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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Incorporating FLEX Strategies in Multi-Unit Probabilistic Risk Assessment

The catastrophic Fukushima nuclear accident reminded nuclear community about the possibilities of extreme accident scenarios, including those involving multiple reactor units on the same site. As the responses to the Fukushima accident, the nuclear power industry developed and implemented a series of strategies, including the Diverse and Flexible Coping Strategies (FLEX), to enhance the coping capacities of nuclear power plants (NPPs) under extreme accidents. This study intends to examine the impact of FLEX strategies on the risk from all the reactor units on the same NPP site, including risks from accidents involving a single unit and from accidents involving multiple units. The fundamental, methodological element of this study is Multi-Unit Probabilistic Risk Assessment (MUPRA), which requires a shift in Probabilistic Risk Assessment (PRA) from a “one-reactor-at-a-time” mindset to a “considering all reactors sharing a site as a unity” one. An integrated modeling approach for multi-unit event sequence development will be leveraged to develop MUPRA model and address the intra-unit and inter-unit dependencies. Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE), a PRA software developed and maintained by Idaho National Laboratory (INL) for the U.S. Nuclear Regulatory Commission (NRC), will serve as the platform for MUPRA modeling. This study selects Loss Of Offsite Power (LOOP) as a representative initiating event, which may occur on a generic two-unit NPP site and impacts both reactor units on the site. First, an MUPRA model, including multi-unit event trees, will be developed to obtain single-unit and multi-unit accident scenarios. Next, different FLEX strategies will be assumed, including FLEX equipment for a unit cannot be used for another unit, FLEX equipment for multiple units can be used in a cross-connected manner, etc. Lastly, the effectiveness of each postulated FLEX strategy will be evaluated by incorporating the corresponding FLEX equipment and deployment logic in the MUPRA model.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2012

Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the other uses the daily SPoRT/MODIS GVFs. Finally, snapshots of the LIS land surface fields are used to initialize two different simulations of the NU-WRF, one running with climatology LIS and GVFs, and the other running with experimental LIS and NASA/SPoRT GVFs. In this paper/presentation, case study results will be highlighted in regions with significant differences in GVF between the NCEP climatology and SPoRT product during severe weather episodes.

Case, Jonathan L.↗
DOE OSTI2019

Feynman’s proof of the commutativity of the Calogero integrals of motion

During his last year of life Feynman became interested in integrable models. In the course of his study of one dimensional particles with inverse square potentials (the Calogero model) he came up with a field theory-inspired proof of the commutativity of the integrals of motion of the system, which remained unpublished. I present an exact transcript of the proof together with a scan of the first page of Feynman’s original manuscript and some historical background.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Soil Respiration Responses to Throughfall Exclusion Are Decoupled From Changes in Soil Moisture for Four Tropical Forests, Suggesting Processes for Ecosystem Models

Abstract Climatic drying is predicted for many tropical forests yet models remain poorly parameterized for these ecosystems, hampering predictions of forest‐climate interactions. We applied an integrated model–experiment approach, parameterizing an ecosystem model with tropical forest observational data and comparing model predictions to a field drying manipulation. We hypothesized that drying suppresses soil CO 2 fluxes (i.e., respiration) in relatively dry tropical forests but increases CO 2 fluxes in wetter tropical forests by alleviating anaerobiosis. We measured soil CO 2 fluxes during wet‐dry cycles from 2015 to 2022 in four Panamanian forests that vary in rainfall and soil fertility. Measured soil CO 2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in lower soil moisture optima for respiration than previously modeled. We then parameterized the model using field data and the new moisture‐respiration response functions. The updated model predicted increased soil CO 2 fluxes with drying in wetter and fertile forests and suppressed fluxes in drier, infertile forests. In contrast to model predictions, a chronic throughfall exclusion experiment initially suppressed soil respiration across forests, with sustained suppression for four years in the wettest forest only (−28% ± 4% during the dry season). In the fertile forest, drying eventually elevated CO 2 fluxes over this period (+75% ± 28% during the late wet season). The unexpected negative drying effect in the wettest, infertile forest could have resulted from reduced vertical flushing of nutrients into soils. Including hydro‐nutrient interactions in ecosystem models could improve predictions of tropical forest‐climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Greenhouse gas consequences of the China dual credit policy

For over ten years, China has been the largest vehicle market in the world. In order to address energy security and air quality concerns, China issued the Dual Credit policy to improve vehicle efficiency and accelerate New Energy Vehicle adoption. In this paper, a market-penetration model is combined with a vehicle fleet model to assess implications on greenhouse gas (GHG) emissions and energy demand. Here we use this integrated modeling framework to study several scenarios, including hypothetical policy tweaks, oil price, battery cost and charging infrastructure for the Chinese passenger vehicle fleet. The model shows that the total GHGs of the Chinese passenger vehicle fleet are expected to peak in 2032 under the Dual Credit policy. A significant reduction in GHG emissions is possible if more efficient internal combustion engines continue to be part of the technology mix in the short term with more New Energy Vehicle penetration in the long term.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Thermal acclimation of plant photosynthesis and autotrophic respiration in a northern peatland

Peatlands contain one-third of global soil carbon (C), but the responses of peatland ecosystems to long-term warming are not well understood. Here, we pursue an emergent understanding of warming effects on ecosystem C fluxes at peatlands by constraining a process-oriented model, the terrestrial ECOsystem model, with observational data from a long-term warming experiment at the Spruce and Peatland Responses Under Changing Environments site. Model-based assessments show that ecosystem-level photosynthesis and autotrophic respiration exhibited significant thermal acclimation, with temperature sensitivities being linearly decreased with warming. Using the thermal-acclimated parameter values, simulated gross primary production, net primary production, and plant autotrophic respiration (R a ), were all lower than those simulated with non-thermal acclimated parameter values. In contrast, ecosystem respiration simulated with thermal acclimated parameter values was higher than that simulated with non-thermal acclimated parameter values. Net ecosystem CO 2 exchange was much higher after constraining model parameters with observational data from the warming treatments, releasing C at a rate of 28.3 g C m -2 yr -1 °C -1 . Our data-model integration study suggests that peatlands are likely to release more C than previously estimated. Earth system models may overestimate C uptake by peatlands under warming if physiological thermal acclimation of plants is not incorporated. Thus, it is critical to consider the long-term physiological thermal acclimation of plants in the models to better predict global C dynamics under future climate and their feedback to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Understanding and Predicting Pedestal Scenarios on NSTX-U (Final Technical Report)

This project investigated gyrokinetic instabilities in the NSTX pedestal, identified the major transport mechanisms (some of them novel) in the NSTX pedestal, and developed and validated a predictive modeling capability for pedestal transport in spherical tokamaks, with particular emphasis on NSTX discharges. The work combined first-principles gyrokinetic simulations, reduced transport models, integrated transport calculations with ASTRA, and exploratory machine learning tools. The central outcome is a practical modeling capability for pedestal temperature profiles based on reduced models informed and constrained by gyrokinetic physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2010

Exploration Life Support Critical Questions for Future Human Space Missions

Exploration Life Support (ELS) is a current project under NASA's Exploration Systems Mission Directorate. The ELS Project plans, coordinates and implements the development of advanced life support technologies for human exploration missions in space. Recent work has focused on closed loop atmosphere and water systems for long duration missions, including habitats and pressurized rovers. But, what are the critical questions facing life support system developers for these and other future human missions? This paper explores those questions and how progress in the development of ELS technologies can help answer them. The ELS Project includes the following Elements: Atmosphere Revitalization Systems, Water Recovery Systems, Waste Management Systems, Habitation Engineering, Systems Integration, Modeling and Analysis, and Validation and Testing, which includes the Sub-Elements Flight Experiments and Integrated Testing. Systems engineering analysis by ELS seeks to optimize overall mission architectures by considering all the internal and external interfaces of the life support system and the potential for reduction or reuse of commodities. In particular, various sources and sinks of water and oxygen are considered along with the implications on loop closure and the resulting launch mass requirements. Systems analysis will be validated through the data gathered from integrated testing, which will demonstrate the interfaces of a closed loop life support system. By applying a systematic process for defining, sorting and answering critical life support questions, the ELS project is preparing for a variety of future human space missions

Kwert, Michael K.↗
DOE OSTI2019

An Open Source Modeling Framework for Interdependent Energy-Transportation-Communication Infrastructure in Smart and Connected Communities

Infrastructure in future smart and connected communities is envisioned as an aggregate of public services, including energy, transportation, and communication systems, all intertwined with each other. The intrinsic interdependency among these systems may exert the underlying influence on both design and operation of the heterogeneous infrastructures. Yet, few prior studies have tapped into the interdependency among these systems in order to quantify their potential impacts during standard operation. In response to this, this paper proposes an open-source, flexible, integrated modeling framework suitable for designing coupled energy, transportation, and communication systems and for assessing the impact of their interdependencies. First, a novel multi-level, multi-layer, multi-agent approach is proposed to enable flexible modeling of the interconnected systems. Then, for the framework's proof of concept, preliminary component and system-level models for different systems are designed and implemented using Modelica, an equation-based object-oriented modeling language. Finally, three case studies of gradually increasing complexity are presented (energy, energy + transportation, and energy + transportation + communication) to evaluate the interdependencies among the three systems. Quantitative analyses show that the deviation of the average velocity on the road can be 10.5% and the deviation of the power drawn from the grid can be 7% with or without considering the transportation and communication system at the peak commute time, indicating the presence of notable interdependencies. The proposed modeling framework also has the potential to be further extended for various modeling purposes and use cases, such as dynamic modeling and optimization, resilience analysis, and integrated decision making in future connected communities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Soil Carbon Change and Net Energy Associated with Biofuel Production on Marginal Lands: A Regional Modeling Perspective

The use of marginal lands (MLs) for biofuel production has been contemplated as a promising solution for meeting biofuel demands. However, there have been concerns with spatial location of MLs, their inherent biofuel potential, and possible environmental consequences with the cultivation of energy crops. Here, we developed a new quantitative approach that integrates high-resolution land cover and land productivity maps and uses conditional probability density functions for analyzing land use patterns as a function of land productivity to classify the agricultural lands. We subsequently applied this method to determine available productive croplands (P-CLs) and non-crop marginal lands (NC-MLs) in a nine-county Southern Michigan. Furthermore, Spatially Explicit Integrated Modeling Framework (SEIMF) using EPIC (Environmental Policy Integrated Climate) was used to understand the net energy (NE) and soil organic carbon (SOC) implications of cultivating different annual and perennial production systems.

Bandaru, Varaprasad↗
DOE OSTI2026

Hierarchical Testing of a Hybrid Machine Learning‐Physics Global Atmosphere Model

Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic-scale atmospheric dynamics, their performance across timescales and under out-of-distribution forcing, such as +3K or +4K uniform-warming forcings, and the sources of biases remain elusive, to establish the model's reliability for Earth science. Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño-Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out-of-distribution uniform-warming forcings, NeuralGCM simulates similar responses in global-average temperature and precipitation and reproduces large-scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper-level warming and stratospheric circulation responses to SST warming compared to physics-based ESMs. The causes of these weaknesses were explored. Despite the noted weaknesses, NeuralGCM reproduces responses across experiments reasonably and performs comparably to ESMs. By integrating a dynamical core with ML, NeuralGCM shows potential for developing ML-based ESMs.

global warming↗
DOE OSTI2019

Modeling the Grid Integration of Variable Renewable Energy (VRE) Resources

The presentation was given at the November 21-23, 2018 'CEM Days - Integration of Renewables in the Electric Sector: Paths and Challenges to Energy Planning' in Rio de Janeiro, Brazil. It provides a summary of NREL's current modeling approaches, datasets, and considerations for integrating renewables into the power system. It also covers current model method improvement efforts, including our vision for a truly integrated modeling approach.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Data-model integration plays a critical role in assessing and improving our capacity to predict ecosystem dynamics. Similarly, the ability to attach quantitative statements of uncertainty around model forecasts is crucial for model assessment and interpretation and for setting field research priorities. Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints. However, the Markov chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally demanding models and large datasets. We employ an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity. Analysis involved informative priors constructed from a meta-analysis of the primary literature and specification of both model and data uncertainties, and it introduced novel approaches to autocorrelation corrections on multiple data streams and emulating the sufficient statistics surface. We report the integration of this method within an ecological workflow management software, Predictive Ecosystem Analyzer (PEcAn), and its application and validation with two process-based terrestrial ecosystem models: SIPNET and ED2. In a test against a synthetic dataset, the emulator was able to retrieve the true parameter values. A comparison of the emulator approach to standard brute-force MCMC involving multiple data constraints showed that the emulator method was able to constrain the faster and simpler SIPNET model's parameters with comparable performance to the brute-force approach but reduced computation time by more than 2 orders of magnitude. The emulator was then applied to calibration of the ED2 model, whose complexity precludes standard (brute-force) Bayesian data assimilation techniques. Both models are constrained after assimilation of the observational data with the emulator method, reducing the uncertainty around their predictions. Performance metrics showed increased agreement between model predictions and data. Our study furthers efforts toward reducing model uncertainties, showing that the emulator method makes it possible to efficiently calibrate complex models.

58 GEOSCIENCES↗
DOE OSTI2017

A Grid of Synthetic Spectra for Hot DA White Dwarfs and Its Application in Stellar Population Synthesis

In this work we present a grid of LTE and non-LTE synthetic spectra of hot DA white dwarfs (WDs). In addition to its usefulness for the determination of fundamental stellar parameters of isolated WDs and in binaries, this grid will be of interest for the construction of theoretical libraries for stellar studies from integrated light. The spectral grid covers both a wide temperature and gravity range, with 17,000 K ≤ T {sub eff} ≤ 100,000 K and 7.0 ≤ logg ≤ 9.5. The stellar models are built for pure hydrogen and the spectra cover a wavelength range from 900 Å to 2.5 μm. Additionally, we derive synthetic HST/ACS, HST/WFC3, Bessel UBVRI, and SDSS magnitudes. The grid was also used to model integrated spectral energy distributions of simple stellar populations and our modeling suggests that DAs might be detectable in ultraviolet bands for populations older than ∼8 Gyr.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2023

Ames Contrail Simulation Model: Modeling Aviation Induced Contrails and the Computation of Contrail Radiative Forcing Using Air Traffic Data

NASA is developing traffic flow management strategies to reduce the impact of aviation on climate and improve efficiency of aircraft routes in the presence of limited airport and airspace capacity constraints, which are partly due to convective weather and natural calamities. Development of Strategic Traffic Flow Management (STFM) requires models of aircraft emissions and contrails, and models of their effect on climate. There is extensive literature on the modeling of contrails and the Radiative Forcing (RF) associated with contrails. This report captures the latest NASA developments in this research area and describes the integrated modeling, analysis, and software development to support STFM. Aircraft contrails are long, thin and often linear clouds triggered by aircraft engine exhausts in the high-altitude ice-saturated atmosphere. Contrails, similar to that of natural cirrus clouds, can impact global climate by reflecting shortwave radiation and trapping longwave radiation. Recent studies from the Intergovernmental Panel on Climate Change (IPCC) have shown that aircraft contrails are estimated to have greater impact on global warming than aircraft CO 2 emissions. The Ames Contrail Simulation Model (ACSM) presented in this report simulates the full life cycle of aircraft contrails, including their formation, dynamic evolution, and dissipation, and calculates the associated RF with actual meteorological and air traffic data. ACSM combines models from previous studies that focus on contrail formation and persistence based on the Schmidt-Appleman theoretical criteria, and it incorporates elements of cloud dynamics, microphysics, and climate modeling found in other surveyed contrail models, while also making adjustment for improved computational efficiency. In addition, ACSM is integrated with NASA's state-of-the-art flight simulation software for rapid assessment of aircraft contrail impacts. Applications include assessments of long-term global climate impact resulting from aviation-induced contrails and the design of optimal contrail-mitigation aircraft operation strategies.

aviation contrails↗
DOE OSTI2014

Integrated optical and electrical modeling of plasmon-enhanced thin film photovoltaics: A case-study on organic devices

The nanoscale light control for absorption enhancement of organic photovoltaic (OPV) devices inevitably produces strongly non-uniform optical fields. These non-uniformities due to the localized optical modes are a primary route toward absorption enhancement in OPV devices. Therefore, a rigorous modeling tool taking into account the spatial distribution of optical field and carrier generation is necessary. Presented here is a comprehensive numerical model to describe the coupled optical and electrical behavior of plasmon-enhanced polymer:fullerene bulk heterojunction (BHJ) solar cells. In this model, a position-dependent electron-hole pair generation rate that could become highly non-uniform due to photonic nanostructures is directly calculated from the optical simulations. By considering the absorption and plasmonic properties of nanophotonic gratings included in two different popular device architectures, and applying the Poisson, current continuity, and drift/diffusion equations, the model predicts quantum efficiency, short-circuit current density, and desired carrier mobility ratios for bulk heterojunction devices incorporating nanostructures for light management. In particular, the model predicts a significant degradation of device performance when the carrier species with lower mobility are generated far from the collecting electrode. Consequently, an inverted device architecture is preferred for materials with low hole mobility. This is especially true for devices that include plasmonic nanostructures. Additionally, due to the incorporation of a plasmonic nanostructure, we use simulations to theoretically predict absorption band broadening of a BHJ into energies below the band gap, resulting in a 4.8% increase in generated photocurrent.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗