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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

The Second Catalog of Flaring Gamma-Ray Sources from theFermi All-sky Variability Analysis

We present the second catalog of flaring gamma-ray sources (2FAV) detected with the Fermi All-sky Variability Analysis (FAVA), a tool that blindly searches for transients over the entire sky observed by the Large Area Telescope (LAT) on board the Fermi Gamma-ray Space Telescope. With respect to the first FAVA catalog, this catalog benefits from a larger data set, the latest LAT data release (Pass 8), as well as from an improved analysis that includes likelihood techniques for a more precise localization of the transients. Applying this analysis to the first 7.4 years of Fermi observations, and in two separate energy bands 0.1–0.8 GeV and 0.8–300 GeV, a total of 4547 flares were detected with significance greater than 6σ (before trials), on the timescale of one week. Through spatial clustering of these flares, 518 variable gamma-ray sources were identified. Based on positional coincidence, likely counterparts have been found for 441 sources, mostly among the blazar class of active galactic nuclei. For 77 2FAV sources, no likely gamma-ray counterpart has been found. For each source in the catalog, we provide the time, location, and spectrum of each flaring episode. Studying the spectra of the flares, we observe a harder-when-brighter behavior for flares associated with blazars, with the exception of BL Lac flares detected in the low-energy band. The photon indexes of the flares are never significantly smaller than 1.5. For a leptonic model, and under the assumption of isotropy, this limit suggests that the spectrum of freshly accelerated electrons is never harder than p∼2.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Spatial associations between social groups and ozone air pollution exposure in the Beijing urban area

Highlights: • Greater percentages of persons living alone and migrants were associated with higher O{sub 3} exposure. • Social groups divided by Hukou status had distinct spatial distributions in the Beijing urban area. • Migrants bore a double burden of social deprivation and O{sub 3} exposure due to city development planning and the Hukou system. • Areas with greater percentages of seniors had lower O{sub 3} exposure but higher NO{sub 2} exposure. Few studies have linked social factors to air pollution exposure in China. Unlike the race or minority concepts in western countries, the Hukou system (residential registration system) is a fundamental reason for the existence of social deprivation in China. To assess the differences in ozone (O{sub 3}) exposure among social groups, especially groups divided by Hukou status, we assigned estimates of O{sub 3} exposure to the latest census data of the Beijing urban area using a kriging interpolation model. We developed simultaneous autoregressive (SAR) models that account for spatial autocorrelation to identify the associations between O{sub 3} exposure and social factors. Principal component regression was used to control the multicollinearity bias as well as explore the spatial structure of the social data. The census tracts (CTs) with higher proportions of persons living alone and migrants with non-local Hukou were characterized by greater exposure to ambient O{sub 3}. The areas with greater proportions of seniors had lower O{sub 3} exposure. The spatial distribution patterns were similar among variables including migrants, agricultural population and household separation (population status with separation between Hukou and actual residences), which fit the demographic characteristics of the majority of migrants. Migrants bore a double burden of social deprivation and O{sub 3} pollution exposure due to city development planning and the Hukou system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics

Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. Here, we propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota’s MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation

Abstract While various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Spatial representation of organic carbon and active-layer thickness of high latitude soils in CMIP5 earth system models

Soil properties such as soil organic carbon (SOC) stocks and active-layer thickness are used in earth system models (F.SMs) to predict anthropogenic and climatic impacts on soil carbon dynamics, future changes in atmospheric greenhouse gas concentrations, and associated climate changes in the permafrost regions. Accurate representation of spatial and vertical distribution of these soil properties in ESMs is a prerequisite for redudng existing uncertainty in predicting carbon-climate feedbacks. We compared the spatial representation of SOC stocks and active-layer thicknesses predicted by the coupled Modellntercomparison Project Phase 5 { CMIP5) ESMs with those predicted from geospatial predictions, based on observation data for the state of Alaska, USA. For the geospatial modeling. we used soil profile observations {585 for SOC stocks and 153 for active-layer thickness) and environmental variables (climate, topography, land cover, and surficial geology types) and generated fine-resolution (50-m spatial resolution) predictions of SOC stocks (to 1-m depth) and active-layer thickness across Alaska. We found large inter-quartile range (2.5-5.5 m) in predicted active-layer thickness of CMIP5 modeled results and small inter-quartile range (11.5-22 kg m-2) in predicted SOC stocks. The spatial coefficient of variability of active-layer thickness and SOC stocks were lower in CMIP5 predictions compared to our geospatial estimates when gridded at similar spatial resolutions (24.7 compared to 30% and 29 compared to 38%, respectively). However, prediction errors. when calculated for independent validation sites, were several times larger in ESM predictions compared to geospatial predictions. Primaly factors leading to observed differences were ( 1) lack of spatial heterogeneity in ESM predictions, (2) differences in assumptions concerning environmental controls, and (3) the absence of pedogenic processes in ESM model structures. Our results suggest that efforts to incorporate these factors in F.SMs should reduce current uncertainties associated with ESM predictions of carbon-climate feedbacks.

Active-layer thickness↗
DOE OSTI2025

Controlling matric potential in microfluidics to examine microbial dynamics in unsaturated porous media

The use of microfluidics for the study of soil microbial ecology is an emerging field. Most microfluidic studies of biological systems, however, have been performed under fully saturated conditions that are not representative of natural soil. Therefore, while microfluidics offer many unique capabilities that other methodologies cannot, they are not currently suited to address the effects of matric potential, an important variable defining the microbial moisture niche. Here, a methodology is presented that allows the user to control the aqueous conditions within microfluidic networks by manipulating matric potential using a hanging water column. The method relies on hydrophilic surface treatment of the microfluidic device using polyvinyl alcohol (PVA) and incorporating a bed of small pores at the network boundaries, which serve as a porous ceramic plate analogue (PPA). The method was validated on a simple capillary bundle and then on a more complex pore network. A water retention curve, exhibiting hysteresis, was generated for the pore network over a narrow matric potential range of 0 to – 5 kPa. Both the drainage and wetting curves were reproducible, as were the spatial configuration and the number of fragmented moisture niches in the pore network, particularly on the drainage curve. In contrast, the wetting curve exhibited greater variability in spatial configuration due to the “ink bottle effect,” where capillarity was interrupted by wider pore bodies. Ultimately, the methodology provides realistic pore-scale moisture conditions that can be easily manipulated and maintained, enabling new opportunities to explore soil biophysics and microbial biogeography in unsaturated porous media. As a brief example, images showing the localization of fluorescently tagged Pantoea sp. YR343 at −4.3 kPa are presented, highlighting bacterial distributions in water films and air-water interfaces.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Altimeter-era emergence of the patterns of forced sea-level rise in climate models and implications for the future

The satellite altimeter record has provided an unprecedented database for understanding sea-level rise and has recently reached a major milestone at 25 years in length. A challenge now exists in understanding its broader significance and its consequences for sea-level rise in the coming decades and beyond. A key question is whether the pattern of altimeter-era change is representative of longer-term trends driven by anthropogenic forcing. In this work, two multimember climate ensembles, the Community Earth System Model (CESM) and the Earth System Model Version 2M (ESM2M), are used to estimate patterns of forced change [also known as the forced response (FR)] and their magnitudes relative to internal variability. It is found that the spatial patterns of 1993–2018 trends in the ensembles correlate significantly with the contemporaneous FRs (0.55 ± 0.10 in the CESM and 0.61 ± 0.09 in the ESM2M) and the 1950–2100 FRs (0.43 ± 0.10 in the CESM and 0.51 ± 0.11 in the ESM2M). Unforced runs for each model show such correlations to be extremely unlikely to have arisen by chance, indicating an emergence of both the altimeter-era and long-term FRs and suggesting a similar emergence in nature. Projected patterns of the FR over the coming decades resemble those simulated during the altimeter era, suggesting a continuation of the forced pattern of change in nature in the coming decades. Notably, elevated rates of rise are projected to continue in regions that are susceptible to tropical cyclones, exacerbating associated impacts in a warming climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data

Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management. Without accessibility to other data sources, the onsite observed temperatures or the airport temperatures are used in forecast models. In this paper, we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations (PWS) to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework. The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs. Our approach is distinguished from existing literature in various aspects. First, we leverage the crowdsourcing weather data from PWS in addition to public data sources. In this way, the data is at much finer time resolution (e.g., at 5-minute frequency) and spatial resolution (e.g., arbitrary location vs grid). Second, our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance. Here, we demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio, Texas, USA. In general, the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50% compares with persistent model and has 90% chance to outperform airport forecast in short-term forecasting. In a real-time setting, the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon. Moreover, we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance. Finally, we implement our model in other cities such as Syracuse and Chicago to test the model's performance in different landforms and climate types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

Abstract Soil respiration (Rs), the soil‐to‐atmosphere CO 2 flux produced by microbes and plant roots, is a critical but uncertain component of the global carbon cycle. Our current understanding of the variability and dynamics is limited by the coarse spatial resolution of existing estimates. We predicted annual Rs and associated uncertainty across the world at 1‐km resolution using a quantile regression forest algorithm trained with observations from the global Soil Respiration Database spanning from 1961 to 2011. This model yielded a global annual Rs estimate of 87.9 Pg C/year with an associated global uncertainty of 18.6 (mean absolute error) and 40.4 (root mean square error) Pg C/year. The estimated annual heterotrophic respiration (Rh), derived from empirical relationships with Rs, was 49.7 Pg C/year over the same period. Predicted Rs rates and associated uncertainty varied widely across vegetation types, with the greatest predicted rates of Rs in evergreen broadleaf forests (accounting for 20.9% of global Rs). The greatest prediction uncertainties were in northern latitudes and arid to semiarid ecosystems, suggesting that these areas should be targeted in future measurement campaigns. This study provides predictions of Rs (and associated prediction uncertainty) at unprecedentedly high spatial resolution across the globe that could help constrain local‐to‐global process‐based models. Furthermore, it provides insights into the large variability of Rs and Rh across vegetation classes and identifies regions and vegetation types with poor model performance that should be prioritized for future data collection.

Warner, D. L.↗
DOE OSTI2017

A Transient Transit Signature Associated with the Young Star RIK-210

We find transient transit-like dimming events within the K2 time series photometry of the young star RIK-210 in the Upper Scorpius OB association. These dimming events are variable in depth, duration, and morphology. High spatial resolution imaging revealed that the star is single and radial velocity monitoring indicated that the dimming events cannot be due to an eclipsing stellar or brown dwarf companion. Archival and follow-up photometry suggest the dimming events are transient in nature. The variable morphology of the dimming events suggests they are not due to a single spherical body. The ingress of each dimming event is always shallower than egress, as one would expect for an orbiting body with a leading tail. The dimming events are periodic and synchronous with the stellar rotation. However, we argue it is unlikely the dimming events could be attributed to anything on the stellar surface based on the observed depths and durations. Variable obscuration by a protoplanetary disk is unlikely on the basis that the star is not actively accreting and lacks the infrared excess associated with an inner disk. Rather, we explore the possibilities that the dimming events are due to magnetospheric clouds, a transiting protoplanet surrounded by circumplanetary dust and debris, eccentric orbiting bodies undergoing periodic tidal disruption, or an extended field of dust or debris near the corotation radius.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

The Two-Column Aerosol Project: Phase I-Overview and impact of elevated aerosol layers on aerosol optical depth

The Two-Column Aerosol Project (TCAP), conducted from June 2012 through June 2013, was a unique study designed to provide a comprehensive data set that can be used to investigate a number of important climate science questions, including those related to aerosol mixing state and aerosol radiative forcing. The study was designed to sample the atmosphere between and within two atmospheric columns; one fixed near the coast of North America (over Cape Cod, MA) and a second moveable column over the Atlantic Ocean several hundred kilometers from the coast. The U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) was deployed at the base of the Cape Cod column, and the ARM Aerial Facility was utilized for the summer and winter intensive observation periods. One important finding from TCAP is that four of six nearly cloud-free flight days had aerosol layers aloft in both the Cape Cod and maritime columns that were detected using the nadir pointing second-generation NASA high-spectral resolution lidar (HSRL-2). In addition, these layers contributed up to 60% of the total observed aerosol optical depth (AOD). Many of these layers were also intercepted by the aircraft configured for in situ sampling, and the aerosol in the layers was found to have increased amounts of biomass burning material and nitrate compared to aerosol found near the surface. Lastly, while there was a great deal of spatial and day-to-day variability in the aerosol chemical composition and optical properties, no systematic differences between the two columns were observed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A new look at ocean ventilation time scales and their uncertainties

Abstract A suite of eddy‐resolving ocean transient tracer model simulations are first compared to observations. Observational and model pCFC‐11 ages agree quite well, with the eddy‐resolving model adding detail. The CFC ages show that the thermocline is a barrier to interior ocean exchange with the atmosphere on time scales of 45 years, the measureable CFC transient, although there are exceptions. Next, model simulations are used to quantify effects on tracer ages of the spatial dependence of internal ocean tracer variability due to stirring from eddies and biases from nonstationarity of the atmospheric transient when there is mixing. These add to tracer age uncertainties and biases, which are large in frontal boundary regions, and small in subtropical gyre interiors. These uncertainties and biases are used to reinterpret observed temporal trends in tracer‐derived ventilation time scales taken from observations more than a decade apart, and to assess whether interpretations of changes in tracer ages being due to changes in ocean ventilation hold water. For the southern hemisphere subtropical gyres, we infer that the rate of ocean ventilation 26–27.2 σ θ increased between the mid‐1990s and the decade of the 2000s. However, between the mid‐1990s and the decade of the 2010s, there is no significant trend—perhaps except for South Atlantic. Observed age/AOU/ventilation changes are linked to a combination of natural cycles and climate change, and there is regional variability. Thus, for the future it is not clear how strong or steady in space and time ocean ventilation changes will be.

58 GEOSCIENCES↗
DOE OSTI2023

Nitrogen fixation rates in the Guinea Dome and the equatorial upwelling regions in the Atlantic Ocean

Abstract Biological nitrogen fixation is a key process balancing the loss of combined nitrogen in the marine nitrogen cycle. Its relevance in upwelling or high nutrient regions is still unclear, with the few available studies in these regions of the ocean reporting rates that vary widely from below detection limit to > 100 nmol N L −1 d −1 . In the eastern tropical Atlantic Ocean, two open ocean upwelling systems are active in boreal summer. One is the seasonal equatorial upwelling, where the residual phosphorus associated with aged upwelled waters is suggested to enhance nitrogen fixation in this season. The other is the Guinea Dome, a thermal upwelling dome. We conducted two surveys along 23° W across the Guinea Dome and the Equator from 15° N to 5° S in September 2015 and August–September 2016 with high latitudinal resolution (20–60 nm between stations). The abundance of Trichodesmium colonies was characterized by an Underwater Vision Profiler 5 and the total biological nitrogen fixation in the euphotic layer was measured using the 15 N 2 technique. The highest abundances of Trichodesmium colonies were found in the area of the Guinea Dome (9°–15° N) with a maximum of 3 colonies L −1 near the surface. By contrast, colonies were almost absent in the Equatorial band between 2° N and 5° S. The highest nitrogen fixation rate was measured at the northern edge of the Guinea Dome in 2016 (ca. 31 nmol N L −1 d −1 ). In this region, where diazotrophs thrived on a sufficient supply of both phosphorus and iron, a patchy distribution was unveiled by our increased spatial resolution scheme. In the Equatorial band, rates were considerably lower, ranging from below detection limit to ca. 4 nmol N L −1 d −1 , with a clear difference in magnitude between 2015 (rates close to zero) and 2016 (average rates around 2 nmol N L −1 d −1 ). This difference seemed triggered by a contrasting supply of phosphorus between years. Our study stresses the importance of surveys with sampling at fine-scale spatial resolution, and shows unexpected high variability in the rates of nitrogen fixation in the Guinea Dome, a region where diazotrophy is a significant process supplying new nitrogen into the euphotic layer.

Fernández-Carrera, Ana (ORCID:0000000183470996)↗
DOE OSTI2024

A multi-objective optimization model for cropland design considering profit, biodiversity, and ecosystem services

More sustainable agricultural methods are needed to alleviate the decreases in biodiversity and ecosystem services that have occurred because of industrial agriculture. One such method is the inclusion of alternative crops into croplands that can support biodiversity, reduce erosion and chemical runoff, and sequester carbon in the soil. However, the question of where such crops should be planted to balance competing economic and environmental objectives remains open. To this end, we develop a mixed-integer quadratically constrained program to optimize the layout of a cropland considering economic, biodiversity, greenhouse gas emissions, and water quality objectives. We include spatially varying fertilization as a decision variable in addition to crop establishment location. We further include the effect of core area and edges between different crops on biodiversity. To demonstrate the applicability of the model, we apply it to an example field, showing how the optimal cropland design changes as a decision-maker prioritizes different objectives and as edges have different impacts on biodiversity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Mechanism of co-seismic water level change following four great earthquakes – insights from co-seismic responses throughout the Chinese mainland

In this study, we analyze the co-seismic groundwater level responses to four great earthquakes recorded by China's network of groundwater monitoring wells. The large number of operational wells (164 wells for the 2007 Mw 8.5 Sumatra earthquake, 245 wells for the Mw 7.9 Wenchuan earthquake, 228 wells for the Mw 9.0 Tohoku earthquake and 223 wells for 2012 Mw 8.6 Sumatra earthquake) and co-seismic responses provide an opportunity to test hypotheses on mechanisms for co-seismic water level changes. Overall, the co-seismic water level responses are complex over large spatial scales, and there is great variability both in the sign and amplitude of water level responses in the data set. As shown in previous studies, permeability change, rather than static strain, is a more plausible mechanism to explain most of the co-seismic responses. However, we find through tidal analysis of water level responses to solid Earth tide that only one third of these wells that showed a sustained post-seismic response can be explained by earthquake-induced permeability change in aquifers, and these wells had sustained (>30 days) water level changes. Lastly, wells that did not show sustained changes are more likely affected by permeability changes only immediately adjacent to the wellbore.

58 GEOSCIENCES↗
DOE OSTI2026

System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study

Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2021

Optimal design of acoustic metamaterial cloaks under uncertainty

In this work, we consider the problem of optimal design of an acoustic cloak under uncertainty and develop scalable approximation and optimization methods to solve this problem. The design variable is taken as an infinite-dimensional spatially-varying field that represents the material property, while an additive infinite-dimensional random field represents the variability of the material property or the manufacturing error. Discretization of this optimal design problem results in high-dimensional design variables and uncertain parameters. To solve this problem, we develop a computational approach based on a Taylor approximation and an approximate Newton method for optimization, which is based on a Hessian derived at the mean of the random field. We show our approach is scalable with respect to the dimension of both the design variables and uncertain parameters, in the sense that the necessary number of acoustic wave propagations is essentially independent of these dimensions, for numerical experiments with up to one million design variables and half a million uncertain parameters. Additionally, we demonstrate that, using our computational approach, an optimal design of the acoustic cloak that is robust to material uncertainty is achieved in a tractable manner. The optimal design under uncertainty problem is posed and solved for the classical circular obstacle surrounded by a ring-shaped cloaking region, subjected to both a single-direction single-frequency incident wave and multiple-direction multiple-frequency incident waves. Finally, we apply the method to a deterministic large-scale optimal cloaking problem with complex geometry, to demonstrate that the approximate Newton method’s Hessian computation is viable for large, complex problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Evidence for a genetic basis in functional trait tradeoffs with microbial growth rate but not growth yield

Tradeoffs in microbial functional traits have been a focus of recently described ecological frameworks and of mathematical models of microbial community functioning. Tradeoffs in key traits such as growth rate, growth yield, resource acquisition, and stress tolerance may have either a genetic basis or a physiological basis, and the type of tradeoff can inform how traits are modeled and measured. Here we provide evidence that growth rate/decomposition and growth rate/stress tolerance tradeoffs have a primarily genetic basis in a phylogenetically diverse suite of ten leaf litter-inhabiting fungi. In contrast, growth yield tradeoffs with functional traits are more likely to have a physiological basis. Consideration of the type of tradeoff, genetic or physiological, should help to inform efforts to model microbial contributions to ecosystem processes, especially when considering different scales. Consideration of physiological tradeoffs may be important for understanding short-term variability (e.g., pulse events) and fine spatial scales, whereas genetic tradeoffs are likely to be useful for understanding regional- to continental-scale and medium- to long-term contributions of microbes to ecosystem processes.

59 BASIC BIOLOGICAL SCIENCES↗