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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0

Abstract. Water flowing under ice sheets and glaciers can have a strong influence on ice dynamics, particularly through pressure changes, suggesting that a comprehensive ice sheet model should include the effect of basal hydrology. Modeling subglacial hydrology remains a challenge, however, mainly due to the range of spatial and temporal scales involved – from subglacial channels to vast subglacial lakes. Additionally, networks of subglacial drainage channels dynamically evolve over time. To address some of these challenges, we have developed an adaptive mesh refinement (AMR) model based on the Chombo software framework. We extend the model proposed by Sommers et al. (2018) with a small but significant change to accommodate the transition from unresolved to resolved flow features. We handle the strong nonlinearities present in the equations by resorting to an efficient nonlinear full approximation scheme multigrid (FAS-MG) algorithm. We outline the details of the algorithm and present convergence analysis results demonstrating its good performance. Additionally, we present results validating our approach, using test cases from the Subglacial Hydrology Model Intercomparison Project (SHMIP) (de Fleurian et al., 2018). We finish by presenting a more complex, 100 km-by-100 km synthetic test case with peaks and valleys that we use to investigate the effective pressure distribution as the number of AMR levels increases. These preliminary results suggest that a minimum spatial resolution is needed to properly capture channel features, but additional work is required to precisely quantify this and its impact on accurately modeling the coupled ice sheet–hydrology system. The efficiency of our approach, relying on localized refinement, is also demonstrated. Future work will include coupling the SUbglacial Hydrology MOdel (SUHMO) with the BISICLES AMR ice sheet model (Cornford et al., 2013), both built on the same numerical framework.

58 GEOSCIENCES↗
DOE OSTI2016

A multiscale hydro-geochemical-mechanical approach to analyze faulted CO 2 reservoirs: Original Research Article: A multiscale hydro-geochemical-mechanical approach to analyze faulted CO 2 reservoirs

This paper applies a multiscale hydro-geochemical-mechanical approach to analyze faulted CO 2 reservoirs using the STOMP-CO 2 -R code that is coupled to the ABAQUS® finite element package. STOMP-CO 2 -R models the reactive transport of CO 2 causing mineral volume fraction changes that are captured by an Eshelby-Mori-Tanka model implemented in ABAQUS®. A three-dimensional (3D) STOMP-CO 2 -R model for a reservoir containing an inclined fault was built to analyze a formation containing a reaction network with 5 minerals: albite, anorthite, calcite, kaolinite and quartz. A 3D finite element mesh that exactly maps the STOMP-CO 2 -R grid is developed for coupled hydro-geochemical-mechanical analyses. The model contains alternating sandstone and shale layers. The impact of reactive transport of CO 2 on the geomechanical properties of reservoir rocks and seals are studied in terms of mineral composition changes that affect their geomechanical responses. Simulations assuming extensional and compressional stress regimes with and without coupled geochemistry are performed to study the stress regime effect on the risk of hydraulic fracture. The tendency for the fault to slip is examined in terms of stress regime, geomechanical and geochemical-mechanical effects as well as fault inclination. The results show that mineralogical changes due to long-term injection of CO 2 reduce the permeability and elastic modulus of the reservoir, leading to increased risk of hydraulic fracture in the injection location and at the caprock seal immediately above the injection zone. Fault slip is not predicted to occur. However, fault inclination and stress regime have an important impact on the slip tendency factor.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Mota Mapper: A Multi-Objective Topology-Aware Mapper Library

Mota is a library that provides several heuristics for mapping tasks to compute ranks (processes) on a networked computer cluster or supercomputer. It was originally designed for task placement of adaptive mesh refinement codes (AMR). It is multi-objective in the sense that it simultaneously balances (potentially multiple types of) computational load on each rank as well as the communication traffic between the boxes. The heuristics used for mapping include algorithms such as greedy list assignment and space-filling curves, as well as algorithms from graph analysis such as adjacency matrix bandwidth reduction, recursive bisection, and greedy graph placement.

Chan, Cy↗
DOE OSTI2022

SAM-ML: Integrating data-driven closure with nuclear system code SAM for improved modeling capability

Advanced reactors often involve complicated thermal-fluid (T-F) phenomena. Modeling such phenomena with the traditional one-dimensional (1-D) system code is a challenging task. The System Analysis Module (SAM), a modern nuclear system code, has developed a coarse mesh multi-dimensional (multi-D) flow model to capture the spatial effect of T-F phenomena in advanced reactors. As a coarse mesh solver, constitutive relations are required for SAM's multi-D model for unresolved fine-scale physics, such as turbulence. Here this work presents a novel approach that integrates neural networks as data-driven closure for SAM's multi-D flow model. The data-driven closure is trained with fine-resolution data to ensure its accuracy while maintaining a coarse mesh setup to ensure its efficiency and consistency with SAM. We demonstrate the applicability of this SAM-ML capability in an open volume thermal stratification problem, where a neural network model serves as the eddy viscosity closure. A customized interface between the neural network and SAM is developed to ensure flexible and efficient data exchange. The SAM-ML results demonstrate superior performance compared to SAM's built-in zero-equation eddy viscosity closure. The case study shows that although the generalization capability of the data-driven closure still needs to be improved for different transient case or different geometric setup, SAM -ML demonstrates good potential for challenging simulation problems with improved accuracy and computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Modeling of Hydraulic Fracture Propagation at the kISMET Site Using a Fully Coupled 3D Network-Flow and Quasi- Static Discrete Element Model

Aimed at supporting the design of hydraulic fracturing experiments at the kISMET site, ~1500 m below ground in a deep mine, we performed pre-experimental hydraulic fracturing simulations in order to estimate the breakdown pressure, propagation pressure, fracture geometry, and the magnitude of induced seismicity using a newly developed fully coupled three-dimensional (3D) network flow and quasi-static discrete element model (DEM). The quasi-static DEM model, which is constructed by Delaunay tessellation of the rock volume, considers rock fabric heterogeneities by using the “disordered” DEM mesh and adding random perturbations to the stiffness and tensile/shear strengths of individual DEM elements and the elastic beams between them. A conjugate 3D flow network based on the DEM lattice is constructed to calculate the fluid flow in both the fracture and porous matrix. One distinctive advantage of the model is that fracturing is naturally described by the breakage of elastic beams between DEM elements. It is also extremely convenient to introduce mechanical anisotropy into the model by simply assigning orientation-dependent tensile/shear strengths to the elastic beams. In this paper, the 3D hydraulic fracturing model was verified against the analytic solution for a penny-shaped crack model. We applied the model to simulate fracture propagation from a vertical open borehole based on initial estimates of rock mechanical properties and in-situ stress conditions. The breakdown pressure and propagation pressure are directly obtained from the simulation. In addition, the released elastic strain energies of individual fracturing events were calculated and used as a conservative estimate for the magnitudes of the potential induced seismic activities associated with fracturing. The comparisons between model predictions and experimental results are still ongoing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Post-Fracturing Experiment Simulation of Hydraulic Fracture Propagation in a Deep Mine Using a Fully Coupled 3D Network-Flow and Quasi-static Discrete Element Model

Aimed at supporting the hydraulic fracturing experiments in a vertical borehole in the Poorman phyllite at approximately 1500 m depth at the Sanford Underground Research Facility (SURF), we perform post-hydraulic fracturing experiment simulations and compare the simulated borehole pressure changes and flow rates with actual measurements using a newly developed fully coupled three-dimensional (3D) network flow and quasi-static discrete element model (DEM). The quasi-static DEM model, which is constructed by Delaunay tessellation of the rock volume, considers rock fabric heterogeneities by using the “disordered” DEM mesh and adding random perturbations to the stiffness and tensile/shear strengths of individual DEM elements and the elastic beam between them. A conjugate 3D flow network based on DEM lattice is constructed to calculate the fluid flow in both the fracture and porous matrix. One distinctive advantage of the DEM model is that fracturing is naturally described by the breakage of elastic beams between DEM elements. It is also extremely convenient to introduce mechanical anisotropy into the model by simply assigning orientation-dependent tensile/shear strengths to the elastic beams.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Textile Inspired Lithium-Oxygen Battery Cathode with Decoupled Oxygen and Electrolyte Pathways

The lithium–air (Li–O 2 ) battery has been deemed one of the most promising next–generation energy–storage devices due to its ultrahigh energy density. However, in conventional porous carbon–air cathodes, the oxygen gas and electrolyte often compete for transport pathways, which limit battery performance. Here, a novel textile–based air cathode is developed with a triple–phase structure to improve overall battery performance. The hierarchical structure of the conductive textile network leads to decoupled pathways for oxygen gas and electrolyte: oxygen flows through the woven mesh while the electrolyte diffuses along the textile fibers. Due to noncompetitive transport, the textile–based Li–O 2 cathode exhibits a high discharge capacity of 8.6 mAh cm –2 , a low overpotential of 1.15 V, and stable operation exceeding 50 cycles. The textile–based structure can be applied to a range of applications (fuel cells, water splitting, and redox flow batteries) that involve multiple phase reactions. In conclusion, the reported decoupled transport pathway design also spurs potential toward flexible/wearable Li–O 2 batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Utah FORGE: 2023 Large Upscaled Discrete Fracture Network Models

This dataset includes the data and a report on the large upscaled discrete fracture network modeling done for the Utah FORGE project in 2023. The FORGE modeling team is making five discrete fracture network (DFN) realizations of a large reservoir model available to researchers. These models have been upscaled to a continuum mesh or grid at resolutions of 10 meters and 20 meters providing reservoir properties for fracture porosity, permeability, and compressibility. The models are available in both the reference global coordinate frame and a local coordinate frame aligned with principal stress directions.

15 GEOTHERMAL ENERGY↗
NASA NTRS1996

Full Mesh Audio Conferencing Using the Point-to-Multipoint On-Board Switching Capability of ACTS

The purpose of this paper is to describe an implementation of audio conferencing using the ACTS T1-VSAT network. In particular, this implementation evaluates the use of the on-board switching capability of the satellite as a viable alternative for providing the multipoint connectivity normally provided by terrestrial audio bridge equipment The system that was implemented provides full mesh, full-duplex audio conferencing, with end-to-end voice paths between all participants requiring only a single hop (i.e. 250 msec. delay). Moreover, it addresses the lack of spontaneity in current systems by allowing a user to easily start a conference from any standard telephone handset connected to an ACTS earth station, and quickly add new members to the conference at any time using the 'hook flash' capability. No prior scheduling of resources is required and there is no central point of control, thereby providing the user with the spontaneity desired in audio conference control.

Rivett, Mary L.↗
DOE OSTI2025

Solving sparse finite element problems on neuromorphic hardware

The finite element method (FEM) is one of the most important and ubiquitous numerical methods for solving partial differential equations (PDEs) on computers for scientific and engineering discovery. Applying the FEM to larger and more detailed scientific models has driven advances in high-performance computing for decades. Here we demonstrate that scalable spiking neuromorphic hardware can directly implement the FEM by constructing a spiking neural network that solves the large, sparse, linear systems of equations at the core of the FEM. We show that for the Poisson equation, a fundamental PDE in science and engineering, our neural circuit achieves meaningful levels of numerical accuracy and close to ideal scaling on modern, inherently parallel and energy-efficient neuromorphic hardware, specifically Intel’s Loihi 2 neuromorphic platform. We illustrate extensions to irregular mesh geometries in both two and three dimensions as well as other PDEs such as linear elasticity. Our spiking neural network is constructed from a recurrent network model of the brain’s motor cortex and, in contrast to black-box deep artificial neural network-based methods for PDEs, directly translates the well-understood and trusted mathematics of the FEM to a natively spiking neuromorphic algorithm.

Applied mathematics↗
NASA NTRSDate not supplied

Toward Time Synchronization in Delay Tolerant Network based Solar System Internetworking

The expanding presence in space will place an increased dependency on networked communications– a scalable communications infrastructure; that is, the Solar System Internet (SSI). Upcoming developments towards a SSI include NASA’s upcoming LunaNet, or lunar Internet, which provides multi-hop multi-path communications using Delay Tolerant Networking (DTN). DTN has been an active area of research and development, particularly in routing, security, and optimization. DTNs are marked by mobility, disconnection, and a wide variance of latencies (propagation and processing delays). In this paper, we outline progress towards a theory of time synchronization across such a network. An underlying assumption of DTN is that the network is time synchronized already, rather than synchronization being provided as a service. While this is necessary for schedule-based routing, which is necessarily prevalent in DTNs, it is so deeply ingrained as to be built into the primary unit of data in DTNs– the bundle. Indeed, a bundle’s creation timestamp and its time to live (called the lifetime) are based on time, and there are special recommendations for systems that lack accurate clocks. The assumption of time synchronization makes sense when limiting considerations to smaller-scale and more traditional space communication. However, just as end-to-end connectivity cannot be guaranteed in DTNs, neither can access to a reference or authoritative clock. In this more general case, it might be necessary to synchronize over time-varying meshes, and perhaps even to consider relativistic effects. Moreover, by imposing synchronization restrictions in order to sustain a network, the effectiveness of the network to achieve scalability will be necessarily muted. To work towards a time synchronization theory for DTNs, we build upon past successes in modeling DTNs using time-varying graphs and sheaves. This includes error and limitation estimation, which allows one to define domains over which schedule-based routing is possible, up to some threshold sensitivity. Despite the theoretical nature of these results, the approaches taken are also algorithmic, and hence lend themselves to practical implementations. The paper concludes with comparisons of the various methods along with suggestions for future work.

Delay Tolerant Networking↗
DOE OSTI2020

Optimized active layer morphology toward efficient and polymer batch insensitive organic solar cells

Abstract Morphology control in laboratory and industry setting remains as a major challenge for organic solar cells (OSCs) due to the difference in film-drying kinetics between spin coating and the printing process. A two-step sequential deposition method is developed to control the active layer morphology. A conjugated polymer that self-assembles into a well-defined fibril structure is used as the first layer, and then a non-fullerene acceptor is introduced into the fibril mesh as the second layer to form an optimal morphology. A benefit of the combined fibril network morphology and non-fullerene acceptor properties was that a high efficiency of 16.5% (certified as 16.1%) was achieved. The preformed fibril network layer and the sequentially deposited non-fullerene acceptor form a robust morphology that is insensitive to the polymer batches, solving a notorious issue in OSCs. Such progress demonstrates that the utilization of polymer fibril networks in a sequential deposition process is a promising approach towards the fabrication of high-efficiency OSCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Evaluating an Exterior Insulation and Finish System for Deep Energy Retrofits

This report provides a case study of a deep energy retrofit using a site-applied exterior insulation and finish system (EIFS). Prefabricated EIFS panels are also discussed as a potential wall insulation retrofit solution. EIFSs are common in new and retrofit commercial construction. Such products typically consist of five layers installed over the top of a water resistive barrier as follows: (1) Adhesive (2) Foam insulation (3) Base coat (4) Reinforcing mesh embedded in the base coat (5) Top or finish coat. They can be applied to new or existing buildings that employ wood or masonry construction, and can utilize an insulation thickness of up to 16 in. However, such systems are rarely used in lowrise residential retrofits. Reasons for this might include a lack of demand for or knowledge of the systems in residential retrofit industry, high cost, and/or lack of suitable distribution channels serving the residential market. While the final appearance of an EIFS installation resembles stucco, it is a distinctly different system. Stucco is a generic cementitious-based material, whereas EIFSs are proprietary synthetic formulations distributed by manufacturers to a network of authorized applicators. An EIFS is composed of polymeric (organic) bonded aggregate and cement reinforced with a glass mesh. Stucco is made of inorganic cement, sand, and lime. In this project, a home was retrofitted using a site-applied 4-in.-thick EIFS. Site-specific details were developed as required for the residential retrofit application. Site work and the costs of the EIFS were documented. The case study home was modeled using version 2 of the National Renewable Energy Laboratory-developed Building Energy Optimization (BEopt) energy and cost analysis software. While a package utilizing the 4-in. EIFS is the optimal retrofit of the options analyzed in terms of annualized energy-related costs, it is higher cost than replacing the siding with midrange vinyl alone. However, compared to high-end vinyl, the EIFS package has a lower annualized energy cost. This held true for both the Islip, New York (climate zone 4) and Boston (climate zone 5) case studies. The report discusses the risks, selection criteria, interactions with other building systems, cost, performance, and other aspects of using an EIFS in a deep energy retrofit. EIFSs do not require special site safety precautions beyond general construction site precautions. Moisture design is important. Trapping moisture between two vapor/air barriers is a risk of all wall systems, including EIFS cladding applications if an interior vapor barrier exists and if there is not adequate ventilation through the drainage plane behind the insulation layer. Face-sealed approaches that rely on exposed sealants do not provide acceptable rain control or durability and are very risky. Generally, fire codes permit EIFSs for most building types and conditions.

airtightness↗
NASA NTRS2005

A Hexapod Robot to Demonstrate Mesh Walking in a Microgravity Environment

The JPL Micro-Robot Explorer (MRE) Spiderbot is a robot that takes advantage of its small size to perform precision tasks suitable for space applications. The Spiderbot is a legged robot that can traverse harsh terrain otherwise inaccessible to wheeled robots. A team of Spiderbots can network and can exhibit collaborative efforts to SUCCeSSfUlly complete a set of tasks. The Spiderbot is designed and developed to demonstrate hexapods that can walk on flat surfaces, crawl on meshes, and assemble simple structures. The robot has six legs consisting of two spring-compliant joints and a gripping actuator. A hard-coded set of gaits allows the robot to move smoothly in a zero-gravity environment along the mesh. The primary objective of this project is to create a Spiderbot that traverses a flexible, deployable mesh, for use in space repair. Verification of this task will take place aboard a zero-gravity test flight. The secondary objective of this project is to adapt feedback from the joints to allow the robot to test each arm for a successful grip of the mesh. The end result of this research lends itself to a fault-tolerant robot suitable for a wide variety of space applications.

Foor, David C.↗
NASA NTRS2013

A Floating Node Method for the Modelling of Discontinuities Within a Finite Element

This paper focuses on the accurate numerical representation of complex networks of evolving discontinuities in solids, with particular emphasis on cracks. The limitation of the standard finite element method (FEM) in approximating discontinuous solutions has motivated the development of re-meshing, smeared crack models, the eXtended Finite Element Method (XFEM) and the Phantom Node Method (PNM). We propose a new method which has some similarities to the PNM, but crucially: (i) does not introduce an error on the crack geometry when mapping to natural coordinates; (ii) does not require numerical integration over only part of a domain; (iii) can incorporate weak discontinuities and cohesive cracks more readily; (iv) is ideally suited for the representation of multiple and complex networks of (weak, strong and cohesive) discontinuities; (v) leads to the same solution as a finite element mesh where the discontinuity is represented explicitly; and (vi) is conceptually simpler than the PNM.

Pinho, Silvestre T.↗
DOE OSTI2021

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Design Parameters for Subwavelength Transparent Conductive Nanolattices

Recent advancements with the directed assembly of block copolymers have enabled the fabrication over cm 2 areas of highly ordered metal nanowire meshes, or nanolattices, which are of significant interest as transparent electrodes. Compared to randomly dispersed metal nanowire networks that have long been considered the most promising next-generation transparent electrode material, such ordered nanolattices represent a new design paradigm that is yet to be optimized. Here in this paper, through optical and electrical simulations, we explore the potential design parameters for such nanolattices as transparent conductive electrodes, elucidating relationships between the nanowire dimensions, defects, and the nanolattices’ conductivity and transmissivity. We find that having an ordered nanowire network significantly decreases the length of nanowires required to attain both high transmissivity and high conductivity, and we quantify the network’s tolerance to defects in relation to other design constraints. Furthermore, we explore how both optical and electrical anisotropy can be introduced to such nanolattices, opening an even broader materials design space and possible set of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Predictions of first passage times in sparse discrete fracture networks using graph-based reductions

Here, we present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We also derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. We obtain accurate estimates of first passage times with an order of magnitude reduction of CPU time and mesh size using the proposed method.

54 ENVIRONMENTAL SCIENCES↗