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DOE OSTI2020

Data-driven learning of nonlocal physics from high-fidelity synthetic data

A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori. Here, we extract nonlocal models from data, circumventing these challenges and providing data-driven justification for the resulting model form. Extracting data-driven surrogates is a major challenge for machine learning (ML) approaches, due to nonlinearities and lack of convexity — it is particularly challenging to extract surrogates which are provably well-posed and numerically stable. Our scheme not only yields a convex optimization problem, but also allows extraction of nonlocal models whose kernels may be partially negative while maintaining well-posedness even in small-data regimes. To achieve this, based on established nonlocal theory, we embed in our algorithm sufficient conditions on the non-positive part of the kernel that guarantee well-posedness of the learnt operator. These conditions are imposed as inequality constraints to meet the requisite conditions of the nonlocal theory. We demonstrate this workflow for a range of applications, including reproduction of manufactured nonlocal kernels; numerical homogenization of Darcy flow associated with a heterogeneous periodic microstructure; nonlocal approximation to high-order local transport phenomena; and approximation of globally supported fractional diffusion operators by truncated kernels.

42 ENGINEERING↗

DOE OSTI2020

Time-domain modelling and thermometry of the CH 4 v 1 Q-branch using hybrid femtosecond/picosecond coherent anti-Stokes Raman scattering

Here, we present time-domain measurements and modelling of the CH 4 v 1 vibrational Q-branch, as well as simultaneous temperature acquisition from the CH 4 and N 2 vibrational Q-branches using broadband hybrid femtosecond/picosecond coherent anti-Stokes Raman scattering (fs/ps CARS). Accurate measurements with fs/ps CARS require knowledge of the time-domain response of the probed molecule at different temperatures and pressures. In this work, a fs/ps CARS model was developed for theCH 4 v 1 Q-branch and validated using time-resolved probe delay scans at pressures between 70 and 600 Torr and temperatures between 295 and 1000 K. A simplified modified exponential energy gap model was used to determine the linewidths of the Raman transitions within the Q-branch. Total Q-branch linewidths for the entire explored parameter space were also determined from the probe delay scans. This enabled quantitative measurements of CH 4 with fs/ps CARS as well as the temperature through the hot bands of the CH 4 v 1 Q-branch. With a broadband fs laser pulse in a two-beam phase matching scheme, the N 2 Q-branch and CH 4 v 1 Q-branch were simultaneously measured in a heated cell, and the temperatures from each molecule's spectra was evaluated. Here, the fitted temperatures corresponded well with differences ranging from 4 to 17%. It was also shown that exchanging 90% of CH 4 for N 2 or Ar at 295 K and 500 Torr did not significantly impact the normalized time-domain signal prior to a 100 picosecond probe delay. Spatially resolved one-dimensional (1-D) thermometry using the CH 4 Q-branch was also demonstrated with an imaged spatial resolution of 40 µm. With the developed model, 1-D quantitative measurements of CH 4 concentration and temperature can be measured with time resolution of tens of picoseconds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Techno-economic analysis of converting oil & gas produced water into valuable resources

Managing produced water from oil and gas wells constitutes a significant portion of the costs of operating a well. Here, in this work, we have designed two different centralized water treatment facilities capable of managing produced water from oil and gas wells in Texas and Louisiana, both of which convert the produced water into the following valuable resources: ten-pound brine and fresh water. The two main designs each use commercially available technology with varying levels of establishment in treating produced water. Both treatment processes remove oil and grease and suspended solids, reduce the divalent ion concentrations, and concentrate the brines to a near-saturation state. The baseline design uses chemical precipitation to remove the divalent ions to meet the reuse specifications, whereas the advanced design uses nanofiltration (NF) membranes to separate divalent ions and uses reverse osmosis (RO) membranes to partially concentrate the brine. Both models use mechanical vapor recompression to concentrate the brine up to NaCl saturation. The baseline process is shown to be cost-effective for low-hardness brines. In the case of high hardness, the chemical precipitation step is cost-prohibitive. We find that NF membranes are a promising alternative to chemical precipitation as a means of separating monovalent and divalent ions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Climate and air pollution implications of potential energy infrastructure and policy measures in India

India is a rapidly developing economy with interrelated air quality, sustainable development, and climate change mitigation goals. There are unique challenges to achieving each of these goals as well as potential tradeoffs among them. This study examines the implications of possible future energy, climate, and air pollution control policies and measures in India through 2050. We take a scenario approach using the GCAM global energy-climate-land model combined with the Hector simple climate model and the TM5-FASST air quality source-receptor model to examine energy, climate and air quality outcomes. Reducing use of traditional biomass in buildings can reduce primary carbonaceous particulate emissions well below 2015 levels. However, policies that are more ambitious than current plans would likely be required to reduce SO 2 and NO x emissions well below 2015 levels. Among single policy cases considered, pricing of greenhouse gas (GHG) emissions and expansion of natural gas infrastructure have the largest impacts on overall energy system changes relative to the reference scenario. Ambitious air pollution control and GHG policies lead to the largest reductions in air pollution concentrations and radiative forcing, respectively. However, ambitious air pollution control and GHG policies differ in the extent to which they support or impede other policy objectives. Forcing increases due to reduced aerosols from ambitious air pollution policies can be mitigated, at least in part, by applying air pollution control and GHG policies together.

India↗

DOE OSTI2021

Electronic structure of reduced CeO 2 (111) surfaces interacting with hydrogen as revealed through electron energy loss spectroscopy in comparison with theoretical investigations

Based on both, ab-initio cluster calculations as well as periodic density functional based loss function calculations, we have assigned the origins of the valence electron excitation regime in electron energy loss spectra of well-ordered ceria films in (111) orientation at various states of reduction as well as after exposing the reduced films to hydrogen from the gas phase (Li et al, Angew. Chem. Int. Ed., 58 (2019) 14686–14693). The explicit calculation of intensity distributions using the dipole approximation allow us to draw conclusions about the nature of oxygen vacancies, which occur upon reduction and how those interact with hydrogen to form hydride species. Herein, we also reports a brief discussion of vibrational excitations of hydrogen loaded ceria and corroborates previous interpretations, based on inelastic neutron scattering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Smarter building start – A distributed solution

A significant focus of research and new technologies for reducing energy use in buildings is on operating the systems more efficiently during the times when the systems are active. However, there is a large potential for energy savings in determining the periods when the systems should or should not be active. This scheduling aspect of operation is often overlooked even though relatively simple solutions can unlock substantial energy savings. In this paper we describe a smart building start (SBS) algorithm that considers multiple zones in a building to determine individual schedules for room controllers as well as the central systems based on solving a simple optimization problem. Application of the SBS algorithm to multiple interconnected systems enables a staggered start-up that minimizes peak loads and also ensures comfort is within a target range with minimal system run time. The SBS algorithm extends the capability of traditional optimal start and is designed to be simple to deploy and robust. Simulation results as well as results from tests in a real building with a VAV system are presented. The presented algorithm is applicable to any type of building with a zonal or multi-zone HVAC system. To function, it needs to be able to change setpoints in rooms and monitor room temperatures, as well as, if desired, turn the central system on or off.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

A High-Efficient Solution for Electro-Hydraulic Actuators with Energy Regeneration Capability

This paper proposes a cost-effective, robustly practical solution for a high-efficient electro-hydraulic actuator (EHA) for linear drives. The solution addresses the challenges of high costs and limited efficiency that limit the application of EHA for the electrification of many off-road vehicles. Based on a variable-speed electric motor and a low-cost technology for a fixed-displacement pump, the proposed solution is suitable for a compact implementation and it is capable of energy regeneration under overrunning load conditions. Two different architectures—an open circuit and a closed circuit one—are considered to determine the most practical and efficient configuration as well as the control features. Both architectures minimize throttling losses associated with the conventional regulation of hydraulic actuators. Actuator velocities below the minimum and beyond the maximum allowable pump flow rate are achieved in both architectures using a proportional valve that bypasses the pump flow. The paper describes the design methodology as well as the simulation and experimental activities performed to verify the control strategies as well as energy efficiency. Considering an EHA system of about 20 kW, a complete efficiency map of the system is determined for all the possible four quadrants of operation thus including both resistive and overrunning loads, where energy recuperation is implemented. Simulation and experimental results show that the energy efficiency of the hydraulic systems can reach 84.7% in resistive phases. In assistive phases, the hydraulic system can recover up to 81.8% of the actuator energy. Here, the comparison between open-circuit and closed-circuit structures shows the advantages of the former in terms of energy efficiency. The proposed solution is suitable for electrified hydraulic actuation systems, in both industrial and mobile applications. With the advantages of the low cost of the hydraulic unit and high energy efficiency, it is particularly suitable for mobile applications such as construction machines, that are recently experiencing a significant electrification trend.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Understanding implications of cathode architecture on energy density of solid-state batteries

Next generation solid-state batteries (SSB) will need to leverage high voltage cathodes, as well as metallic anodes to achieve the realistic performance targets necessary to replace liquid electrolyte-based batteries in cutting-edge applications including electric vehicles. However, limitations arising from mass and charge transports, kinetics and chemo-mechanical degradation at the electrode | electrolyte interface limit the performance of present day SSBs. Optimizing composite cathode architecture, which is an integral part of solid-state batteries, is vital to realize the high-energy density and high-performance goals for next-generation solid-state batteries. Additionally, cathode architecture needs to be optimized for high loadings of active material, well-percolated ion and electron transport pathways and increased resilience against electrochemical stresses. This paper provides a first report of framework for geometric modeling of composite cathode architectures and evaluates the impact of cathode architecture on cell-level energy density using hierarchical models. Packing around primary and secondary active material particles are simulated for a range of active material particle size and solid electrolyte size distributions in the composite cathode. Impact of packing architecture on processing parameters of a given cathode composition and thickness, as well as on achievable energy density is evaluated for a range of commonly used solid electrolyte and cathode materials. Overall, the proposed framework offers a facile exploratory methodology for establishing initial metrics for scalable processing of practical and competent SSBs.

25 ENERGY STORAGE↗

DOE OSTI2021

Extended cycle life implications of fast charging for lithium-ion battery cathode

Enabling extreme fast charging (XFC, =10–15 min charging) requires a comprehensive understanding of its implications. While lithium plating is a key bottleneck for the anode, the full extent of limitations for the cathode are not well-understood, particularly in extended-cycle settings with well-defined battery designs and conditions. This article presents cycle-life implications of XFC on cathodes at multiple length scales, combining electrochemical analyses, degradation modeling, and post-test characterizations. The comprehensive test matrix includes 41 well-defined gr/NMC pouch cells under varied fast-charge rates (1–9C) and state-of-charges cycled up to 1000 times. Cathode issues remain minimal in early cycling, but begin to accelerate in later life, when distinct cracking is found and identified as a fatigue mechanism. The bulk structure of cathodes remains intact, but distinct particle surface reconstruction is observed; however, this shows less pronounced effect on cathode aging than does cracking.

25 ENERGY STORAGE↗

DOE OSTI2023

Synthesis and morphological characterization of linear and miktoarm star poly(solketal methacrylate)- block -polystyrene copolymers

The synthesis, molecular characterization, and morphological evaluation of AB n, (n = 2,3) miktoarm star block copolymers consisting of poly(glycerol monomethacrylate) (PGMA) and polystyrene (PS) with varying molecular weights and compositions is described. The system is known to demonstrate a remarkably high Flory-Huggins interaction parameter. The corresponding linear diblock copolymer analogues, were synthesized as well, and comparisons with regard to feature dimensions and morphologies are provided. Well-ordered nanostructures of various morphologies were formed with domain spacing as low as 7.2 nm. Different morphologies were attained by some of the topological isomers indicating that in miktoarm star block copolymers the phase boundaries were strongly shifted. Additionally, a triblock ABA analogue was studied to investigate the effect of triblock copolymer conformations. Noteworthy is that for copolymers with different macromolecular architecture leading to similar morphology; different domain spacings were obtained. The synthesis of all the samples was carried out by high-vacuum anionic polymerization techniques. Molecular characterization with Size Exclusion Chromatography (SEC) and Proton Nuclear Magnetic Resonance Spectroscopy ( 1 H NMR) confirmed well-defined copolymers obtained. The morphological characterization was accomplished by Small-Angle X-ray Scattering (SAXS). In conclusion, the observations from this study highlight the potential of incorporating macromolecular architecture in the self-assembly of strongly immiscible block copolymers to attain ultra-small nanofeatures with desired morphologies.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Convergent reductive evolution and host adaptation in Mycoavidus bacterial endosymbionts of Mortierellaceae fungi

Intimate associations between fungi and intracellular bacterial endosymbionts are becoming increasingly well understood. Phylogenetic analyses demonstrate that bacterial endosymbionts of Mucoromycota fungi are related either to free-living Burkholderia or Mollicutes species. The so-called Burkholderia-related endosymbionts or BRE comprise Mycoavidus, Mycetohabitans and Candidatus Glomeribacter gigasporarum. These endosymbionts are marked by genome contraction thought to be associated with intracellular selection. However, the conclusions drawn thus far are based on a very small subset of endosymbiont genomes, and the mechanisms leading to genome streamlining are not well understood. The purpose of this study was to better understand how intracellular existence shapes Mycoavidus and BRE functionally at the genome level. To this end we generated and analyzed 14 novel draft genomes for Mycoavidus living within the hyphae of Mortierellomycotina fungi. We found that our novel Mycoavidus genomes were significantly reduced compared to free-living Burkholderiales relatives. Using a genome-scale phylogenetic approach including the novel and available existing genomes of Mycoavidus, we show that the genus is an assemblage composed of two independently derived lineages including three well supported clades of Mycoavidus. Using a comparative genomic approach, we shed light on the functional implications of genome reduction, documenting shared and unique gene loss patterns between the three Mycoavidus clades. We found that many endosymbiont isolates demonstrate patterns of vertical transmission and host-specificity, but others are present in phylogenetically disparate hosts. We discuss how reductive evolution and host specificity reflect convergent adaptation to the intrahyphal selective landscape, and commonalities of eukaryotic endosymbiont genome evolution.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗

DOE OSTI2022

Model development for flash boiling spray and validations with isooctane, hexane, ethanol and their binary mixtures

The objective of this study is to develop a comprehensive model for spray atomization under the consideration of flash boiling, which includes nucleation, bubble growth, and droplet breakup. Although flash boiling has been previously studied, there is still no well-established analytical and numerical model of their mechanisms, or general guidelines on the implementation of this atomization strategy. In this study, detailed formulations are elaborated for the unified spray atomization model under both non-flash and flash boiling conditions. Next, the model was implemented into CFD software and 3-D simulations were performed for model validations. Two fuel blends are tested: ethanol-isooctane blends and hexane-isooctane blends. Simulation results were compared with the experimental measurements conducted using two methodologies, DBI (Diffuse Backlight Imaging) system for spray images captured by a high-speed camera, and a PDA (Phase Doppler Anemometry) system for droplet size measurement during the spray injection process. It was found that the current flash boiling model can accurately capture the spray characteristics under different environment conditions, such as the spray plume interactions and liquid penetration. The averaged Sauter mean diameter (SMD) calculations match the measurement sufficiently well. The spray structures for both single- and multi-component fuels during the injection process are well represented by current model.

42 ENGINEERING↗

DOE OSTI2021

Soil organic matter is principally root derived in an Ultisol under oak forest

To a large degree, the sources and stability of soil organic carbon remain poorly constrained. A clear understanding of links among the components of the soil C cycle is hampered by the complexity of the system as well as challenges associated with partitioning bulk soil C into meaningful fractions. A large accidental 14 CO 2 release at the Oak Ridge Reservation in Tennessee, USA provided a strong label pulse into adjacent, well-studied oak forests, resulting in highly elevated Δ 14 C values in leaf litter (~1000‰) and roots (~260–450‰). A four-year manipulative study was conducted to determine the relative contribution of litter versus roots to the bulk mineral soil C pool, as well as to free light, occluded light and heavy fractions. The heavy fraction was further split into fractions with densities of 1.7–2.4 g cm –3 and >2.4 g cm –3 to test the homogeneity of the mineral-associated fraction of C. Substantial concentrations of label were detected in all soil fractions within a year of the 14 CO 2 release, indicating rapid incorporation of newly fixed photosynthates in all fractions of soil organic C, regardless of differences in stability inferred by previous work. This rapid incorporation of label occurred only in treatments where roots were labeled, indicating that roots are the major source of inputs to mineral soil C stocks at these sites. Separation of the heavy fraction into subfractions of intermediate (1.7–2.4 g cm –3 ) and high (>2.4 g cm –3 ) density indicated that both subfractions incorporated label at similar rates, despite significant differences in degree of microbial processing. In general, the rate of label incorporation suggested a much faster turnover for all fractions than indicated by natural radiocarbon abundance values. This study suggests that within each soil fraction there are portions of slow-cycling and fast-cycling materials, and the determination of an average turnover time or mean age is dependent on experimental approach. The rapid incorporation of label into all fractions within a year regardless of inferred stability implies a high degree of heterogeneity in all fractions regardless of how finely the soils are partitioned. Further refinement of the nature and drivers of this heterogeneity could yield important insights into the soil C cycle.

58 GEOSCIENCES↗

DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Thermal shock resistance of lightweight cements developed for geothermal conditions

Cements are a critical component in well construction, as they act to prevent well fluid and gas escape, prevent corrosion of the casing, and strengthen the wellbore to prevent deformation. Under the high temperature/pressure conditions common in geothermal systems, the injection of cold water for energy production is expected to induce cyclic damage to the borehole cement through the rapid temperature fluctuations. These “thermal shocks” are expected to cause casing shrinkage, annulus formation, and cement tensile stresses. To understand the effect of cold water injection on the wellbore environment, a set of rock-cement-steel samples were created to simulate the structure of a geothermal well. Lightweight thermally-insulating cement blends were tested under thermal shock conditions in this study. In each test, the samples were pressurized to an effective pressure of ~3.5 MPa and placed at high temperatures. Thermal shocks were performed by injecting cold water (~10-15 °C) through the samples at a constant rate while keeping the samples at high temperatures until the sample temperature stopped decreasing and deformation ceased. Eight thermal shock tests were conducted with each sample – two at 100 °C and six at 200 °C. Post-tests analysis was then conducted by cutting open each sample to examine the damage in each component of the simulated wellbore. Experimental results suggest that all samples experienced similar degrees of axial and lateral contraction during cold water injection, but for the most part this contraction is recoverable when injection halts. Post-test analysis revealed that fly ash cenosphere pre-treatment had the best effect on improving thermal shock resistance in the cement blends. Thermomechanical modeling of likely stress paths experienced by the cements during heating/cooling cycles shows that elasto-plastic cement constitutive behavior results in most plastic strain occurring during the initial heating steps, with mostly elastic strain occurring during the thermal shock cycles. In conclusion, this agrees with experimental evidence, suggesting that cement damage from shocking occurs via other mechanisms such as chemical alteration, corrosion, and fatigue.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A missing layer in COVID-19 studies: Transmission of enveloped viruses in mucus-rich droplets

Here we evaluate the influence of mucus layers on the evaporation time and transport of enveloped viruses, including SARS-CoV-2. Enveloped viruses must remain moist to be fully infective. Yet, the Wells model based on water droplets divides respiratory droplets into either quickly evaporated aerosolized particles termed droplet nuclei (<10 s) or liquid droplets that fall to the nearest surface, leaving no physical mechanism for airborne transmission of fully infective enveloped viruses over large distances (greater than a few meters). Yet, the role of mucus layers on evaporation times has not been considered even though the formation of mucus shells around liquid cores of respiratory droplets has been shown experimentally. Here we show that mucus shells increase the drying time by orders of magnitude so that enveloped virions may remain well hydrated and, thus, fully infective at substantial distances. Further, this provides a mechanism by which infective enveloped virus particles can transmit as aerosols within buildings and between buildings over extended distances. This analysis is important because public health agencies typically follow the Wells model to establish health policies including social/physical distancing guidelines.

60 APPLIED LIFE SCIENCES↗