxes show a nonlinear relationship with the magnitude of VHEFs, where high VHEFs introduce O 2 into riverbed sediments, which inhibit CH 4 production and induce CH 4 oxidation, and low VHEFs cause transient reduction in CH 4 flux (relative to production) due to reduced advective CH 4 transport. In addition, VHEFs lead to the hysteresis of temperature rise and CH 4 emissions because high river discharge caused by snowmelt in spring leads to strong downwelling flow that offsets increasing CH 4 production with temperature rise. Our findings reveal how the interplay between in-stream hydrologic flux besides fluvial-wetland connectivity and microbial metabolic pathways that compete with methanogenic pathways can produce complex patterns in CH 4 production and emission in riverbed alluvial sediments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Preparation for quantum simulation of the ( 1 + 1 ) -dimensional O(3) nonlinear σ model using cold atoms

The 1+1D O(3) non-linear σ-model is a model system for future quantum lattice simulations of other asymptotically-free theories, such as non-Abelian gauge theories. We find that utilizing dimensional reduction can make efficient use of two-dimensional layouts presently available on cold atom quantum simulators. A new definition of the renormalized coupling is introduced, which is applicable to systems with open boundary conditions and can be measured using analog quantum simulators. Monte Carlo and tensor network calculations are performed to determine the quantum resources required to reproduce perturbative short-distance observables. In particular, we show that a rectangular array of 48 Rydberg atoms with existing quantum hardware capabilities should be able to adiabatically prepare low-energy states of the perturbatively-matched theory. Furthermore, these states can then be used to simulate non-perturbative observables in the continuum limit that lie beyond the reach of classical computers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Universal Compiling and (No-)Free-Lunch Theorems for Continuous-Variable Quantum Learning

Quantum compiling, where a parameterized quantum circuit is trained to learn a target unitary, is an important primitive for quantum computing that can be used as a subroutine to obtain optimal circuits or as a tomographic tool to study the dynamics of an experimental system. While much attention has been paid to quantum compiling on discrete-variable hardware, less has been paid to compiling in the continuous-variable paradigm. Here we motivate several, closely related, short-depth continuous-variable algorithms for quantum compilation. We analyze the trainability of our proposed cost functions and numerically demonstrate our algorithms by learning arbitrary Gaussian operations and Kerr nonlinearities. We further make connections between this framework and quantum learning theory in the continuous-variable setting by deriving no-free-lunch theorems. These generalization bounds demonstrate a linear resource reduction for learning Gaussian unitaries using entangled coherent-Fock states and an exponential resource reduction for learning arbitrary unitaries using two-mode-squeezed states.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Tunable Plasma Linearizer for Compensation of Nonlinear Energy Chirp

The removal of undesired nonlinear time-energy correlation (energy chirp) present in relativistic electron beams is crucial for many accelerator-based scientific applications, such as free-electron lasers, high-energy electron radiography, and MeV ultrafast electron microscopy. Here, we propose and demonstrate that a low-density plasma section can be used as a passive “linearizer” to significantly compensate for the nonlinear energy chirp imprinted on a beam by the curvature of the radio-frequency field in a conventional accelerator. Physically, the passage of the beam through the plasma excites a strong quasi-cosinoidal longitudinal decelerating wakefield that acts to mitigate the beam’s nonlinear energy chirp by superimposing a reverse chirp on the beam. Time-resolved phase-space measurements, combined with high-fidelity three-dimensional particle-in-cell simulations show that the longitudinal phase space of the beam core is almost completely linearized, leading to a fourfold reduction of the beam overall energy spread from 0.148% to 0.036% (FWHM).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

High-Fidelity Large-Signal Order Reduction Approach for Composite Load Model

With the increasing penetration of electronic loads and distributed energy resources, conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high-order non-linear system with multi-time-scale property, which poses challenges on power system studies with heavy computational burden. In order to reduce the model complexity, the authors firstly develop a large-signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow ones to preserve transient characteristics of the former. Then, accuracy assessment conditions are proposed and embedded into the LSOR to improve and guarantee the accuracy of reduced-order model. Finally, the reduced-order WECC composite load model is derived by using the proposed algorithm. Overall, simulation results show that the reduced-order large-signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Modification of ion-temperature-gradient turbulence by impurities in stellarator plasmas

Recent nonlinear gyrokinetic simulations have shown that impurities can strongly modify the turbulent heat flux in stellarator plasmas. Here, the ion-temperature-gradient (ITG) dispersion relation in a plasma containing impurities is analytically solved in certain limits and an expression for the modification of the ITG growth rate by impurities is derived. The analytical expression is the sum of three terms corresponding to three different physical causes (impurity density gradient, impurity temperature gradient and dilution) of the change in the growth rate. The scalings predicted analytically for the modification of the growth rate are shown to be reproduced by linear gyrokinetic simulations. The conditions for reduction or increase of the ITG growth by impurities are also correctly predicted by the analytical solution to the dispersion relation. Finally, a remarkable correlation is found between the analytical expression for the modification of the growth rate and the modification of the turbulent heat flux obtained from nonlinear gyrokinetic simulations.

Calvo, Iván [Research Centre for Energy, Environme↗
DOE OSTI2021

422 Million intrinsic quality factor planar integrated all-waveguide resonator with sub-MHz linewidth

High quality-factor (Q) optical resonators are a key component for ultra-narrow linewidth lasers, frequency stabilization, precision spectroscopy and quantum applications. Integration in a photonic waveguide platform is key to reducing cost, size, power and sensitivity to environmental disturbances. However, to date, the Q of all-waveguide resonators has been relegated to below 260 Million. Here, we report a Si 3 N 4 resonator with 422 Million intrinsic and 3.4 Billion absorption-limited Qs. The resonator has 453 kHz intrinsic, 906 kHz loaded, and 57 kHz absorption-limited linewidths and the corresponding 0.060 dB m -1 loss is the lowest reported to date for waveguides with deposited oxide upper cladding. These results are achieved through a careful reduction of scattering and absorption losses that we simulate, quantify and correlate to measurements. This advancement in waveguide resonator technology paves the way to all-waveguide Billion Q cavities for applications including nonlinear optics, atomic clocks, quantum photonics and high-capacity fiber communications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Invertible Neural Networks for Aerodynamic Design of Wind Turbine Blades

The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. Our design technique is a significant improvement over the state-of-the-practice linearized blade element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades.

aerodynamic design↗
DOE OSTI2024

Learning Nonlinear Reduced Models from Data with Operator Inference

This review discusses Operator Inference, a nonintrusive reduced modeling approach that incorporates physical governing equations by defining a structured polynomial form for the reduced model, and then learns the corresponding reduced operators from simulated training data. The polynomial model form of Operator Inference is sufficiently expressive to cover a wide range of nonlinear dynamics found in fluid mechanics and other fields of science and engineering, while still providing efficient reduced model computations. The learning steps of Operator Inference are rooted in classical projection-based model reduction; thus, some of the rich theory of model reduction can be applied to models learned with Operator Inference. This connection to projection-based model reduction theory offers a pathway toward deriving error estimates and gaining insights to improve predictions. Furthermore, through formulations of Operator Inference that preserve Hamiltonian and other structures, important physical properties such as energy conservation can be guaranteed in the predictions of the reduced model beyond the training horizon. This review illustrates key computational steps of Operator Inference through a large-scale combustion example.

Mechanics↗
DOE OSTI2026

A Generalized Grain-Scale Model for the Non-Plasma and Plasma-Assisted Hydrogen Direct Reduction of Iron Ore

Direct Reduction of Iron ore using hydrogen (H-DRI) is a promising pathway towards efficient steelmaking and accurate predictive models are a necessity for scale-up and optimization of this technology. However, accurate models of this process remain limited because existing models oversimplify grain-scale phenomena, such as nonlinearity inside grain, self-sufficient porosity, surface reactions, and the role of plasma species. These phenomena are important for flash steelmaking and plasma-assisted H-DRI processes. To address this need, we present a phenomenological model for simulating H-DRI at the scale of a single micron-sized grain of the iron ore. We call this the Transient Reactive Grain Model (TRGM). TRGM incorporates key physical process: gas species transport, a chemical kinetics of material conversion, nanopore structural evolution and, adsorption-desorption surface kinetics at the reactive nanopore surface. The important contribution of this work is that the model provides a dependence on different reductant species, specifically hydrogen atoms versus molecules, so that role of hydrogen plasma reduction can be clarified compared to the use of pure hydrogen gas reduction. TRGM predictions agree well with experimental data for both molecular H2 reduction of Fe2O3 and plasma hydrogen reduction of Fe3O4. Results reveal species concentration gradients with a diffuse reaction zone, and enhanced hydrogen diffusion at the grain outer surface due to evolving porosity. These findings challenge common assumptions in existing models, including sharp reaction fronts, quasi-steady diffusion and kinetics, and the neglect of surface chemistry. As a generalized grain-scale model for H-DRI processes, TRGM has practical applications in flash steelmaking and in-flight reduction using both molecular and plasma hydrogen.

08 HYDROGEN↗
DOE OSTI2024

Physics-informed State-space Neural Networks for transport phenomena

This work introduces Physics -informed State -space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transportdominated systems such as chemical, biomedical, and power plants. Traditional data -driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics -informing using components' Partial Differential Equations (PDEs), resulting in a physics -constrained, end -to -end differentiable forward dynamics model. Further, through two in silico experiments - a heated channel and a cooling system loop - we demonstrate that PSMs offer a more accurate approach than a purely data -driven model. In the former experiment, PSMs demonstrated significantly lower average root -mean -square errors across test datasets compared to a purely data -driven neural network, with reductions of 44 %, 48 %, and 94 % in predicting pressure, velocity, and temperature, respectively. Beyond accuracy, PSMs demonstrate a compelling multitask capability, making them highly versatile. In this work, we showcase two: supervisory control of a nonlinear system through a sequentially updated state -space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates PSMs' ability to handle constant and time -dependent constraints, while the latter illustrates their value in system diagnostics and fault detection.

42 ENGINEERING↗
DOE OSTI2025

Towards a Quantum Algorithm for the Incompressible Nonlinear Navier-Stokes Equations

In this work, we present novel concepts for quantum algorithms to solve transient, nonlinear partial differential equations (PDEs). The challenge lies in how to effectively represent, encode, process, and evolve the nonlinear system of PDEs on quantum computers. We will discuss the new techniques using the incompressible Navier-Stokes equations as an example, because it represents the fundamental nonlinear feature and yet removes certain complexity in physics, allowing us to focus on the design of quantum algorithms. Previous attempts solving nonlinear PDEs in quantum computation have often involved storing multiple copies of solutions or employing linearizations. Neither is practical due to exponential scaling with evolution time or insufficient solution accuracy. We propose a new framework based on matrix product states (MPSs) and matrix product operators (MPOs), in addition to the Krylov subspace methods. For example, the solution variables of the Navier-Stokes equations are represented by MPSs, and the linear and nonlinear terms are processed by MPOs. The time evolution of the operators is attained by a fast-forwarding algorithm using Krylov subspace methods. Furthermore, we discuss various techniques for efficient encoding of MPSs, measurement reduction for MPOs, and use of tensor operations to treat multi-variate, multi-physics characteristics of Navier-Stokes.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗