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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fluid-Dynamic Mechanisms Underlying Wind Turbine Wake Control with Strouhal-Timed Actuation

A reduction in wake effects in large wind farms through wake-aware control has considerable potential to improve farm efficiency. This work examines the success of several emerging, empirically derived control methods that modify wind turbine wakes (i.e., the pulse method, helix method, and related methods) based on Strouhal numbers on the O(0.3). Drawing on previous work in the literature for jet and bluff-body flows, the analyses leverage the normal-mode representation of wake instabilities to characterize the large-scale wake meandering observed in actuated wakes. Idealized large-eddy simulations (LES) using an actuator-line representation of the turbine blades indicate that the n=0 and ±1 modes, which correspond to the pulse and helix forcing strategies, respectively, have faster initial growth rates than higher-order modes, suggesting these lower-order modes are more appropriate for wake control. Exciting these lower-order modes with periodic pitching of the blades produces increased modal growth, higher entrainment into the wake, and faster wake recovery. Modal energy gain and the entrainment rate both increase with streamwise distance from the rotor until the intermediate wake. This suggests that the wake meandering dynamics, which share close ties with the relatively well-characterized meandering dynamics in jet and bluff-body flows, are an essential component of the success of wind turbine wake control methods. A spatial linear stability analysis is also performed on the wake flows and yields insights on the modal evolution. In the context of the normal-mode representation of wake instabilities, these findings represent the first literature examining the characteristics of the wake meandering stemming from intentional Strouhal-timed wake actuation, and they help guide the ongoing work to understand the fluid-dynamic origins of the success of the pulse, helix, and related methods.

17 WIND ENERGY↗
DOE OSTI2014

Nodal soliton solutions for generalized quasilinear Schrödinger equations

This paper is concerned with constructing nodal radial solutions for generalized quasilinear Schrödinger equations in R{sup N} which arise from plasma physics, fluid mechanics, as well as high-power ultashort laser in matter. For any given integer k ⩾ 0, by using a change of variables and minimization argument, we obtain a sign-changing minimizer with k nodes of a minimization problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Selection of software for mechanical engineering undergraduates

A major problem with the undergraduate mechanical course is the limited exposure of students to software packages coupled with the long learning curve on the existing software packages. This work proposes the use of appropriate software packages for the entire mechanical engineering curriculum to ensure students get sufficient exposure real life design problems. A variety of software packages are highlighted as being suitable for undergraduate work in mechanical engineering, e.g. simultaneous non-linear equations; uncertainty analysis; 3-D modeling software with the FEA; analysis tools for the solution of problems in thermodynamics, fluid mechanics, mechanical system design, and solid mechanics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Reinforcement learning for bluff body active flow control in experiments and simulations

Significance Reinforcement learning (RL) has been applied effectively in games and robotic manipulation. We demonstrate the effectiveness of RL in experimental fluid mechanics by applying it to reduce the drag of circular cylinders in turbulent flow, a canonical fluid–structure interaction problem. Although physics agnostic, RL managed to reduce the drag by 30 % or reach another specified optimum point very quickly. Following this discovery, we used high-fidelity simulations to probe the underlying physical mechanisms so that the discovered control techniques can be generalized to other similar flow problems. More broadly, RL-guided active control can lead to efficient exploration of additional flow-control strategies in experimental fluid mechanics, potentially paving the way for accelerating scientific discovery and different designs in flow-related engineering problems.

42 ENGINEERING↗
DOE OSTI2021

On closures for reduced order models - A spectrum of first-principle to machine-learned avenues

For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr–Sommerfeld stability equation and numerous vortex models, of which the von Karman vortex street is one of the most prominent. Subsequent ROMs typically relied on first principles, like mathematical Galerkin models, weakly nonlinear stability theory, and two- and three-dimensional vortex models pioneered the data-driven proper orthogonal decomposition (POD) modeling. In early POD modeling, available data were used to build an optimal basis, which was then utilized in a classical Galerkin procedure to construct the ROM, but data have made a profound impact on ROMs beyond the Galerkin expansion. In this paper, we take a modest step and illustrate the impact of data-driven modeling on one significant ROM area. Specifically, we focus on ROM closures, which are correction terms that are added to the classical ROMs in order to model the effect of the discarded ROM modes in under-resolved simulations. Through simple examples, we illustrate the main modeling principles used to construct the classical ROMs, motivate and introduce modern ROM closures, and show how data-driven modeling, artificial intelligence, and machine learning have changed the standard ROM methodology over the last two decades. Finally, we outline our vision on how the state-of-the-art data-driven modeling can continue to reshape the field of reduced order modeling.

42 ENGINEERING↗
DOE OSTI2015

An Eulerian projection method for quasi-static elastoplasticity

A well-established numerical approach to solve the Navier-Stokes equations for incompressible fluids is Chorin's projection method, whereby the fluid velocity is explicitly updated, and then an elliptic problem for the pressure is solved, which is used to orthogonally project the velocity field to maintain the incompressibility constraint. In this paper, we develop a mathematical correspondence between Newtonian fluids in the incompressible limit and hypo-elastoplastic solids in the slow, quasi-static limit. Using this correspondence, we formulate a new fixed-grid, Eulerian numerical method for simulating quasi-static hypo-elastoplastic solids, whereby the stress is explicitly updated, and then an elliptic problem for the velocity is solved, which is used to orthogonally project the stress to maintain the quasi-staticity constraint. We develop a finite-difference implementation of the method and apply it to an elasto-viscoplastic model of a bulk metallic glass based on the shear transformation zone theory. We show that in a two-dimensional plane strain simple shear simulation, the method is in quantitative agreement with an explicit method. Like the fluid projection method, it is efficient and numerically robust, making it practical for a wide variety of applications. We also demonstrate that the method can be extended to simulate objects with evolving boundaries. Here, we highlight a number of correspondences between incompressible fluid mechanics and quasi-static elastoplasticity, creating possibilities for translating other numerical methods between the two classes of physical problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

MyCrunchGPT: A LLM Assisted Framework for Scientific Machine Learning

Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. Here, the objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deformation of Lie–Poisson algebras and chirality

Linearization of a Hamiltonian system around an equilibrium point yields a set of Hamiltonian symmetric spectra: If λ is an eigenvalue of the linearized generator, -λ and $\barλ$ (hence, -$\barλ$) are also eigenvalues—the former implies a time-reversal symmetry, while the latter guarantees the reality of the solution. However, linearization around a singular equilibrium point (which commonly exists in noncanonical Hamiltonian systems) works out differently, resulting in breaking of the Hamiltonian symmetry of spectra; time-reversal asymmetry causes chirality. This interesting phenomenon was first found in analyzing the chiral motion of the rattleback, a boat-shaped top having misaligned axes of inertia and geometry [Z. Yoshida et al., Phys. Lett. A 381, 2772–2777 (2017)]. To elucidate how chiral spectra are generated, we study the three-dimensional Lie–Poisson systems and classify the prototypes of singularities that cause symmetry breaking. The central idea is the deformation of the underlying Lie algebra; invoking Bianchi’s list of all three-dimensional Lie algebras, we show that the so-called class-B algebras, which are produced by asymmetric deformations of the simple algebra s o ( 3 ) , yield chiral spectra when linearized around their singularities. The theory of deformation is generalized to higher dimensions, including the infinite-dimensional Poisson manifolds relevant to fluid mechanics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Sensitivity of Thermal Predictions to Uncertain Surface Tension Data in Laser Additive Manufacturing

To understand the process-microstructure relationships in additive manufacturing (AM), it is necessary to predict the solidification characteristics in the melt pool. This study investigates the influence of Marangoni driven fluid flow on the predicted melt pool geometry and solidification conditions using a continuum finite volume model. A calibrated laser absorptivity was determined by comparing the model predictions (neglecting fluid flow) against melt pool dimensions obtained from single laser melt experiments on a nickel super alloy 625 (IN625) plate. Using this calibrated efficiency, predicted melt pool geometries agree well with experiments across a range of process conditions. When fluid mechanics is considered, a surface tension gradient recommended for IN625 tends to overpredict the influence of convective heat transfer, but the use of an intermediate value reported from experimental measurements of a similar nickel super alloy produces excellent experimental agreement. Despite its significant effect on the melt pool geometry predictions, fluid flow was found to have a small effect on the predicted solidification conditions compared to processing conditions. We find that this result suggests that under certain circumstances, a model only considering conductive heat transfer is sufficient for approximating process-microstructure relationships in laser AM. Extending the model to multiple laser passes further showed that fluid flow also has a small effect on the solidification conditions compared to the transient variations in the process. Furthermore, limitations of the current model and areas of improvement, including uncertainties associated with the phenomenological model inputs are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Principles of aerosol jet printing

Aerosol jet printing (AJP) has emerged as a promising method for microscale digital additive manufacturing using functional nanomaterial inks. While compelling capabilities have been demonstrated in the research community in recent years, the development and refinement of inks and process parameters largely follows empirical observations, with an extensive phase space over which to optimize. While this has led to general qualitative guidelines and ink- and machine-specific correlations, a more fundamental understanding based on principles of aerosol physics and fluid mechanics is lacking. This contrasts with more mature printing technologies, for which foundational physical principles have been rigorously examined. Presented here is a broad framework for describing the AJP process. Simple analytical models are employed to ensure generality and accessibility of the results, while experimental validation using a silver nanoparticle ink supports the physical relevance of the approach. In conclusion, this basic understanding enables a description of process limitations grounded in fundamental principles, as well as guidelines for improved printer design, ink formulation, and print parameter optimization.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.

Computer Science↗
DOE OSTI2015

Contribution of the numerical simulations in the development of the French HLLW vitrification Processes - 15126

For many years, the CEA (Commissariat a l'energie Atomique et aux energies Alternatives) Marcoule France has developed various processes dedicated to radioactive waste confinement, especially vitrification processes for HLLW. For 10 years now, the numerical simulation has become an important tool for research and development projects held in the CEA-AREVA Joint Vitrification Laboratory (LCV). Induction heating, fluid mechanics and thermal simulations take part of all new R and D projects. The contributions of such simulations are, first, the enhancement of the working knowledge of existing process. Those data are very useful to define optimisation choices, for example upgrades made on the hot metallic melter used since the 90's at La Hague facility. Second, the simulations are, of course, also used at the conception stage of new processes as a tool allowing wide ranges parametric tests. This has been extensively used in the design of the cold crucible inductive melter (CCIM) commissioned in 2010 at La Hague plant. Finally, it is a powerful and relatively cheap tool for prospective studies for processes of the future. Whatever the purpose, the potential benefits are gains on the reliability, the output capacity and the life time. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Applications of Fusion Energy Sciences Research - Scientific Discoveries and New Technologies Beyond Fusion

Since the 1950s, scientists and engineers in the U.S. and around the world have worked hard to make an elusive goal to be achieved on Earth: harnessing the reaction that fuels the stars, namely fusion. Practical fusion would be a source of energy that is unlimited, safe, environmentally benign, available to all nations and not dependent on climate or the whims of the weather. Significant resources, most notably from the U.S. Department of Energy (DOE) Office of Fusion Energy Sciences (FES), have been devoted to pursuing that dream, and significant progress is being made in turning it into a reality. However, that is only part of the story. The process of creating a fusion-based energy supply on Earth has led to technological and scientific achievements of far-reaching impact that touch every aspect of our lives. Those largely unanticipated advances, spanning a wide variety of fields in science and technology, are the focus of this report. There are many synergies between research in plasma physics (the study of charged particles and fluids interacting with self-consistent electric and magnetic fields), high-energy physics, and condensed matter physics dating back many decades. For instance, the formulation of a mathematical theory of solitons, solitary waves which are seen in everything from plasmas to water waves to Bose-Einstein Condensates, has led to an equal span of applications, including the fields of optics, fluid mechanics and biophysics. Another example, the development of a precise criterion for transition to chaos in Hamiltonian systems, has offered insights into a range of phenomena including planetary orbits, two-person games and changes in the weather. Seven distinct areas of fusion energy sciences were identified and reviewed which have had a recent impact on fields of science, technology and engineering not directly associated with fusion energy: Basic plasma science; Low temperature plasmas; Space and astrophysical plasmas; High energy density laboratory plasmas and inertial fusion energy; Particle accelerator technology; Fusion nuclear science; and Magnetically confined plasmas. Individual sections within the report summarize applications associated with each of these areas. These sections were also informed by a survey that went out to the community, and the subcommittee wishes to thank those who responded, as well as to the national labs and universities that contributed photographs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models

Due to costs and practical constraints, field campaigns in the atmospheric boundary layer typically only measure a fraction of the atmospheric volume of interest. Machine learning techniques have previously successfully reconstructed unobserved regions of flow in canonical fluid mechanics problems and two-dimensional geophysical flows, but these techniques have not yet been demonstrated in the three-dimensional atmospheric boundary layer. Here, we conduct a numerical analogue of a field campaign with spatially limited measurements using large-eddy simulation. We pose flow reconstruction as an inpainting problem, and reconstruct realistic samples of turbulent, three-dimensional flow with the use of a latent diffusion model. The diffusion model generates physically plausible turbulent structures on larger spatial scales, even when input observations cover less than 1% of the volume. Through a combination of qualitative visualization and quantitative assessment, we demonstrate that the diffusion model generates meaningfully diverse samples when conditioned on just one observation. These samples successfully serve as initial conditions for a large-eddy simulation code. We find that diffusion models show promise and potential for other applications for other turbulent flow reconstruction problems.

17 WIND ENERGY↗
DOE OSTI2013

Chemical composition and temperature dependent performance of ZnO-thin film transistors deposited by pulsed and continuous spray pyrolysis

Zinc oxide thin film transistors (TFTs) deposited by continuous and pulsed spray pyrolysis were investigated to analyze process kinetics which make reduction of process temperature possible. Thus, fluid mechanics, chemical composition, electrical performance, and deposition and annealing temperature were systematically analyzed. It was found that ZnO layers continuously deposited at 360 °C contained zinc oxynitrides, CO{sub 3}, and hydro carbonate groups from pyrolysis of basic zinc acetate. Statistically, every second wurtzite ZnO unit cell contained an impurity atom. The purity and performance of the ZnO-TFTs increased systematically with increasing deposition temperature due to an improved oxidation processes. At 500 °C the zinc to oxygen ratio exceeded a high value of 0.96. Additionally, the ZnO film was not found to be in a stabilized state after deposition even at high temperatures. Introducing additional subsequent annealing steps stabilizes the film and allows the reduction of the overall thermal stress to the substrate. Further improvement of device characteristics was obtained by pulsed deposition which allowed a more effective transport of the by-products and oxygen. A significant reduction of the deposition temperature by 140 °C was achieved compared to the same performance as in continuous deposition mode. The trap density close to the Fermi energy could be reduced by a factor of two to 4 × 10{sup 17} eV{sup −1} cm{sup −3} due to the optimized combustion process on the surface. The optimization of the deposition processes made the fabrication of TFTs with excellent performance possible. The mobility was high and exceeded 12 cm{sup 2}/V s, the subthreshold slope was 0.3 V dec{sup −1}, and an on-set close to the ideal value of 0 V was achieved.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗