Results for “model exploration” — Page 62

NASA NTRS2012

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

Celaya, Jose R.↗

NASA NTRS2012

Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

Galvan, Jose Ramon↗

NASA NTRS2023

Numerical Investigation of Occupant Injury Risks During A Realistic Transport Aircraft Crash Conditions

Researchers at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) have conducted a full-scale crash test of a Fokker F28 MK1000 aircraft to investigate the performance of transport aircraft under realistic crash conditions. This crash test was computationally recreated using finite element (FE) human body models (HBMs) to further explore potential injury risks to occupants and analyze the utilization of HBMs in the aerospace crash environment. The Global Human Body Model Consortium (GHBMC) male 50th percentile occupant detailed model (v6.0) and the Toyota Human Model for Safety (THUMS) male 50th percentile occupant model (v6.1) were selected to be used in the crash simulations. The HBMs were simulated in conditions matching those of anthropomorphic test device (ATD) experiments included within the aircraft cabin during the crash test. Seven occupant locations within the cabin were simulated utilizing each of the models. The models were positioned in a neutral upright posture with hands resting on the legs and the feet contacting the floor. Head, brain, neck, and lumbar vertebra injury metrics were calculated for all trials. Both HBM models required minor modifications to stabilize these simulations. The GHBMC model required added erosion for six parts while the THUMS model only required one in order to complete the full simulation. The THUMS model, however, required a much smaller timestep for stability and therefore took significantly more computational time. In addition the GHBMC model includes integrated instrumentation while the THUMS model requires development and implementation of instrumentation. Both models predicted 100% injury risk for lumbar vertebra fracture in all test conditions. This prediction was in family with high lumbar load values measured by the ATDs during the crash test. The THUMS model consistently predicted lower injury risks than the GHBMC model in all three other metrics varying depending on the crash pulse. Overall, the THUMS model required less modifications to allow for this study. However, the GHBMC models significantly faster run time and integrated instrumentation make it a more intuitive model for this research.

Crashworthiness↗

DOE OSTI2023

High-Performance Accelerator Modeling: Toward Improving Controls and Diagnostics for High-Brightness Beams in Experiment

One of the main charges of beam physics is to improve the overall beam quality delivered to experiments and applications, which is generally quantified by the beam brightness. High brightness beams have numerous applications, including x-ray free electron lasers and ultrafast electron diffraction (UED). To these ends, this thesis details efforts to use high-performance models --- high-fidelity models that execute quickly --- for controls and diagnostics. To demonstrate the generality of these techniques, this thesis focuses on three of the most successful photoinjector designs currently used worldwide: the UCLA/SLAC/BNL-type high-gradient S-band gun, the continuous-wave high-repetition rate VHF APEX gun and the L-band DESY-PITZ-type gun. Work will be shown from Pegasus (UCLA), HiRES (LBNL) and FAST (FNAL).Specifically, data-driven models for online virtual diagnostics are presented, in this case, in the context of UED at HiRES, leading to a temporal resolution improvement. Methods for improving the fidelity of physics-based models are discussed, with examples at HiRES, Pegasus and FAST. Markov-chain Monte Carlo analysis is applied to match simulations, in the context of photocathode studies at HiRES and Pegasus. Lastly, the augmentation of online model-based predictions with model-independent optimization is explored in a fluctuating environment at Pegasus and HiRES. A central theme of this dissertation is working with parameter fluctuations when modeling an accelerator beamline. Long-term drifts and shot-to-shot jitter exist in every accelerator to a varying degree and therefore play an important role in every chapter of this dissertation. This thesis attempts to address the issues associated with these fluctuations when trying to develop faithful model representations of the system.

43 PARTICLE ACCELERATORS↗

DOE OSTI2019

Fingerprinting Anomalous Computation with RNN for 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 four generations of GPU machines demonstrate that the workload classification framework can tell apart the illicit authorized workloads with a high accuracy of over 95%.

Zou, Pengfei↗

DOE OSTI2026

AI-Driven Frameworks for Characterizing Urban Energy Systems

We develop AI-driven frameworks to characterize urban energy systems with the goal of transforming planning by reducing the labor of model generation, scaling scenario exploration, and improving accuracy for localized analysis. The approach integrates top-down and bottom-up data to train different AI models that predict missing information and generate inputs and targeted scenarios for district-scale models. The result is a scalable framework that provides actionable insights for reliable and efficient planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

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 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↗

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↗