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At least 145 records · Page 8

Major facilitator family transporters specifically enhance caffeyl alcohol uptake during C‐lignin biosynthesis

The mode of transport of lignin monomers to the sites of polymerization in the apoplast remains controversial. C-Lignin is a recently discovered form of lignin found in some seed coats that is composed exclusively of units derived from caffeyl alcohol. RNA-seq and proteome analyses identified a number of transporters co-expressed with C-lignin deposition in the seed coat of Cleome hassleriana. Cloning and influx/efflux analysis assays in yeast identified two low-affinity transporters, ChPLT3 and ChSUC1, that were active with caffeyl alcohol but not with the classical monolignols p-coumaryl, coniferyl, and sinapyl alcohols, consistent with molecular modeling and docking studies. Expression of ChPLT3 in Arabidopsis seedlings enhanced root growth in the presence of caffeyl alcohol, and expression of ChPLT3 and ChSUC1 correlated with lignin C-unit content in hairy roots of Medicago truncatula. We present a model, consistent with phylogenetic and evolutionary considerations, whereby passive caffeyl alcohol transport may be supplemented by hitchhiking on secondary active transporters to ensure the synthesis of C-lignin, and inhibition of synthesis of G-lignin, in the apoplast.

59 BASIC BIOLOGICAL SCIENCES↗

Ensemble‐Based, Large‐Eddy Reconstruction of Wind Turbine Inflow in a Near‐Stationary Atmospheric Boundary Layer Through Generative Artificial Intelligence

ABSTRACT To validate the second‐by‐second dynamics of turbines in field experiments, it is necessary to accurately reconstruct the winds going into the turbine. Current time‐resolved inflow reconstruction techniques estimate wind behavior in unobserved regions using relatively simple spectral‐based models of the atmosphere. Here, we develop a technique for time‐resolved inflow reconstruction that is rooted in a large‐eddy simulation model of the atmosphere. Our “large‐eddy reconstruction” technique blends observations and atmospheric model information through a diffusion model machine learning algorithm, allowing us to generate probabilistic ensembles of reconstructions for a single 10‐min observational period. Our generated inflows can be used directly by aeroelastic codes or as inflow boundary conditions in a large‐eddy simulation. We verify the second‐by‐second reconstruction capability of our technique in three synthetic field campaigns, finding positive Pearson correlation coefficient values () between ground‐truth and reconstructed streamwise velocity, as well as smaller positive correlation coefficient values for unobserved fields (spanwise velocity, vertical velocity, and temperature). We validate our technique in three real‐world case studies by driving large‐eddy simulations with reconstructed inflows and comparing to independent inflow measurements. The reconstructions are visually similar to measurements, follow desired power spectra properties, and track second‐by‐second behavior ().

17 WIND ENERGY↗

Modeling Plant Nutrient Acquisition Strategies Alters Projections of Carbon and Nitrogen Dynamics in Bioenergy Agroecosystems

Plant strategies to acquire nutrients from limited environments help shape ecosystem carbon (C) and nitrogen (N) cycling and response to environmental change. The effects of plant strategies on ecosystem dynamics are largely uncharacterized in bioenergy agroecosystems, where the impacts could determine bioenergy's ability to meet its sustainability goals of storing C and reducing N loss. We used FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms and Protection), a plant–microbe interaction model of coupled plant nutrient uptake and soil organic matter decomposition, to simulate the effects of nutrient acquisition strategies on soil microbial activity and ecosystem nutrient cycling in bioenergy feedstocks miscanthus (Miscanthus × giganteus) and sorghum (Sorghum bicolor (L.) Moench). We examined the model's ability to reproduce the relative effects of belowground nutrient uptake on microbial activity using a reanalysis of empirical data showing that miscanthus root exudation provoked a larger soil microbial response than sorghum. From baseline model simulations, we found that the ability of miscanthus to retranslocate N resulted in higher N uptake at a lower C cost than the sorghum/soybean rotation and that soil C and N pools increased under perennial (miscanthus) and decreased under annual (sorghum/soybean) cultivation. The model also predicted that greater root exudation increased soil C accumulation, highlighting the role of roots in forming stable soil C. Overall, the baseline model was unable to reproduce field observations of miscanthus root exudation stimulating microbial activity more than sorghum. To improve the model, we updated the soil microbial parameters in miscanthus to have faster decomposition, a higher C/N ratio, and greater carbon use efficiency. These changes improved the simulated soil microbial response to miscanthus root exudation, supporting the hypothesis that miscanthus soils foster a microbial community that is more responsive to root exudation than that of sorghum.

09 BIOMASS FUELS↗

Comparison of three DWM-based wake models at above-rated wind speeds

In this study we investigate three mid-fidelity wind turbine wake models based on the dynamic wake meandering (DWM) model principle, and compare their performance with a reference dataset, produced with large-eddy simulations using the actuator line model. The models are compared with respect to flow field, power, and loads on a row of four 5MW reference turbines experiencing above-rated wind conditions. In general, the DWM models show fairly good agreement with large-eddy simulation for the time-averaged flow fields, blade forces and power, with increasing differences along the turbine row. Also when comparing fatigue loads of blade root moments, the differences between the models increase further into the row, with deviations up to 25 % of the reference case. However, while the development in blade root moment fatigue along the turbine row is predominantly driven by the energy content at the frequency corresponding to the turbine's rotational period (1 P ) for the DWM models, the large-eddy simulation results suggest that the key drivers for the blade root and tower loads are the increase in meandering and energy at higher frequencies (> 1 P ) deeper into the turbine row. For the tower loads, the DWM models highly underestimate the fatigue for the waked turbines. From these results, we suggest priorities for future model developments so that robust model implementations can be used in wind farm design and operation.

17 WIND ENERGY↗

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology

Abstract The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum . The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

59 BASIC BIOLOGICAL SCIENCES↗

Predictive links between microbial communities and biological oxygen utilization in the Arctic Ocean

Microbial metabolism influences rates of net community production (NCP), exerting a direct biological control on marine oxygen and carbon fluxes. In the Arctic, it is increasingly important to understand and quantify this process, as ecological and oceanographic conditions shift due to changing climate. Here, we describe potential ecological links between pelagic microbial diversity and an NCP precursor, biological oxygen utilization, using machine learning and paired observations of community structure and metabolic activity from a seasonally and spatially variable transect of the Arctic Ocean (2019–2020 MOSAiC Expedition). Community structure was determined using 16S (prokaryotic) and 18S (eukaryotic) rRNA gene amplicon sequencing, and metabolic activity was derived from ΔO 2 /Ar. Using self-organizing maps, we identified clear successional patterns in observed microbial community structure that were seasonally driven in the upper ocean and vertically stratified with depth. Metabolic activity was also stratified, with a primarily net heterotrophic water column (median −1.5% biological oxygen saturation), excepting periodic oxygen supersaturation (maximum: 13.6%) within the mixed layer. Using DNA sequences as predictor variables, we then constructed a random forest regression model that reliably reconstructed biological oxygen concentrations (root mean squared error = 4.14 μmol kg −1 ). Top predictors from this model were from heterotrophic (bacteria) or potentially mixotrophic (dinoflagellate) taxa. These analyses highlight biologically driven diagnostic tools that can be used to expand biogeochemical datasets and improve the microbial perspectives and metabolisms represented in ecological models of net productivity and carbon flux in a changing Arctic Ocean.

Chamberlain, Emelia J. [Univ. of San Diego, San Di↗

Artificial ground reflector size and position effects on energy yield and economics of single‐axis‐tracked bifacial photovoltaics

Abstract Artificial ground reflectors improve bifacial energy yield by increasing both front and rear‐incident irradiance. Studies have demonstrated an increase in energy yield due to the addition of artificial reflectors; however, they have not addressed the effect of varying reflector dimensions and placement on system performance and the impact of these parameters on the reflectors' financial viability. We studied the effect of high albedo (70% reflective) artificial reflectors on single‐axis‐tracked bifacial photovoltaic systems through ray‐trace modeling and field measurements. In the field, we tested a range of reflector configurations by varying reflector size and placement and demonstrated that reflectors increased daily energy yield up to 6.2% relative to natural albedo for PERC modules. To confirm the accuracy of our model, we compared modeled and measured power and found a root mean square error (RMSE) of 5.4% on an hourly basis. We modeled a typical meteorological year in Golden, Colorado, to demonstrate the effects of artificial reflectors under a wide range of operating conditions. Seventy percent reflective material can increase total incident irradiance by 1.9%–8.6% and total energy yield by 0.9%–4.5% annually after clipping is considered with a DC–AC ratio of 1.2. Clipping has a significant effect on reflector impact and must be included when assessing reflector viability because it reduces reflector energy gain. We calculated a maximum viable cost for these improvements of up to $2.50–4.60/m 2 , including both material and installation, in Golden. We expanded our analysis to cover a latitude range of 32–48°N and demonstrated that higher‐latitude installations with lower energy yield and higher diffuse irradiance content can support higher reflector costs. In both modeling and field tests, and for all locations, the ideal placement of the reflectors was found to be directly underneath the module due to the optimized rear irradiance increase.

14 SOLAR ENERGY↗

Topographical shifts in fine root lifespan in a mixed, mesic temperate forest

Root lifespan, often is estimated in landscape- and ecosystem-level carbon models using linear approximations. In water manipulation experiments, fine root lifespan can vary with soil water content. Soil water content is generally structured by complex topography, which is largely unaccounted for in landscape- and ecosystem-scale carbon models. Topography governs the range of soil water content experienced by roots which may impact their lifespan. We hypothesized that root lifespan varied nonlinearly across a temperate, mesic, forested catchment due to differences in soil water content associated with topographic position. We expected regions of the landscape that were too wet or too dry would have soils that were not optimal for roots and thus result in shorter root lifespans. Specifically, we hypothesized that root lifespan would be longest in areas that consistently had soil water content in the middle of the soil water content spectrum, while in soils at either very low or very high soil water content, root lifespan would be relatively short. We tested this hypothesis by collecting and analyzing two years of minirhizotron and soil moisture data in plots widely distributed in the Shale Hills catchment of the Susquehanna-Shale Hills Critical Zone Observatory in Pennsylvania. We found that fine root lifespans were longer in traditionally wetter topographic regions, but detected no short term (biweekly) effect of soil moisture on root lifespan. Additionally, depth in soil, soil series, slope face orientation, and season of birth strongly affected root lifespans across the catchment. In contrast, lifespan was unaffected by root diameter or mycorrhizal association. Failure to account for these variables could result in erroneous estimates of fine root lifespan and, consequentially, carbon flux in temperate forested regions.

54 ENVIRONMENTAL SCIENCES↗

Plant roots stimulate the decomposition of complex, but not simple, soil carbon

Roots release carbon into soil and can alleviate energy limitation of microbial organic matter decomposition. We know little about the effects of roots on microbial decomposition of different organic matter substrates, despite the importance for soil carbon stocks and turnover. Through implementing root-microbe interactions, the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model was previously shown to represent dynamics of total soil carbon in temperate forest field experiments. However, the model permits alternative hypotheses concerning microbial-substrate affinity. We investigated how root inputs affect decomposition of soil organic carbon (SOC) with variable decomposability. We simulated SOC stocks in CORPSE and compared microbial degradation of two substrates types with varying root-microbe interactions under two alternative hypotheses that varied in microbial-substrate affinity. We compared our modeled hypotheses to a forest field experiment where we quantified decomposition of isotopically-labeled starch and leaf tissues in soils with manipulated root access to microbes. Here, we tested the hypothesis that decomposition of leaves would be more sensitive to root inputs than decomposition of starch, corresponding to the alternative model hypothesis. In the field study, leaf decomposition increased with root density while starch decomposition was unchanged by root density. Microbial biomass and enzyme activity consistently increased with root inputs in CORPSE and the field study. Our field experiment supported the CORPSE simulations with high microbial-substrate affinity. Roots stimulated microbial growth and enzyme production, which increased degradation of more complex substrates such as leaf tissues. Substrates that were easily decomposed, such as starch, may already be degrading at a maximum rate in the absence of rhizosphere influence because their decomposition rate was unchanged by root inputs. We found that the degree to which roots stimulate microbial decomposition depends on the substrate being decomposed, and that root-microbe interactions influenced SOC stocks in both our model and field experiment. Environmental changes that alter root-microbe interactions could, therefore, alter soil C stocks and biogeochemical cycling, and models of these interactions should incorporate differential influence of rhizosphere inputs on different substrates.

59 BASIC BIOLOGICAL SCIENCES↗

Tropical dry forest response to nutrient fertilization: a model validation and sensitivity analysis

Abstract. Soil nutrients, especially nitrogen (N) and phosphorus (P), regulate plant growth and hence influence carbon fluxes between the land surface and atmosphere. However, how forests adjust biomass partitioning to leaves, wood, and fine roots in response to N and/or P fertilization remains puzzling. Recent work in tropical forests suggests that trees increase fine root production under P fertilization, but it is unclear whether mechanistic models can reproduce this dynamic. In order to better understand mechanisms governing nutrient effects on plant allocation and improve models, we used the nutrient-enabled ED2 model to simulate a fertilization experiment being conducted in a secondary tropical dry forest in Costa Rica. We evaluated how different allocation parameterizations affected model performance. These parameterizations prescribed a linear relationship between relative allocation to fine roots and soil P concentrations. The slope of the linear relationship was allowed to be positive, negative, or zero. Some parameterizations realistically simulated leaf, wood, and fine root production, and these parameterizations all assumed a positive relationship between relative allocation to fine roots and soil P concentration. Model simulations of a 30-year timeframe indicated strong sensitivity to parameterization and fertilization treatment. Without P fertilization, the simulated aboveground biomass (AGB) accumulation was insensitive to the parameterization. With P fertilization, the model was highly sensitive to the parameterization and the greatest AGB accumulation occurred when relative allocation to fine roots was independent of soil P. Our study demonstrates the need for simultaneous measurements of leaf, wood, and fine root production in nutrient fertilization experiments and for longer-term experiments. Models that do not accurately represent allocation to fine roots may be highly biased in their simulations of AGB, especially on multi-decadal timescales.

Environmental Sciences & Ecology↗

How deep should we go to understand roots at the top of the world?

Harsh environmental conditions and the short summers of northern, high-latitude biomes impose unique constraints on the plants that live in the arctic tundra and the boreal forest. To escape the harsh aboveground environment, plants in these habitats often allocate a large portion of their biomass belowground to facilitate nutrient acquisition. In turn, the proximity of living plant roots to vast stores of sequestered soil carbon in these biomes means that shifts in rooting depth distribution and the size of the root–soil interface could significantly contribute to ongoing climate change. Indeed, plant ‘priming’ of rhizosphere decomposition via root exudation, particularly from shallowly distributed roots, can lead to losses of carbon from tundra soils. While we have a hard-won understanding of the distribution of plant communities across the arctic tundra and the boreal forest from direct field observations scaled to the landscape level using climate-informed mapping techniques (i.e. the Circumpolar Arctic Vegetation Map (CAVM); Walker et al. , 2005), these vegetation maps are only the tip of the iceberg (Iversen et al. , 2015). Root form and function remain hidden beneath the land surface. In an article recently published in New Phytologist , Blume-Werry et al. (2023, 10.1111/nph.18998) asked whether rooting depth distribution, and ensuing carbon emissions, could be inferred from commonly used vegetation mapping classifications across the pan-Arctic. An important question to guide our understanding, mapping, and prediction of belowground characteristics and ecosystem feedbacks at the top of the world. Unfortunately, they found that the answer was ‘not quite’. While rooting depth distribution varied demonstrably, in turn causing substantial changes in modeled carbon emissions via rhizosphere ‘priming’, variation across rooting depth profiles did not correspond with vegetation mapping classes. If we are unable to predict belowground rooting depth distributions across large spatial scales by leveraging aboveground vegetation community distributions, how then should belowground researchers proceed?

59 BASIC BIOLOGICAL SCIENCES↗

Isotherm Modeling and Techno-Economic Analysis of Contactor Technologies for New Tetraamine-Appended MOF for NGCC Applications

Recently, a family of tetraamine-functionalized metal-organic frameworks (MOF) has been reported as promising sorbent materials for capturing CO2 from flue gas conditions relevant to natural gas combined cycle (NGCC) applications. The main advantages of these materials are their two-step cooperative CO2 adsorption, which gives rise to unusual two step-shaped CO2 adsorption profiles and their high thermal stability. This work presents the modelling of the two-transition isotherm of the tetraamine-appended MOF, N,N'-bis(3-aminopropyl)-1,4-diaminobutane (3-4-3)-appended Mg2(dobpdc), and the techno-economic analysis (TEA) of carbon capture processes utilizing this sorbent. Due to the unusual isotherm shapes of the experimental CO2 adsorption data for tetraamine-appended Mg2(dobpdc) and the strong nonlinearity of CO2 loading with respect to temperature and pressure, we tested two different models which use logistic functions for representing the different isotherm behaviors in the different pressure ranges. The first model uses the quadratic isotherm model in the low-pressure region, the Langmuir isotherm model in the middle pressure range, and the dual site Langmuir isotherm model in the high-pressure range. To model the transition between regions we used the arctangent functions independent of temperature and the thermal effect was accounted by using the Clausius-Clapeyron relation]. The second model is an extension of the weighted dual-site Langmuir isotherm model presented by Hughes et al.. In this extended model, the dual-site Langmuir isotherm is employed in the three transition regions, using temperature-dependent logistic functions to activate or deactivate the isotherm model in the low, middle, and high-pressure ranges. Both models fit the experimental data quite well with root mean squared errors (RMSE’s) of 0.41 and 0.17 for model 1 and model 2, respectively. Since model 2 resulted in a lower RMSE, it was leveraged for the development of the gas/solid contactor models used by the TEA. Specifically, two different contactor models, an axial-flow fixed bed and moving bed contactor, were developed as part of this work. These models are dynamic, pressure-driven, and consist of mass, energy, and momentum conservation equations. A kinetic model was also developed by performing parameter estimation using experimental fixed bed breakthrough data. These models are then used to simulate CO2 capture processes from the flue gas generated from a ~600 gross MW NGCC power plant. A cost model was developed which considers the capital cost of the reactors and the significant operating costs such as steam and electricity. Using NETL’s Framework for the Optimization and Quantification of Uncertainty of Uncertainty and Surrogates tool (FOQUS), which has the capability of linking models built using numerous modelling platforms with derivative-free optimization solvers, a techno-economic optimization of the carbon capture processes was performed which minimizes the cost of capture.

Caballero, Daison↗

Influence of tower shadow on downwind flexible rotors: Field tests and simulations

Abstract As wind turbine rotors become larger, the blades become more flexible, requiring extra stiffness and cost to avoid the risk of tower strike. Wind turbines in a downwind configuration have a reduced risk of tower strike because the rotor thrust acts away from the tower. However, downwind blades pass through the wake of the tower, and the resulting load variation may contribute to blade fatigue. To date, there have been no field tests to quantify this tower shadow effect on unsteady blade moments. The present study reports on the first field testing of a flexible, downwind, coned rotor and compares the experimental data against simulations run in OpenFAST. The tower shadow effect is simulated using the conventional Powles model and a new Eames model (developed herein), which includes the influence of upstream turbulence. Both models reasonably predict the blade root out‐of‐plane bending moment data and the tower shadow dip magnitude when compared to field test data in Region 3. Tower shadow was found to increase the short‐term Damage Equivalent Loads (DELs) by less than 10% compared to other effects (gravity, shear, and turbulence), and the predictions were consistent with experiments. These results indicate that the tower shadow effect can be reasonably modeled with the simpler Powles model and that the tower shadow effect can be small compared to the effect of turbulence. However, long‐term fatigue due to tower shadow should be included in detailed structural analysis and design of the rotor and the tower.

Simpson, Juliet G.↗

Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

Building energy models are used to simulate heat and mass transfer and estimate end-use load in buildings. With the proliferation of solar photovoltaics on residential and commercial buildings, increasingly, buildings are expected to provide grid services, for which accurate and computationally efficient building energy simulations and end-use load prediction are imperative. Existing building energy simulation tools, however, have significant computational overhead that make them less practical in real-time deployment for optimization, design, uncertainty quantification and control in building energy management systems. Here this article presents a data-driven machine learning model based on light gradient boosting method (LightGBM) as a surrogate for a physics-based simulator for residential buildings to predict end-use load. The machine learning based surrogate model accounts for time-series related variables, seasonality and trend component of end-use load, and history of end-use load. The accuracy of the surrogate model is assessed on the prediction of the load profiles of 100 different houses in Cook County, Illinois, USA. The LightGBM surrogate model is shown to reduce the root-mean-squared error by 53% relative to a reference decision tree (DT) based model reported previously in the literature. Moreover, the model predicts the load spikes and high-ramp rate events throughout the year which are often the Achilles heel of other models in the literature. The machine learning based surrogate model is demonstrated to be computationally efficient, with a ten-fold reduction in the computational time compared to a physics-based building energy simulation, and suitable for uncertainty analysis and real-time control of building characteristics in response to uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify if the software is reliable (or unreliable) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify software reliability (or unreliability) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multi-objective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solvent-diffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO 4 /Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically "rediscovered" using machine learning.

25 ENERGY STORAGE↗

On the use of air temperature and precipitation as surrogate predictors in soil respiration modelling

Soil respiration (R S ), the soil-to-atmosphere CO 2 flux that is a major component of the global carbon cycle, is strongly influenced by local soil temperature (T soil ) and water content (SWC). Regional to global-scale R S modelling thus requires this information at local scales, but few high-quality, wall-to-wall (global) T soil and SWC data exist. As a result, such modelling efforts commonly use air temperature (T air ) and monthly precipitation (P m ) as surrogate predictors, but their site-scale accuracy and potential bias are unknown. In this report we used monthly data from 880 sites across a wide variety of different environmental conditions (i.e., climate, ecosystem type, elevation, vegetation leaf habit and drainage conditions) to determine the suitability of T air as a surrogate for T soil , and data from 507 sites to examine the suitability of P m as a surrogate for SWC. Site-specific linear and second-order exponential non-linear models were compared using model evaluation metrics (i.e., slope, p-value of slope, root mean square error [RMSE], index of agreement and model efficiency). We found that T soil and T air are highly correlated and explain similar R S variability. In contrast, P m is not a good surrogate for SWC, even though P m explains a similar amount of R S variability to SWC. The wide variability in the site-specific relationships between R S and SWC means that no single relationship can be used for large-scale modelling. The results from this study support the use of T air in continental-to-global scale R S models, and highlight the urgent need for continental-to-global scale SWC datasets for the modelling and evaluation of future soil carbon dynamics under global climate change.

54 ENVIRONMENTAL SCIENCES↗