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429 records · Page 24

All sources NASA NTRS DOE OSTI

NASA NTRS2023

Fan Noise Predictions of the NASA Source Diagnostic Test Using Unsteady Simulations with LAVA Part I: Near-Field Aerodynamics and Turbulence

A sliding mesh technique within the Launch, Ascent, and Vehicle Aerodynamics (LAVA) computational framework is validated using the experimental dataset collected as part of the NASA Source Diagnostic Test (SDT) campaign. Two modeling approaches are explored: the unsteady Reynolds-Averaged Navier Stokes (URANS) with Spalart-Allmaras (SA) turbulence model closure, and a hybrid Reynolds-Averaged Navier Stokes/Large Eddy Simulation (RANS/LES) paradigm employing a Zonal Detached Eddy Simulation (ZDES) closure with enhanced shielding protection. Fan stage performance metrics, aerodynamic quantities and turbulent flow structures are analyzed in this work. Initial studies focusing on grid and time-step sensitivity are presented. Sensitivity to different variants of the SA turbulence model is analyzed, supporting the use of the baseline SA model in the production runs. Two conditions are analyzed in detail using URANS and hybrid RANS/LES (HRLES). Mean flow quantities are well-captured by both methods in the low-speed (approach) regime. While URANS misses all the upstream-propagating noise in the inlet due to the rotor-locked tones being evanescent in nature at subsonic fan tip speeds, HRLES captures this broadband component in its pressure field. At the high-speed (sideline) condition, URANS shows better agreement with the SDT data than HRLES in the interstage flow-field. In this regime, URANS captures the tonal content propagating through the inlet, since the tones are now cut-on. Both methods are suitable to capture fan stage performance metrics and mean flow quantities, but only HRLES is able to resolve the fine turbulent structures responsible for broadband noise. The results support the use of the sliding mesh technique implemented in this work for future turbomachinery applications within the LAVA solver framework.

AATT↗

NASA NTRS2023

Fan Noise Predictions of the NASA Source Diagnostic Test Using Unsteady Simulations with LAVA Part I: Near-Field Aerodynamics and Turbulence

AATT↗

NASA NTRS2022

LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2

The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5°C) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we examine the role of cloud parameterizations in simulating the LGM cooling in CESM2. Through substituting different versions of cloud schemes in the atmosphere model, we attribute the excessive LGM cooling to the new CESM2 schemes of cloud microphysics and ice nucleation. Further exploration suggests that removing an inappropriate limiter on cloud ice number (NoNimax) and decreasing the time-step size (substepping) in cloud microphysics largely eliminate the excessive LGM cooling. NoNimax produces a more physically consistent treatment of mixed-phase clouds, which leads to an increase in cloud ice content and a weaker shortwave cloud feedback over mid-to-high latitudes and the Southern Hemisphere subtropics. Microphysical substepping further weakens the shortwave cloud feedback. Based on NoNimax and microphysical substepping, we have developed a paleoclimate-calibrated CESM2 (PaleoCalibr), which simulates well the observed twentieth century warming and spatial characteristics of key cloud and climate variables. PaleoCalibr has a lower ECS (∼4°C) and a 20% weaker aerosol-cloud interaction than CESM2. PaleoCalibr represents a physically more consistent treatment of cloud microphysics than CESM2 and is a valuable tool in climate change studies, especially when a large climate forcing is involved. Our study highlights the unique value of paleoclimate constraints in informing the cloud parameterizations and ultimately the future climate projection.

equilibrium climate sensitivity↗

DOE OSTI2021

An Orthogonal Recursive Bisection (ORB) Based Time Advancement Algorithm for CFD-DEM Solvers

The time integration of the granular phase in coupled computational fluid dynamics (CFD) – discrete element method (DEM) simulations presents a unique computational challenge brought about by the large variations in particle collisional time scales. Particles in the dilute regions of the computational domain can be advanced with large time steps while dense regions require much smaller time increments. However, the time step size in most solvers is globally set as the limit for accuracy and stability imposed by the collisions and is typically orders of magnitude less than that required away from collisions. This work addresses this precise issue and provides a strategy to avoid the use of a global conservative small time step size for the entire set of particles.A novel time stepping algorithm for CFD-DEM solvers using a partitioning approach using orthogonal recursive bisection (ORB) that allows for variable time steps among particles is described and its computational performance is compared against baseline explicit methods, typically used in several CFD-DEM solvers. ORB has advantages of being relatively quick and easy to update incrementally and has the required heuristic behavior (i.e., it will split the region in half with a cluster on each side) when groups of particles are well separated (clustered). The algorithm presented in this work uses a local time stepping approach to resolve collisional time scales for subsets of particles that are present at the leaves of the ORB, thereby resulting in substantial reduction of computational cost. The parallel implementation of this method where a ``knapsack” algorithm is used in tandem with ORB for effective load-balancing is also presented, where a best possible partitioning is obtained based on number of particles and local time-stepping costs. The algorithm is tested against benchmark problems with varying particle distributions that include fluidized bed and riser flow scenarios. Preliminary results indicate that the approach is 2-3X faster than traditional explicit methods for problems that involve both dense and dilute regions, while maintaining the same level of accuracy.

adaptive timestepping↗

DOE OSTI2022

4.2.1.31 Integrated Life Cycle Sustainability Analysis

This project provides the Department of Energy's Bioenergy Technologies Office (BETO) with strategic decision-support for the evaluation of its R&D portfolio by developing, validating, and applying a coherent methodology and consistent model framework to quantify the net effects of an expanding US bioeconomy. The framework fills an analysis gap previously identified by Peer Review and supports a related milestone in BETO's Multi-Year Program Plan. The framework was scoped with inputs from practitioners in academia, national laboratories, and federal agencies. The model is a top-down, economy-wide framework using a coherent methodology to compute environmental and socio-economic metrics. It is purposefully complementary to existing bottom-up, process-based techno-economic and life cycle assessment BETO tools and uses their data as inputs. Presently, the model covers several commercial and near-commercial biofuel routes and an emerging pathway for plastics upcycling. It covers temporal detail across four time-steps and is currently being expanded with a prospective modeling capability. The model has provided analyses for the Third Triennial Report to Congress (RtC3) on the environmental impacts of the Renewable Fuel Standard (RFS2), among others. As part of this project, NREL also provides scientific support to BETO in the International Energy Agency's Technology Collaboration Program on Bioenergy (IEA Bioenergy) Task 45 on Sustainability. Here, NREL evaluates and synthesizes activities that develop, compare, or apply metrics, methods, and tools to quantify sustainability effects of bioeconomy products. NREL also coordinates related national lab involvement and a BETO Working Group on Sustainable Land Management.

bioeconomy↗

DOE OSTI2022

Distribution Function Instead of Steady-State Assumption in Time-Series Simulation

The quasi-steady-state assumption in time-series simulation is inadequate to model phenomena of interest to energy system integration such as inverter clipping, sell-back of excess electricity to a utility and utility hosting capacity, and battery throughput. Researchers are working on stochastic modeling, higher-resolution time series data, and machine learning approaches to address this need. This poster describes a distribution function that allows a maximum value, minimum value, and shape to the curve that can vary within each time-step based only on data inputs at the resolution of that time step (eg. Hourly). The distribution function is not used to synthesize high-resolution data, rather scalar integrals are used to calculate the quantities of interest within each time step. The form of the distribution function shows significant reduction in error when compared to 1 minute data and commercial software employing the distribution function (HOMER Pro version 14.3 and higher) shows a much improved estimate of inverter clipping in an example.

battery↗

DOE OSTI2020

Issue Resolution During the Development of the Performance Assessment for the Savannah River Site Saltstone Disposal Facility - 20125

In 2019, Savannah River Remediation developed a revision to the performance assessment (PA) on behalf of the U.S. Department of Energy (DOE) Savannah River Operations Office (SR) for the near-surface disposal of low-level waste at the Savannah River Site (SRS) Saltstone Disposal Facility (SDF). Soluble waste from SRS Tank Farms undergoes salt processing to remove cesium and other high-activity constituents. The low-activity decontaminated salt solution (DSS) is then immobilized by mixing it into a cementitious waste form known as saltstone. After mixing, the saltstone is poured into leak-tight concrete vaults, known as saltstone disposal units (SDUs), where the waste form cures. By the time of facility closure, the SDF is expected to consist of 15 SDUs with a combined capacity of 1.06 E+09 L (280 Mgal) of cured saltstone. The facility operates under a Disposal Authorization Statement from DOE and a permit from the South Carolina Department of Health and Environmental Control (SCDHEC). Since the start of operations in 1990, the SDF has received almost 6.7 E+07 L (18 Mgal) of DSS, resulting in the safe disposal of 2.7 E+16 Bq (7.3 E+05 Ci) of activity. Due to the radioactive decay of short-lived contaminants, the total remaining activity in the disposed waste is estimated to be approximately 1.4 E+16 Bq (3.9 E+05 Ci), as of September 2018. The Disposal Authorization Statement requires a demonstration that the system of engineered and natural features of the disposal facility will limit releases from the facility and be protective of human health and the environment for at least the next 1,000 years. The long-term performance of the facility was evaluated under the requirements of the DoE's Radioactive Waste Management Manual (US DOE Manual 435.1-1). Simulations were performed to demonstrate that the disposal facility would meet performance objectives specified in the manual. The evaluation was based on numerical models that simulate the releases of contaminants from the saltstone waste form. Contaminants were transported through groundwater and air pathways to points of assessment to evaluate compliance (i.e., 100 m from the SDUs). In addition, the potential consequences of an inadvertent human intrusion (IHI) were also evaluated. A number of issues were overcome during the development of these simulations. These issues were identified as part of internal technical reviews. Simulations are developed by people and people make mistakes, so the internal technical review process is a vital step in PA development. Specific examples of resolved issues include a unit-conversion error, an inappropriate definition for a model boundary condition, a model time-stepping issue, and an error in the calculation for the buildup of contaminants in soil. Actions taken to address these issues resulted in an improved product with a better supported technical basis and more defensible results. The identification and correction of these issues are discussed. By understanding these issues, model developers and technical reviewers working on PAs in the future may avoid repeating these types of mistakes. Transparency with respect to these mistakes builds trust between waste management sites, regulators, and stakeholders. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations

Neural operators such as the Fourier Neural Operator (FNO) have been shown to provide resolution-independent deep learning models that can learn mappings between function spaces. For example, an initial condition can be mapped to the solution of a partial differential equation (PDE) at a future time-step using a neural operator. Despite the popularity of neural operators, their use to predict solution functions over a domain given only data over the boundary (such as a spatially varying Dirichlet boundary condition) remains unexplored. In this paper, we refer to such problems as boundary-to-domain problems; they have a wide range of applications in areas such as fluid mechanics, solid mechanics, heat transfer etc. We present a novel FNO-based architecture, named Lifting Product FNO (or LP-FNO) which can map arbitrary boundary functions defined on the lower-dimensional boundary to a solution in the entire domain. Specifically, two FNOs defined on the lower-dimensional boundary are lifted into the higher dimensional domain using our proposed lifting product layer. We demonstrate the efficacy and resolution independence of the proposed LP-FNO for the 2D Poisson equation.

Kashi, Aditya↗

DOE OSTI2024

Multiphysics Time-Integration for Turbulent Combustion at the Exascale

Turbulent reacting flow systems are often modeled with coupled time-dependent partial differential equations (PDEs). Solving such equations can easily tax the world's largest supercomputers. One pragmatic strategy for attacking such problems is to split the PDEs into components that can more easily be solved in isolation. This generic operator-splitting strategy leads to a set of ordinary differential equations (ODEs) that need to be solved as part of an "outer-loop" time-stepping approach. In many combustion applications, the ODEs to be solved can be very stiff, exhibiting timescales that span many orders of magnitude. The SUNDIALS library provides a plethora of robust time integration algorithms for solving these ODEs on exascale-capable computing hardware, yet for many complex applications (such multicomponent fuels or emissions predictions), the chemical models remain too complex to solve using reasonable resources. The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of the simulations. In this talk, I will discuss the use of the SUDIALS library of ODE solvers together with automatic code generation tools to solve complex turbulent reacting flow problems using QSSA models.

chemistry↗

DOE OSTI2022

Combustion Feature Characterization using Computer Vision Diagnostics within Rotating Detonation Combustors

Rotating detonation engines (RDEs) theoretically achieve pressure gain by continuously propagating detonation waves in a cylindrical annulus. Research aims to implement them in gas turbines for improved propulsion and power generation efficiency. Current diagnostic methods, such as point measurements and optical diagnostics, face limitations due to high data acquisition rates required to analyze rapid detonation interactions. In contrast, image classification and time series classification achieve real-time capabilities with rates of 4 and 5 Hz, respectively. Object detection offers the highest time-step resolution at 20 μsec, while real-time methods require longer resolutions of 80 msec, highlighting the advancements in diagnostic capabilities for RDEs.

May, Kristyn Johnson↗

DOE OSTI2024

Development of a Performance Portable Non-Equilibrium Plasma Fluid Solver on Adaptive Grids

This presentation will describe the numerical techniques, programming paradigms, verification, and performance of a non-equilibrium plasma fluid solver that can effectively utilize current and upcoming central processing and graphics processing unit (CPU+GPU) architectures. Our plasma fluid model solves the conservation equations for self-consistent electrostatic Poisson, electron and heavy species transport, and electron temperature on adaptive Cartesian grids. Our solver is written using performance portable adaptive mesh management library, AMReX (Zhang et al., JOSS, 4 (37) 1370, 2019), and can be built and run on widely available vendor specific GPU architectures (NVIDIA/AMD/Intel). We utilize a non-subcycled second order semi-implicit time-stepping method where all adaptive mesh refinement (AMR) levels are advanced with the same time step. The composite multi-level multigrid solver from within AMReX is used for each of the governing equations that are cast into a Helmholtz equation form. We have also developed a python based chemical mechanism parser framework that uses a similar format as CANTERA (Goodwin et al., Zenodo, 2018) yaml files as input. Our custom parser reads the yaml file and provides C++ files with transport and production rate functions that can be executed on both host (CPU) and device (GPU). We present verification of our solver using method of manufactured solutions that indicate formal second order accuracy with central diffusion and fifth order weighted-essentially-non-oscillatory (WENO) advection scheme. We also verify our solver with published literature on low-pressure capacitive and high-pressure streamer discharges. Our initial performance studies indicate 10X speed-up using 20 NVIDIA GPUs versus 200 CPUs for an atmospheric streamer discharge problem solved on a 512 x 1024 x 512 grid.

graphics processing units↗

DOE OSTI2023

MiGRIDS

MiGRIDS is a software that models islanded microgrid power systems with different controls and components. For example, using load and resource data from a microgrid, you could model it with additional wind turbines, battery etc. You could also try out different dispatch schemes to see which one worked best. MiGRIDS is designed to help optimize the size and dispatch of grid components in a microgrid. While a grid connect feature is expected to be added in the future, islanded operation is the focus. Note that this is a basic implementation and more features and functionality (such as a GUI) are coming! MiGRIDS runs time-step energy balance simulations for different grid components and controls. In smaller microgrid environments, dispatch decisions are being made on the order of seconds. In order to fully capture their effect, this tool lets you run simulations on the order of seconds. The end result is a more realistic representation of what can be achieved by integrating different components and control strategies in a grid.

Morgan, Tawna↗

DOE OSTI2020

Adaptive multilevel space-time-stepping scheme for transport in heterogeneous porous media (ADM-LTS)

Not Available

42 ENGINEERING↗

DOE OSTI2024

Multilevel Parareal Algorithm with Averaging for Oscillatory Problems

The present study is an extension of the work done by Peddle, Haut, and Wingate and Haut and Wingate, where a two-level Parareal method with mapping and averaging is examined. The method proposed in this paper is a multilevel Parareal method with arbitrarily many levels, which is not restricted to the two-level case. We give an asymptotic error estimate which reduces to the two-level estimate for the case when only two levels are considered. Introducing more than two levels has important consequences for the averaging procedure, as we choose separate averaging windows for each of the different levels, which is an additional new feature of the present study. The different averaging windows make the proposed method especially appropriate for nonlinear multiscale problems, because we can introduce a level for each intrinsic scale of the problem and adapt the averaging procedure such that we reproduce the behavior of the model on the particular scale resolved by the level. The method is applied to nonlinear differential equations. The nonlinearities can generate a range of frequencies in the problem. The computational cost of the new method is investigated and studied on several examples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A novel approach for tetrahedral-element-based finite element simulations of anisotropic hyperelastic intervertebral disc behavior

Intervertebral discs are microstructurally complex spinal tissues that add greatly to the flexibility and mechanical strength of the human spine. Attempting to provide an adjustable basis for capturing a wide range of mechanical characteristics and to better address known challenges of numerical modeling of the disc, we present a robust finite-element-based model formulation for spinal segments in a hyperelastic framework using tetrahedral elements. We evaluate the model stability and accuracy using numerical simulations, with particular attention to the degenerated intervertebral discs and their likely skewed and narrowed geometry. To this end, 1) annulus fibrosus is modeled as a fiber-reinforced Mooney-Rivlin type solid for numerical analysis. 2) An adaptive state-variable dependent explicit time step is proposed and utilized here as a computationally efficient alternative to theoretical estimates. 3) Tetrahedral-element-based FE models for spinal segments under various loading conditions are evaluated for their use in robust numerical simulations. For flexion, extension, lateral bending, and axial rotation load cases, numerical simulations reveal that a suitable framework based on tetrahedral elements can provide greater stability and flexibility concerning geometrical meshing over commonly employed hexahedral-element-based ones for representation and study of spinal segments in various stages of degeneration.

59 BASIC BIOLOGICAL SCIENCES↗