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DOE OSTI2021

Machine-learning based prediction of injection rate and solenoid voltage characteristics in GDI injectors

We report that current state-of-the-art gasoline direct-injection (GDI) engines use multiple injections as one of the key technologies to improve exhaust emissions and fuel efficiency. For this technology to be successful, secured adequate control of fuel quantity for each injection is mandatory. However, nonlinearity and variations in the injection quantity can deteriorate the accuracy of fuel control, especially with small fuel injections. Therefore, it is necessary to understand the complex injection behavior and to develop a predictive model to be utilized in the development process. This study presents a methodology for rate of injection (ROI) and solenoid voltage modeling using artificial neural networks (ANNs) constructed from a set of Zeuch-style hydraulic experimental measurements conducted over a wide range of conditions. A quantitative comparison between the ANN model and the experimental data shows that the model is capable of predicting not only general features of the ROI trend, but also transient and non-linear behaviors at particular conditions. In addition, the end of injection (EOI) could be detected precisely with a virtually generated solenoid voltage signal and the signal processing method, which applies to an actual engine control unit. A correlation between the detected EOI timings calculated from the modeled signal and the measurement results showed a high coefficient of determination.

42 ENGINEERING↗

DOE OSTI2020

3D Characterization of the Columnar-to-Equiaxed Transition in Additively Manufactured Inconel 718

Additive manufacturing (AM) provides enormous processing flexibility, enabling novel part geometries and optimized designs. Access to a local heat source further permits the potential for local microstructure control on the scale of individual melt pools, which can enable local control of part properties. In order to design tailored processing strategies for target microstructures, models predicting the columnar-to-equiaxed transition must be extended to the high solidification velocities and complex thermal histories present in AM. Here, we combine 3D characterization with advanced modeling techniques to develop a more complete understanding of the solidification process and evolution of microstructure during electron beam melting (EBM) of Inconel 718. Full calibration of existing microstructure prediction models demonstrates the differences between AM processes and more conventional welding techniques, underlying the need for accurate determination of key parameters that can only be measured directly in 3D. The ability to combine multisensor data in a consistent 3D framework via data fusion algorithms is essential to fully leverage these advanced characterization approaches. Thermal modeling provides insight on microstructure development within isolated solidification events and demonstrates the role of Marangoni effects on controlling solidification behavior.

Polonsky, Andrew↗

DOE OSTI2023

A constitutive model for sheared dense fiber suspensions

Here we propose a constitutive model to predict the viscosity of fiber suspensions, which undergoes shear thinning, at various volume fractions, aspect ratios, and shear stresses/rates. We calibrate the model using the data from direct numerical simulation and prove the accuracy by predicting experimental measurements from the literature. We use a friction coefficient decreasing with the normal load between the fibers to quantitatively reproduce the experimentally observed shear thinning in fiber suspensions. In this model, the effective normal contact force, which is directly proportional to the bulk shear stress, determines the effective friction coefficient. A rise in the shear stress reduces the effective friction coefficient in the suspension. As a result, the jamming volume fraction increases with the shear stress, resulting in a shear thinning in the suspension viscosity. Moreover, we extend the model to quantify the effects of fiber volume fraction and aspect ratio in the suspension. We calibrate this model using the data from numerical simulations for the rate-controlled shear flow. Once calibrated, we show that the model can be used to predict the relative viscosity for different volume fractions, shear stresses, and aspect ratios. The model predictions are in excellent agreement with the available experimental measurements from the literature. The findings of this study can potentially be used to tune the fiber size and volume fraction for designing the suspension rheology in various applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Carbon-water Cycling in the Critical Zone: Understanding Ecosystem Process Variability Across Complex Terrain

One of the largest knowledge gaps in environmental science is the ability to understand and predict how ecosystems will respond to future climate variability. The links between vegetation, hydrology, and climate that control carbon sequestration in plant biomass and soils remain poorly understood. Soil respiration is the second largest carbon flux of terrestrial ecosystems, yet there is no consensus on how respiration will change as water availability and temperature co-vary. To address this knowledge gap, we use the variation in soil development and topography across an elevation and climate gradient on the Front Range of Colorado to conduct a natural experiment that enables us to examine the co-evolution of soil carbon, vegetation, hydrology, and climate in an accessible field laboratory. The goal of this project is to further our ability to combine plant water availability, carbon flux and storage, and topographically driven hydrometrics into a watershed scale predictive model of carbon balance. We hypothesize: (i) landscape structure and hydrology are important controls on soil respiration as a result of spatial variability in both physical and biological drivers: (ii) variation in rates of soil respiration during the growing season is due to corresponding shifts in belowground carbon inputs from vegetation; and (iii) aboveground carbon storage (biomass) and species composition are directly correlated with soil moisture and therefore, can be directly related to subsurface drainage patterns.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2011

Improved Creep-Fatigue Models on Advanced Materials for SFR Applications (Final Report)

This report provides an update on the materials performance criteria and methodology relevant to sodium-cooled fast reactors (SFRs). The report is the second deliverable (Level 2) in FY11 (M2A11AN040303) under the work package A-11AN040303 “Materials Performance Criteria and Methodology” as part of the Advanced Structural Materials Program for the Advanced Reactor Concepts. The overall objective of the Materials Performance Criteria and Methodology work project is to evaluate the key requirements for the ASME Code qualification and the Nuclear Regulatory Commission (NRC) approval of advanced structural materials in support of the design and performance of sodium-cooled fast reactors. Advanced materials are a critical element in the development of fast reactor technologies. Enhanced materials performance not only improves safety margins and provides design flexibility, but also is essential for the economics of future advanced fast reactors. Qualification and licensing of advanced materials are prominent needs for the development and implementation of advanced fast reactor technologies. Nuclear structural component designs in the U.S. comply with the ASME Boiler and Pressure Vessel (B&PV) Code Section III (Rules for Construction of Nuclear Facility Components), and the NRC grants licensing. As the SFRs will operate at higher temperatures than the current light water reactors (LWRs), the design of elevated-temperature components must comply with ASME Section III Subsection NH (Class 1 Components in Elevated Temperature Service). A number of technical issues relevant to materials performance criteria and high temperature design methodology in the SFR were identified and presented in earlier reports. A viable approach to resolve these issues and the R&D priority were also recommended. The development of mechanistically based creep-fatigue interaction models for life prediction and reliable data extrapolation was chosen to be the central focus in near-term efforts. Our current focus is on the creep-fatigue damage issue in high-strength ferritic/martensitic steels such as mod.9Cr-1Mo (G91) and NF616 (G92) steels. The current ASME creep-fatigue design rule puts severe limits of fatigue and creep loads for G91 steel, the lead structural material for fast reactors. High-strength ferritic/martensitic steels behave fundamentally differently from austenitic stainless steels, for which the current ASME creep-fatigue design rules were developed. The unique deformation and damage characteristics in G91 steel, e.g. cyclic softening, degradation of creep and rupture strength during cyclic service, demands a new creep-fatigue design procedure that explicitly accounts for the material’s unique creep-fatigue behavior. G92 steel, a variant of G91 steel, is the lead candidate in the Advanced Alloy Development program. There is currently no creep-fatigue design rule available for this advanced alloy. To support the predictive model development and resolve the over-conservative issue with the ASME design rule for G91 steel, we recovered stress relaxation data from thirteen creep-fatigue tests conducted in the late 1980s and early 1990s at Oak Ridge National Laboratory, and conducted extensive data analysis in FY10. Based on this database and available literature data, we have developed a Cyclic Softening Model and a Stress Relaxation Model specifically applied to G91 steel. The Cyclic Softening Model describes the cyclic stress variation as a function of cycle number during creep-fatigue loading, and well captures the cyclic softening effect in G91 steel. The Stress Relaxation Model predicts the stress relaxation curve during the hold time of cyclic loading, and its dependence on the cyclic softening effect of G91 steel. Two creep-fatigue test systems have been set up to conduct model-oriented creep-fatigue experiments. Results from three creep-fatigue experiments on Heat 30176 of G91 steel are presented in the report: (1) a test performed at 550°C with total strain amplitude of 0.25% and a hold time of 60 s applied at peak strains in both tension and compression, (2) a test performed at 400°C with total strain amplitude of 0.5% and a tension and compression hold time of 60 s, and (3) a test performed at 20°C with total strain amplitude of 0.5% and a tension and compression hold time of 60 s. Three different test temperatures were chosen for two primary reasons: to determine the transition temperature of creep-fatigue interaction from a plasticity-dominated process to a creep-dominated process; to develop unified creep-fatigue models applicable for a wide range of temperatures. With the detailed information obtained in the creep-fatigue experiments, we have further refined the Cyclic Softening Model and Stress Relaxation Model. The Cyclic Softening Model and Stress Relaxation Model were incorporated into the Bilinear Creep-Fatigue Damage Model, forming a powerful framework for the life prediction by an improved creep-fatigue evaluation methodology. The cyclic softening and consecutive stress relaxation responses in G91 steel can be considered for each cycle of the entire cyclic process in the model calculation. The creep-fatigue damage in G91 steel was evaluated using the improved Bilinear Creep-Fatigue Damage Model without any safety margins. It was found that due to the significant cyclic softening in G91 steel, the unit creep damage in each cycle decreased continuously with increasing number of cycles. This continuous change in creep damage with cycling has significant implication in the evaluation of the total creep-fatigue damage. The total creep damage can be one order of magnitude lower when the unit creep damage at half-life was used in the evaluation in comparison with the calculations using the unit creep damage at cycle 10. An important conclusion was that the accurate assessment of the creep-fatigue damage in G91 steel must consider the history of cycling. This analysis provides a more realistic assessment of creep-fatigue damage and identifies critical factors that must be considered in the development of future advanced models for high-strength ferritic/martensitic alloys. An accelerated creep-fatigue testing methodology is proposed for the design of model-oriented experiments to guide and validate model developments. Future work will focus on creep-fatigue experiments of both G91 and G92 steels, and development of creep-fatigue models applicable to advanced ferritic/martensitic alloys. The Cyclic Softening Model and Stress Relaxation Model will be improved by incorporating microstructural parameters of dislocation density, subgrain structure, and second-phase particles. Collaborative modeling of combined Cyclic Softening Model, Stress Relaxation Model, and Interactive Damage Rate Model will be pursued for life prediction under creep-fatigue loading with extended hold time. Accelerated, well-controlled creep-fatigue tests will be conducted to provide high-quality experimental data to refine key material parameters and validate models.

Li, Meimei↗

DOE OSTI2018

Large-Signal Impedance Modeling of Three-Phase Voltage Source Converters

Resonance problems have become a major hurdle in the large-scale grid integration of renewable energy. It is important to be able to predict the amplitude of resonance-induced distortions for the protection and control design of power electronic converters. We recently presented an impedance-based approach for the prediction of resonance amplitude. It was shown that the impedance response of a converter starts changing with the amplitude of resonance; this blocks the amplitude growth beyond a certain point where the converter forms a limit cycle mode of sustained oscillations. The large-signal impedance of a converter captures the change in its impedance response with the resonance amplitude, and it can be used to predict the resonance amplitude under different operation conditions. This paper presents large-signal sequence impedance modeling for three-phase voltage source converters (VSC) with dq-current control and PLL. Modeling challenges encountered in the absence of the small-signal approximation are addressed by a) leveraging the dominating influence of hard nonlinearities such as pulse-width modulation saturation and limiters over soft nonlinearities in shaping the converter large-signal behavior and b) modeling the large-signal gain of hard nonlinearities using appropriate describing functions. Developed large-signal impedance models are validated using numerical simulations. Measurements of the large-signal impedance responses of a commercial 1-MW VSC-based inverter are also presented to experimentally demonstrate the effects of the injected perturbation amplitude on impedance response as predicted by the developed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Intrinsic kinetics of water-inhibited ultra-lean methane oxidation over PtPd-Mg/ θ -Al 2 O 3 catalyst

Here, this study develops intrinsic methane oxidation kinetics for ultra-lean methane conditions in the presence of water over a highly active and stable PtPd–Mg/θ-Al 2 O 3 catalyst. Comprehensive laboratory experiments were conducted over a wide range of methane concentrations (150–1200 ppm CH 4 ), water contents (1–5% H 2 O), and industrially relevant space velocities (80,000 ≤ GHSV ≤ 160,000 h -1 ). These systematic experiments informed a two-dimensional, axisymmetric, multiscale reactor model that was used to develop and validate methane oxidation kinetics under practically relevant conditions, including non-isothermal operation and high conversion regimes. Combined experimental and modeling results revealed significant intraparticle diffusion resistance and transport-induced reaction exotherm at elevated temperatures, which limited catalyst utilization despite high intrinsic activity. These transport effects were explicitly incorporated into the reactor model, enabling accurate estimation of intrinsic kinetic parameters without reliance on conventional effectiveness-factor corrections. The resulting kinetic model successfully captured both kinetically controlled and mass-transfer-limited regimes and reliably predicted CH 4 conversion across broad ranges of temperature, methane concentration, and water content

Heat and mass transfer limitations↗

DOE OSTI2023

Modification of the MTS model for high strain-rate behavior of TI-6AL-4V

The Mechanical Threshold Stress (MTS) model provides excellent predictive capabilities for the material constitutive response for a wide range of temperatures and strain rates. However, the MTS model fails to capture the rapidly increasing yield stress at high strain rate behavior as the deformation controlling mechanism transitions from thermal activation to drag mechanisms, only capturing the linear behavior. Further, the model typically over predicts the flow stress behavior at yield and post yield due to its use of a constant work hardening rate parameter derived from the stress–strain response at constant saturation stress. An alternative approach to fitting portions of the MTS model is investigated and mathematical models are developed to address these issues. The results show that with appropriate experimental data, the mechanical threshold stress and work hardening rate parameters within the MTS model can quite easily and accurately be modified to extend applicability to high strain rate behavior and more accurately model the initial flow stress behavior at early work hardening rates without modification of the functions core to the MTS model itself.

36 MATERIALS SCIENCE↗

DOE OSTI2022

When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickness. Herein, we develop a model-based design of experiment (MBDoE) framework that integrates physics-informed models, nonlinear regression, and information criteria to postulate, select and calibrate the best model to describe and optimize the AJP manufacturing process. Starting with already available data from system commissioning (e.g., prior single variable sensitivity analysis), four candidate physics-informed models are postulated and trained. MBDoE identifies a single additional optimal experiment to validate these predictive models with quantified uncertainties, which are then used to determine the best experimental conditions to control printed film thickness. As a comparative benchmark, the analysis is repeated using the same dataset with nonparametric Gaussian process regression (GPR) model that does not incorporate physical information. Using MBDoE principles, we find that only five experiments are necessary to calibrate the nonlinear physics-informed parametric model, and with said limited data, this model outperforms the black-box machine learning GPR model. This key result underscores an emerging trend in the data science community: incorporating physical information into predictive models often drastically reduces the data requirements. Leveraging MBDoE further increased the data efficiency. By design, the proposed data science framework is general in nature and can be easily extended to other experimental and additive manufacturing systems beyond AJP.

Aerosol jet printing↗

DOE OSTI2017

Model Order Reduction Algorithm for Estimating the Absorption Spectrum

The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propagator’s eigenspectrum for medium-to-large sized systems, traditional approaches based on the partial diagonalization of the propagator often encounter oscillatory and stagnating convergence. Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location. This generality comes at a seemingly high computational cost associated with solving a large linear system for each spectral shift in some discretization of the spectral domain of interest. In this work, we present a novel, adaptive solution to this high computational overhead based on model order reduction techniques via interpolation. Model order reduction reduces the computational complexity of mathematical models and is ubiquitous in the simulation of dynamical systems and control theory. The efficiency and effectiveness of the proposed algorithm in the ab initio prediction of X-ray absorption spectra is demonstrated using a test set of challenging water clusters which are spectrally dense in the neighborhood of the oxygen K-edge. On the basis of a single, user defined tolerance we automatically determine the order of the reduced models and approximate the absorption spectrum up to the given tolerance. We also illustrate that, for the systems studied, the automatically determined model order increases logarithmically with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window. Furthermore, we observed that the computational cost of the proposed algorithm only scales quadratically with respect to the problem dimension.

IDREAM, K-Edge, theoretical, computational, electr↗

DOE OSTI2015

Nomograms for Prediction of Outcome With or Without Adjuvant Radiation Therapy for Patients With Endometrial Cancer: A Pooled Analysis of PORTEC-1 and PORTEC-2 Trials

Background: Postoperative radiation therapy for stage I endometrial cancer improves locoregional control but is without survival benefit. To facilitate treatment decision support for individual patients, accurate statistical models to predict locoregional relapse (LRR), distant relapse (DR), overall survival (OS), and disease-free survival (DFS) are required. Methods and Materials: Clinical trial data from the randomized Post Operative Radiation Therapy for Endometrial Cancer (PORTEC-1; N=714 patients) and PORTEC-2 (N=427 patients) trials and registered group (grade 3 and deep invasion, n=99) were pooled for analysis (N=1240). For most patients (86%) pathology review data were available; otherwise original pathology data were used. Trial variables which were clinically relevant and eligible according to data constraints were age, stage, given treatment (pelvic external beam radiation therapy (EBRT), vaginal brachytherapy (VBT), or no adjuvant treatment, FIGO histological grade, depth of invasion, and lymph-vascular invasion (LVSI). Multivariate analyses were based on Cox proportional hazards regression model. Predictors were selected based on a backward elimination scheme. Model results were expressed by the c-index (0.5-1.0; random to perfect prediction). Two validation sets (n=244 and 291 patients) were used. Results: Accuracy of the developed models was good, with training accuracies between 0.71 and 0.78. The nomograms validated well for DR (0.73), DFS (0.69), and OS (0.70), but validation was only fair for LRR (0.59). Ranking of variables as to their predictive power showed that age, tumor grade, and LVSI were highly predictive for all outcomes, and given treatment for LRR and DFS. The nomograms were able to significantly distinguish low- from high-probability patients for these outcomes. Conclusions: The nomograms are internally validated and able to accurately predict long-term outcome for endometrial cancer patients with observation, pelvic EBRT, or VBT after surgery. These models facilitate decision support in daily clinical practice and can be used for patient counseling and shared decision making, selecting patients who benefit most from adjuvant treatment, and generating new hypotheses.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Conditioning multi-model ensembles for disease forecasting

In this study we investigate how an ensemble of disease models can be conditioned to observational data, in a bid to improve its predictive skill. We use the ensemble of influenza forecasting models gathered by the US Centers for Disease Control and Prevention (CDC) as the exemplar. This ensemble is used every year to forecast the annual influenza outbreak in the United States. The models constituting this ensemble draw on very different modeling assumptions and approximations and are a diverse collection of methods to approximate epidemiological dynamics. Currently, each models' predictions are accorded the same importance, or weight, when compiling the ensemble's forecast. We consider this equally-weighted ensemble as the baseline case which has to be improved upon. In this study, we explore whether an ensemble forecast can be improved by "conditioning" the ensemble to whatever observational data is available from the ongoing outbreak. "Conditioning" can imply according the ensemble's members different weights which evolve over time, or simply perform the forecast using the top k (equally-weighted) models. In the latter case, the composition of the "top-k-see of models evolves over time. This is called "model averaging" in statistics. We explore four methods to perform model-averaging, three of which are new. We find that the CDC ensemble responds best to the "top-k-models" approach to model-averaging. All the new MA methods perform better than the baseline equally-weighted ensemble. The four model-averaging methods treat the models as black-boxes and simply use their forecasts as inputs i.e., one does not need access to the models at all, but rather only their forecasts. The model-averaging approaches reviewed in this report thus form a general framework for model-averaging any model ensemble.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Large-Signal Impedance Modeling of Three-Phase Voltage Source Converters: Preprint

Resonance problems have become a major hurdle in the large-scale grid integration of renewable energy. It is important to be able to predict the amplitude of resonance-induced distortions for the protection and control design of power electronic converters. We recently presented an impedance-based approach for the prediction of resonance amplitude. It was shown that the impedance response of a converter starts changing with the amplitude of resonance; this blocks the amplitude growth beyond a certain point where the converter forms a limit cycle mode of sustained oscillations. The large-signal impedance of a converter captures the change in its impedance response with the resonance amplitude, and it can be used to predict the resonance amplitude under different operation conditions. This paper presents large-signal sequence impedance modeling for three-phase voltage source converters (VSC) with dq-current control and PLL. Modeling challenges encountered in the absence of the small-signal approximation are addressed by a) leveraging the dominating influence of hard non-linearities such as pulse-width modulation saturation and limiters over soft non-linearities in shaping the converter large-signal behavior and b) modeling the large-signal gain of hard non-linearities using appropriate describing functions. Developed large-signal impedance models are validated using numerical simulations. Measurements of the large-signal impedance responses of a commercial 1-MW VSC-based inverter are also presented to experimentally demonstrate the effects of the injected perturbation amplitude on impedance response as predicted by the developed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

ATMOSPHERIC CIRCULATION OF HOT JUPITERS: DAYSIDE–NIGHTSIDE TEMPERATURE DIFFERENCES

The full-phase infrared light curves of low-eccentricity hot Jupiters show a trend of increasing dayside-to-nightside brightness temperature difference with increasing equilibrium temperature. Here, we present a three-dimensional model that explains this relationship, in order to provide insight into the processes that control heat redistribution in tidally locked planetary atmospheres. This three-dimensional model combines predictive analytic theory for the atmospheric circulation and dayside–nightside temperature differences over a range of equilibrium temperatures, atmospheric compositions, and potential frictional drag strengths with numerical solutions of the circulation that verify this analytic theory. The theory shows that the longitudinal propagation of waves mediates dayside–nightside temperature differences in hot Jupiter atmospheres, analogous to the wave adjustment mechanism that regulates the thermal structure in Earth’s tropics. These waves can be damped in hot Jupiter atmospheres by either radiative cooling or potential frictional drag. This frictional drag would likely be caused by Lorentz forces in a partially ionized atmosphere threaded by a background magnetic field, and would increase in strength with increasing temperature. Additionally, the amplitude of radiative heating and cooling increases with increasing temperature, and hence both radiative heating/cooling and frictional drag damp waves more efficiently with increasing equilibrium temperature. Radiative heating and cooling play the largest role in controlling dayside–nightside temperature differences in both our analytic theory and numerical simulations, with frictional drag only being important if it is stronger than the Coriolis force. As a result, dayside–nightside temperature differences in hot Jupiter atmospheres increase with increasing stellar irradiation and decrease with increasing pressure.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Accurate spatiotemporal predictions of daily stream temperature from statistical models accounting for interactions between climate and landscape

Spatial and temporal patterns in stream temperature are primary factors determining species composition, diversity and productivity in stream ecosystems. The availability of spatially and temporally continuous estimates of stream temperature would improve the ability of biologists to fully explore the effects of stream temperature on biota. Most statistical stream temperature modeling techniques are limited in their ability to account for the influence of variables changing across spatial and temporal gradients. We identified and described important interactions between climate and spatial variables that approximate mechanistic controls on spatiotemporal patterns in stream temperature. With identified relationships we formed models to generate reach-scale basin-wide spatially and temporally continuous predictions of daily mean stream temperature in four Columbia River tributaries watersheds of the Pacific Northwest, USA. Models were validated with a testing dataset composed of completely distinct sites and measurements from different years. While some patterns in residuals remained, testing dataset predictions of selected models demonstrated high accuracy and precision (averaged RMSE for each watershed ranged from 0.85–1.54 °C) and was only 17% higher on average than training dataset prediction error. Aggregating daily predictions to monthly predictions of mean stream temperature reduced prediction error by an average of 23%. The accuracy of predictions was largely consistent across diverse climate years, demonstrating the ability of the models to capture the influences of interannual climatic variability and extend predictions to timeframes with limited temperature logger data. Results suggest that the inclusion of a range of interactions between spatial and climatic variables can approximate dynamic mechanistic controls on stream temperatures.

Siegel, Jared E.↗

DOE OSTI2023

Denitrification and the challenge of scaling microsite knowledge to the globe

Here, our knowledge of microbial processes—who is responsible for what, the rates at which they occur, and the substrates consumed and products produced—is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are—by themselves—insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait-based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

An Assessment of the General Applicability of the Relationship Between Nucleation of CO Bubbles and Mass Transfer of Phosphorus in Liquid Iron Alloys

The current paper seeks to demonstrate the general applicability of the authors’ recently developed treatment of surface renewal during decarburization of Fe-C-S alloys and its effect on the mass transport of phosphorus in the metal phase. The proposed model employs a quantitative model of CO bubble nucleation in the metal to predict the rate of surface renewal, which can then in turn be used to predict the mass-transfer coefficient for phosphorus. A model of mixed transport control in the slag and metal phases was employed to investigate the dephosphorization kinetics between a liquid iron alloy and oxidizing slag. Based on previous studies of the mass-transfer coefficient of FeO in the slag, it was possible to separate the mass transfer coefficient of phosphorus in metal phase, k m , from the overall mass-transfer coefficient k o . Using this approach, k m was investigated under a wide range of conditions and shown to be represented reasonably by the mechanism proposed. The mass-transfer model was tested against results from the literature over a wide range of conditions. The analysis showed that the FeO content in the slag, silicon in the metal and the experimental temperature have strong impact on, k m , almost entirely because of their effect on decarburization behavior.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Understanding the Performance of Automotive Catalysts via Spatial Resolution of Reactions inside Honeycomb Monoliths

By directly resolving spatial and temporal species distributions within operating honeycomb monolith catalysts, spatially resolved capillary inlet mass spectrometry (SpaciMS) provides a uniquely enabling perspective for advancing automotive catalysis. Specifically, the ability to follow the spatiotemporal evolution of reactions throughout the catalyst is a significant advantage over inlet-and-effluent-limited analysis. Intracatalyst resolution elucidates numerous catalyst details including the network and sequence of reactions, clarifying reaction pathways; the relative rates of different reactions and impacts of operating conditions and catalyst state; and reaction dynamics and intermediate species that exist only within the catalyst. These details provide a better understanding of how the catalyst functions and have basic and practical benefits; e.g., catalyst system design; strategies for on-road catalyst state assessment, control, and on-board diagnostics; and creating robust and accurate predictive catalyst models. Moreover, such spatiotemporally distributed data provide for critical model assessment, and identification of improvement opportunities that might not be apparent from effluent assessment; i.e., while an incorrectly formulated model may provide correct effluent predictions, one that can accurately predict the spatiotemporal evolution of reactions along the catalyst channels will be more robust, accurate, and reliable. In such ways, intracatalyst diagnostics comprehensively enable improved design and development tools, and faster and lower-cost development of more efficient and durable automotive catalyst systems. Beyond these direct contributions, SpaciMS has spawned and been applied to enable other analytical techniques for resolving transient distributed intracatalyst performance. This chapter focuses on SpaciMS applications and associated catalyst insights and improvements, with specific sections related to lean NOx traps, selective catalytic reduction catalysts, oxidation catalysts, and particulate filters. The objective is to promote broader use and development of intracatalyst analytical methods, and thereby expand the insights resulting from this detailed perspective for advancing automotive catalyst technologies.

Partridge Jr, William↗