Results for “varying data rates” — Page 2

DOE OSTI2021

ACCELERATED CREEP TEST (ACT) QUALIFICATION OF CREEP RESISTANCE USING THE WCS CONSTITUTIVE MODEL AND STEPPED ISOSTRESS METHOD (SSM)

In this study, a qualification of accelerated creep-resistance of Inconel 718 is assessed using the novel Wilshire-Cano-Stewart (WCS) model and the stepped isostress method (SSM) and predictions are made to conventional creep data. Conventional creep testing (CCT) is a long-term continuous process, in fact, the ASME B&PV III requires that 10,000+ hours of experiments must be conducted to each heat for materials employed in boilers and/or pressure vessel components. This process is costly and not feasible for rapid development of new materials. As an alternative, accelerated creep testing techniques have been developed to reduce the time needed to characterize the creep resistance of materials. Most techniques are based upon the time-temperature-stress superposition principle (TTSSP) that predicts minimum-creep-strain-rate (MCSR) and stress-rupture behaviors but lack the ability to predict creep deformation and consider deformation mechanisms that occur for experiments of longer duration. The stepped isostress method (SSM) has been developed which enables the prediction of creep deformation response as well as reduce the time needed for qualification of materials. The SSM approach has been successful for polymer, polymeric composites, and recently has been introduced for metals. In this study, the WCS constitutive model, calibrated to SSM test data, qualifies the creep resistance of Inconel 718 at 750°C and predictions are compared to CCT data. The WCS model has proven to make long-term predictions for stress-rupture, minimum-creep-strain-rate (MCSR), creep deformation, and damage in metallic materials. The SSM varies stress levels after time interval adding damage to the material, which can be tracked by the WCS model. The SSM data is calibrated into the model and the WCS model generates realistic predictions of stress-rupture, MSCR, damage, and creep deformation. The calibrated material constants are used to generate predictions of stress-rupture and are post-audit validated using the National Institute of Material Science (NIMS) database. Similarly, the MCSR predictions are compared from previous studies. Finally the creep deformation predictions are compared with real data and is determined that the results are well in between the expected boundaries. Material characterization and mechanical properties can be determined at a faster rate and with a more cost-effective method. This is beneficial for multiple applications such as in additive manufacturing, composites, spacecraft, and Industrial Gas Turbines (IGT).

36 MATERIALS SCIENCE↗

DOE OSTI2023

Recent Developments in Hydrogeologic Applications for Strain Tensor Analyses

Changes in fluid pressure deform porous media and this effect occurs in a variety of hydrogeologic processes, from the change in storage during pumping or injection to fluctuations in water levels caused by barometric pressure. We have developed instruments for measuring small strains in porous media, and we have used the resulting strain data to evaluate well testing, hydraulic fracturing, manual loading at the ground surface, and ambient hydrologic processes, like rainfall and evaporation. A particularly important application is the use of strain tensor data measured at shallow depths to analyze well tests or hydraulic fractures conducted at much greater depths. An early demonstration of this technique was conducted at the North Avant Field north of Tulsa, Oklahoma, where Pennsylvanian sandstone creates a confined aquifer and oil reservoir at a depth of 530m. We have showed that the strains caused by injecting into the aquifer could be measured at a depth of 30m and used to evaluate the properties of the aquifer. We recently expanded the array of strainmeters at the North Avant Field by deploying three more instruments at shallow depth (30m) along with a deep instrument at 520m depth in the winter, 2021. The deep instrument is deployed in shale caprock slightly above the aquifer. To our knowledge, the deep strainmeter at the North Avant Field is the deepest strainmeter ever deployed and this required refining methods originally developed for shallow deployments. The instrument was lowered to depth on oil field tubing and cemented in place using techniques and materials developed for use in oil wells. Optical fiber used to communicate with the instrument was cemented in the annulus on the outside of tubing. This is significant because the techniques we used could readily be extended to greater depth, suggesting that strainmeters can be deployed over a wide range of depths for monitoring critical subsurface processes. For example, it suggests that strainmeters could be deployed through the caprock to monitor for leaks from underlying CO2 storage reservoirs. The strainmeter array at the North Avant Field has been used to characterize deformation during a series of injection tests in the spring and summer, 2021. All the new strainmeters respond to pumping, and the strainmeters we installed earlier also responded. To our knowledge, this is the first application of well testing in a deep aquifer that was monitored by an array of strainmeters—our earlier work used strainmeters at a single location. We are currently analyzing the strain data using an analytical solution, a proxy-based Bayesian inversion algorithm, and other methods. Strainmeter data has also been used to characterize periodic pumping tests by us and Riley Blais. A periodically varying pumping rate causes both hydraulic head and strain signals that vary with the same period as the pumping. The peaks and troughs of the head in monitoring wells lag behind the peaks and troughs of the head in the pumping well, and this lag time increases with distance from the pumping well. The lag time of the pressure and the distance to the monitoring well can be used in a simple analysis to estimate the hydraulic diffusivity of the aquifer. The lag time determined from strain data can be used to estimate aquifer properties using the same analysis that works for the pressure only for strain data measured at particular locations. That is because the strain field in a confining unit advances upward, laterally and then downward even though the pressure in the underlying aquifer only advances laterally, according to our recent simulations. We have field data showing that a small periodic signal superimposed on an injection rate at the North Avant Field will create a periodic strain signal at shallow strainmeters. The field data and the recent simulations suggest that including a periodic component to injection or pumping and then monitoring the resulting strain signal could be a way to monitor the subsurface.

Murdoch, Larry↗

DOE OSTI2023

Recent Developments in Hydrogeologic Applications for Strain Tensor Analyses

Changes in fluid pressure deform porous media and this effect occurs in a variety of hydrogeologic processes, from the change in storage during pumping or injection to fluctuations in water levels caused by barometric pressure. We have developed instruments for measuring small strains in porous media, and we have used the resulting strain data to evaluate well testing, hydraulic fracturing, manual loading at the ground surface, and ambient hydrologic processes, like rainfall and evaporation. A particularly important application is the use of strain tensor data measured at shallow depths to analyze well tests or hydraulic fractures conducted at much greater depths. An early demonstration of this technique was conducted at the North Avant Field north of Tulsa, Oklahoma, where Pennsylvanian sandstone creates a confined aquifer and oil reservoir at a depth of 530m. We have showed that the strains caused by injecting into the aquifer could be measured at a depth of 30m and used to evaluate the properties of the aquifer. We recently expanded the array of strainmeters at the North Avant Field by deploying three more instruments at shallow depth (30m) along with a deep instrument at 520m depth in the winter, 2021. The deep instrument is deployed in shale caprock slightly above the aquifer. To our knowledge, the deep strainmeter at the North Avant Field is the deepest strainmeter ever deployed and this required refining methods originally developed for shallow deployments. The instrument was lowered to depth on oil field tubing and cemented in place using techniques and materials developed for use in oil wells. Optical fiber used to communicate with the instrument was cemented in the annulus on the outside of tubing. This is significant because the techniques we used could readily be extended to greater depth, suggesting that strainmeters can be deployed over a wide range of depths for monitoring critical subsurface processes. For example, it suggests that strainmeters could be deployed through the caprock to monitor for leaks from underlying CO2 storage reservoirs. The strainmeter array at the North Avant Field has been used to characterize deformation during a series of injection tests in the spring and summer, 2021. All the new strainmeters respond to pumping, and the strainmeters we installed earlier also responded. To our knowledge, this is the first application of well testing in a deep aquifer that was monitored by an array of strainmeters—our earlier work used strainmeters at a single location. We are currently analyzing the strain data using an analytical solution, a proxy-based Bayesian inversion algorithm, and other methods. Strainmeter data has also been used to characterize periodic pumping tests by us and Riley Blais. A periodically varying pumping rate causes both hydraulic head and strain signals that vary with the same period as the pumping. The peaks and troughs of the head in monitoring wells lag behind the peaks and troughs of the head in the pumping well, and this lag time increases with distance from the pumping well. The lag time of the pressure and the distance to the monitoring well can be used in a simple analysis to estimate the hydraulic diffusivity of the aquifer. The lag time determined from strain data can be used to estimate aquifer properties using the same analysis that works for the pressure only for strain data measured at particular locations. That is because the strain field in a confining unit advances upward, laterally and then downward even though the pressure in the underlying aquifer only advances laterally, according to our recent simulations. We have field data showing that a small periodic signal superimposed on an injection rate at the North Avant Field will create a periodic strain signal at shallow strainmeters. The field data and the recent simulations suggest that including a periodic component to injection or pumping and then monitoring the resulting strain signal could be a way to monitor the subsurface.

Murdoch, Larry↗

DOE OSTI2023

Nonparametric Inference for the Reproductive Rate in Generalized Compartmental Models

We develop a tractable nonparametric model for the time-varying reproductive rate of infectious diseases that combines the structure of a deterministic compartmental model and a stochastic model for incidence data. We use Bayesian inference to estimate, with uncertainty, the reproductive rate of the Coronavirus 2019 outbreak in the U.S. states of California, Florida, Michigan, New Mexico, New York, and Texas from January 2020 to March 2022. Employing the inferred reproductive rates, we estimate the posterior distribution of the time-varying reproduction numbers for each state. Compering the time-varying reproduction numbers across the states, we identify some epidemic waves, potentially driven from changes in human behavior and virus mutations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Deep learning-based model for progress variable dissipation rate in turbulent premixed flames

A deep neural network (DNN) based large eddy simulation (LES) model for progress variable dissipation rate in turbulent premixed flames is presented. The DNN model is trained using filtered data from direct numerical simulations (DNS) of statistically planar turbulent premixed flames with n-heptane as fuel. Training data was comprised of flames with varying turbulence levels leading to a range of Karlovitz numbers. Through a-priori tests the DNN model is shown to predict the subfilter contribution to progress variable dissipation rate accurately over a range of filter widths and for all Karlovitz numbers examined in this study. Superior performance of the DNN model relative to an established physics-based model is also demonstrated. Additionally, transferability of the DNN model is highlighted by a-priori evaluation of the model using filtered DNS data from multiple cases with different Karlovitz numbers and fuel species than those that were used for training the model.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Compact high repetition rate Thomson parabola ion spectrometer

Here, we present the development of a compact Thomson parabola ion spectrometer capable of characterizing the energy spectra of various ion species of multi-MeV ion beams from >10 20 W/cm 2 laser produced plasmas at rates commensurate with the highest available from any of the current and near-future PW-class laser facilities. This diagnostic makes use of a polyvinyl toluene based fast plastic scintillator (EJ-260), and the emitted light is collected using an optical imaging system coupled to a thermoelectrically cooled scientific complementary metal–oxide–semiconductor camera. This offers a robust solution for data acquisition at a high repetition rate, while avoiding the added complications and nonlinearities of micro-channel plate based systems. Different ion energy ranges can be probed using a modular magnet setup, a variable electric field, and a varying drift-distance. We have demonstrated operation and data collection with this system at up to 0.2 Hz from plasmas created by irradiating a solid target, limited only by the targeting system. With the appropriate software, on-the-fly ion spectral analysis will be possible, enabling real-time experimental control at multi-Hz repetition rates.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

NIR fluorescence lifetime macroscopic imaging with a time-gated SPAD camera

The performance of SwissSPAD2 (SS2), a large scale, widefield time-gated CMOS SPAD imager developed for fluorescence lifetime imaging, has recently been described in the context of visible range and fluorescence lifetime imaging microscopy (FLIM) of dyes with lifetimes in the 2.5 – 4 ns range. Here, we explore its capabilities in the NIR regime relevant for small animal imaging, where its sensitivity is lower and typical NIR fluorescent dye lifetimes are much shorter (1 ns or less). We carry out this study in a simple macroscopic imaging setup based on a compact NIR picosecond pulsed laser, an engineered diffuser-based illumination optics, and NIR optimized imaging lens suitable for well-plate or small animal imaging. Because laser repetition rates can vary between models, but the synchronization signal frequency accepted by SS2 is fixed to 20 MHz, we first checked that a simple frequency-division scheme enables data recording for different laser repetition rates. Next, we acquired data using different time gate widths, including gates with duration longer than the laser period, and analyzed the resulting data using both standard nonlinear least-square fit (NLSF) and phasor analysis. We show that the fixed synchronization rate and large gate widths characterizing SS2 (10 ns and over) are not an obstacle to accurately extracting lifetime in the 1 ns range and to distinguishing between close lifetimes. As a result, SS2 and similar very large gated SPAD imagers appear as a versatile alternative to other widefield time-resolved detectors for NIR fluorescence lifetime imaging, including preclinical molecular applications.

FLIM↗

DOE OSTI2020

Visco-inertial gas flow through wellbore cement fractures

Understanding the nature of fluid flow through fractured wellbore cement is fundamental for evaluating the leakage potential and risk assessments of leaky wellbores. In this study, the conditions that require considering visco-inertial flow for describing the gas flow through wellbore cement fractures were investigated. Nitrogen gas flow tests were conducted on fractured cement samples under varying pressure conditions and flow rates, covering both viscous and visco-inertial flow regimes. The data substantially deviated from Darcy's law at higher flowrates and were well-fit to Forchheimer's equation for visco-inertial flow. The inertial coefficient and critical Reynolds number were expressed as a function of the hydraulic aperture. Finally, the empirical function obtained from the experiments was used as an input to numerical simulations which showed the significant role of visco-inertial flow in wellhead pressure build-up and leakage rates, and demonstrated the importance of visco-inertial flow when modeling gas flow through wellbore cement fractures.

03 NATURAL GAS↗

DOE OSTI2025

BioSTEAMDevelopmentGroup/BLocS

BioSTEAM Location-Specific Evaluation. This module allows BioSTEAM users to consider the impacts of economic and environmental parameters that vary by location. Current location-specific data includes income, property, fuel producer, and sales tax rates; feedstock prices (corn, corn stover, and sugarcane); electricity prices; natural gas prices; location capital cost factors (LCCFs), and tax incentives (available as of February 2020) for all 50 states in the US. Stewart et al. is the first paper to utilize BLocS to explore the influence of policy incentives and location-specific economic parameters on the financial viability of three different biorefineries.

Stewart, Dalton↗

DOE OSTI2026

Towards Robust Calibration of the AWSD Reactive Burn Model

Calibration of a reactive burn model for detonation of high explosive is an important step towards predictive hy drodynamic simulations of detonation. A typical calibration consists of varying model parameters (e.g., rate constants, activation energies) until results of hydrodynamic simulations match the experimental data for a certain set of ex periments. Hydrodynamic simulations of the dependence of steady detonation velocity on the radius of a cylindrical high-explosive charge - often used in such calibrations - can be computationally expensive. In this work, we propose a method where such expensive simulations are performed infrequently, and only to parameterize and refine a surrogate model for the dependence of the detonation velocity on calibrated parameters. The method is developed, implemented and applied to an example problem - calibration of the AWSD reactive burn model for important high explosive PBX 9502. Two different flavors of the surrogate model are investigated, and the calibration is performed successfully.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data

Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. Further, the accuracy of the force field is demonstrated by calculating rate constants for the H + HO 2 → H 2 + 3 O 2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Microwave-Assisted Plastic Upcycling: Dynamic Data Reconciliation, Parameter Estimation, and Kinetic Modeling

Microwave (MW)-assisted catalytic pyrolysis offers a promising pathway for efficient plastic upcycling. This work develops an integrated modeling framework combining dynamic data reconciliation, a temperature-dependent rate model, and a yield model to represent the time-varying production rate of components in MW-assisted LDPE pyrolysis conducted in a batch reactor. An Arrhenius-type rate model with a temperature-dependent reaction order is developed. A biexponential correlation is proposed for the yield of gaseous products that enables to capture the evolving product formation behavior during conversion. In the yield correlation, one term is used to represent the initial increase in yield, reflecting the rapid formation of intermediate or primary products at the early stages of the reaction when a larger fraction of the reactant remains available. As conversion progresses, the influence of this term gradually diminishes. The other term accounts for the subsequent decrease in the predicted yield, representing secondary reactions such as further cracking or coke formation that reduce the concentration of certain products at higher conversion. The model is found to accurately represent reconciled experimental flow rate profiles from an in-house MW-assisted catalytic batch reactor for major products, including ethylene, ethane, 1-butene, and benzene, across 250−350 °C. Ethylene remains the dominant product but decreases from about 41.95% at 250 °C to 30.14% at 350 °C, while heavier products increase significantly, with 1-butene rising to nearly 8.37% and benzene reaching 2.17% at intermediate temperatures. The model shows that the ethylene production rate can be maximized at around 270 °C. The models developed in this work can be utilized for process optimization, reactor design and scale-up of microwave-assisted plastic conversion technologies, and economic analysis.

Damahe, Harish [West Virginia Univ., Morgantown, W↗

DOE OSTI2025

Global Evaluation of Process Conditions and Wave Modes in a Rotating Detonation Engine

Rotating Detonation Engines (RDEs) show significant promise for enhancing the efficiency of gas turbine engines while maintaining low 𝑁𝑂𝑥 emissions. This work investigates the predictability of wave modes in a water-cooled RDE under varying operational conditions. Experimental data comprising over 6,700 samples was collected, including parameters such as flow rates, temperatures, pressures, and equivalence ratios. A machine learning approach using the XGBoost library was used to build a multi-class classifier, predicting wave modes based on these inputs. The model achieved a high accuracy of 97%, demonstrating that wave modes are not random but deterministic based on the process conditions. SHAP analysis was used to identify the most influential parameters affecting wave mode prediction. The results show that for the water-cooled NETL RDE, wave mode is determinant and predictable based on the process parameters.

Weber, Justin [NETL] (ORCID:0000000218487035)↗

DOE OSTI2023

Experimental study on kinetic oxidation of graphite IG-110 by steam

Graphite is proposed for use in High-temperature Gas-cooled Reactors (HTGRs) as the fuel matrix, neutron moderator/reflector, and core structural material. One important property of nuclear grade graphite is their resistance to oxidation in high-temperature environment. Extensive investigation has been performed in the literature for graphite oxidation by air. However, available experimental data are still limited for graphite oxidation by steam under conditions comparable to a postulated steam ingress accident in HTGRs. In this study, the oxidation rate of graphite IG-110 by steam was measured at temperatures from 850 to 1100 °C with the steam partial pressure varying from 0.5 to 20.0 kPa and the hydrogen partial pressure varying from 0 to 2.0 kPa. Further analysis confirms the oxidation process in this present study is dominated by the chemical kinetics, which lends credit to the data for being used to develop numerical models. It was observed that the increase of the kinetic oxidation rate with the steam partial pressure tends to become less apparent if the steam partial pressure keeps increasing. In addition, it was found that the partitioning of hydrogen inhibits the graphite-steam reaction process even with the steam partial pressure up to 20.0 kPa. However, this inhibiting effect starts to become saturated when the hydrogen partial pressure exceeds 1.0 kPa. The oxidation rates were fitted to the conventional Langmuir-Hinshelwood (LH) and Boltzmann-enhanced Langmuir-Hinshelwood (BLH) models by a multivariable optimization algorithm. The BLH model exhibits a better accuracy than the LH model within the specified experimental conditions. The predicted oxidation rate using the BLH model shows a mean relative difference of about 24% with the maximum difference of about 55% when compared with our experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Techno-Economic Case Study: Fermentation Cost Impacts from Selected Critical Material Attributes

This report summarizes analysis conducted to support a case study under the Feedstock-Conversion Interface Consortium (FCIC) focused on techno-economic analysis (TEA) modeling to quantify the economic implications of biomass hydrolysate substrate variability on fermentation performance and resultant biorefinery fuel yields. It is known that fermentation inhibitors or byproducts, as may either come from constituents in the biomass feedstock or imparted through biorefinery processing operations, can detrimentally impact fermentation rates and yields. However, it is often difficult to isolate fermentation impacts to individual components given the complex nature of biomass varying simultaneously in multiple attributes from one lot to another. For this study, we worked with FCIC researchers to obtain data on key fermentation performance metrics, namely productivity rates and conversion yields, across a number of material attribute species previously selected by the researchers as "critical" attributes which may be found in hydrolysate used as microbial carbon sources during fermentation. These data were run through TEA models to estimate resultant impacts on biorefinery economics, reported as minimum fuel selling price (MFSP). Overall, the trends in MFSP closely followed fuel yields, in turn tied to fermentation process yields and selectivity, with minimal economic impact from variations in fermentation productivity.

09 BIOMASS FUELS↗

DOE OSTI2021

Large Evolutionary Rate Heterogeneity among and within HIV-1 Subtypes and CRFs

HIV-1 is a fast-evolving, genetically diverse virus presently classified into several groups and subtypes. The virus evolves rapidly because of an error-prone polymerase, high rates of recombination, and selection in response to the host immune system and clinical management of the infection. The rate of evolution is also influenced by the rate of virus spread in a population and nature of the outbreak, among other factors. HIV-1 evolution is thus driven by a range of complex genetic, social, and epidemiological factors that complicates disease management and prevention. Here, we quantify the evolutionary (substitution) rate heterogeneity among major HIV-1 subtypes and recombinants by analyzing the largest collection of HIV-1 genetic data spanning the widest possible geographical (100 countries) and temporal (1981–2019) spread. We show that HIV-1 substitution rates vary substantially, sometimes by several folds, both across the virus genome and between major subtypes and recombinants, but also within a subtype. Across subtypes, rates ranged 3.5-fold from 1.34 × 10−3 to 4.72 × 10−3 in env and 2.3-fold from 0.95 × 10−3 to 2.18 × 10−3 substitutions site−1 year−1 in pol. Within the subtype, 3-fold rate variation was observed in env in different human populations. It is possible that HIV-1 lineages in different parts of the world are operating under different selection pressures leading to substantial rate heterogeneity within and between subtypes. We further highlight how such rate heterogeneity can complicate HIV-1 phylodynamic studies, specifically, inferences on epidemiological linkage of transmission clusters based on genetic distance or phylogenetic data, and can mislead estimates about the timing of HIV-1 lineages.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

High-Fidelity Heavy-Duty Vehicle Modeling Using Sparse Telematics Data

Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate. Captured in the dataset was geospatial information, time series measurements, and vehicle-specific metadata from a subset of 96 vehicles from varied geographic regions across North America. A series of custom algorithms was developed to process vehicle data and derive both vehicle model input parameters and representative drive cycles. Derived models provide a basis on which to simulate real-world vehicles and iterate on vehicle aerodynamics, auxiliary power loads, transmission shift schedules, and other parameters to achieve reduced fuel consumption and increase energy efficiency. Notably, these models were developed without the use of expensive field data collection, using only data collected through fleet telematics. Processed representative drive cycles are used to validate the fuel economy of derived models. The models developed through this research allow for more representative vehicle simulations with increased flexibility regarding vehicle-to-vehicle variations.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Defining the scope for altering rice leaf anatomy to improve photosynthesis: a modelling approach

Summary Leaf structure plays an important role in photosynthesis. However, the causal relationship and the quantitative importance of any single structural parameter to the overall photosynthetic performance of a leaf remains open to debate. In this paper, we report on a mechanistic model, eLeaf, which successfully captures rice leaf photosynthetic performance under varying environmental conditions of light and CO 2 . We developed a 3D reaction‐diffusion model for leaf photosynthesis parameterised using a range of imaging data and biochemical measurements from plants grown under ambient and elevated CO 2 and then interrogated the model to quantify the importance of these elements. The model successfully captured leaf‐level photosynthetic performance in rice. Photosynthetic metabolism underpinned the majority of the increased carbon assimilation rate observed under elevated CO 2 levels, with a range of structural elements making positive and negative contributions. Mesophyll porosity could be varied without any major outcome on photosynthetic performance, providing a theoretical underpinning for experimental data. eLeaf allows quantitative analysis of the influence of morphological and biochemical properties on leaf photosynthesis. The analysis highlights a degree of leaf structural plasticity with respect to photosynthesis of significance in the context of attempts to improve crop photosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗