Results for “model exploration” — Page 92

DOE OSTI2020

Detecting Anomalous Computation with RNNs on GPU-Accelerated HPC Machines

This paper presents a workload classification framework that discriminates illicit computation from authorized workloads on GPU-accelerated HPC systems. As such systems become more and more powerful, they are exploited by attackers to run malicious and for-profit programs that typically require extremely high computing ability to be successful. Our classification framework leverages the distinctive signatures between illicit and authorized workloads, and explore machine learning methods to learn the workloads and classify them. The framework uses lightweight, non-intrusive workload profiling to collect model input data, and explores multiple machine learning methods, particularly recurrent neural network (RNN) that is suitable for online anomalous workload detection. Evaluation results on three generations of GPU machines demonstrate that the workload classification framework can tell apart the illicit authorized workloads with a high accuracy of over 95%.

Pengfei, Zou↗

DOE OSTI2022

The climate change mitigation potential of annual grasslands under future climates

Abstract Composted manure and green waste amendments have been shown to increase net carbon (C) sequestration in rangeland soils and have been proposed as a means to help lower atmospheric CO 2 concentrations. However, the effect of climate change on soil organic C (SOC) stocks and greenhouse gas emissions in rangelands is not well understood, and the viability of climate change mitigation strategies under future conditions is even less certain. We used a process‐based biogeochemical model (DayCent) at a daily time step to explore the long‐term effects of potential future climate changes on C and greenhouse gas dynamics in annual grassland ecosystems. We then used the model to explore how the same ecosystems might respond to climate change following compost amendments to soils and determined the long‐term viability of net SOC sequestration under changing climates. We simulated net primary productivity (NPP), SOC, and greenhouse gas fluxes across seven California annual grasslands with and without compost amendments. We drove the DayCent simulations with field data and with site‐specific daily climate data from two Earth system models (CanESM2 and HadGEM‐ES) and two representative concentration pathways (RCP4.5 and RCP8.5) through 2100. NPP and SOC stocks in unamended and amended ecosystems were surprisingly insensitive to projected climate changes. A one‐time amendment of compost to rangeland acted as a slow‐release organic fertilizer and increased NPP by up to 390–814 kg C ha −1 year −1 across sites. The amendment effect on NPP was not sensitive to Earth system model or emissions scenario and endured through the end of the century. Net SOC sequestration amounted to 1.96 ± 0.02 Mg C ha −1 relative to unamended soils at the maximum amendment effect. Averaged across sites and scenarios, SOC sequestration peaked 22 ± 1 years after amendment and declined but remained positive throughout the century. Though compost stimulated nitrous oxide (N 2 O) emissions, the cumulative net emissions (in CO 2 equivalents) due to compost were far less than the amount of SOC sequestered. Compost amendments resulted in a net climate benefit of 69.6 ± 0.5 Tg CO 2 e 20 ± 1 years after amendment if applied to similar ecosystems across the state, amounting to 39% of California's rangeland. These results suggest that the biogeochemical benefits of a single amendment of compost to rangelands in California are insensitive to climate change and could contribute to decadal‐scale climate change mitigation goals alongside emissions reductions.

Mayer, Allegra↗

DOE OSTI2014

Efficient Algorithm for Locating and Sizing Series Compensation Devices in Large Transmission Grids: Model Implementation (PART 1)

We explore optimization methods for planning the placement, sizing and operations of Flexible Alternating Current Transmission System (FACTS) devices installed to relieve transmission grid congestion. We limit our selection of FACTS devices to Series Compensation (SC) devices that can be represented by modification of the inductance of transmission lines. Our master optimization problem minimizes the l 1 norm of the inductance modification subject to the usual line thermal-limit constraints. We develop heuristics that reduce this non-convex optimization to a succession of Linear Programs (LP) which are accelerated further using cutting plane methods. The algorithm solves an instance of the MatPower Polish Grid model (3299 lines and 2746 nodes) in 40 seconds per iteration on a standard laptop—a speed up that allows the sizing and placement of a family of SC devices to correct a large set of anticipated congestions. We observe that our algorithm finds feasible solutions that are always sparse, i.e., SC devices are placed on only a few lines. In a companion manuscript, we demonstrate our approach on realistically-sized networks that suffer congestion from a range of causes including generator retirement. In this manuscript, we focus on the development of our approach, investigate its structure on a small test system subject to congestion from uniform load growth, and demonstrate computational efficiency on a realistically-sized network.

25 ENERGY STORAGE↗

DOE OSTI2013

EXCLUSION OF COSMIC RAYS IN PROTOPLANETARY DISKS: STELLAR AND MAGNETIC EFFECTS

Cosmic rays (CRs) are thought to provide an important source of ionization in the outermost and densest regions of protoplanetary disks; however, it is unknown to what degree they are physically present. As is observed in the solar system, stellar winds can inhibit the propagation of CRs within the circumstellar environment and subsequently into the disk. In this work, we explore the hitherto neglected effects of CR modulation by both stellar winds and magnetic field structures and study how these processes act to reduce disk ionization rates. We construct a two-dimensional protoplanetary disk model of a T-Tauri star system, focusing on ionization from stellar and interstellar FUV, stellar X-ray photons, and CRs. We show that stellar winds can power a heliosphere-like analog, i.e., a ''T-Tauriosphere,'' diminishing CR ionization rates by several orders of magnitude at low to moderate CR energies (E{sub CR} {<=} 1 GeV). We explore models of both the observed solar wind CR modulation and a highly simplified estimate for ''elevated'' CR modulation as would be expected from a young T-Tauri star. In the former (solar analog) case, we estimate the ionization rate from galactic CRs to be {zeta}{sub CR} {approx} (0.23-1.4) Multiplication-Sign 10{sup -18} s{sup -1}. This range of values, which we consider to be the maximum CR ionization rate for the disk, is more than an order of magnitude lower than what is generally assumed in current models for disk chemistry and physics. In the latter elevated case, i.e., for a ''T-Tauriosphere,'' the ionization rate by CRs is {zeta}{sub CR} {approx}< 10{sup -20} s{sup -1}, which is 1000 times smaller than the interstellar value. We discuss the implications of a diminished CR ionization rate on the gas physics by estimating the size of the resulting magnetorotational instability dead zones. Indeed, if winds are as efficient at CR modulation as predicted here, short-lived radionuclides (now extinct) would have provided the major source of ionization ({zeta}{sub RN} {approx} 7.3 Multiplication-Sign 10{sup -19} s{sup -1}) in the planet-forming zone of the young solar nebula.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Machine-learned closure of URANS for stably stratified turbulence: connecting physical timescales & data hyperparameters of deep time-series models

Stably stratified turbulence (SST), a model that is representative of the turbulence found in the oceans and atmosphere, is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying scenarios. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations—used to numerically simulate approximate solutions for such turbulent flows. Rather than attempting to model each term in URANS separately, it is attractive to explore the capability of machine learning (ML) to model groups of terms, i.e. to directly model the force balances. We develop deep time-series ML for closure modeling of the URANS equations applied to SST. We consider decaying SST which are homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. We consider two time-series ML models: long short-term memory and neural ordinary differential equation. Both models perform accurately and are numerically stable in a posteriori (online) tests. Furthermore, we explore the data requirements of the time-series ML models by extracting physically relevant timescales of the complex system. We find that the ratio of the timescales of the minimum information required by the ML models to accurately capture the dynamics of the SST corresponds to the Reynolds number of the flow. The current framework provides the backbone to explore the capability of such models to capture the dynamics of high-dimensional complex dynamical system like SST flows.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Photometric Correction of Hayabusa2's NIRS3 Spectra of Asteroid 162173 Ryugu Using Empirical Photometric Models

In 2018, Japanese Aerospace Exploration Agency’s spacecraft Hayabusa2 began a near infrared spectroscopic imaging survey of near-Earth asteroid 162173 Ryugu. Hayabusa2 is a successful sample-return mission with an overarching goal to provide a better understanding of the origin and evolution of our solar system. The target, Ryugu, is a low-albedo carbonaceous asteroid (Cb-type) that is linked to carbonaceous chondrite meteorites. It is thought to have originated in the main asteroid belt and migrated inward to become a near-Earth asteroid, and therefore reachable by spacecraft. Preliminary findings from processed NIRS3data have shown that hydroxyl-bearing minerals are present on the surface of Ryugu, and it is likely the result of impact fragments from an aqueously altered parent body.In this project, we have used newly calibrated and processed NIRS3 data using updated shape models of Ryugu. NIRS3 spectra have both a thermal and reflectance component. The thermal component (beyond 2.5 microns) was modeled and removed from all NIRS3 spectra. Ryugu spectra were taken at different viewing geometries, and a photometric model needed to be developed to normalize all of the spectra at a common geometry for each mission phase. We have used three empirical models: Minnaert, Lommel-Seeliger, and ROLO (RObotic LunarOrbiter). These models were chosen for their ability to relate the surface reflectance to the viewing geometry, as well as their compatibility with the asteroid’s albedo range. The three models provide the global light scattering properties of Ryugu’s surface and subsequently enable us to calculate the geometric albedo, phase integral, spherical bond albedo, and the average surface normal albedo for Ryugu.

Lucille Grace Williamson↗

DOE OSTI2024

USAID Haiti Training Session #2: Introduction to Engage [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-89369 for the French translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

USAID Haiti Training Session #3: Capacity Expansion [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-89365 for the French translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Session de formation de l'USAID en Haiti n2: Introduction a l'outil Engage [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-88836 for the English translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Seance de formation de l'USAID en Haiti n3: Expansion des capacites [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-88837 for the English translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

USAID Haiti Training Session #4: Resilience and Power Sector Modeling Part II [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-89412 for the French translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Seance de formation n4: Modelisation de la resilience et du secteur de l'electricite - Partie I [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-88838 for the English translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Seance de formation n4: Modelisation de la resilience et du secteur de l'electricite - Partie II [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-89389 for the English translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2013

The Regional Nature of Global Challenges. A Need and Strategy for Integrated Regional Modeling

In this paper, we explore the regional nature of global environmental challenges. We take a broad approach by examining the scientific foundation that is needed to support policy and decision making and identifying some of the most important barriers to progress that are truly scale-dependent. In so doing, we hope to show that understanding global environmental changes requires understanding a number of intrinsically regional phenomena, and that successful decision making likewise requires an integrated approach that accounts for a variety of regional Earth system processes—which we define to include both human activities and environmental systems that operate or interact primarily at sub-continental scales. Understanding regional processes and phenomena, including regional decision-making processes and information needs, should thus be an integral part of the global change research agenda. To address some of the key issues and challenges, we propose an integrated regional modeling approach that accounts for the dynamic interactions among physical, ecological, biogeochemical, and human processes and provides relevant information to regional decision makers and stakeholders.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

Testing Dissipative Magnetosphere Model Light Curves and Spectra with Fermi Pulsars

We explore the emission properties of a dissipative pulsar magnetosphere model introduced by Kalapotharakos et al. comparing its high-energy light curves and spectra, due to curvature radiation, with data collected by the Fermi LAT. The magnetosphere structure is assumed to be near the force-free solution. The accelerating electric field, inside the light cylinder (LC), is assumed to be negligible, while outside the LC it rescales with a finite conductivity (sigma). In our approach we calculate the corresponding high-energy emission by integrating the trajectories of test particles that originate from the stellar surface, taking into account both the accelerating electric field components and the radiation reaction forces. First, we explore the parameter space assuming different value sets for the stellar magnetic field, stellar period, and conductivity. We show that the general properties of the model are in a good agreement with observed emission characteristics of young gamma-ray pulsars, including features of the phase-resolved spectra. Second, we find model parameters that fit each pulsar belonging to a group of eight bright pulsars that have a published phase-resolved spectrum. The sigma values that best describe each of the pulsars in this group show an increase with the spin-down rate (E˙ ) and a decrease with the pulsar age, expected if pair cascades are providing the magnetospheric conductivity. Finally, we explore the limits of our analysis and suggest future directions for improving such models.

acceleration of particles↗

DOE OSTI2015

Testing dissipative magnetosphere model light curves and spectra with FERMI pulsars

We explore the emission properties of a dissipative pulsar magnetosphere model introduced by Kalapotharakos et al. comparing its high-energy light curves and spectra, due to curvature radiation, with data collected by the Fermi LAT. The magnetosphere structure is assumed to be near the force-free solution. The accelerating electric field, inside the light cylinder (LC), is assumed to be negligible, while outside the LC it rescales with a finite conductivity (σ). In our approach we calculate the corresponding high-energy emission by integrating the trajectories of test particles that originate from the stellar surface, taking into account both the accelerating electric field components and the radiation reaction forces. First, we explore the parameter space assuming different value sets for the stellar magnetic field, stellar period, and conductivity. We show that the general properties of the model are in a good agreement with observed emission characteristics of young γ-ray pulsars, including features of the phase-resolved spectra. Second, we find model parameters that fit each pulsar belonging to a group of eight bright pulsars that have a published phase-resolved spectrum. The σ values that best describe each of the pulsars in this group show an increase with the spin-down rate ( E-dot ) and a decrease with the pulsar age, expected if pair cascades are providing the magnetospheric conductivity. Finally, we explore the limits of our analysis and suggest future directions for improving such models.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Exploring the Impact of Dust on North Atlantic Hurricanes in a High-Resolution Climate Model

Herein the relationship between African dust and the climatology of tropical cyclones (TCs) in the North Atlantic is explored using the Community Atmosphere Model at a global horizontal resolution of 28 km. A simulation in which the aerosol model is modified to significantly reduce the amount of airborne dust is compared to a standard simulation. The simulation with reduced dust increases TC frequency globally, with the largest increase occurring in the North Atlantic. The increase in TC activity in the North Atlantic is consistent with an environment that is more conducive for the genesis and intensification of storms. TCs are more frequent (27%) and on average significantly longer lived (13%) in the low dust configuration but only slightly stronger (3%). This results in a 57% increase in accumulated cyclone energy per hurricane season on average. This work has implications for projections of future climate and resulting changes in TC activity.

58 GEOSCIENCES↗

DOE OSTI2023

gLaSDI: Parametric physics-informed greedy latent space dynamics identification

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic nonlinear latent representations of high-dimensional data, while dynamics identification (DI) models capture local latent-space dynamics. Here, an interactive training algorithm is adopted for the autoencoder and local DI models, which enables identification of simple latent-space dynamics and enhances accuracy and efficiency of data-driven reduced-order modeling. To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual-based error indicator and random-subset evaluation is introduced to search for the optimal training samples on the fly. Further, to exploit local latent-space dynamics captured by the local DI models for an improved modeling accuracy with a minimum number of local DI models in the parameter space, a -nearest neighbor convex interpolation scheme is employed. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including Burgers equations, nonlinear heat conduction, and radial advection. The proposed adaptive greedy sampling outperforms the conventional predefined uniform sampling in terms of accuracy. Compared with the high-fidelity models, gLaSDI achieves 17 to 2,658× speed-up with 1 to 5% relative errors.

97 MATHEMATICS AND COMPUTING↗