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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6

Allometric Scaling of Hyporheic Respiration Across Basins in the Pacific Northwest United States

Abstract Hyporheic zones regulate biogeochemical processes in streams and rivers, but high spatiotemporal heterogeneity makes it difficult to predict how these processes scale from individual reaches to river basins. Recent work applying allometric scaling (i.e., power‐law relationships between size and function) to river networks provides a new paradigm for understanding cumulative hyporheic biogeochemical processes. We used previously published model predictions of reach‐scale hyporheic aerobic respiration to explore patterns in allometric scaling across two climatically divergent basins with differing characteristics in the Pacific Northwest, United States. In the model, hydrologic exchange fluxes (HEFs) regulate hyporheic respiration, so we examined how HEFs might influence allometric scaling of respiration. We found consistent scaling behaviors where HEFs were either very low or very high, but differences between basins when HEFs were moderate. Our findings provide initial model‐generated hypotheses for factors influencing allometric scaling of hyporheic respiration. These hypotheses can be used to optimize new data generation efforts aimed at developing predictive understanding of allometries that can, in turn, be used to scale biogeochemical dynamics across watersheds.

59 BASIC BIOLOGICAL SCIENCES↗

Predicting 3D moisture sorption behavior of materials from 1D investigations

Abstract Moisture in materials can be a source of future outgassing and exacerbate unwanted changes in physical and chemical properties. Here, we investigate the effect of sample size and shape on the moisture transport phenomena through a combined experimental and modeling approach. Several different materials varying in size and shape were investigated over a wide range of relative humidities (0–90%) and temperatures ( $$30{-}70^{\,\circ } \hbox {C}$$ 30 - 70 ∘ C ) using gravimetric type dynamic vapor sorption (DVS). A dynamic triple-mode sorption model, developed previously, was employed to describe the experimental results with good success; the model includes absorption, adsorption, pooling (clustering) of species, and molecular diffusion. Here we show that the full triple-mode sorption model is robust enough to predict the dynamic uptake and outgassing of 3-dimensional (3D) samples using parameters derived from quasi-1D samples. This successful demonstration on three different materials (filled polydimethylsiloxane (PDMS), unfilled PDMS, and ceramic inorganic composite) illustrates that the model is robust at describing the scale-independent physics and chemistry of moisture sorption and diffusion materials. This work demonstrates that while sorption mechanisms manifest in testing of all sample sizes, some of these mechanisms were so subtle that they were overlooked in our initial modeling and assessment, illustrating the importance of multi-scale experiments in the development of robust predictive capabilities. Our study also outlines the challenges and viable solutions for global optimization of a multi-parameter model. The ability to quantify moisture sorption and diffusion, independent of scale, using 1D lab-scale experiments enables prediction of long-term bulk materials behavior in real applications.

36 MATERIALS SCIENCE↗

Development of NDE/NDT Tools for High-Volume & High-Speed Inspection of CFRP Structures in Automotive Manufacturing

Main advantages of the air-coupled ultrasound testing (ACUT) and electromagnetic testing (EMT) techniques for NDE of CFRP composites were non-contact sensing, scalability for high-speed inspection, cost-effectiveness, and non-hazardous operation. Despite these advantages, no systems that would satisfy the project requirements were commercially available. Hence, one of the major efforts of the Michigan State University (MSU) team at the initial stage of the project was to close this technological gap by developing, optimizing, and validating array sensors that would provide sufficient sensitivity, spatial coverage, and resolution for robust defect detection. Optimization of the ACUT and EMT sensor designs was performed using experimentally validated finite element models. Initial experiments using array probes were conducted on relatively flat CFRP samples. In parallel, the MSU team designed and assembled a portable platform with two robotic arms. The robots were equipped with newly designed sensors that enabled high-speed NDE of curved CFRP parts. Presently, the developed robotic platform can be used as a demo/template NDE system, which is easily adaptable to manufacturing environments and in-line NDE. The ACUT NDE system developed by the MSU team used a high-power 4-channel pulser receiver for parallel data acquisition. The array probes were designed by stacking commercially available ACUT transducers, which operated in the frequency range between 100 kHz and 500 kHz. MSU optimized the excitation procedure and developed wave focusing cones so as to reduce the crosstalk between the transducers and to provide higher pulse repletion frequency (PRF). The through-transmission (TT) and single-side access (SSA) inspection modes were successfully implemented. In the TT-ACUT, structural defects in CFRP were detected by passing ultrasonic waves through the test part. Hence, the ACUT transmitters and receivers needed to be placed on the opposite sides of the test part. In the SSA-ACUT, guided waves (GW) were excited in the test part using the transmitters and were sensed by the receivers from the same side. Multi-channel TT-ACUT and SSA-ACUT provided high-speed NDE, and were successfully validated on CFRP test samples with interlaminar delaminations and other embedded defects The EM techniques developed by the MSU team included: 1) eddy current testing (ECT), 2) capacitive imaging (CI) and hybrid dual-mode imaging. In ECT, structural damage was detected in CFRP using coils sensor arrays. In ECT, the excitation magnetic field is generated by passing an alternating current through a coil, which is placed above the test sample. The excitation field penetrates the conductive sample and induces the eddy currents in its transect. In turn, the eddy currents generate the reaction field, which affects the total field sensed by a coil. Hence, the presence of structural flaws will alter the eddy current flow and the picked-up signal. ECT is mostly sensitive to local changes of the electric conductivity of the test sample, and CFRPs are mostly conductive in the direction of carbon fibers. Hence, ECT was well suited for the detection of fiber damage/fiber irregularities. The MSU team developed printed circuit boards (PCB) with coil sensor arrays optimized for NDE of CFRP. Unlike most commercial probes designed for ECT of metallic structures, the MSU array probes were designed for operation in [1-10] MHz frequency range, which was optimal for low-conductive CFRP. Multiple sensing topologies (coil groups excitation/sensing arrangements) were implemented and successfully validated. Capacitive Imaging (CI) technique developed by MSU was complementary to ECT. In contrast to ECT, which was sensitive to local changes of the electrical conductivity, the CI was sensitive to local changes of the dielectric constant. Therefore, CI could provide information about matrix damage/matrix irregularities in CFRP. The MSU CI sensor arrays were made of multiple circular or rectangular open-plate capacitors printed on PCB. Sensors of this type are not commercially available. In addition to ECT and CI, the MSU team developed a hybrid (dual-mode) inductive/capacitive measurement technique that synergistically combined the benefits of inductive and capacitive sensing for rapid NDE of fiber reinforced polymer (FRP) composite structures. Fiber damage and fiber irregularities in FRPs were detected by configuring hybrid sensors as coil sensors. Similarly, matrix damage, matrix irregularities and interlaminar delaminations were detected by configuring hybrid sensors as capacitive sensors. ECT and CI were performed sequentially by means of electronic switching. Hence, eliminating the need for mounting two separate sensor arrays on the probe. Portable robotic platform was developed by MSU for multi-technique high-speed NDE of CFRP test parts. The platform had two 6-axis robots, which enabled inspection of curved parts in approximately a 6×6×6 ft 3 active scan area. On the software side, the MSU team integrated scripts for NDE hardware control with scripts for robot motion control. MSU also implemented automated path planning for the robots, reconstruction of part’s surfaces via stereovision, 3D rendering of inspection data, and image processing algorithms for enhanced defect detection. Automotive composite parts manufactured by Plasan Composites from Phase I were used to validate the ACUT and EMT techniques on representative testbeds. Among those parts were three X-braces for a Dodge Viper, one composite calibration plaque with known defects at known locations, and four other test sections, including sections from a front splitter, a corner section from a composite hood, and a high-pressure RTM panel made using non crimp fabric. Other test samples included CFRP and GFRP calibration plates with fiber/matrix defects fabricated at MSU/CVRC.

36 MATERIALS SCIENCE↗

Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow

This work presents a new engineering analytical model that predicts the effect of both the turbine yaw misalignment and the inflow wind veer on the wake flow distribution downwind of a wind turbine. To consider the veered inflow, two methods were examined. In the first method, the curled shape of the wake due to the yaw offset is initially modelled. The wake shape is then laterally skewed at each height due to the wind veer based on the assumption that the turbine wake is transported downstream by the incoming flow. The second method is a more realistic approach that accounts for the effect of wind veer on the wind velocity direction and the yaw angle seen by the wind turbine. This models the wake region in a local coordinate system defined based on the wind direction at each height. A coordinate transformation is then performed to represent the wake flow distribution in the global coordinate system attached to the ground. The results show that while the two methods provide similar outputs for small variations in the wind direction across the rotor, the difference becomes more evident with an increase in wind veer. High-fidelity simulations for a turbine subject to a neutral atmospheric boundary layer were employed to validate model predictions for different operating conditions.

17 WIND ENERGY↗

Tanana River Test Site Model Verification Using the Marine and Hydrokinetic Toolkit (MHKiT)

The marine energy (ME) industry historically lacked a standardized data processing toolkit for common tasks such as data ingestion, quality control, and visualization. The marine and hydrokinetic toolkit (MHKiT) solved this issue by providing a public software deployment (open-source and free) toolkit for the ME industry to store and maintain commonly used functionality for wave, tidal, and river energy. This paper demonstrates an initial model verification study in MHKiT. Using Delft3D, a numerical model of the Tanana River Test Site (TRTS) at Nenana, Alaska was created. Field data from the site was collected using an Acoustic Doppler Current Profiler (ADCP) at the proposed Current Energy Converter (CEC) locations. MHKiT is used to process model simulations from Delft3D and compare them to the transect data from the ADCP measurements at TRTS. The ability to use a single tool to process simulation and field data demonstrates the ease at which the ME industry can obtain results and collaborate across specialties, reducing errors and increasing efficiency.

13 HYDRO ENERGY↗

IDAES-PSE 2.6.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.6.0 Release Highlights Upcoming Changes IDAES will be switching to the new Pyomo solver interface in the next release. Whilst this will hopefully be a smooth transition for most users, there are a few important changes to be aware of. The new solver interface uses a different version of the IPOPT writer (“ipopt_v2”) and thus any custom configuration options you might have set for IPOPT will not carry over and will need to be reset. By default, the new Pyomo linear presolver will be activated with ipopt_v2. Whilst are working to identify any bugs in the presolver, it is possible that some edge cases will remain. IDAES will begin deploying a new set of scaling tools and APIs over the next few releases that make use of the new solver writers. The old scaling tools and APIs will remain for backward compatibility but will begin to be deprecated. New Models, Tools and Features New Intersphinx extension automatically linking Jupyter notebook examples to project documentation New end-to-end diagnostics example demonstrated on a real problem New complementarity formulation for VLE with cubic equations of state, backward compatibility for old formulation New solver interface with presolve (ipopt_v2) in support of upcoming changes to the initialization and APIs methods, with default set to ipopt to maintain backwards compatibility; this will deprecate once all examples have been updated New forecaster and parameterized bidder methods within grid integration library Updated surrogates API and examples to support Keras 3, with backwards compatibility for older formats such as TensorFlow SavedModel (TFSM) Updated costing base dictionary to include the 2023 cost year index value Updated ProcessBlock to include information on the constructing block class Updated Flowsheet Visualizer to allow visualize() method to return value and functions Bug Fixes Fixed bug in the Modular Property Framework that would cause errors when trying to use phase-based material balances with phase equilibria. Fixed bug in Modular Properties Framework that caused errors when initializing models with non-vapor-liquid phase equilibria. Fixed typos flagged by June update to crate-ci/typos and removed DMF-related exceptions Minor corrections of units of measurement handling in power plant waste/transport costing expressions, control volume material holdup expressions, and BTX property package parameters Fixed throwing >7500 numpy deprecation warnings by replacing scalar value assignment with element extraction and item iteration calls Testing and Robustness Migrated slow tests (>10s) to integration, impacting test coverage but also yielding a nearly 30% decrease in local test runtime Pinned pint to avoid issues with older supported Python versions Pinned codecov versions to avoid tokenless upload behavior with latest version Bumped extensions to version 3.4.2 to allow pointing to non-standard install location Deprecations and Removals Python 3.8 is no longer supported. The supported Python versions are 3.9 through 3.12 The Data Management Framework (DMF) is no longer supported. Importing idaes.core.dmf will cause a deprecation warning to be displayed until the next release The SOFC Keras surrogates have been removed. The current version of the SOFC surrogate model in the examples repository is a PySMO Kriging model.

AS↗

Improving Seasonal Forecast Using Probabilistic Deep Learning

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

54 ENVIRONMENTAL SCIENCES↗

Local-scale Arctic tundra heterogeneity affects regional-scale carbon dynamics

In northern Alaska nearly 65% of the terrestrial surface is composed of polygonal ground, where geomorphic tundra landforms disproportionately influence carbon and nutrient cycling over fine spatial scales. Process-based biogeochemical models used for local to Pan-Arctic projections of ecological responses to climate change typically operate at coarse-scales (1km 2 –0.5°) at which fine-scale (<1km 2 ) tundra heterogeneity is often aggregated to the dominant land cover unit. Here, we evaluate the importance of tundra heterogeneity for representing soil carbon dynamics at fine to coarse spatial scales. We leveraged the legacy of data collected near Utqiagvik, Alaska between 1973 and 2016 for model initiation, parameterization, and validation. Simulation uncertainty increased with a reduced representation of tundra heterogeneity and coarsening of spatial scale. Hierarchical cluster analysis of an ensemble of 21 st -century simulations reveals that a minimum of two tundra landforms (dry and wet) and a maximum of 4km 2 spatial scale is necessary for minimizing uncertainties (<10%) in regional to Pan-Arctic modeling applications.

54 ENVIRONMENTAL SCIENCES↗

IDAES-PSE 2.5.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.5.0 Release Highlights Upcoming Changes IDAES will be switching to the new Pyomo solver interface in the next release. Whilst this will hopefully be a smooth transition for most users, there are a few important changes to be aware of. The new solver interface uses a different version of the IPOPT writer (“ipopt_v2”) and thus any custom configuration options you might have set for IPOPT will not carry over and will need to be reset. By default, the new Pyomo linear presolver will be activated with ipopt_v2. Whilst are working to identify any bugs in the presolver, it is possible that some edge cases will remain. IDAES will begin deploying a new set of scaling tools and APIs over the next few releases that make use of the new solver writers. The old scaling tools and APIs will remain for backward compatibility but will begin to be deprecated. New Models, Tools and Features New diagnostics check for near-parallel variables and constraints. New diagnostics tools for identifying causes of infeasibility in models. New example for creating a custom model of a liquid-liquid extractor unit operation. Bug Fixes Fixed bug in Gibbs reactor that caused it to appear to have additional spurious degrees of freedom. Fixed bug in the Modular Property Framework that would cause errors when trying to use phase-based material balances with phase equilibria. Fixed bug in Modular Properties Framework that caused errors when initializing models with non-vapor-liquid phase equilibria. Testing and Robustness Deployed the IDAES Diagnostics Toolbox to confirm that there are no structural or numerical issues in the core model libraries. Additional robustness tests for core model, and some associated improvements in the converge tester class. Fixed a number of issues that were causing unexpected warnings to be emitted during testing. Deprecations and Removals Removed examples for RIPE tool which has not been supported for a number of releases.

AS↗

New Predictive Capabilities for Nuclear Weapons in Composite Fires

The prevalence of flammable carbon-based composite airframe materials and their use in high-temperature nuclear weapon re-entry systems requires analysts to address the abnormal thermal environment hazards associated with composite material fires. These fires tend to burn very differently than conventional fuel fires, usually burning less intensely, but much longer. This could lead to challenges in understanding margins in classic safety themes. The technical challenges in modeling the phenomena associated with these new types of fires are considerable, but new models have been developed. Their predictions have been compared with well-documented measurements of a vertical porous burner fire, known as a "wall fire" (a "wall-fire" validation simulation is reflected in the figures below). These measurements were conducted at FMGlobal, a mutual insurance company with a strong fire risk management program, as part of an ongoing collaboration between Sandia and FMGlobal. To date, the "wall-fire" scenario has been set up and initial model assessments with grid refinement studies have been conducted focusing on mesh resolutions suitable for full weapon system simulations. This work will continue with further verification and validation tasks assessing the predictions of the new model. Future work will address specific aspects of the wall models that are lacking in their predictive ability.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Five-day track forecast skills of WRF model for the western North Pacific tropical cyclones

In this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF Model) are verified. We utilize 12-km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. A total of 125 forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moving northward in the subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.

54 ENVIRONMENTAL SCIENCES↗

Development of a kinetic-thermodynamic model for lime-stabilization of Na-bentonite

This study presents the first kinetic model to predict the solid and pore solution composition of Na-bentonite clay reacting with slaked lime over a period of 720 days. The model successfully accounts for most experimental data using a single kinetic rate constant. The following sequence of reactions was predicted by the model: initial rapid dissolution of portlandite within the first 7 days, leading to a decrease in pH and dissolved calcium, and concurrent formation of calcium silicate hydrates (C-S-H: jennite), calcium aluminate hydrate (C-A-H: C₄AH₁₃), calcium aluminosilicate hydrates (stratlingite) and hydrotalcite. After 7 days, jennite and stratlingite are predicted to transform into tobermorite-II, contributing to strength development up to 28 days. From 28 to 90 days, continued montmorillonite dissolution is predicted, along with minor formation of ettringite, partial tobermorite-II dissolution, and precipitation of secondary phases such as albite and talc. Experimentally, portlandite dissolution was confirmed by TGA and XRD and found to be complete within 7 days, in agreement with model predictions. However, other predicted solid-phase transformations (e.g., tobermorite-II formation and dissolution, ettringite, albite, and talc formation) could not be conclusively verified through experimental techniques. Aqueous phase measurements confirmed that the pH and Ca trends in solution, and that equilibrium was reached by 90 days.

Chemical kinetics↗

Use of Shallow Ice Core Measurements to Evaluate and Constrain 1980–1990 Global Reanalyses of Ice Sheet Precipitation Rates

Abstract Sea‐level rise (SLR) projections by Earth System Models (ESMs) depend on ice sheet surface mass balances. Accurate, global atmosphere reanalyses would be ideal for providing equilibrated ice sheet model initial conditions in fully coupled ESM simulations. Here we present the first evaluation of 1980–1990 global reanalysis precipitation over Greenland and Antarctica that uses independent observations of net accumulation rates derived from shallow ice cores. Precipitation distributions from both the European Centre for Medium‐Range Weather Forecast's Reanalysis (ERA5) and the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) are highly correlated with contemporaneous co‐located net accumulation rates from Greenland ( r 2 > 0.95) and West Antarctica ( r 2 > 0.7). Three other commonly used reanalyses (WFDE5, CRUNCEP, and GSWP3) exhibit significantly weaker correlations on one or both ice sheets. Our findings imply that ESMs should use ERA5 or MERRA‐2 in data‐forced simulations to validate ice sheet model dynamics and precondition firn for SLR projections.

Schneider, Adam↗

Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure

In many non-canonical data science scenarios, obtaining, detecting, attributing, and annotating enough high-quality training data is the primary barrier to developing highly effective models. Moreover, in many problems that are not sufficiently defined or constrained, manually developing a training dataset can often overlook interesting phenomena that should be included. To this end, we have developed and demonstrated an iterative self-supervised learning procedure, whereby models are successfully trained and applied to new data to extract new training examples that are added to the corpus of training data. Successive generations of classifiers are then trained on this augmented corpus. Using low-frequency acoustic data collected by a network of infrasound sensors deployed around the High Flux Isotope Reactor and Radiochemical Engineering Development Center at Oak Ridge National Laboratory, we test the viability of our proposed approach to develop a powerful classifier with the goal of identifying vehicles from continuously streamed data and differentiating these from other sources of noise such as tools, people, airplanes, and wind. Using a small collection of exhaustively manually labeled data, we test several implementation details of the procedure and demonstrate its success regardless of the fidelity of the initial model used to seed the iterative procedure. Finally, we demonstrate the method’s ability to update a model to accommodate changes in the data-generating distribution encountered during long-term persistent data collection.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

n-Propylbenzene Droplet Combustion: Experiments and Numerical Modeling

n-Propylbenzene (PB, C9H12, 432K boiling point) is important as a constituent in surrogates for some transportation fuels but little is understood about its droplet burning characteristics. We present an experimental and numerical study of a fuel droplet burning under conditions where the sole means of gas transport is fuel evaporation at the droplet surface thereby creating a one-dimensional gas transport that is amenable to detailed numerical modeling. The one-dimensional gas transport that results is amenable to detailed numerical modeling. Initial droplet diameters (Do) ranged from 0.3 mm to about 5 mm to better understand the mechanisms that control burning over such a wide range of Do. For Do < 1 mm droplets were burned while anchored to 14 m SiC fibers within a sealed container in free-fall during which time the droplets were ignited by spark discharge and the burning process recorded by video cameras. Larger and free-floating droplets were studied in the spaced-based platform of the orbiting International Space Station for Do > 1.0 mm which provided unlimited times for experimental observation. Measurements of droplet, flame and soot shell diameters were made and are discussed in the presentation. The experimental results showed formation of optically thick soot clouds developing during burning that required a robust image analysis algorithm to enable measurement of droplet diameter. The measured droplet diameters were well predicted by a numerical model that included soot formation, a PB kinetic mechanism comprising 576 species and 27369 reactions, unsteady gas and liquid transport, variable properties and radiative transport. Flame diameters showed more variability compared to the simulations, depending on how the flame was defined (peak gas temperature and OH concentration). Droplets burned slower as Do was increased and the PB sooting propensity increased with Do. Flame extinction was observed for Do > 3.3 mm indicating a strong effect of radiation as Do was increased compared to the smaller droplet diameters examined where extinction was not experimentally observed. For the largest droplets examined the possibility that the observed extinction is indicative of a transition to cool flame burning is still under investigation.

Guo, Songtao↗

An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting

Microalgae have received increasing attention as a potential feedstock for biofuel or biobased products. Forecasting the microalgae growth is beneficial for managers in planning pond operations and harvesting decisions. This study proposed a biomass forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2), ensemble data assimilation (DA), and numerical weather prediction Global Ensemble Forecast System (GEFS) ensemble meteorological forecasts. The novelty of this study is to seek the use of ensemble DA to improve both BGM and MASS2 model initial conditions with the assimilation of biomass and water temperature measurements and consequently improve short-term biomass forecasting skills. This study introduces the theory behind the proposed integrated biomass forecasting system, with an application undertaken in pseudo-real-time in three outdoor ponds cultured with Chlorella sorokiniana in Delhi, California, United States. Results from all three case studies demonstrate that the biomass forecasting system improved the short-term (i.e., 7-day) biomass forecasting skills by about 60% on average, comparing to forecasts without using the ensemble DA method. Given the satisfactory performances achieved in this study, it is probable that the integrated BGM-MASS2-DA forecasting system can be used operationally to inform managers in making pond operation and harvesting planning decisions.

59 BASIC BIOLOGICAL SCIENCES↗

Implementation of Triply Periodic Minimal Surfaces (TPMS) as surface objects in OpenMC

Triply Periodic Minimal Surfaces (TPMS) represent a promising geometry for future fuel designs due to their significant surface-to-volume ratio, which facilitates efficient cooling of nuclear fuel, a crucial factor for safety and efficiency. Demonstrating the remarkable capabilities of TPMS fuel requires initial modeling and simulation. This paper presents an implementation of TPMS in the Monte Carlo code OpenMC, enabling reactor physics modeling of TPMS. Here, the primary advantages over traditional methods using CAD files include reduced memory requirements for computations and high-fidelity implementation. This implementation has been tested against CAD files loaded in Serpent2, yielding promising results with low biases in the $k_{\textrm{eff}}$, comparable to biases in the material balance sheet. The implementation presented in this work will be used in future reactor physics computations related to new reactor designs involving TPMS-based fuels.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Monitoring spatiotemporal evolution of fractures during hydraulic stimulations at the first EGS collab testbed using anisotropic elastic-waveform inversion

The EGS Collab project acquired continuous active-source seismic monitoring (CASSM) data before, during, and after hydraulic stimulations at the first testbed at the depth of 4850 ft (1478 m) at the Sanford Underground Research Facility in Lead, South Dakota, for monitoring fracture creation and evolution. CASSM acquisition was conducted using 24 hydrophones, 18 accelerometers, and 17 piezoelectric sources within four fracture-parallel wells and two orthogonal wells. 3D anisotropic traveltime tomography and anisotropic elastic-waveform inversion of the campaign cross-borehole seismic data show that the rock within the stimulation region is a heterogeneous horizontal transverse isotropic medium. Here we use these inversion results as the initial models and apply 3D anisotropic first-arrival traveltime tomography and 3D anisotropic elastic-waveform inversion to the CASSM data acquired after each stimulation in May, 2018 and December, 2018. We observe the spatiotemporal evolution of seismic velocities and anisotropic parameters caused by hydraulic fracture stimulations, showing the regions of rock alternation caused by hydraulic fracture stimulation.

15 GEOTHERMAL ENERGY↗