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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.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Disentangling the Black Hole Mass Spectrum with Photometric Microlensing Surveys

Abstract From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens class of any single event. We investigate this method’s effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of 30 M ⊙ . On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ≈25%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Estimating reaction parameters in mechanism-enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach

In order to quantitatively predict nano- as well as other particle-size distributions, one needs to have both a mathematical model and estimates of the parameters that appear in these models. Here, we show how one can use Bayesian inversion to obtain statistical estimates for the parameters that appear in recently derived mechanism-enabled population balance models (ME-PBM) of nanoparticle growth. The Bayesian approach addresses the question of “how well do we know our parameters, along with their uncertainties?.” The results reveal that Bayesian inversion statistical analysis on an example, prototype $\mathrm{lr(0)_n}$ nanoparticle formation system allows one to estimate not just the most likely rate constants and other parameter values, but also their SDs, confidence intervals, and other statistical information. Moreover, knowing the reliability of the mechanistic model's parameters in turn helps inform one about the reliability of the proposed mechanism, as well as the reliability of its predictions. Importantly, the paper can also be seen as a tutorial with the additional goal of achieving a “Gold Standard” Bayesian inversion ME-PBM benchmark that others can use as a control to check their own use of this methodology for other systems of interest throughout nature. Overall, the results provide strong support for the hypothesis that there is substantial value in using a Bayesian inversion methodology for parameter estimation in particle formation systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Population balance models for polymer upcycling: signatures of the mechanism in the molecular weight evolution

Chemical and catalytic upcycling processes could help realize a circular plastics economy, but current models for testing mechanistic hypotheses and designing catalysts remain primitive. This work shows how proposed catalytic mechanisms can be incorporated into population balance models to predict the time evolution of molecular weight distributions. We develop models for homogeneous and heterogeneous catalysts, including catalysts that cut at chain ends and catalysts that cut at random locations. For heterogeneous catalysts, we illustrate the effect of adsorption constants that depend on polymer chain length. Here, we discuss ongoing efforts and challenges in measuring and modeling the time evolving molecular weight distributions in polymer upcycling processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Accuracy of environmental tracers and consequences for determining the Type Ia supernova magnitude step

Type Ia Supernovae (SNe Ia) are standardizable candles that allow us to measure the recent expansion rate of the Universe. Due to uncertainties in progenitor physics, potential astrophysical dependencies may bias cosmological measurements if not properly accounted for. The dependency of the intrinsic luminosity of SNe Ia with their host-galaxy environment is often used to standardize SNe Ia luminosity and is commonly parameterized as a step function. This functional form implicitly assumes two-populations of SNe Ia. In the literature, multiple environmental indicators have been considered, finding different, sometimes incompatible, step function amplitudes. We compare these indicators in the context of a two-populations model, based on their ability to distinguish the two populations. We show that local Hα-based specific star formation rate (lsSFR) and global stellar mass are better tracers than, for instance, host galaxy morphology. We show that tracer accuracy can explain the discrepancy between the observed SNe Ia step amplitudes found in the literature. Using lsSFR or global mass to identify the two populations can explain all other observations, though lsSFR is favoured. As lsSFR is strongly connected to age, our results favour a prompt and delayed population model. In any case, there exists two populations that differ in standardized magnitude by at least 0.121 ± 0.010 mag.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Compound Poisson Generator Approach to Point-source Inference in Astrophysics

Abstract The identification and description of point sources is one of the oldest problems in astronomy, yet even today the correct statistical treatment for point sources remains one of the field’s hardest problems. For dim or crowded sources, likelihood-based inference methods are required to estimate the uncertainty on the characteristics of the source population. In this work, a new parametric likelihood is constructed for this problem using compound Poisson generator (CPG) functionals that incorporate instrumental effects from first principles. We demonstrate that the CPG approach exhibits a number of advantages over non-Poissonian template fitting (NPTF)—an existing method—in a series of test scenarios in the context of X-ray astronomy. These demonstrations show that the effect of the point-spread function, effective area, and choice of point-source spatial distribution cannot, generally, be factorized as they are in NPTF, while the new CPG construction is validated in these scenarios. Separately, an examination of the diffuse-flux emission limit is used to show that most simple choices of priors on the standard parameterization of the population model can result in unexpected biases: when a model comprising both a point-source population and diffuse component is applied to this limit, nearly all observed flux will be assigned to either the population or to the diffuse component. A new parameterization is presented for these priors that properly estimates the uncertainties in this limit. In this choice of priors, CPG correctly identifies that the fraction of flux assigned to the population model cannot be constrained by the data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The APOGEE Library of Infrared SSP Templates (A-LIST): High-resolution Simple Stellar Population Spectral Models in the H Band

Integrated light spectroscopy from galaxies can be used to study the stellar populations that cannot be resolved into individual stars. This analysis relies on stellar population synthesis (SPS) techniques to study the formation history and structure of galaxies. However, the spectral templates available for SPS are limited, especially in the near-infrared (near-IR). We present A-LIST (APOGEE Library of Infrared SSP Templates), a new set of high-resolution, near-IR SSP spectral templates spanning a wide range of ages (2–12 Gyr), metallicities ( − 2.2 < [M/H] < + 0.4) and α abundances ( − 0.2 < [α/M] < + 0.4). This set of SSP templates is the highest resolution (R ∼ 22, 500) available in the near-IR, and the first such based on an empirical stellar library. Our models are generated using spectra of ∼300,000 stars spread across the Milky Way, with a wide range of metallicities and abundances, from the APOGEE survey. We show that our model spectra provide accurate fits to M31 globular cluster spectra taken with APOGEE, with best-fit metallicities agreeing with those of previous estimates to within ∼0.1 dex. We also compare these model spectra to lower-resolution E-MILES models and demonstrate that we recover the ages of these models to within ∼1.5 Gyr. This library is available in https://github.com/aishashok/ALIST-library.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Satellite Embedding-Based Population Imputation for Areas with Missing Building Footprint Data: A Computer Vision-Based Approach

High-resolution population modeling is important for supporting effective decision-making across diverse sectors. LandScan Mosaic generates population estimates at the level of individual buildings and aggregates them to 3 arc-second grids, and this approach performs well in regions where building footprint data are comprehensive and reliable. However, large portions of the globe still suffer from incomplete, sparse, or entirely missing building stock datasets, creating a structural limitation for strictly building-based population models. To address this research gap, this study proposes a computer vision-based framework that employs Google Earth Engine satellite embeddings and UNet, which allows us to directly impute grid-level population estimates in building-data-deficient areas. Applied to Taiwan as a case study, the framework achieved strong predictive performance with R$^{2}$ of 0.89, RMSE of 18.70, and MAE of 8.41, outperforming traditional machine learning approaches. Notably, the proposed framework effectively addressed building false-positive errors inherent in Global Human Settlement Layer (GHSL) data, correctly identifying uninhabited areas that were erroneously classified as populated. The framework also offers significant advantages for global population mapping, particularly in terms of scalability and temporal consistency, thereby extending the coverage and accuracy of high-resolution population products in data-scarce regions worldwide. Urban planners, decision makers, and related stakeholders can obtain granular population distributions to support more accurate and targeted infrastructure investment, service delivery, resource allocation, and risk assessment decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Nanoparticle Catalyst Formation and Subsequent Sintering: Size-Distribution Fundamental Understanding and Catalyst Control via Mechanism-Enabled Population Balance Modeling (Final Report)

The challenge of elucidating the chemical mechanisms by which nano- and other particles form, and then using those mechanisms to understand, analyze, and ideally predict particle-size and particle-size distributions, was undertaken. Thanks to multi-year, longer-term DOE support each of these challenges was successfully met. The community is now able to both predict, and control in cases where the underlying mechanism of particle formation has been determined, the key PS and PSD that are, in turn, critical properties that strongly influence the catalytic and other physical properties of nano- and larger particles across nature.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Population balance modeling of polyurethane foam formation with pressure‐dependent growth kernel

Abstract Polyurethane foams are widely used materials often chosen for their useful characteristics such as low thermal conductivity, ease of application, and high strength‐to‐weight ratios. Computational models are needed to predict the dynamics of the flow and expansion, and the resulting material properties, to improve manufacturing processes. In this paper, a model for PMDI, a water‐blown polyurethane foam, is presented. By extending a kinetics‐based approach by adding bubble‐scale information via a population balance equation (PBE) using the quadrature method of moments, we can track bubble size distributions during foaming. We present results from a three‐dimensional computational fluid dynamics model using arbitrary Lagrangian–Eulerian interface tracking implemented in finite element software. The model compares favorably with experimental data, including dynamics, bubble distributions measured by both camera and diffusion wave spectroscopy, and post‐test bubble size from scanning electron microscopy and density measurements from x‐ray computed tomography.

Ortiz, Weston↗
DOE OSTI2021

Linking habitat suitability with a longleaf pine-hardwood model: Building a species-predictive fire-land management framework

Active management of fire-dependent ecosystems for specific species leads to complex tradeoffs, which affect conservation outcomes to other species. Therefore a multi-species evaluation of management actions is required. Habitat Suitability Models (HSMs) can help in predicting the likelihood of species occurrence using corresponding environmental variables and empirical relationships that link occurrence with specific environmental conditions. Incorporating multiple species into HSMs and relating them to habitat dynamics is crucial for ecosystems that require active management with prescribed fire. To address this issue, we developed multi-species HSM driven within an existing population model of the longleaf pine-hardwood ecosystem to assess the suitability of an ecosystem given different fire management strategies and environmental conditions. The population model used in this study provides spatial and temporal changes of longleaf pine-hardwood habitat structure in response to fire. These habitat values are used by the HSM to calculate habitat suitability for three threatened and endangered faunal species of this ecosystem, which all thrive with frequent fire, but have unique habitat requirements. Transient habitat conditions are traced to predict longleaf pine ecosystem trajectories under various management strategies, thereby evaluating current land management actions, such as thinning or prescribed fire frequencies. We tested a suite of environmental conditions to emphasize the sensitivity of the species to different fire management actions. The results of our modeling suggest that maximum suitable habitat for all three species can be achieved with fire frequency occurring at approximately once every three years. The modeling results support current management actions and provide a new habitat assessment tool that incorporates ecological factors for multiple species, thus providing for habitat optimization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Estimating IDP Origins Using ACLED Political Violence Events: Validation with Lebanon IDP Flow Data

A key input in modeling population distribution and flows following conflict events is the inclusion of Internally Displaced Person (IDP) flows between administrative units within a country. These flows are critical for capturing population movement and redistribution driven by current events, particularly conflict. In some cases, IDP destination data are available while origin data is incomplete or unavailable. This creates a gap in understanding where displacement is occurring, limiting the ability to model population redistribution accurately. Without origin data, it is not possible to reallocate population flows or accurately represent where displacement is occurring within the country. This report evaluates whether Armed Conflict Location Event Data (ACLED) political violence event data can be used to estimate IDP origin distributions when direct origin data are unavailable. The approach is validated using historical IDP flow data from Lebanon, where both origins and destinations are observed. Results show that ACLED event distributions strongly correspond to observed IDP-origin patterns, particularly when using cumulative 60-day event windows. The method is most reliable for identifying major origin districts and approximating proportional origin shares. However, it is not intended to reconstruct exact individual displacement flows, but rather to provide a probabilistic spatial allocation of displacement origins.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Aggregation of Leslie matrix models with application to ten diverse animal species

Some grouping is necessary when constructing a Leslie matrix model because it involves discretizing a continuous process of births and deaths. Here, the level of grouping is determined by the number of age classes and frequency of sampling. It is largely unknown what is lost or gained by using fewer age classes, and I address this question using aggregation theory. I derive an aggregator for a Leslie matrix model using weighted least squares, determine what properties an aggregated matrix inherits from the original matrix, evaluate aggregation error, and measure the influence of aggregation on asymptotic and transient behaviors. To gauge transient dynamics, I employ reactivity of the standardized Leslie matrix. I apply the aggregator to 10 Leslie models developed for animal populations drawn from a diverse set of species. Several properties are inherited by the aggregated matrix: (a) it is a Leslie matrix; (b) it is irreducible whenever the original matrix is irreducible; (c) it is primitive whenever the original matrix is primitive; and (d) its stable population growth rate and stable age distribution are consistent with those of the original matrix if the least squares weights are equal to the original stable age distribution. In the application, depending on the population modeled, when the least squares weights do not follow the stable age distribution, the stable population growth rate of the aggregated matrix may or may not be approximately consistent with that of the original matrix. Transient behavior is lost with high aggregation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Search for Neutrino Point-source Populations in 7 yr of IceCube Data with Neutrino-count Statistics

The presence of a population of point sources in a data set modifies the underlying neutrino-count statistics from the Poisson distribution. This deviation can be exactly quantified using the non-Poissonian template fitting technique, and in this work we present the first application of this approach to the IceCube high-energy neutrino data set. Using this method, we search in 7 yr of IceCube data for point-source populations correlated with the disk of the Milky Way, the Fermi bubbles, the Schlegel, Finkbeiner, and Davis dust map, or with the isotropic extragalactic sky. No evidence for such a population is found in the data using this technique, and in the absence of a signal, we establish constraints on population models with source-count distribution functions that can be described by a power law with a single break. Here, the derived limits can be interpreted in the context of many possible source classes. In order to enhance the flexibility of the results, we publish the full posterior from our analysis, which can be used to establish limits on specific population models that would contribute to the observed IceCube neutrino flux.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Floating solar panels on reservoirs impact phytoplankton populations: A modelling experiment

Floating solar photovoltaic (FPV) deployments are increasing globally as the switch to renewable energy intensifies, representing a considerable water surface transformation. FPV installations can potentially impact aquatic ecosystem function, either positively or negatively. However, these impacts are poorly resolved given the challenges of collecting empirical data for field or modelling experiments. In particular, there is limited evidence on the response of phytoplankton to changes in water body thermal dynamics and light climate with FPV. Given the importance of understanding phytoplankton biomass and species composition for managing ecosystem services, we use an uncertainty estimation approach to simulate the effect of FPV coverage and array siting location on a UK reservoir. FPV coverage was modified in 10% increments from a baseline with 0% coverage to 100% coverage for three different FPV array siting locations based on reservoir circulation patterns. Results showed that FPV coverage significantly impacted thermal properties, resulting in highly variable impacts on phytoplankton biomass and species composition. The impacts on phytoplankton were often dependent on array siting location as well as surface coverage. Changes to phytoplankton species composition were offset by the decrease in phytoplankton biomass associated with increasing FPV coverage. We identified that similar phytoplankton biomass reductions could be achieved with less FPV coverage by deploying the FPV array on the water body's faster-flowing area than the central or slower flowing areas. The difference in response dependent on siting location could be used to tailor phytoplankton management in water bodies. Simulation of water body-FPV interactions efficiently using an uncertainty approach is an essential tool to rapidly develop understanding and ultimately inform FPV developers and water body managers looking to minimise negative impacts and maximise co-benefits.

14 SOLAR ENERGY↗
DOE OSTI2021

Pursuing sources of heterogeneity in modeling clustered population

Abstract Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are many candidate predictors, it is not only of interest to identify the predictors that are associated with the outcome, but also to distinguish the true sources of heterogeneity , that is, to identify the predictors that have different effects among the clusters and thus are the true contributors to the formation of the clusters. We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously. We develop an efficient algorithm and show that our approach can achieve both estimation and selection consistency. Simulation studies further demonstrate the effectiveness of our method under various practical scenarios. Three applications are presented, namely, an imaging genetics study for linking genetic factors and brain neuroimaging traits in Alzheimer's disease, a public health study for exploring the association between suicide risk among adolescents and their school district characteristics, and a sport analytics study for understanding how the salary levels of baseball players are associated with their performance and contractual status.

Li, Yan↗