Search NASA⌕ Search

SEARCH · Search NASA

Results for “simulation output analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Landrush: Rethinking In-Situ Analysis for GPGPU Workflows

In-situ analysis on the output data of scientific simulations has been made necessary by ever-growing output data volumes and increasing costs of data movement as supercomputing is moving towards exascale. With hardware accelerators like GPUs becoming increasingly common in high end machines, new opportunities arise to co-locate scientific simulations and online analysis performed on the scientific data generated by the simulations. However, the asynchronous nature of GPGPU programming models and the limited context-switching capabilities on the GPU pose challenges to co-locating the scientific simulation and analysis on the same GPU. This paper dives deeper into these challenges to understand how best to co-locate analysis with scientific simulations on the GPUs in HPC clusters. Specifically, our 'Landrush' approach to GPU sharing proposes a solution that utilizes idle cycles on the GPU to provide an improved time-to-answer, that is, the total time to run the scientific simulation and analysis of the generated data. Landrush is demonstrated with experimental results obtained from leadership high-end applications on ORNL's Titan supercomputer, which show that (i) GPU-based scientific simulations have varying degrees of idle cycles to afford useful analysis task co-location, and (ii) the inability to context switch on the GPU at instruction granularity can be overcome by careful control of the analysis kernel launches and software-controlled early completion of analysis kernel executions. Results show that Landrush is superior in terms of time-to-answer compared to serially running simulations followed by analysis or by relying on the GPU driver and hardwired thread dispatcher to run analysis concurrently on a single GPU.

Computer Science↗
DOE OSTI2016

The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): Understanding sea ice through climate-model simulations

A better understanding of the role of sea ice for the changing climate of our planet is the central aim of the diagnostic Coupled Model Intercomparison Project 6 (CMIP6)-endorsed Sea-Ice Model Intercomparison Project (SIMIP). To reach this aim, SIMIP requests sea-ice-related variables from climate-model simulations that allow for a better understanding and, ultimately, improvement of biases and errors in sea-ice simulations with large-scale climate models. This then allows us to better understand to what degree CMIP6 model simulations relate to reality, thus improving our confidence in answering sea-ice-related questions based on these simulations. Furthermore, the SIMIP protocol provides a standard for sea-ice model output that will streamline and hence simplify the analysis of the simulated sea-ice evolution in research projects independent of CMIP. To reach its aims, SIMIP provides a structured list of model output that allows for an examination of the three main budgets that govern the evolution of sea ice, namely the heat budget, the momentum budget, and the mass budget. Furthermore, we explain the aims of SIMIP in more detail and outline how its design allows us to answer some of the most pressing questions that sea ice still poses to the international climate-research community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Rethinking Sensitivity Analysis of Nuclear Simulations with Topology

In nuclear engineering, understanding the safety margins of the nuclear reactor via simulations is arguably of paramount importance in predicting and preventing nuclear accidents. It is therefore crucial to perform sensitivity analysis to understand how changes in the model inputs affect the outputs. Modern nuclear simulation tools rely on numerical representations of the sensitivity information -- inherently lacking in visual encodings -- offering limited effectiveness in communicating and exploring the generated data. In this paper, we design a framework for sensitivity analysis and visualization of multidimensional nuclear simulation data using partition-based, topology-inspired regression models and report on its efficacy. We rely on the established Morse-Smale regression technique, which allows us to partition the domain into monotonic regions where easily interpretable linear models can be used to assess the influence of inputs on the output variability. The underlying computation is augmented with an intuitive and interactive visual design to effectively communicate sensitivity information to the nuclear scientists. Our framework is being deployed into the multi-purpose probabilistic risk assessment and uncertainty quantification framework RAVEN (Reactor Analysis and Virtual Control Environment). We evaluate our framework using an simulation dataset studying nuclear fuel performance.

computational topology↗
DOE OSTI2019

Automated Multiscale Approach To Predict Self-Diffusion from a Potential Energy Field

For large-scale screening studies there is a need to estimate the diffusion of gas molecules in nanoporous materials more efficiently than (brute force) molecular dynamics. In particular for systems with low diffusion coefficients molecular dynamics can be prohibitively expensive. An alternative is to compute the hopping rates between adsorption sites using transition state theory. For large-scale screening this requires the automatic detection of the transition states between the adsorption sites along the different diffusion paths. Here an algorithm is presented that analyzes energy grids for the moving particles. It detects the energies at which diffusion paths are formed, together with their directions. This allows for easy identification of nondiffusive systems. For diffusive systems, it partitions the grid coordinates assigned to energy basins and transitions states, permitting a transition state theory based analysis of the diffusion. We test our method on CH 4 diffusion in zeolites, using a standard kinetic Monte Carlo simulation based on the output of our grid analysis. We find that it is accurate, fast, and rigorous without limitations to the geometries of the diffusion tunnels or transition states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Leaf-scale carbon assimilation simulated by the multi-assumption architecture & testbed for process sensitivity analysis

Raw and processed output from the Multi-Assumption Architecture & Testbed (MAAT, https://github.com/walkeranthonyp/MAAT, tag: v1.2.1_walker2020; Walker et al., 2018). The dataset contains leaf-scale carbon assimilation data from three MAAT simulations and their post-processing and plotting scripts. Simulation 1, labelled "FvC_4proc_proc," is a four-process, process sensitivity analysis (Dai et al., 2017) of carbon assimilation based on the Farquhar et al. (1980) photosynthesis model, and its variant proposed by Collatz et al. (1991). Simulation 2, labelled "FvC_4proc_salt," is a 14 parameter sensitivity analysis (Saltelli et al., 2010) of carbon assimilation based on the Farquhar and Collatz photosynthesis models. Simulation 3, labelled "FvC," is a factorial combination of the alternative process representations and parameter values presented in those original papers across a factorial combination of incident photosynthetically active radiation (50-2000 µmol m-2 s-1, in increments of 50) and atmospheric CO2 concentration (50-1500 µmol m-2 s-1, in increments of 50). Additional scripts/functions for post-processing and plotting are part of the MAAT repository. These data were generated to evaluate and compare the Farquhar and Collatz models of leaf-scale photosynthesis across a range of CO2 and incident light conditions. Analysis of results can be found in Walker et al. (2020). This dataset is comprised of R scripts to process data and produce figures; .RDS files, a binary format specific to R and read with the 'readRDS' function, to store raw MAAT output; R and bash scripts (.bs) that help to run a MAAT simulation, .XML files that are output from MAAT describing a run, and .csv files which contain the calculated sensitivity indexes published in Walker et al. (2020).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

WebGIobe - A cloud-based geospatial analysis framework for interacting with climate data

While climate models have evolved over time to produce high fidelity and high resolution climate forecasts, visualization and analysis of the output of the model simulations has been limited, typically constrained to single dimensional charts for visualization and basic aggregate statistics for analytics. Same is true for the large troves of observational data available from meteorological stations all over the world. For richer understanding of climate and the impact of climate change, one needs computational tools that allow researchers, policymakers, and general public, to interact with the climate data. In this paper, we describe, webGlobe, a browser based GIS framework for interacting with climate data, and other datasets available in similar format. webGlobe is a unique resource that allows unprecedented access to climate data through a browser. The framework also allows for deploying machine learning based analytical applications on the climate data without putting computational burden on the client. Instead, webGlobe uses a client-server framework, where the server, deployed on a cloud infrastructure, allows for dynamic allocation of resources for running compute-intensive applications. The capabilities of the framework will be discussed in context of a use case: identifying extreme events from real and simulated climate data using a Gaussian process based change detection algorithm.

Sharma, Arun↗
DOE OSTI2023

CERESMIP: a climate modeling protocol to investigate recent trends in the Earth's Energy Imbalance

The Clouds and the Earth's Radiant Energy System (CERES) project has now produced over two decades of observed data on the Earth's Energy Imbalance (EEI) and has revealed substantive trends in both the reflected shortwave and outgoing longwave top-of-atmosphere radiation components. Available climate model simulations suggest that these trends are incompatible with purely internal variability, but that the full magnitude and breakdown of the trends are outside of the model ranges. Unfortunately, the Coupled Model Intercomparison Project (Phase 6) (CMIP6) protocol only uses observed forcings to 2014 (and Shared Socioeconomic Pathways (SSP) projections thereafter), and furthermore, many of the ‘observed' drivers have been updated substantially since the CMIP6 inputs were defined. Most notably, the sea surface temperature (SST) estimates have been revised and now show up to 50% greater trends since 1979, particularly in the southern hemisphere. Additionally, estimates of short-lived aerosol and gas-phase emissions have been substantially updated. These revisions will likely have material impacts on the model-simulated EEI. We therefore propose a new, relatively low-cost, model intercomparison, CERESMIP, that would target the CERES period (2000-present), with updated forcings to at least the end of 2021. The focus will be on atmosphere-only simulations, using updated SST, forcings and emissions from 1990 to 2021. The key metrics of interest will be the EEI and atmospheric feedbacks, and so the analysis will benefit from output from satellite cloud observation simulators. The Tier 1 request would consist only of an ensemble of AMIP-style simulations, while the Tier 2 request would encompass uncertainties in the applied forcing, atmospheric composition, single and all-but-one forcing responses. We present some preliminary results and invite participation from a wide group of models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Offshore Wind to Hydrogen - Modeling, Analysis, Testing and International Collaboration Work

This project explores electrolytic hydrogen production from an offshore wind turbine by: Modeling: Simulate an offshore wind turbine and generate power output profiles; Analysis: Analyze offshore conditions and determine operational modes of the stack; Testing: Perform hardware testing to evaluate dynamic characteristics; and International Collaboration Work: Share learnings and publish. Potential Outcomes: Accelerate development of an integrated, in-turbine offshore wind hydrogen system; and Support two DOE Energy Earthshots - Hydrogen and Floating Offshore Wind.

electrolyzer↗
DOE OSTI2026

Reservoir Characterization and Techno-Economic Analysis of Enhanced Geothermal and Closed-Loop Systems in the Wattenberg Field of the Denver-Julesburg Basin, Colorado

This report provides details of reservoir characterization and techno-economic analysis for enhanced geothermal systems and closed-loop geothermal systems in the Denver-Julesburg Basin Wattenberg area. The analysis contributes to the Geothermal Limitless Approach to Drilling Efficiencies (GLADE) project, a multi-institutional research initiative focused on advancing geothermal energy development by reducing drilling costs and improving the rate of penetration. The GLADE project brings together national laboratories, academic institutions, and industry partners, including the U.S. Department of Energy's Geothermal Technologies Office, Occidental Petroleum, the National Laboratory of the Rockies (NLR), Los Alamos National Laboratory, Colorado School of Mines, Louisiana State University, Texas A&M University, and several drilling technology companies. As part of this effort, NLR developed and applied slender-body theory modeling tools and the GEOPHIRES simulator to support techno-economic analysis. The slender-body theory wellbore simulator evaluated thermal performance in U-loop, Eavor-type multilateral, and enhanced geothermal system well configurations. Results indicated that an optimized single U-loop design delivers an outlet temperature of 168.8 degrees Celsius,stabilizing at a final outlet temperature of 135.3 degrees Celsius. Heat production also improved significantly, with an average thermal output of 16.9 MWth and a final thermal output of 11.0 MWth. These results provided critical baseline data for selecting optimized systems for techno-economic analysis. Building on these outputs, NLR applied the GEOPHIRES techno-economic simulator to estimate the levelized cost of electricity for both the Eavor multilateral and enhanced geothermal systems. The sensitivity analysis showed that drilling costs exert a particularly strong impact on the levelized cost of electricity of multilateral systems. Therefore, advances in drilling technology and improved drilling rates could yield significant gains in the economic performance of these systems. In this context, the GLADE project directly supports the U.S. Department of Energy's mission to reduce the cost of geothermal energy and accelerate the deployment of next-generation geothermal technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

iTOUGH2 v7.1

iTOUGH2 (inverse TOUGH2) provides inverse modeling capabilities for TOUGH2, a simulator for multi-dimensional, multi-phase, multi-component, non-isothermal flow and transport in fractured porous media. iTOUGH2 performs sensitivity analyses, data-worth analyses, parameter estimation, and uncertainty propagation analyses in geosciences and reservoir engineering and other application areas. iTOUGH2 supports a number of different combinations of fluids and components (equation-of-state (EOS) modules). In addition, the optimization routines implemented in iTOUGH2 can also be used for sensitivity analysis, automatic model calibration, and uncertainty quantification of any external code that uses text-based input and output files using the PEST protocol. iTOUGH2 solves the inverse problem by minimizing a non-linear objective function of the weighted differences between model output and the corresponding observations. Multiple minimization algorithms (derivative-free, gradient-based, and second-order; local and global) are available. iTOUGH2 also performs Latin Hypercube Monte Carlo simulations for uncertainty propagation analyses. A detailed residual and error analysis is provided. This upgrade includes (a) global sensitivity analysis methods, (b) dynamic memory allocation (c) additional input features and output analyses, (d) increased forward simulation capabilities, (e) parallel execution on multicore PCs and Linux clusters, and (f) bug fixes.

Finsterle, Stefan↗
DOE OSTI2018

Equilibration and analysis of first-principles molecular dynamics simulations of water

First-principles molecular dynamics (FPMD) simulations based on density functional theory are becoming increasingly popular for the description of liquids. In view of the high computational cost of these simulations, the choice of an appropriate equilibration protocol is critical. In this study, we assess two methods of estimation of equilibration times using a large dataset of first-principles molecular dynamics simulations of water. The Gelman-Rubin potential scale reduction factor [A. Gelman and D. B. Rubin, Stat. Sci. 7, 457 (1992)] and the marginal standard error rule heuristic proposed by White [Simulation 69, 323 (1997)] are evaluated on a set of 32 independent 64-molecule simulations of 58 ps each, amounting to a combined cumulative time of 1.85 ns. The availability of multiple independent simulations also allows for an estimation of the variance of averaged quantities, both within MD runs and between runs. We analyze atomic trajectories, focusing on correlations of the Kohn-Sham energy, pair correlation functions, number of hydrogen bonds, and diffusion coefficient. The observed variability across samples provides a measure of the uncertainty associated with these quantities, thus facilitating meaningful comparisons of different approximations used in the simulations. We find that the computed diffusion coefficient and average number of hydrogen bonds are affected by a significant uncertainty in spite of the large size of the dataset used. Lastly, a comparison with classical simulations using the TIP4P/2005 model confirms that the variability of the diffusivity is also observed after long equilibration times. Complete atomic trajectories and simulation output files are available online for further analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The Aerosol Model Benchmarking Repository: A toolkit for model intercomparison

The Aerosol Model Benchmarking Repository and Standards (AMBRS) project was initiated to provide tools and to establish community standards for benchmarking aerosol models. This report describes a set of open-source tools for building, running, and analyzing aerosol box model simulations in a standardized framework. The framework consists of three core components: AMBuilder, a CMake-based build system that compiles supported models consistently; AMBRS, a Python module that defines unified numerical experiments and executes them with aligned inputs; and PyParticle, an aerosol analysis package that standardizes output, computes diagnostics, and visualizes simulation results. Together, these tools enable reproducible intercomparison of aerosol schemes and support process-level evaluation of how model simplifications affect predictions of size distributions, cloud condensation nuclei activity, and other relevant properties relevant for the Earth-Energy system. Beyond its role in benchmarking, AMBRS provides a platform for studying aerosol processes across scales and can be used to generate training data for AI/ML applications in support of a broader hierarchical aerosol modeling strategy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Stochastic simulations of temporally and spatially variable Fe cycling within a floodplain aquifer

This data package contains model input and output files for local and global sensitivity analysis (SA) of a reactive transport model simulating redox cycling within a floodplain aquifer. The reaction network is implemented in CrunchFlow and focuses on spatially and temporally heterogeneous Fe cycling, though it contains several other species and redox pathways (37 aqueous species and 7 minerals in total). The package contains data from 600 Monte Carlo simulations (for global SA) and 104 one-at-a-time simulations (for local SA). It includes all input files required to run the CrunchFlow model, as well as limited model output – .csv files containing time series of primary species concentrations at 21 grid cells within the domain. These time series files contain the following species: H+, CO32-, SO42-, Cl-, Ca2+, Mg2+, Na+, K+, Fe(II), Fe(III), HS-, CH3COO-, O2(aq), S(aq), and Br-.These data were used to demonstrate the application of a novel form of global SA, distance-based generalized sensitivity analysis (DGSA) to a reactive transport model. The 2D model simulates the export of reduced species from pockets of fine-grained, organic-rich sediments embedded in a coarse sand aquifer. Stochastic simulations jointly varied 17 key model input parameters across several orders of magnitude, including the spatial variability of each parameter. DGSA on the model results reveal that the amplitude and variability of exported Fe(II) are most sensitive to the interaction between sand permeability and individual reaction rates. By contrast, the propagation of reducing conditions downgradient from the fine-grained lenses depends most heavily on the rate of dissolved organic carbon and sulfate release from the fines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Master of Puppets: Cooperative Multitasking for In Situ Processing

Modern scientific and engineering simulations track the time evolution of billions of elements. For such large runs, storing most time steps for later analysis is not a viable strategy. It is far more efficient to analyze the simulation data while it is still in memory. Here, we present a novel design for running multiple codes in situ: using coroutines and position-independent executables we enable cooperative multitasking between simulation and analysis, allowing the same executables to post-process simulation output, as well as to process it on the fly, both in situ and in transit. We present Henson, an implementation of our design, and illustrate its versatility by tackling analysis tasks with different computational requirements. This design differs significantly from the existing frameworks and offers an efficient and robust approach to integrating multiple codes on modern supercomputers. The techniques we present can also be integrated into other in situ frameworks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Henson v1.0

Modern scientific and engineering simulations track the time evolution of billions of elements. For such large runs, storing most time steps for later analysis is not a viable strategy. It is far more efficient to analyze the simulation data while it is still in memory. The developers present a novel design for running multiple codes in situ: using coroutines and position-independent executables they enable cooperative multitasking between simulation and analysis, allowing the same executables to post-process simulation output, as well as to process it on the fly, both in situ and in transit. They present Henson, an implementation of our design, and illustrate its versatility by tackling analysis tasks with different computational requirements. Our design differs significantly from the existing frameworks and offers an efficient and robust approach to integrating multiple codes on modern supercomputers. The presented techniques can also be integrated into other in situ frameworks.

Monozov, Dmitriy↗
DOE OSTI2016

Henson v1.0

Modern scientific and engineering simulations track the time evolution of billions of elements. For such large runs, storing most time steps for later analysis is not a viable strategy. It is far more efficient to analyze the simulation data while it is still in memory. The developers present a novel design for running multiple codes in situ: using coroutines and position-independent executables they enable cooperative multitasking between simulation and analysis, allowing the same executables to post-process simulation output, as well as to process it on the fly, both in situ and in transit. They present Henson, an implementation of our design, and illustrate its versatility by tackling analysis tasks with different computational requirements. Our design differs significantly from the existing frameworks and offers an efficient and robust approach to integrating multiple codes on modern supercomputers. The presented techniques can also be integrated into other in situ frameworks.

Monozov, Dmitriy↗
DOE OSTI2016

Program for Climate Model Diagnosis and Intercomparison (PCMDI) (Fact Sheet)

Established by the U.S. Department of Energy at Lawrence Livermore National Laboratory, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) fosters and engages in research aimed at providing a systematic and comprehensive evaluation of climate models. Working with international partners, PCMDI has inspired a fundamental cultural shift in the climate research community, which now expects access to output from climate simulations, enabling widespread scrutiny and analysis.

54 ENVIRONMENTAL SCIENCES↗