Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Model fitting”

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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

X-ray perspective of the twisted magnetospheres of magnetars

Anomalous X-ray pulsars (AXPs) and soft gamma-ray repeaters (SGRs) are recognized as the most promising magnetar candidates, as indicated by their energetic bursts and rapid spin-downs. It is expected that the strong magnetic field leaves distinctive imprints on the emergent radiation both by affecting the radiative processes in atmospheres of magnetars and by scattering in the upper magnetospheres. We construct a self-consistent physical model that incorporates emission from the magnetar surface and its reprocessing in the three-dimensional (3D) twisted magnetosphere using a Monte Carlo technique. The synthetic spectra are characterized by four parameters: surface temperature kT, surface magnetic field strength B, magnetospheric twist angle Δϕ, and the normalized electron velocity β. We also create a tabular model (STEMS3D) and apply it to a large sample of XMM-Newton spectra of magnetars. The model successfully fits nearly all spectra, and the obtained magnetic field for 7 out of the 11 sources are consistent with the values inferred from the spin-down rates. We conclude that the continuum-fitting by our model is a robust method to measure the magnetic field strength and magnetospheric configuration of AXPs and SGRs. Investigating the multiple observations of variable sources, we also study the mechanism of their spectral evolution. Our results suggest that the magnetospheres in these sources are highly twisted (Δϕ>1), and the behavior of magnetospheric twisting and untwisting is revealed in the 2002 outburst of 1E 2259+586.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Wiggly whipped inflation

Motivated by BICEP2 results on the CMB polarization B-mode which imply primordial gravitational waves are produced when the Universe has the expansion rate of about H ≈ 10{sup 14} GeV, and by deviations from a smooth power-law behavior for multipoles ℓ <50 in the CMB temperature anisotropy power spectrum found in the WMAP and Planck experiments, we have expanded our class of large field inflationary models that fit both the BICEP2 and Planck CMB observations consistently. These best-fitted large field models are found to have a transition from a faster roll to the slow roll V(φ)=m{sup 2} φ{sup 2}/2 inflation at a field value around 14.6 M{sub Pl} and thus a potential energy of V(φ) ∼ (10{sup 16} GeV){sup 4}. In general this transition with sharp features in the inflaton potential produces not only suppression of scalars relative to tensor modes at small k but also introduces wiggles in the primordial perturbation spectrum. These wiggles are shown to be useful to explain some localized features in the CMB angular power spectrum and can also have other observational consequences. Thus, primordial GW can be used now to make a tomography of inflation determining its fine structure. The resulting Wiggly Whipped Inflation scenario is described in details and the anticipated perturbation power spectra, CMB power spectra, non-Gaussianity and other observational consequences are calculated and compared to existing and forthcoming observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Characterizing the gene–environment interaction underlying natural morphological variation in Neurospora crassa conidiophores using high-throughput phenomics and transcriptomics

Abstract Neurospora crassa propagates through dissemination of conidia, which develop through specialized structures called conidiophores. Recent work has identified striking variation in conidiophore morphology, using a wild population collection from Louisiana, United States of America to classify 3 distinct phenotypes: Wild-Type, Wrap, and Bulky. Little is known about the impact of these phenotypes on sporulation or germination later in the N. crassa life cycle, or about the genetic variation that underlies them. In this study, we show that conidiophore morphology likely affects colonization capacity of wild N. crassa isolates through both sporulation distance and germination on different carbon sources. We generated and crossed homokaryotic strains belonging to each phenotypic group to more robustly fit a model for and estimate heritability of the complex trait, conidiophore architecture. Our fitted model suggests at least 3 genes and 2 epistatic interactions contribute to conidiophore phenotype, which has an estimated heritability of 0.47. To uncover genes contributing to these phenotypes, we performed RNA-sequencing on mycelia and conidiophores of strains representing each of the 3 phenotypes. Our results show that the Bulky strain had a distinct transcriptional profile from that of Wild-Type and Wrap, exhibiting differential expression patterns in clock-controlled genes (ccgs), the conidiation-specific gene con-6, and genes implicated in metabolism and communication. Combined, these results present novel ecological impacts of and differential gene expression underlying natural conidiophore morphological variation, a complex trait that has not yet been thoroughly explored.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

SU-E-T-751: Three-Component Kinetic Model of Tumor Growth and Radiation Response for Stereotactic Radiosurgery

Purpose: To develop and study a kinetic model of tumor growth and its response to stereotactic radiosurgery (SRS) by assuming that the cells in irradiated tumor volume were made of three types. Methods: A set of ordinary differential equations (ODEs) were derived for three types of cells and a tumor growth rate. It is assumed that the cells were composed of actively proliferating cells, lethally damaged-dividing cells, and non-dividing cells. We modeled the tumor volume growth with a time-dependent growth rate to simulate the saturation of growth. After SRS, the proliferating cells were permanently damaged and converted to the lethally damaged cells. The amount of damaged cells were estimated by the LQ-model. The damaged cells gradually stopped dividing/proliferating and died with a constant rate. The dead cells were cleared from their original location with a constant rate. The total tumor volume was the sum of the three components. The ODEs were numerically solved with appropriate initial conditions for a given dosage. The proposed model was used to model an animal experiment, for which the temporal change of a rhabdomyosarcoma tumor volume grown in a rat was measured with time resolution sufficient to test the model. Results: To fit the model to the experimental data, the following characteristics were needed with the model parameters. The α-value in the LQ-model was smaller than the commonly used value; furthermore, it decreased with increasing dose. At the same time, the tumor growth rate after SRS had to increase. Conclusions: The new 3-component model of tumor could simulate the experimental data very well. The current study suggested that the radiation sensitivity and the growth rate of the proliferating tumor cells may change after irradiation and it depended on the dosage used for SRS. These preliminary observations must be confirmed by future animal experiments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning

This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. Additionally, the diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Stochastic Models, Indices & Optimization Algorithms for Pricing & Hedging Reliability Risks in Modern Power Grids: Data Plan - Princeton

We collected and cleaned the synthetic grid data produced by NREL for the Texas and New York synthetic grids. We developed a high dimensional joint stochastic model for load at the zone level, and solar and wind power productions at the asset level, capturing the spatial and temporal dependencies between all the variables, and demonstrated how such a model could be fitted to historical data. We designed and implemented a simulation engine which can produce Monte Carlo scenarios for the hourly day-ahead values of load, and solar and wind power productions at the spatial and temporal resolutions of the historical data used to fit the model. Finally we developed an open-source Python package which can, from an input grid model, efficiently use forecasts and large numbers of Monte Carlo scenarios to provide unit commitment and economic dispatch for each of these scenarios. The high dimensional stochastic model and the subsequent Monte Carlo simulation engine were implemented in the package PGscen and the corresponding UC and ED optimization programs in the package Vatic.

14 SOLAR ENERGY↗
DOE OSTI2015

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

flux balance analysis↗
DOE OSTI2017

Improved Dynamical Constraints on the Mass of the Central Black Hole in NGC 404

We explore the nucleus of the nearby 10{sup 9} M{sub ⊙} early-type galaxy, NGC 404, using Hubble Space Telescope (HST)/STIS spectroscopy and WFC3 imaging. We first present evidence for nuclear variability in UV, optical, and infrared filters over a time period of 15 years. This variability adds to the already substantial evidence for an accreting black hole at the center of NGC 404. We then redetermine the dynamical black hole mass in NGC 404 including modeling of the nuclear stellar populations. We combine HST/STIS spectroscopy with WFC3 images to create a local color–M/L relation derived from stellar population modeling of the STIS data. We then use this to create a mass model for the nuclear region. We use Jeans modeling to fit this mass model to adaptive optics stellar kinematic observations from Gemini/NIFS. From our stellar dynamical modeling, we find a 3σ upper limit on the black hole mass of 1.5×10{sup 5} M{sub ⊙}. Given the accretion evidence for a black hole, this upper limit makes NGC 404 the lowest mass central black hole with dynamical mass constraints. We find that the kinematics of H{sub 2} emission line gas show evidence for non-gravitational motions preventing the use of gas dynamical modeling to constrain the black hole mass. Our stellar population modeling also reveals that the central, counter-rotating region of the nuclear cluster is dominated by ∼1 Gyr old populations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Paramagnetic alignment of small grains: A novel method for measuring interstellar magnetic fields

We present a novel method to measure the strength of interstellar magnetic fields using ultraviolet (UV) polarization of starlight that is in part produced by weakly aligned, small dust grains. We begin with calculating the degrees of the paramagnetic alignment of small (size a ∼ 0.01 μm) and very small (a ∼ 0.001 μm) grains in the interstellar magnetic field due to the Davis-Greenstein relaxation and resonance relaxation. To calculate the degrees of paramagnetic alignment, we use Langevin equations and take into account various interaction processes essential for the rotational dynamics of small grains. We find that the alignment of small grains is necessary to reproduce the observed polarization in the UV, although the polarization arising from these small grains is negligible at the optical and infrared (IR) wavelengths. Based on fitting theoretical models to observed extinction and polarization curves, we find that the best-fit model for the case with the peak wavelength of polarization λ{sub max} < 0.55 μm requires a higher degree of alignment of small grains than for the typical case with λ{sub max} = 0.55 μm. We interpret the correlation between the systematic increase of the UV polarization relative to maximum polarization (i.e., of p(6 μm{sup –1})/p{sub max}) with λ{sub max}{sup −1} for cases of low λ{sub max} by appealing to the higher degree of alignment of small grains. We utilize the correlation of the paramagnetic alignment of small grains with the magnetic field strength B to suggest a new way to measure B using the observable parameters λ{sub max} and p(6 μm{sup –1})/p{sub max}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Empirical estimation of weather-driven yield shocks using biophysical characteristics for U.S. rainfed and irrigated maize, soybeans, and winter wheat

Agricultural yields are highly susceptible to changes in weather system patterns, including both annual and sub-annual changes in temperature and precipitation. Understanding the impacts of future changes in meteorological variables on crop yields have been widely studied in both empirical and process-based models. This work presents a structural econometric approach that combines historical weather data with information about the biophysical growth cycles of maize, winter wheat, and soy to predict the year-to-year yield responses for rainfed crops. Modeled soil moisture and temperature are taken as key predictors, which allows testing the fitted rainfed model’s ability to predict irrigated yields by assuming irrigation produces the numerically optimal level of soil moisture. This approach is grounded in known biophysical processes, which improves confidence in the model predictions. It produces estimates of interannual variability in yields, which enable study of the effects of extreme events on agricultural productivity. Finally, it enables prediction of the potential impacts of changing weather patterns on irrigated crops in areas that are currently primarily rainfed. We present the results of the empirical model, fitted with rainfed data; out-of-sample validation on irrigated crops; and projections of yield shocks under multiple future climate scenarios. Under a bias-corrected GFDL RCP8.5 scenario, this approach predicts average yield change, relative to 2006-2020 average yields, across U.S. counties in the 2040-2060 period of -16.9% and -13.4% for rainfed and irrigated maize, of -19.1% and -18.4% for rainfed and irrigated soy, and of -4.0% and -2.0% for rainfed and irrigated winter wheat. And in the 2070-2090 period of -30.3% and -28.8 % for rainfed and irrigated maize, of -33.1% and -32.8% for rainfed and irrigated soy, and of -6.5% and -5.2%for rainfed and irrigated winter wheat.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Atacama Cosmology Telescope: A Measurement of the DR6 CMB Lensing Power Spectrum and Its Implications for Structure Growth

We present new measurements of cosmic microwave background (CMB) lensing over 9400 deg of the sky. These lensing measurements are derived from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) CMB data set, which consists of five seasons of ACT CMB temperature and polarization observations. We determine the amplitude of the CMB lensing power spectrum at 2.3% precision (43σ significance) using a novel pipeline that minimizes sensitivity to foregrounds and to noise properties. To ensure that our results are robust, we analyze an extensive set of null tests, consistency tests, and systematic error estimates and employ a blinded analysis framework. Our CMB lensing power spectrum measurement provides constraints on the amplitude of cosmic structure that do not depend on Planck or galaxy survey data, thus giving independent information about large-scale structure growth and potential tensions in structure measurements. The baseline spectrum is well fit by a lensing amplitude of A lens = 1.013 ± 0.023 relative to the Planck 2018 CMB power spectra best-fit ΛCDM model and A lens = 1.005 ± 0.023 relative to the ACT DR4 + WMAP best-fit model. From our lensing power spectrum measurement, we derive constraints on the parameter combination $S^{CMBL}_{8}$ ≡ σ 8 (Ω m /0.3) 0.25 of $S^{CMBL}_{8}$ = 0.818 ± 0.022 from ACT DR6 CMB lensing alone and $S^{CMBL}_{8}$ = 0.813 ± 0.018 when combining ACT DR6 and Planck NPIPE CMB lensing power spectra. These results are in excellent agreement with ΛCDM model constraints from Planck or ACT DR4 + WMAP CMB power spectrum measurements. Our lensing measurements from redshifts z ~ 0.5–5 are thus fully consistent with ΛCDM structure growth predictions based on CMB anisotropies probing primarily z ~ 1100. We find no evidence for a suppression of the amplitude of cosmic structure at low redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Developing Practical Models of Complex Salts for Molten Salt Reactors

Molten salt reactors (MSRs) utilize salts as coolant or as the fuel and coolant together with fissile isotopes dissolved in the salt. It is necessary to therefore understand the behavior of the salts to effectively design, operate, and regulate such reactors, and thus there is a need for thermodynamic models for the salt systems. Molten salts, however, are difficult to represent as they exhibit short-range order that is dependent on both composition and temperature. A widely useful approach is the modified quasichemical model in the quadruplet approximation that provides for consideration of first- and second-nearest-neighbor coordination and interactions. Its use in the CALPHAD approach to system modeling requires fitting parameters using standard thermodynamic data such as phase equilibria, heat capacity, and others. A shortcoming of the model is its inability to directly vary coordination numbers with composition or temperature. Another issue is the difficulty in fitting model parameters using regression methods without already having very good initial values. The proposed paper will discuss these issues and note some practical methods for the effective generation of useful models.

Besmann, Theodore M. (ORCID:0000000155980550)↗
DOE OSTI2023

Encoding nonlinear and unsteady aerodynamics of limit cycle oscillations using nonlinear sparse Bayesian learning

This article investigates the applicability of a recently proposed, nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate the complex aerodynamics of limit cycle oscillations. NSBL provides a semi-analytical framework for determining the data-optimal sparse model nested within a (potentially) over-parameterized model. This is particularly relevant to nonlinear dynamical systems where modelling approaches involve the use of physics-based and data-driven components. In such cases, the data-driven components, where analytical descriptions of the physical processes are not readily available, are often prone to overfitting, meaning that the empirical aspects of these models will often involve the calibration of an unnecessarily large number of parameters. While an overparameterized model may fit the observed data well, such models may be inadequate for making predictions in regimes that are different from those wherein the data were recorded. In view of this, it is desirable to not only calibrate the model parameters, but also identify the optimal compromise between data fit and model complexity. In this article, we exhibit the optimal model discovery for an aeroelastic system wherein the structural dynamics are well-known and described by a differential equation model, coupled with a semi-empirical aerodynamic model for laminar separation flutter, resulting in low-amplitude limit cycle oscillations (LCO). To illustrate the performance of the algorithm, in this article, we use synthetic data and demonstrate the ability of the algorithm to correctly rediscover the optimal model and model parameters, given a known data-generating model. The synthetic data are generated from a forward simulation of a known differential equation model with parameters selected so as to mimic the dynamics observed in wind-tunnel experiments. Subsequently, we demonstrate the performance of the algorithm for model selection using noisy LCO data from wind tunnel experiments. As there is no ground truth available for the experimental data case, we provide a comparison between NSBL and Bayesian model selection to validate the results, and demonstrate the use of NSBL as an efficient alternative to traditional methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

ESTIMATING THE PROPERTIES OF HARD X-RAY SOLAR FLARES BY CONSTRAINING MODEL PARAMETERS

We wish to better constrain the properties of solar flares by exploring how parameterized models of solar flares interact with uncertainty estimation methods. We compare four different methods of calculating uncertainty estimates in fitting parameterized models to Ramaty High Energy Solar Spectroscopic Imager X-ray spectra, considering only statistical sources of error. Three of the four methods are based on estimating the scale-size of the minimum in a hypersurface formed by the weighted sum of the squares of the differences between the model fit and the data as a function of the fit parameters, and are implemented as commonly practiced. The fourth method is also based on the difference between the data and the model, but instead uses Bayesian data analysis and Markov chain Monte Carlo (MCMC) techniques to calculate an uncertainty estimate. Two flare spectra are modeled: one from the Geostationary Operational Environmental Satellite X1.3 class flare of 2005 January 19, and the other from the X4.8 flare of 2002 July 23. We find that the four methods give approximately the same uncertainty estimates for the 2005 January 19 spectral fit parameters, but lead to very different uncertainty estimates for the 2002 July 23 spectral fit. This is because each method implements different analyses of the hypersurface, yielding method-dependent results that can differ greatly depending on the shape of the hypersurface. The hypersurface arising from the 2005 January 19 analysis is consistent with a normal distribution; therefore, the assumptions behind the three non-Bayesian uncertainty estimation methods are satisfied and similar estimates are found. The 2002 July 23 analysis shows that the hypersurface is not consistent with a normal distribution, indicating that the assumptions behind the three non-Bayesian uncertainty estimation methods are not satisfied, leading to differing estimates of the uncertainty. We find that the shape of the hypersurface is crucial in understanding the output from each uncertainty estimation technique, and that a crucial factor determining the shape of hypersurface is the location of the low-energy cutoff relative to energies where the thermal emission dominates. The Bayesian/MCMC approach also allows us to provide detailed information on probable values of the low-energy cutoff, E{sub c} , a crucial parameter in defining the energy content of the flare-accelerated electrons. We show that for the 2002 July 23 flare data, there is a 95% probability that E{sub c} lies below approximately 40 keV, and a 68% probability that it lies in the range 7-36 keV. Further, the low-energy cutoff is more likely to be in the range 25-35 keV than in any other 10 keV wide energy range. The low-energy cutoff for the 2005 January 19 flare is more tightly constrained to 107 {+-} 4 keV with 68% probability. Using the Bayesian/MCMC approach, we also estimate for the first time probability density functions for the total number of flare-accelerated electrons and the energy they carry for each flare studied. For the 2002 July 23 event, these probability density functions are asymmetric with long tails orders of magnitude higher than the most probable value, caused by the poorly constrained value of the low-energy cutoff. The most probable electron power is estimated at 10{sup 28.1} erg s{sup -1}, with a 68% credible interval estimated at 10{sup 28.1}-10{sup 29.0} erg s{sup -1}, and a 95% credible interval estimated at 10{sup 28.0}-10{sup 30.2} erg s{sup -1}. For the 2005 January 19 flare spectrum, the probability density functions for the total number of flare-accelerated electrons and their energy are much more symmetric and narrow: the most probable electron power is estimated at 10{sup 27.66{+-}0.01} erg s{sup -1} (68% credible intervals). However, in this case the uncertainty due to systematic sources of error is estimated to dominate the uncertainty due to statistical sources of error.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The age and age spread of the Praesepe and Hyades clusters: A consistent, ∼800 Myr picture from rotating stellar models

We fit the upper main sequence of the Praesepe and Hyades open clusters using stellar models with and without rotation. When neglecting rotation, we find that no single isochrone can fit the entire upper main sequence at the clusters’ spectroscopic metallicity: more massive stars appear, at high significance, to be younger than less massive stars. This discrepancy is consistent with earlier studies, but vanishes when including stellar rotation. The entire upper main sequence of both clusters is very well-fit by a distribution of 800 Myr old stars with the spectroscopically measured [Fe/H] = 0.12. The increase over the consensus age of ∼600–650 Myr is due both to the revised solar metallicity (from Z{sub ⊙}≈0.02 to Z{sub ⊙}≈0.014) and to the lengthening of main-sequence lifetimes and increase in luminosities with rapid rotation. Our results show that rotation can remove the need for large age spreads in intermediate-age clusters, and that these clusters may be significantly older than is commonly accepted. A Hyades/Praesepe age of ∼800 Myr would also require a recalibration of rotation/activity age indicators.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗