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At least 1,009 records · Page 56

Learning About Nucleosynthesis from Multi-dimensional Simulations of Core-Collapse Supernovae

For more than two decades, we have understood that the development of a successful core-collapse supernova is inextricably linked to neutrino heating and three dimensional fluid flows, with large scale hydrodynamic instabilities allowing successful explosions that spherical symmetry would prevent. Unfortunately, our understanding of the nucleosynthesis that occurs in these supernovae, and therefore the impact of supernovae on galactic chemical evolution, has generally ignored much that we have learned about the central engine of these supernovae over the past two decades. Now, with two and three dimensional simulations of core-collapse supernovae run to sufficient duration, we are learning how the multi-dimensional, neutrino-driven character of the explosions directly impacts the nucleosynthesis and other observables of core-collapse supernovae. Here we focus on lessons from multi-dimensional models which implement realistic nuclear reaction networks.

Hix, W Raphael↗

Abstract for CRADA among NETL, Mälardalen University, University College Cork (acting through its Tyndall National Institute), and Micro Electricity Generation Association

GeoCoHorT aims to accelerate the transition to 4 th generation district heating and cooling (4GDHC) in Europe and globally, by assessing, optimizing, and demonstrating the integration of shallow geothermal heat extraction with other renewable sources and smart buildings. The target geo-source is ground water from River Shannon (Limerick, Ireland), which will provide both an efficiency and noise-reduction benefit to the micro-district and a method of combatting climate-change-warming of the Shannon Estuary. The heat from this low-temperature source will be recycled and supplied to a smart district located in Limerick, Ireland, through heat pumps powered by renewable electricity and by means of a suitable DH network design. The mandate is to find solutions that work for entire communities in a fair and sustainable manner, with the involvement of the communities themselves to highlight their needs. The project brings together four partners from Ireland, Sweden, and the U.S.A. with strong multi-disciplinary competencies. The Tyndall Institute at University of Cork (UCC) and Mälardalen University (MDU) will design and optimize the 4GDH micro-district system, and the Micro Electricity Generation Association (MEGA) will work with the community to build a sense of ownership of the Climate Challenge and ensure close community involvement. The National Energy Technology Laboratory of the U.S. Department of Energy (NETL) will design the prospective geo-fluid loop to optimize heat-extraction effectiveness with ecological safety and assess the environmental benefits of the proposed solutions. Industrial advisors (Eskilstuna Strängnäs Energi och Miljö (ESEM) and Mimer in Sweden, and Smart MPOWER in Ireland) will steer the technology development to meet the needs of public utilities and consumers. Current infrastructure in the smart district in Limerick (demonstration site of an EU Lighthouse project) will be exploited and modified with support of MEGA, UCC, and MPOWER to integrate micro-DH from low-temperature heat sources, such as the river heat, and to allow increasing penetration of prosumers in the grid. Additional case studies will be developed with the help of industrial advisors to extend the results to other regions (e.g., Sweden and USA) and provide valuable insights for the development of 4GDH worldwide.

15 GEOTHERMAL ENERGY↗

Implementation of a fully-balanced periodic tridiagonal solver on a parallel distributed memory architecture

While parallel computers offer significant computational performance, it is generally necessary to evaluate several programming strategies. Two programming strategies for a fairly common problem - a periodic tridiagonal solver - are developed and evaluated. Simple model calculations as well as timing results are presented to evaluate the various strategies. The particular tridiagonal solver evaluated is used in many computational fluid dynamic simulation codes. The feature that makes this algorithm unique is that these simulation codes usually require simultaneous solutions for multiple right-hand-sides (RHS) of the system of equations. Each RHS solutions is independent and thus can be computed in parallel. Thus a Gaussian elimination type algorithm can be used in a parallel computation and the more complicated approaches such as cyclic reduction are not required. The two strategies are a transpose strategy and a distributed solver strategy. For the transpose strategy, the data is moved so that a subset of all the RHS problems is solved on each of the several processors. This usually requires significant data movement between processor memories across a network. The second strategy attempts to have the algorithm allow the data across processor boundaries in a chained manner. This usually requires significantly less data movement. An approach to accomplish this second strategy in a near-perfect load-balanced manner is developed. In addition, an algorithm will be shown to directly transform a sequential Gaussian elimination type algorithm into the parallel chained, load-balanced algorithm.

Eidson, T. M.↗

Zero Boil-Off System Testing

Cryogenic propellants such as liquid hydrogen (LH2) and liquid oxygen (LO2) are a part of NASA's future space exploration plans due to their high specific impulse for rocket motors of upper stages. However, the low storage temperatures of LH2 and LO2 cause substantial boil-off losses for long duration missions. These losses can be eliminated by incorporating high performance cryocooler technology to intercept heat load to the propellant tanks and modulating the cryocooler temperature to control tank pressure. The technology being developed by NASA is the reverse turbo-Brayton cycle cryocooler and its integration to the propellant tank through a distributed cooling tubing network coupled to the tank wall. This configuration was recently tested at NASA Glenn Research Center in a vacuum chamber and cryoshroud that simulated the essential thermal aspects of low Earth orbit, its vacuum and temperature. This test series established that the active cooling system integrated with the propellant tank eliminated boil-off and robustly controlled tank pressure.

Reverse turbo-Brayton cycle↗

Imaging and quantitative data acquisition of biological cell walls with Atomic Force Microscopy and Scanning Acoustic Microscopy

This chapter demonstrates the feasibility of Atomic Force Microscopy (AFM) and High Frequency Scanning Acoustic Microscopy (HF-SAM) as tools to characterize biological tissues. Both the AFM and the SAM have shown to provide imaging (with different resolution) and quantitative elasticity measuring abilities. Plant cell walls with minimal disturbance and under conditions of their native state have been examined with these two kinds of microscopy. After descriptions of both the SAM and AFM, their special features and the typical sample preparation is discussed. The sample preparation is focused here on epidermal peels of onion scales and celery epidermis cells which were sectioned for the AFM to visualize the inner surface (closest to the plasma membrane) of the outer epidermal wall. The nm-wide cellulose microfibrils orientation and multilayer structure were clearly observed. The microfibril orientation and alignment tend to be more organized in older scales compared with younger scales. The onion epidermis cell wall was also used as a test analog to study cell wall elasticity by the AFM nanoindentation and the SAM V(z) feature. The novelty in this work was to demonstrate the capability of these two techniques to analyze isolated, single layered plant cell walls in their natural state. AFM nanoindentation was also used to probe the effects of Ethylenediaminetetraacetic acid (EDTA), and calcium ion treatment to modify pectin networks in cell walls. The results suggest a significant modulus increase in the calcium ion treatment and a slight decrease in EDTA treatment. To complement the AFM measurements, the HF-SAM was used to obtain the V(z) signatures of the onion epidermis. These measurements were focused on documenting the effect of pectinase enzyme treatment. The results indicate a significant change in the V(z) signature curves with time into the enzyme treatment. Thus AFM and HF-SAM open the door to a systematic nondestructive structure and mechanical property study of complex biological cell walls. A unique feature of this approach is that both microscopes allow the biological samples to be examined in their natural fluid (water) environment.

nanoindentation↗

A dynamic 2D Borehole Thermal Energy Storage (BTES) model for enhanced computational efficiency

Progressing toward a future increasingly reliant on renewable energy sources, the development of effective, durable energy storage solutions becomes essential to balance supply and demand fluctuations. Borehole Thermal Energy Storage (BTES) is a long-duration thermal energy storage technology that captures excess heat generated from renewable energy sources and stores it underground for later use, enabling the efficient utilization of sustainable energy. This approach is particularly valuable in district energy networks when integrated with Ground Source Heat Pumps (GSHP) to provide stable heating and cooling. However, traditional three-dimensional (3D) numerical models of BTES systems demand extensive computational resources, limiting their practicality for real-time and large-scale applications. This study introduces a novel two-dimensional (2D) modeling approach that reduces computational costs while maintaining high accuracy. By employing a radial ring-based discretization method, the model simulates heat injection, retention, and retrieval dynamics over seasonal cycles. A new thermal-mass weighted-average temperature parameter is introduced to evaluate the performance of BTES systems. Model validation against FEFLOW simulations demonstrates a 17-fold improvement in computational speed compared to traditional Computational Fluid Dynamics (CFD) models while achieving a mean absolute percentage error (MAPE) of 2 % during charging and 4 % during discharging. Additionally, a trade-off analysis between computational efficiency and accuracy is conducted, ensuring the model's applicability for real-world scenarios. The findings of this research contribute to the development of computationally efficient BTES models, facilitating better optimization, control, and integration into renewable energy systems. This work provides a foundation for further studies in techno-economic analysis, multi-year performance evaluation, and real-time operational strategies for BTES applications, supporting a more sustainable energy future.

2D modeling↗

Data-driven closure modeling for hypersonic turbulent flows

The Reynolds-averaged Navier–Stokes (RANS) equations remain a workhorse technology for simulating compressible fluid flows of practical interest. Due to model-form errors, however, RANS models can yield erroneous predictions that preclude their use on mission-critical problems. This report summarizes work performed from FY22-FY24 focused on improving RANS models for hypersonic flows using data-driven modeling and scientific machine learning. In this work we: 1. Investigate the current capabilities of RANS models in Sandia’s parallel aerodynamics and re-entry code (SPARC) for hypersonic flows with a focus on shock boundary layer interactions (SBLIs), 2. Assess several established corrections that exist in the literature aimed at improving predictions for SBLIs, 3. Develop improved models for the Reynolds stress tensor using tensor-basis neural networks, 4. Develop a neural-network-based variable turbulent Prandtl number model to reduce errors in wall heating in SBLIs. 5. Begin future investigations including employing the LIFE framework to improve wall heating predictions in SBLIs as well as the ensemble Kalman filter. We find that current RANS models in SPARC are deficient for complex SBLI flows. In particular, no current model jointly predicts wall heat flux, wall shear stress, and wall pressure with reasonable accuracy. Existing corrections help, but do not alleviate this issue altogether. The development of improved models for the Reynolds stress tensor via tensor-basis neural networks results in more predictive RANS models across a suite of low-speed and high-speed cases. For hypersonic boundary layers, the inclusion of the wall-normal Reynolds stress via TBNNs has an appreciable impact on the wall-normal momentum balance and wall quantities. However, we find that improvements to the Reynolds stress tensor do not address the over-prediction in wall heat flux in SBLIs. We find that a neural-network-based variable turbulent Prandtl number model systematically and substantially improves wall heating predictions for a range of SBLI cases.

97 MATHEMATICS AND COMPUTING↗

Large Eddy Simulation of Autoignition Transients in a Model Diesel Injector Configuration

Developing an improved understanding of transient mixing and combustion processes inherent in diesel injection is an important element in the design of advanced engines. This paper provides a detailed analysis of these processes using an idealized benchmark configuration designed to facilitate precise comparisons between different models and numerical methods. The computational domain is similar to the Engine Combustion Network (www.sandia.gov/ECN) Spray-A injector with n-dodecane as the fuel. Quantified idealizations are made in the treatment of boundary conditions to eliminate ambiguities and unknowns associated with the actual injector(s) used in the experiment. These ambiguities hinder comparisons aimed at understanding the accuracy of different models and the coupled effects of potential numerical errors. Prior to understanding the impact of injector imperfections on their performance, it is first necessary to understand how models perform in a well-controlled environment with well-defined boundary conditions. Here, we focus on the latter while accurately matching the operating conditions used in the experiments. Relevant high-pressure phenomena are treated with real-fluid thermodynamics and transport for multicomponent mixtures. A highly resolved calculation is performed to study both scalar-mixing and initiation of combustion. Results are assembled into a database that can facilitate one-to-one comparisons between codes. The combined set of results is analyzed to provide physical insights related to localized broadband transient mixing and combustion processes that are typically not available from experiments.

Hakim, Layal↗

Mass Transport in Global Geophysical Fluids

Mass transports occurring in the atmosphere-hydrosphere-solid Earth-core system (the "global geophysical fluids") are important geophysical phenomena. They occur on all temporal and spatial scales. Examples include air mass and ocean circulations, tides, hydrological water redistribution, mantle processes such as post-glacial rebound, earthquakes and tectonic motions, and core geodynamo activities. With only a few exceptions on the Earth surface, the temporal history and spatial pattern of such mass transport are often not amenable to direct observations. Space geodesy techniques, however, have the capability of monitoring certain direct consequences of the mass transport, including Earth's rotation variations, gravitational field variations, and the geocenter motion. These techniques include the very-long-baseline interferometry, satellite laser ranging and Doppler tracking, and the Global Positioning System, all entail global observational networks. While considerable advances have been made in observing and understanding of the dynamics of Earth's rotation, only the lowest-degree gravitational variations have been observed and limited knowledge of geocenter motion obtained. New space missions, projects and initiatives promise to further improve the measurements and hence our knowledge about the global mass transports. The latter contributes to our understanding and modeling capability of the geophysical processes that produce and regulate the mass transports, as well as the solid Earth's response to such changes in constraining the modeling of Earth's mechanical properties.

Chao, B. F.↗

Interface learning in fluid dynamics: Statistical inference of closures within micro–macro-coupling models

Many complex multiphysics systems in fluid dynamics involve using solvers with varied levels of approximations in different regions of the computational domain to resolve multiple spatiotemporal scales present in the flow. The accuracy of the solution is governed by how the information is exchanged between these solvers at the interface and several methods have been devised for such coupling problems. In this article, we construct a data-driven model by spatially coupling a microscale lattice Boltzmann method (LBM) solver and macroscale finite difference method (FDM) solver for reaction-diffusion systems. The coupling between the micro-macro solvers has one to many mapping at the interface leading to the interface closure problem, and we propose a statistical inference method based on neural networks to learn this closure relation. The performance of the proposed framework in a bifidelity setting partitioned between the FDM and LBM domain shows its promise for complex systems where analytical relations between micro-macro solvers are not available.

42 ENGINEERING↗

Global Design Optimization for Aerodynamics and Rocket Propulsion Components

Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design practices and the need for future research are identified.

Shyy, Wei↗

Three-Dimensional High-Resolution Optical/X-Ray Stereoscopic Tracking Velocimetry

Measurement of three-dimensional (3-D) three-component velocity fields is of great importance in a variety of research and industrial applications for understanding materials processing, fluid physics, and strain/displacement measurements. The 3-D experiments in these fields most likely inhibit the use of conventional techniques, which are based only on planar and optically-transparent-field observation. Here, we briefly review the current status of 3-D diagnostics for motion/velocity detection, for both optical and x-ray systems. As an initial step for providing 3-D capabilities, we nave developed stereoscopic tracking velocimetry (STV) to measure 3-D flow/deformation through optical observation. The STV is advantageous in system simplicity, for continually observing 3- D phenomena in near real-time. In an effort to enhance the data processing through automation and to avoid the confusion in tracking numerous markers or particles, artificial neural networks are employed to incorporate human intelligence. Our initial optical investigations have proven the STV to be a very viable candidate for reliably measuring 3-D flow motions. With previous activities are focused on improving the processing efficiency, overall accuracy, and automation based on the optical system, the current efforts is directed to the concurrent expansion to the x-ray system for broader experimental applications.

Cha, Soyoung S.↗

Three-Dimensional High-Resolution Optical/X-Ray Stereoscopic Tracking Velocimetry

Measurement of three-dimensional (3-D) three-component velocity fields is of great importance in a variety of research and industrial applications for understanding materials processing, fluid physics, and strain/displacement measurements. The 3-D experiments in these fields most likely inhibit the use of conventional techniques, which are based only on planar and optically-transparent-field observation. Here, we briefly review the current status of 3-D diagnostics for motion/velocity detection, for both optical and x-ray systems. As an initial step for providing 3-D capabilities, we have developed stereoscopic tracking velocimetry (STV) to measure 3-D flow/deformation through optical observation. The STV is advantageous in system simplicity, for continually observing 3-D phenomena in near real-time. In an effort to enhance the data processing through automation and to avoid the confusion in tracking numerous markers or particles, artificial neural networks are employed to incorporate human intelligence. Our initial optical investigations have proven the STV to be a very viable candidate for reliably measuring 3-D flow motions. With previous activities focused on improving the processing efficiency, overall accuracy, and automation based on the optical system, the current efforts is directed to the concurrent expansion to the x-ray system for broader experimental applications.

Cha, Soyoung S.↗

Usage of ChatGPT for Engineering Design and Analysis Tool Development

ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT’s application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an open-source, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT’s help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn’s MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model—less than one-fifth of the stress value in the baseline model.

Design↗

Usage of ChatGPT for Engineering Design and Analysis Tool Development

ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT’s application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an open-source, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT’s help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn’s MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model—less than one-fifth of the stress value in the baseline model.

Design↗

Experimental study of crossover from capillary to viscous fingering for supercritical CO 2 - water displacement in a homogeneous pore network

Carbon sequestration in saline aquifers involves displacing resident brine from the pore space by supercritical CO 2 (scCO 2 ). The displacement process is considered unstable due to the unfavorable viscosity ratio (logM < 0). The unstable mechanisms that affect scCO 2 - water displacement under reservoir conditions (i.e., 41 °C, 9 MPa) were investigated in a homogeneous micromodel. A wide range of injection rates (logCa = -7.61~-4.73) was studied in two sets of experiments: discontinuous-rate injection, where the micromodel was first cleaned and saturated with water before each injection rate was imposed, and continuous-rate injection, where the rate was increased after quasi-steady conditions were reached for a certain rate. For the discontinuous-rate experiments, capillary fingering and viscous fingering are the dominant mechanisms for low (logCa <= -6.61) and high injection rates (logCa >= -5.21), respectively. Crossover from capillary to viscous fingering was observed for logCa = -5.91~-5.21, resulting in a large decrease in scCO 2 saturation. The discontinuous-rate experimental results confirmed the decrease in nonwetting fluid saturation during crossover from capillary to viscous fingering predicted by numerical simulations by Lenormand et al. (1988).1 Capillary fingering was the only mechanism that dominates all injection rates in the continuous-rate experiment, and resulted in monotonic increase in scCO 2 saturation.

Environmental Molecular Sciences Laboratory↗

Review of NASA's (National Aeronautics and Space Administration) Numerical Aerodynamic Simulation Program

NASA has planned a supercomputer for computational fluid dynamics research since the mid-1970's. With the approval of the Numerical Aerodynamic Simulation Program as a FY 1984 new start, Congress requested an assessment of the program's objectives, projected short- and long-term uses, program design, computer architecture, user needs, and handling of proprietary and classified information. Specifically requested was an examination of the merits of proceeding with multiple high speed processor (HSP) systems contrasted with a single high speed processor system. The panel found NASA's objectives and projected uses sound and the projected distribution of users as realistic as possible at this stage. The multiple-HSP, whereby new, more powerful state-of-the-art HSP's would be integrated into a flexible network, was judged to present major advantages over any single HSP system.

Source record↗

Algorithms for parallel flow solvers on message passing architectures

The purpose of this project has been to identify and test suitable technologies for implementation of fluid flow solvers -- possibly coupled with structures and heat equation solvers -- on MIMD parallel computers. In the course of this investigation much attention has been paid to efficient domain decomposition strategies for ADI-type algorithms. Multi-partitioning derives its efficiency from the assignment of several blocks of grid points to each processor in the parallel computer. A coarse-grain parallelism is obtained, and a near-perfect load balance results. In uni-partitioning every processor receives responsibility for exactly one block of grid points instead of several. This necessitates fine-grain pipelined program execution in order to obtain a reasonable load balance. Although fine-grain parallelism is less desirable on many systems, especially high-latency networks of workstations, uni-partition methods are still in wide use in production codes for flow problems. Consequently, it remains important to achieve good efficiency with this technique that has essentially been superseded by multi-partitioning for parallel ADI-type algorithms. Another reason for the concentration on improving the performance of pipeline methods is their applicability in other types of flow solver kernels with stronger implied data dependence. Analytical expressions can be derived for the size of the dynamic load imbalance incurred in traditional pipelines. From these it can be determined what is the optimal first-processor retardation that leads to the shortest total completion time for the pipeline process. Theoretical predictions of pipeline performance with and without optimization match experimental observations on the iPSC/860 very well. Analysis of pipeline performance also highlights the effect of uncareful grid partitioning in flow solvers that employ pipeline algorithms. If grid blocks at boundaries are not at least as large in the wall-normal direction as those immediately adjacent to them, then the first processor in the pipeline will receive a computational load that is less than that of subsequent processors, magnifying the pipeline slowdown effect. Extra compensation is needed for grid boundary effects, even if all grid blocks are equally sized.

Vanderwijngaart, Rob F.↗