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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Nanomaterials in Extreme Environments: Fundamentals and Applications Rostislav A. Andrievski and Arsen V. Khatchoyan

Nanomaterials in Extreme Environments Rostislav A. Andrievski and Arsen V. Khatchoyan Springer, 2016 106 pages, $99.00 (e-book $69.99) ISBN 978–3-319–25331–2 This slim volume is an extensive review of our current understanding of the response of nanostructured materials to extreme operating conditions, such as high temperature, flux of high energy neutrons, high pressure, mechanical stress, and oxidizing environments. The emphasis is on metallic materials, especially Cu alloys. Graphene-based materials, fullerenes, polymeric materials, nano-glasses and glass-ceramics are not covered by this review. The book has six chapters including an introduction and a brief conclusion. The introduction documents the growth of scientific interest in nanostructured materials and stresses the need to study the behavior of nanomaterials under extreme conditions. This chapter also presents Herbert Gleiter’s classification of nanomaterials into twelve groups based on the shapes of the nanoscale features and chemical composition of the components of the nanostructure. The second chapter deals with the high temperature environment and the thermodynamics and kinetics of grain growth. The authors identify the lack of reliable thermodynamic data as a key limitation in this field. The discussion brings out the interplay of structural relaxation, redistribution of excess free volume, diffusion, and recrystallization in multicomponent nanostructures at elevated temperature. Chapter 3 focuses on the effects of ion and neutron irradiation on the structure and properties of nanomaterials. The authors do a good job of highlighting recent studies on the radiation tolerance of nanocrystalline oxides and rapid grain growth under irradiation. The material addresses both fission and fusion reactor applications. Chapter 4 reviews the effects of severe plastic deformation and cyclic loading on nanostructure formation and phase transformation. This chapter also explores the challenge of achieving high density while retaining nanostructural features during processing under extreme loads and high temperatures. Chapter 5 discusses the effects of corrosion on nanomaterials. The behavior of a variety of alloys and high melting point compounds in liquid media and high temperature oxidizing environments is reviewed. The concluding chapter identifies areas for further research. The book would have benefited from careful copy editing for use of the English language. Moreover, the excessive use of acronyms makes the text difficult to read. Each chapter ends with a section on the application of nanomaterials and a long list of references. The integration of theoretical approaches and simulation results with experimental data offers fresh insights into the behavior of nanomaterials. Overall, this book will serve as useful reference material for researchers interested in nanomaterials driven to extremes. Reviewer: Ram Devanathan is Technical Group Manager of Reactor Materials and Mechanical Design, Pacific Northwest National Laboratory, USA.

Devanathan, Ram↗
DOE OSTI2012

Application of the BISON Fuel Performance Code to the FUMEX-III Coordinated Research Project

INL recently participated in FUMEX-III, an International Atomic Energy Agency sponsored fuel modeling Coordinated Research Project. A main purpose of FUMEX-III is to compare code predictions to reliable experimental data. During the same time period, the INL initiated development of a new multidimensional (2D and 3D) multiphysics nuclear fuel performance code called BISON. Interactions with international fuel modeling researchers via FUMEX-III played a significant and important role in the BISON evolution, particularly influencing the selection of material and behavioral models which are now included in the code. BISON's ability to model integral fuel rod behavior did not mature until 2011, thus the only FUMEX-III case considered was the Riso3-GE7 experiment, which includes measurements of rod outer diameter following pellet clad mechanical interaction (PCMI) resulting from a power ramp late in fuel life. BISON comparisons to the Riso3-GE7 final rod diameter measurements are quite reasonable. The INL is very interested in participation in the next Fuel Modeling Coordinated Research Project and would like to see the project initiated as soon as possible.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Field Trial of a Low-Cost, Distributed Plug Load Monitoring System

Researchers have struggled to inventory and characterize the energy use profiles of the ever-growing category of so-called miscellaneous electric loads (MELs) because plug-load monitoring is cost-prohibitive to the researcher and intrusive to the homeowner. However, these data represent a crucial missing link to our understanding of how homes use energy, and we cannot control what we do not understand. Detailed energy use profiles would enable the nascent automated home energy management (AHEM) industry to develop effective control algorithms that target consumer electronics and other plug loads. If utility and other efficiency programs are to incent AHEM devices, they need large-scale datasets that provide statistically meaningful justification of their investments by quantifying the aggregate energy savings achievable. To address this need, we have investigated a variety of plug-load measuring devices available commercially and tested them in the laboratory to identify the most promising candidates for field applications. The scope of this report centers around the lessons learned from a field validation of one proof-of-concept system, called Smartenit (formerly SimpleHomeNet). The system was evaluated based on the rate of successful data queries, reliability over a period of days to weeks, and accuracy. This system offers good overall performance when deployed with up to ten end nodes in a residential environment, although deployment with more nodes and in a commercial environment is much less robust. We conclude that the current system is useful in selected field research projects, with the recommendation that system behavior is observed over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Field Trial of a Low-Cost, Distributed Plug Load Monitoring System

Researchers have struggled to inventory and characterize the energy use profiles of the ever-growing category of so-called miscellaneous electric loads (MELs) because plug-load monitoring is cost-prohibitive to the researcher and intrusive to the homeowner. However, these data represent a crucial missing link to understanding how homes use energy. Detailed energy use profiles would enable the nascent automated home energy management (AHEM) industry to develop effective control algorithms that target consumer electronics and other plug loads. If utility and other efficiency programs are to incent AHEM devices, they need large-scale datasets that provide statistically meaningful justification of their investments by quantifying the aggregate energy savings achievable. To address this need, NREL researchers investigated a variety of plug-load measuring devices available commercially and tested them in the laboratory to identify the most promising candidates for field applications. This report centers around the lessons learned from a field validation of one proof-of-concept system, called Smartenit (formerly SimpleHomeNet). The system was evaluated based on the rate of successful data queries, reliability over a period of days to weeks, and accuracy. This system offers good overall performance when deployed with up to 10 end nodes in a residential environment, although deployment with more nodes and in a commercial environment is much less robust. NREL concludes that the current system is useful in selected field research projects, with the recommendation that system behavior is observed over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Final Report for Project: Impacts of stratification and non-equilibrium winds and waves on hub-height winds

This project used a combination of turbulence-resolving large-eddy simulations, single-column modeling (where turbulence is parameterized), and currently available observations to improve, assess, and develop a parameterization of the impact of non-equilibrium wave states and stratification on the buoy-observed winds to establish reliable wind data at the turbine hub-height level. Analysis of turbulence-resolving simulations and observations illuminates the non-linear coupling between the atmosphere and the undulating sea surface. This analysis guides modification of existing boundary layer parameterizations to include wave influences for upward extrapolation of surface-based observations through the turbine layer. Our surface roughness modifications account for the interaction between stratification and the effects of swell’s amplitude and wavelength as well as swell’s relative motion with respect to the mean wind direction. The single-column version of the open source Weather and Research Forecasting (WRF) model (Skamarock et al., 2008) serves as our platform to test our proposed planetary boundary layer parameterization modifications that account for wave effects on marine atmospheric boundary layer flows. WRF has been widely adopted for wind resource analysis and forecasting. The single column version is particularly suitable to development, analysis, and testing of new boundary layer parameterizations. We utilize WRF’s single-column version to verify and validate our proposed modifications to the Mellor-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization (Nakanishi and Niino, 2004). We explore the implications of our modifications for two-way coupling between WRF and wave models (e.g.,Wavewatch III). The newly implemented parameterization accounting for marine atmospheric boundary layer-wave coupling is then tested in three-dimensional WRF simulations at grid sizes near 1 km. These simulations identify the behavior of simulated winds at the wind plant scale. Overall project conclusions include; In the presence of fast-moving swell (significant wave height Hs = 6.4 m, and phase speed cp = 18 ms -1 ), the atmospheric boundary layer grows more rapidly when waves propagate opposite to the winds compared to when winds and waves are aligned. Pressure drag increases by nearly a factor of 2 relative to the turbulent stress for the extreme case where waves propagate at 180° compared to the pressure gradient forcing. Net wind speed reduces by nearly 15% at hub-height for the 180°-case compared to the 0°-case, and turbulence intensities increase by nearly a factor of 2. These impacts diminish with decreasing wave age; Stratification increases hub height wind speeds and increases the vertical shear of the mean wind across the rotor plane. Fortuitously, this stability-induced enhanced shear does not influence turbulence intensity at hub height, but does increase (decrease) turbulence intensity below (above) hub height. Increased stability also increases the wave-induced pressure stress by ~ 10%; Off the East Coast of the United States during Coupled Boundary Layers Air-Sea Transfer - Low Wind (CBLAST-Low), cases with short fetch include thin stable boundary layers with depths of only a few tens of meters. In the coastal zone, the relationship between the mean wind and the surface fiction velocity (u*(V )) is significantly related to wind direction for weak winds but is not systematically related to the air sea difference of virtual potential temperature, δθv; since waves generally propagate from the south at the Air-Sea Interaction Tower (ASIT) tower, these results suggest that under weak wind conditions waves likely influence surface stress more than stratification does; and Winds and waves are frequently misaligned in the coastal zone. Stability conditions persist for long duration. Over a four year period, the Forschungsplattformen in Nord- und Ostsee Nr. 1 (FINO1) tower (a site with long fetch) primarily experienced weakly-unstable conditions, while stability at the ASIT tower (with a larger influence of offshore winds) experiences a mix of both unstable and stable conditions, where the summer months are predominantly stable. Wind-wave misalignment likely explains the large scatter in observed non-dimensional surface roughness under swell-dominated conditions. Andreas et al.’s (2012) relationship between u* and the 10-m wind speed under predicts the increased u* produced by wave-induced pressure drag produced by misaligned winds and waves. Incorporating wave-state (speed and direction) influences in parameterizations improves predictive skill. In a broad sense, these results suggest that one needs information on winds, temperature, and wave state to upscale buoy measurements to hub-height and across the rotor plane. Our parameterization of wave-state influences on surface drag has been submitted for inclusion in the next publicly available release. In combination, our project elucidates the impacts of two important physical processes (non-equilibrium wind/waves and stratification) on the atmosphere within which offshore turbines operate. This knowledge should help guide and inform manufacturers making critical decisions surrounding design criteria of future turbines to be deployed in the coastal zone. Reductions in annually averaged hub height wind speed error using our new wave-state-aware surface layer parameterization are relatively modest. However since wind turbine power production depends on the wind speed cubed, the error in estimated power production is close to 5%; which is significant and can substantially impact wind resource assessment and decision making with regards to the viability of particular location for a wind plant location. For a single 30-hour forecast, significant reductions in wind speed prediction errors can yield substantially improved wind power forecast skill, thereby mitigating costs and/or increasing revenue through improved; forecasting for maintenance operations and planning; day-ahead forecasting for power trading and resource allocation; and short-term forecasting for dispatch and grid balancing.

17 WIND ENERGY↗
DOE OSTI2020

Improved Models of Long-Term Creep Behavior of High Performance Structural Alloys

In this program, QuesTek Innovations LLC, a leader in the field of computational materials design, proposes to develop a robust creep-modeling toolkit that expands its computational Materials by Design® technology, in order to predict the long-term creep performance of materials for base alloys and weldments in fossil energy systems under wide thermal and mechanical conditions. The Material Database Group of NIMS (National Institute for Material Science, JAPAN) provides reliable public data for long-term creep behavior over 300,000 hours (> 34 years) for a variety of materials including high Cr steels along with seminal work performed by EPRI (Electric Power Research Institute) on creep failure of these steels. QuesTek would rely on these premier institutions for experimental data needed to calibrate models. The developed tool has wider applications than the state-of-the-art modeling tools. It includes the effects of chemistry variations, microstructure variation (weldments) etc .which is lacking in many available models requiring the model to be calibrated to large number of datasets for accurate predictions. A model based on fundamental mechanism has promises in application to different materials, other than the demonstrated material (Grade 91 steels) in the current program. As a result of such an effort, we envision greatly advancing the state-of-the-art modeling tools for creep life assessments of different materials.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Semiconductor: Supply Chain Deep Dive Assessment

In February 2021, President Biden signed Executive Order (EO) 14017, “America’s Supply Chains,” directing seven executive agencies to evaluate the resilience and security of the nation’s critical supply chains and to craft strategies for six industrial bases that underpin America’s economic and national security. See Sec. 3(b) of E.O. 14017. As part of the one-year response to EO 14017, the U.S. Department of Energy (DOE), through the national laboratories, conducted evaluations of the supply chains that encompass the Energy Sector Industrial Base, with a particular focus on technologies required to decarbonize the U.S. by 2050. This report focuses on one of these technologies, semiconductors, both conventional and wide bandgap (WBG) power electronics (PE). As noted in the 100-Day report, conventional semiconductors are a keystone technology that are essential for the operation of nearly every electronic device, including those that are critical to decarbonization, such as electric vehicles (EVs), industrial and building applications, and electricity generation and end use. In addition, power electronics have been applied to industrial equipment to improve energy efficiency and enhance controllability. Power electronics have also been used to improve the reliability of data centers and critical infrastructure, including the stabilization of the electric grid while subjected to disturbances. Increasingly, power electronics are being used to integrate renewable energy and battery storage systems, enabling new grid services and the development of microgrids. It is estimated that 30% of the electricity used in the U.S. passes through power electronic devices, and studies suggest that this number could reach 80% as power electronics are deployed in more markets. Nearly all forms of electrified transportation will depend on power electronics. Power electronic systems utilize high-capacity semiconductor devices at their core. The continued growth in power electronics depends on sustained innovation in the semiconductor industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Creation Synthetic Data to Train a Digital Twin to Predict Reactor Operations

Understanding techniques to strengthen the nuclear safeguards regime is crucial in preventing nuclear proliferation due to recent advancements in the nuclear energy industry such as Generation IV reactors and microreactors. Prior to the construction of a nuclear power plant, it is necessary to understand the proliferation potential of the plant's reactor. Digital twins serve as a unique solution to recognizing reactor behavior indicative of nuclear proliferation. A digital twin is defined as a virtual model that works in unison to represent a physical asset, with a transference of data between the virtual and physical assets [1]. This work serves as validation for training a digital twin on synthetic data fabricated via means of Serpent reactor physics and point kinetics equations simulations. In this case this work is based on parameters of Idaho State University's AGN-201 reactor. The synthetic data can then be utilized to train machine learning models in the future to further investigate the utility of these methods. The accuracy of the predicted data is measured against real operational data to verify the reliability of the synthetic data creation methods and decide whether these methods should be used in the future to inform inspectors of a reactor's proliferation potential.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Utilizing Physics-Informed Synthetic Data to Train a Digital Twin for Predicting Reactor Operations

Understanding techniques to strengthen the nuclear nonproliferation regime is crucial in reducing the creation of nuclear weaponry on the basis of advancements in the nuclear energy industry. Prior to construction of a nuclear power plant, it is necessary to understand the proliferation potential of the plant’s reactor. Digital twins serve as a unique solution to recognizing reactor behavior indicative of nuclear proliferation. The following research conducted serves as a validation to training a digital twin on synthetic data fabricated via means of reactor physics simulations based on parameters of Idaho State University’s AGN-201 reactor. The synthetic data is utilized to train a long short-term memory (LSTM) recurrent neural network model. The accuracy of the predicted data is measured against real operational data to verify the reliability of the synthetic data creation methods and if these methods should be used in the future to inform inspectors of a reactor’s proliferation capabilities.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Uncertainty Analyses of Molten Salt Property Measurements

Thermophysical and thermochemical property values of molten salts and associated uncertainties are used by reactor developers to evaluate reactor performance and model accident scenarios. These activities support fuel qualification objectives by demonstrating that fundamental safety functions (e.g., limit the release of radioactive materials, remove heat from the reactor, and control reactivity) can be performed effectively in a variety of conditions over the operating life of the reactor. Fuel qualification activities are still in the development phase due to a lack of reliable property data for salt mixtures being considered for use in molten salt reactors (MSR).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A Novel Framework for Performance Evaluation and Design Optimization of PCM Embedded Heat Exchangers for the Built Environment

This research sheds light on the performance evaluation and design optimization of PCM-HXs for the built environment, addressing several barriers to practical issues to PCM-HX commercialization such as modeling aspects (i.e., modeling expertise and computational / time investment, etc.), manufacturing aspects (i.e., at-scale manufacturing, cost assessments, etc.) and experimental performance assessment (i.e., reliable experimental data, assessment of multiple PCM-working fluid combinations, etc.). We present a novel, comprehensive, and experimentally-validated design optimization framework for PCM-HXs capable of simulating any PCM-HX geometry with reasonable accuracy and significant computational time savings when compared to traditional CFD-based design practices. The framework was validated for a wide range of PCM-HX configurations, including a design optimization for a domestic hot water heater application where TES partially replaces electrical heating input. The resulting PCM-HXs were found to deliver 34-68% of the total daily hot water supply with only 5-10% package volume increase from the water heater, thus within U.S. DOE targets for TES systems. To identify the most promising HXs for PCM applications, first-order geometry and cost analyses were conducted based on off-the-shelf HX products. As part of this work, 9 PCM-HX prototypes were manufactured using additive and conventional manufacturing methods. Detailed economy-of-scale assessments were conducted for the most promising PCM-HXs and were found to have a good outlook for the next 5-10 years. The PCM-HX design optimization framework was validated through comprehensive in-house experimental testing using newly-developed PCM-to-fluid test facilities. In total,10 total in-house component-level experiments were conducted using these prototypes, including 9 with water and 1 with refrigerant (R410A) as the working fluid. It was found that the framework can successfully predict experimental thermal-hydraulic performance within ±10-20% the first time without manual design changes, eliminating the need for time-consuming and expensive prototyping efforts as part of the design process. As part of this work, a publicly-available PCM web tool was released which includes a PCM property database (531 PCMs) and PCM-HX modeling tool to assist the design community on common PCM-HX use-cases, e.g., single/multiple flow path(s) fluid-to-PCM and air-to-fluid-to-PCM configurations (https://ceeeweb.umd.edu/pcmapp/). This work will accelerate the design and time to market for next generation PCM-HXs.

25 ENERGY STORAGE↗
DOE OSTI2026

Solar Radiation Research Laboratory (SRRL) Core Project Final Report: Fiscal Years 2022-2024

The Solar Radiation Research Laboratory (SRRL) at the National Laboratory of the Rockies (NLR) is a world-leading solar calibration and measurement facility and maintains and disseminates the World Radiation Reference (essentially the W/m2) for the United States that is essential for traceable and accurate measurements of solar radiation at all solar generation facilities. SRRL operates two calibration facilities that meet International Standards Organization-17025 (ISO-17025) standards and provide unique high-quality calibrations to NREL and other U.S. Department of Energy laboratories. The Baseline Measurement System (BMS) at SRRL provides a high-quality record of solar irradiance and surface meteorological conditions. SRRL capabilities are used to develop (1) improved methods for the calibration of solar radiometers; (2) new standards through the ISO, the International Electrotechnical Commission (IEC), and the American Standards for Testing of Materials (ASTM) International; (c) solar radiation and meteorological models; and (d) advanced instrumentation and methods for operating solar measurement stations. The SRRL datasets are also critical for the validation of new models and datasets, such as the National Solar Radiation Database (NSRDB). The research and development of solar radiation measurement systems and resource modeling techniques are essential for advancing the scientific basis for producing reliable resource data. Specifically, the spatial, temporal, and spectral (wavelength dependency) characteristics of the solar resource are required in several different time frames for various project phases.

14 SOLAR ENERGY↗
DOE OSTI2023

Evaluating various composite sampling modes for detecting pathogenic SARS-CoV-2 virus in raw sewage

Inadequate sampling approaches to wastewater analyses can introduce biases, leading to inaccurate results such as false negatives and significant over- or underestimation of average daily viral concentrations, due to the sporadic nature of viral input. To address this challenge, we conducted a field trial within the University of Tennessee residence halls, employing different composite sampling modes that encompassed different time intervals (1 h, 2 h, 4 h, 6 h, and 24 h) across various time windows (morning, afternoon, evening, and late-night). Our primary objective was to identify the optimal approach for generating representative composite samples of SARS-CoV-2 from raw wastewater. Utilizing reverse transcription-quantitative polymerase chain reaction, we quantified the levels of SARS-CoV-2 RNA and pepper mild mottle virus (PMMoV) RNA in raw sewage. Our findings consistently demonstrated that PMMoV RNA, an indicator virus of human fecal contamination in water environment, exhibited higher abundance and lower variability compared to pathogenic SARS-CoV-2 RNA. Significantly, both SARS-CoV-2 and PMMoV RNA exhibited greater variability in 1 h individual composite samples throughout the entire sampling period, contrasting with the stability observed in other time-based composite samples. Through a comprehensive analysis of various composite sampling modes using the Quade Nonparametric ANCOVA test with date, PMMoV concentration and site as covariates, we concluded that employing a composite sampler during a focused 6 h morning window for pathogenic SARS-CoV-2 RNA is a pragmatic and cost-effective strategy for achieving representative composite samples within a single day in wastewater-based epidemiology applications. This method has the potential to significantly enhance the accuracy and reliability of data collected at the community level, thereby contributing to more informed public health decision-making during a pandemic.

sampling timing↗
DOE OSTI2021

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Eu(III) and Cm(III) Complexation by the Aminocarboxylates NTA, EDTA, and EGTA Studied with NMR, TRLFS, and ITC—An Improved Approach to More Robust Thermodynamics

The complex formation of Eu(III) and Cm(III) was studied via tetradentate, hexadentate, and octadentate coordinating ligands of the aminopolycarboxylate family, viz., nitrilotriacetate (NTA 3- ), ethylenediaminetetraacetate (EDTA 4- ), and ethylene glycol-bis(2-aminoethyl ether)-N,N,N',N'-tetraacetate (EGTA 4- ), respectively. Based on the complexones’ pK a values obtained from 1 H nuclear magnetic resonance (NMR) spectroscopic pH titration, complex formation constants were determined by means of the parallel-factor-analysis-assisted evaluation of Eu(III) and Cm(III) time-resolved laser-induced fluorescence spectroscopy (TRLFS). This was complemented by isothermal titration calorimetry (ITC), providing the enthalpy and entropy of the complex formation. This allowed us to obtain genuine species along with their molecular structures and corresponding reliable thermodynamic data. The three investigated complexones formed 1:1 complexes with both Eu(III) and Cm(III). Besides the established Eu(III)–NTA 1:1 and 1:2 complexes, we observed, for the first time, the existence of a Eu(III)–NTA 2:2 complex of millimolar metal and ligand concentrations. Demonstrated for thermodynamic studies on Eu(III) and Cm(III) interaction with complexones, the utilized approach is commonly applicable to many other metal–ligand systems, even to high-affinity ligands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

The Evolution of the Tully–Fisher Relation between z ∼ 2.3 and z ∼ 0.9 with KMOS{sup 3D}

We investigate the stellar mass and baryonic mass Tully–Fisher relations (TFRs) of massive star-forming disk galaxies at redshift z∼2.3 and z∼0.9 as part of the KMOS{sup 3D} integral field spectroscopy survey. Our spatially resolved data allow reliable modeling of individual galaxies, including the effect of pressure support on the inferred gravitational potential. At fixed circular velocity, we find higher baryonic masses and similar stellar masses at z∼2.3 as compared to z∼0.9. Together with the decreasing gas-to-stellar mass ratios with decreasing redshift, this implies that the contribution of dark matter to the dynamical mass on the galaxy scale increases toward lower redshift. A comparison to local relations reveals a negative evolution of the stellar and baryonic TFR zero points from z = 0 to z∼0.9, no evolution of the stellar TFR zero point from z∼0.9 to z∼2.3, and a positive evolution of the baryonic TFR zero point from z∼0.9 to z∼2.3. We discuss a toy model of disk galaxy evolution to explain the observed nonmonotonic TFR evolution, taking into account the empirically motivated redshift dependencies of galactic gas fractions and the relative amount of baryons to dark matter on galaxy and halo scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A numerical framework for simulating the atmospheric variability of supermicron marine biogenic ice nucleating particles

Abstract. We present a framework for estimating concentrations of episodically elevated high-temperature marine ice nucleating particles (INPs) in the sea surface microlayer and their subsequent emission into the atmospheric boundary layer. These episodic INPs have been observed in multiple ship-based and coastal field campaigns, but the processes controlling their ocean concentrations and transfer to the atmosphere are not yet fully understood. We use a combination of empirical constraints and simulation outputs from an Earth system model to explore different hypotheses for explaining the variability of INP concentrations, and the occurrence of episodic INPs, in the marine atmosphere. In our calculations, we examine the following two proposed oceanic sources of high-temperature INPs: heterotrophic bacteria and marine biopolymer aggregates (MBPAs). Furthermore, we assume that the emission of these INPs is determined by the production of supermicron sea spray aerosol formed from jet drops, with an entrainment probability that is described by Poisson statistics. The concentration of jet drops is derived from the number concentration of supermicron sea spray aerosol calculated from model runs. We then derive the resulting number concentrations of marine high-temperature INPs (at 253 K) in the atmospheric boundary layer and compare their variability to atmospheric observations of INP variability. Specifically, we compare against concentrations of episodically occurring high-temperature INPs observed during field campaigns in the Southern Ocean, the Equatorial Pacific, and the North Atlantic. In this case study, we evaluate our framework at 253 K because reliable observational data at this temperature are available across three different ocean regions, but suitable data are sparse at higher temperatures. We find that heterotrophic bacteria and MBPAs acting as INPs provide only a partial explanation for the observed high INP concentrations. We note, however, that there are still substantial knowledge gaps, particularly concerning the identity of the oceanic INPs contributing most frequently to episodic high-temperature INPs, their specific ice nucleation activity, and the enrichment of their concentrations during the sea–air transfer process. Therefore, targeted measurements investigating the composition of these marine INPs and drivers for their emissions are needed, ideally in combination with modeling studies focused on the potential cloud impacts of these high-temperature INPs.

54 ENVIRONMENTAL SCIENCES↗