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DOE OSTI2024

Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data

Over the past decade, a variety of innovative methodologies have been developed to better characterize the relationships between processing conditions and the physical, morphological, and chemical features of special nuclear material (SNM). Different processing conditions generate SNM products with different features, which are known as “signatures” because they are indicative of the processing conditions used to produce the material. These signatures can potentially allow a forensic analyst to determine which processes were used to produce the SNM and make inferences about where the material originated. This article investigates a statistical technique for relating processing conditions to the morphological features of PuO 2 particles. We develop a Bayesian implementation of seemingly unrelated regression (SUR) to inverse-predict unknown PuO 2 processing conditions from known PuO 2 features. Model results from simulated data demonstrate the usefulness of the technique. Applied to empirical data from a bench-scale experiment specifically designed with inverse prediction in mind, our model successfully predicts nitric acid concentration, while results for Pu concentration and precipitation temperature were equivalent to a simple mean model. Our technique compliments other recent methodologies developed for forensic analysis of nuclear material and can be generalized across the field of chemometrics for application to other materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Promoted Ru/PrOx Catalysts for Mild Ammonia Synthesis

Ammonia synthesis is one of the most important chemical reactions. Due to thermodynamic restrictions and the reaction requirements of the current commercial iron catalysts, it is also one of the worst reactions for carbon dioxide emissions and energy usage. Ruthenium-based catalysts can substantially improve the environmental impact as they operate at lower pressures and temperatures. In this work, we provide a screening of more than 40 metals as possible promoter options based on a Ru/Pr2O3 catalyst. Cesium was the best alkali promoter and was held constant for the series of double-promoted catalysts. Ten formulations outperformed the Ru-Cs/PrOx benchmark, with barium being the best second promoter studied and the most cost-effective option. Designs of experiments were utilized to optimize both the pretreatment conditions and the promoter weight loadings of the doubly promoted catalyst. As a result, optimization led to a more than five-fold increase in activity compared to the unpromoted catalyst, therefore creating the possibility for low-ruthenium ammonia synthesis catalysts to be used at scale. Further, we have explored the roles of promoters using kinetic analysis, X-ray Photoelectron Spectroscopy (XPS), and in situ infrared spectroscopy. Here, we have shown that the role of barium is to act as a hydrogen scavenger and donor, which may permit new active sites for the catalyst, and have demonstrated that the associative reaction mechanism is likely used for the unpromoted Ru/PrOx catalyst with hydrogenation of the triple bond of the dinitrogen occurring before any dinitrogen bond breakage.

Chemistry↗

DOE OSTI2021

Experimental Study of the Behavior of Phase Change Materials during Interrupted Phase Change Processes

This research article explores the behavior of a phase change material (PCM) when it undergoes interrupted melting and freezing, through experimental investigations using a heat flow meter apparatus. A fatty acid-based organic PCM, encapsulated within polyethylene and thin aluminum foil layers, was experimentally tested in this study. Experiments were designed to represent multiple interrupted phase change scenarios that could occur within PCMs applied in buildings. The experimental results were analyzed and compared with previously reported assumptions in numerical models dealing with PCM hysteresis and interrupted phase change processes. These comparisons indicated that the assumptions used in the different numerical models considered can capture the interrupted phase change phenomena with varying degrees of accuracy. The findings also highlighted the need for additional experimental research on different phase change processes that can occur in building applications of PCMs.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Machine Learning-Enabled Quantitative Analysis of Optically Obscure Scratches on Nickel-Plated Additively Manufactured (AM) Samples

Additively manufactured metal components often have rough and uneven surfaces, necessitating post-processing and surface polishing. Hardness is a critical characteristic that affects overall component properties, including wear. This study employed K-means unsupervised machine learning to explore the relationship between the relative surface hardness and scratch width of electroless nickel plating on additively manufactured composite components. The Taguchi design of experiment (TDOE) L9 orthogonal array facilitated experimentation with various factors and levels. Initially, a digital light microscope was used for 3D surface mapping and scratch width quantification. However, the microscope struggled with the reflections from the shiny Ni-plating and scatter from small scratches. To overcome this, a scanning electron microscope (SEM) generated grayscale images and 3D height maps of the scratched Ni-plating, thus enabling the precise characterization of scratch widths. Optical identification of the scratch regions and quantification were accomplished using Python code with a K-means machine-learning clustering algorithm. The TDOE yielded distinct Ni-plating hardness levels for the nine samples, while an increased scratch force showed a non-linear impact on scratch widths. The enhanced surface quality resulting from Ni coatings will have significant implications in various industrial applications, and it will play a pivotal role in future metal and alloy surface engineering.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Quantifying System Level Impact of Connected and Automated Vehicles in an Urban Corridor

Numerous studies have demonstrated significant energy reduction for an ego vehicle by up to 20% leveraging Vehicle-to-Everything (V2X) technologies [1-4]. Some studies have also analyzed the impact of such vehicles on the energy consumption of other vehicles in a suburban or a highway corridor [5, 6], but the impact in an urban setting has not been studied yet. Southwest Research Institute (SwRI), in collaboration with Continental and Hyundai, is currently working on a Department of Energy funded project that is focused on quantifying the impact of multiple ego vehicles (smart vehicles) on the total energy consumption of the corridor under various traffic conditions, vehicle electrification level, vehicle-to-vehicle (V2V) technology penetration, and the number of smart (ego) vehicles in an urban setting. A six-kilometer-long urban corridor from Columbus, Ohio was modeled and calibrated with real-world data in PTV Vissim traffic microsimulation software. Five forward-looking powertrain models, consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles, were developed to estimate the energy consumption of vehicles on the corridor. A comprehensive full factorial simulation study was performed. The simulation results indicate that for a traffic mix based on projected new vehicles sales in 2025, a 15% corridor-level energy consumption reduction can be achieved. The paper details the development and validation of the simulation framework, design of experiments conducted, a discussion of challenges faced, and results under various test conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Demonstration of Ego Vehicle and System Level Benefits of Eco-Driving on Chassis Dynamometer

Eco-Driving with connected and automated vehicles has shown potential to reduce energy consumption of an individual (i.e., ego) vehicle by up to 15%. In a project funded by ARPA-E, a team led by Southwest Research Institute demonstrated an 8-12% reduction in energy consumption on a 2017 Prius Prime. This was demonstrated in simulation as well as chassis dynamometer testing. The authors presented a simulation study that demonstrated corridor-level energy consumption improvements by about 15%. This study was performed by modeling a six-kilometer-long urban corridor in Columbus, Ohio for traffic simulations. Five powertrain models consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles were developed. The design of experiment consisted of sweeps for various levels of traffic, penetration of smart vehicles, penetration of technology, and powertrain electrification. The large-scale simulation study consisted of doing approximately 96,000 powertrain simulations. A sophisticated clustering scheme was built and utilized to down select representative traces for each scenario from the simulation study for vehicle testing on a chassis dynamometer. Furthermore, this paper provides a summary of individual ego vehicle testing as well as a comprehensive overview of the method utilized for down selecting representative traces from large scale simulation studies that can be used to quantify corridor level benefits. Vehicle test results along with corresponding analyses are presented.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO 2 , temperature, water, and nitrogen (version 1.0)

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Dial

A key step in almost all scientific endeavors is answering the question: Given this data I already collected, what new data do I expect will yield the most useful information toward my scientific objective? The area of (sequential) experimental design has long been investigating answers to this question, but in recent years techniques from the machine learning subfield of active learning are increasingly applied. Researchers need a simple software tool for active learning applied to experimental design that can easily integrate into their existing workflows. This computer code, Dial, provides a microservice in ORNL's INTERSECT ecosystem for active learning applied to experimental design. By being part of the INTERSECT ecosystem, Dial is simple to integrate into any INTERSECT-based workflow. Dial provides multiple backend options, where a backend is an implementation of a specific active learning method. Users can select the backend that performs best for their application. Developers can also add new backends as needed. At its core, Dial receives a set of pre-existing measurements and input parameter bounds and then recommends one or more new sets of parameters to measure. Dial also includes interfaces to other microservices in the INTERSECT ecosystem so that it can be incorporated into INTERSECT campaigns. Dial provides a simple, yet powerful interface to convert automated INTERSECT workflows into autonomous workflows that adapt based on the results that are obtained. A shared microservice for active learning prevents duplicated effort by each application team implementing its own adaptive design of experiments tool.

Drane, Lance [Oak Ridge National Laboratory (ORNL)↗

DOE OSTI2026

Investigation of Flux Spreading in a Light-Trapping, Planar-Cavity Receiver for Enclosed Solar Particle Heating

Concentrating solar thermal power (CSP) technology development has recently focused on increasing the operating temperatures to accommodate high efficiency power cycles and thermochemical processes. Inert solid particles as heat transfer media enable solar receivers to operate above 700 degrees Celsius resulting in increased system thermal efficiency compared to the conventional molten salt based CSP system. An open-cavity falling-particle solar receiver that can efficiently heat particles by direct heating from concentrated solar radiation faces challenges with large particle losses from wind and unable to support thermochemical reactions. A light-trapping, planar cavity reiver (LTPCR) where particles are indirectly heated can significantly minimize the particle losses during the operation, support thermochemical reactions, and offer scalability potential. The LTPCR features an array of vertical planar receiver/absorber panels arranged within a cavity configuration. Concentrated solar radiation from heliostats is focused onto the receiver walls, where heat is indirectly transferred to solid particles flowing inside the receiver channels. Heat transfer occurs through direct contact between the receiver panel walls and particles, and can be enhanced by fluidizing particles with air. This fluidization increases particle-wall contact and extends particle residence time, maximizing heat transfer efficiency. The unique vertical planar receiver structure originated from a near-blackbody tubular light absorber, effectively distributing the incoming solar beam spread across the panel walls and trapping light. This flux spreading effect, driven by cosine projection, converts high incident solar flux into a lower, more uniform heat flux on the panel walls. This redistribution enhances heat transfer efficiency between particle-wall or reaction gases-wall, while preventing localized overheating of the receiver panel. Indirect planar cavity solar receivers completely separate solid particles from the ambient environment that can greatly reduce the thermal losses in heated particles resulting in high efficiency at high temperatures above 700 degrees Celsius. This design ensures no particle losses to the environment during the operation while open-cavity designs can experience significant particle losses from wind. An experimental investigation was conducted to observe flux spreading on the receiver panel wall. A lab-scale prototype planar receiver, fabricated using Haynes 230 alloy, was tested under direct concentrated solar radiation using the high-flux solar furnace (HFSF) facility at NREL. The experiment was performed under normal peak radiative heat fluxes ranging from 800 to 1900 kW/m2. A temperature distribution on the panel wall was measured using a thermal imaging camera (FLIR A 6600). To prevent overheating at the receiver front tip, prism-shaped heat shields (Zircar UNIFROM C1) were placed in front of the receiver, and their influence on flux spreading was also studied. Absorbed flux distribution on the panel wall was modeled using SolTrace. The total solar power and flux distributions delivered from HFSF were determined based on the heliostat mirror optical properties, direct normal irradiance (DNI) on the on-sun testing days, peak flux measurement during the on-sun testing, and shutter/attenuator settings Due to the large incident angles of the solar beam on the panel wall, the angular optical properties of Haynes 230 alloy and Zircar heat shields were incorporated into the model. This flux distribution model was then integrated into a computational fluid dynamics (CFD) simulation to predict the receiver panel wall temperature, which was compared with the experimental measurements. Both prediction and measurements identified a temperature hotspot at the backside of the panel, indicating that the incident solar beam can fully reach to the rear of the receiver. The heat shields positioned at the front of the receiver effectively reduced the excessive temperature rise at the receiver front tip. Overall, the temperature was well distributed over the panel wall, with a minor hotspot at the back of the receiver. The model slightly overpredicted the temperature, possibly due to discrepancies in optical properties of the panel and an underprediction of thermal loss in the receiver. The advancement of the particle LTPCR offers a viable alternative to open-cavity receivers by addressing particle loss issues. Additionally, it presents a pathway for enabling solar thermochemical processes, extending CSP technology beyond power generation to fuel and chemical production.

14 SOLAR ENERGY↗

DOE OSTI2020

As-run thermal hydraulics analysis of the EPRI-1 Experiment

The EPRI-1 experiment was designed to irradiate various types of reactor pressure vessel steels at a temperature of 288°C (PLN-3934). The specimens were irradiated in an instrumented test train inside a pressurized water loop in the center lobe of the ATR during cycle 157C. Temperature was monitored using thermocouples placed at the top of the test train. Additional temperature indications were obtained by post-irradiation examination of melt wires placed within the test train and spanning the temperature range 239°C to 327°C. The purpose of this analysis is to calculate specimen temperature using measured data on reactor power and as-run calculations of heating rates of the test train. The accuracy of the model is assessed by comparing the measured and calculated in-pile tube inlet to outlet temperature difference, comparing the measured and calculated thermocouple temperatures, and comparing the calculated specimen temperature to the temperature range indicated by the melt wires.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Neutronic Consideration of TREAT Facility Fuel SiC Recladding

The Transient Reactor Test (TREAT) Facility is an air-cooled reactor composed of graphite-urania fuel encapsulated in zirconium alloy canisters and surrounded by ~2 ft (~61 cm) of nuclear grade graphite. The primary role of TREAT is to provide in-pile nuclear-heated safety research via transient testing [1]. Benchmark efforts have demonstrated sensitivity of TREAT neutronics models to graphite cross section data and key neutron poisons found in the fuel assemblies [2]. During fabrication of TREAT fuel assemblies, a small concentration of boron was inadvertently introduced with variable concentration into the aggregate of fuel blocks prepared for TREAT use. The concentration of boron, and its respective uncertainty, represent the most sensitive component in a neutronics study; unfortunately, nothing can be done to reduce or otherwise mitigate its impact. Hydrogen in the fuel blocks reduces the mean free path of the core and its true quantity and uncertainty is approximated. The hydrogen remains from pitch binder used in the fuel fabrication process and was not completely released due to incomplete fuel graphitization. Destructive fuel testing is necessary to quantify the actual hydrogen content and to improve benchmark model quality. A third neutron poison of some significance is the hafnium impurity in the Zircaloy-3 (Zry-3) assembly cladding. The quantity of hafnium is expected to be within nominal manufacturing limits for nuclear grade zircaloy. Recladding the fuel assemblies with material that is less neutron absorbing is expected to benefit reactor neutron economy. Moreover, TREAT’s total transient energy is restricted based on the rate of Zry-3 oxidation in TREAT’s air coolant environment. Hence, cladding with more benign air reaction kinetics could dramatically uprate the facility’s experimental capability. Silicon carbide (SiC) has been proposed as a candidate material for recladding TREAT assemblies, as SiC/SiC demonstrate favorable properties within nuclear environments [3]. The current operational limits of TREAT are restricted by the oxidation of Zry-3. Change of the cladding to SiC could feasibly double the current temperature limitation. Recladding opportunities allow for previously damaged fuel assemblies to be returned to service, as Zry-3 is not commercially available. The initial effort required to consider proceeding with any recladding effort is to neutronically characterize key reactor parameters while replacing Zry-3 with SiC. A characterization summary, with respective results, is provided herein.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Establishing Maximal Core Excess Reactivity Envelope for TREAT Test Vehicle Insertion

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Rubik’s Cube Topology Based Particle Swarm Algorithm for Bilevel Building Energy Transaction

Following the rapid growth of distributed energy resources (e.g. renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as, reducing power loss, stabilizing the main power grid, etc. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. In addition, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush-Kuhn-Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubiks Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubiks Cube topology based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology based PSO and its scalability on larger scale applications.

Feng, Xiaochun↗

DOE OSTI2021

Nucleon Mass and Charges with Lattice Quantum Chromodynamics

Neutrino oscillation experiments are designed to measure neutrino masses and mixing parameters by scattering them off nuclei such as carbon, oxygen, and argon in detectors. Predictions of neutrino-nuclei cross sections from the Standard Model are needed to extract these parameters, but their theoretical uncertainties remain large due to the complexity of nuclear and hadronic physics. This situation needs to be improved in order to satisfy the precision needs of future experiments.In this dissertation, we focus on working towards a first-principles calculation of the nucleon axial form factor with lattice quantum chromodynamics (QCD). Nucleon axial form factor, which parametrizes the weak responses of a proton or neutron, is difficult to measure experimentally, and it is a dominant uncertainty in neutrino-nuclei cross-section calculations for incoming neutrino energies at around 1 GeV. So a theoretical calculation with lattice QCD provides a non-ambiguous determination of the form factor that could help reducing the uncertainty.The notorious signal-to-noise problem renders calculations of nucleon observables computationally intensive in lattice QCD. In this work, we investigate the use of staggered fermions in nucleon calculations. Staggered fermions are the most computationally efficient fermion discretization in lattice field theory, but certain theoretical issues have so far prevented their applications to nucleon physics. As a stepping stone towards a full calculation of the nucleon axial form factor, this dissertation provides a comprehensive theoretical framework on how to calculate the nucleon mass, vector charge, and axial charge with staggered fermions, together with numerical results demonstrating the methodology. This framework can be generalized to the form factor calculation that will appear in the near future.

Lin, Yin↗

DOE OSTI2020

Gamma Spectrometry Examination of the AGR-3/4 Irradiation

The results from gamma spectrometry examination of the different components from the combined third and fourth U.S. Advanced Gas Reactor (AGR) TRISO-coated particle fuel irradiation tests (AGR-3/4) have been analyzed. This experiment was designed to provide information about in-pile fission product migration. In each of the 12 capsules, a single stack of four compacts with designed-to-fail (DTF) particles surrounded by inner and outer graphite and/or graphitic matrix rings and a graphite sink ring were irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). Gamma spectrometry has been used to evaluate the gamma-emitting fission product inventory of compacts from the irradiation and evaluate the burnup of these compacts based on the activity of the radioactive cesium isotopes (i.e., Cs-134 and Cs-137) in the compacts. Burnup from gamma spectrometry compares well with predicted burnup from simulations. The inner and outer rings were also examined by gamma spectrometry to evaluate the total fission product inventory of the rings, and gamma emission computed tomography (GECT) was used to investigate the spatial distribution of gamma-emitting fission products within the rings. This report focuses on results obtained via the Precision Gamma Scanner (PGS) for the compacts and inner and outer rings. These non-destructive gamma measurements are currently being compared to recent destructive measurements of spatial fission product distributions in the rings and to predictions made via mathematical modeling. A separate report discusses the entire AGR-3/4 fission product mass balance for all irradiation capsule components including the inner and outer rings, graphite sink rings, capsule spacers, capsule foils, through tubes, and felts. This report is similar to the conference proceedings: Harp, J.M., Demkowicz, P.A., and Stempien, J.D., “Initial gamma spectrometry examination of the AGR-3/4 irradiation,” International Topical Meeting High Temperature Reactor Technology (HTR 2016), Las Vegas, NV, USA, November 2016, Paper HTR2016-18593, but it has been updated to reflect all the data that has been collected on AGR-3/4 using PGS. The Appendices contain extensive data from the collected gamma spectra.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

STUDY OF THE η'π 0 SYSTEM IN GLUEX

The η'π is an important decay channel for the potentially exotic hybrid meson π 1 (1600) that has been detected in the COMPASS, VES and E852 experiments. These previous experiments used a charged pion beam as a probe. In contrast, the GlueX experiment is designed to search for the exotic hybrid mesons using a linearly polarized photon beam. This thesis project analyzes the η'π 0 system produced in GlueX by the reaction γp → η'π 0 p. After optimizing the event selection to minimize background contributions, a mass independent partial wave analysis is performed in search of an exotic signature. Within the currently available statistical precision, no signal was found.

Dotel, Rupesh↗

DOE OSTI2022

High CO2 Capture Rate Cost and Performance for F- and H-Class Natural Gas Combined Cycles (NGCC)

The National Energy Technology Laboratory (NETL) is updating its study titled “Cost and Performance Baselines for Fossil Energy Plants, Volume 1: Bituminous Coal and Natural Gas to Electricity.” Revision 4 was published in 2019. Pulverized coal (PC) and natural gas combined cycle (NGCC) cases with carbon capture in that study capture carbon dioxide (CO2) at 90% efficiency. Field-testing of post-combustion CO2 capture technology as well as vendor and industry feedback on projects currently in the planning stages (including FEED projects sponsored by DOE) indicate that capture rates as high as 95% are feasible for both coal- and natural gas-fueled electricity generating units. Technology suppliers (as reflected in vendor-supplied information provided to DOE for this study that included cost and performance estimates for both NGCC (97%) and PC (99%) study cases) as well as subject matter experts acknowledge and support that solvent-based post-combustion CO2 capture technologies are capable of achieving CO2 removal rates beyond 95% on low-purity streams representative of fossil-fueled combustion; however, the relatively limited experience with design and operation of capture systems that can routinely, reliably, and economically achieve very high removal rates (e.g., 97% for NGCC flue gas) requires further study. This presentation will address performance and cost estimates for 90%, 95%, and 97% capture for F- and H-class NGCC plants. This presentation was given at the 2022 Pittsburgh Coal Conference.

Fout, Timothy↗

DOE OSTI2022

A Study on Surface Texture and Wettability of Femtosecond Laser Treated Aluminum Alloys

This research aims to study the effects of femtosecond laser treatment on surface texture and wettability properties. The first portion of this research studies 2024-T3 aluminum alloy concerning surface texture (e.g., roughness), wettability, and surface free energy. SEM images help aid the researchers in detecting minuscule changes in surface texture. Profilometry extracts topographical data from the surface. A contact angle goniometer measures contact angle, which follows the sessile drop method. Owens, Wendt, Rabel, and Kaelble's method calculates the surface free energy in conjunction with the contact angle. Femtosecond laser modification produces relatively uniform surface morphology at the macroscale. When looking at the microscale, surface morphology differs depending on the interplay between material melting and self-assembly, where the surface becomes rougher after laser treatment. Contact angle results show that all but the control sample and sample 7 exhibit hydrophobic behavior, i.e., contact angle >90°, with Sample 1 having the most significant angle of 115°. The effects of laser parameters on surface energy is apparent. More work is needed to establish correlations among these three quantities: contact angle, surface energy, and laser parameters. The second portion of this research studies aluminum alloy 6061 as the subject material with the primary goal of creating a superhydrophobic surface. A central composite design creates the laser surface texturing design of experiments. SEM images help aid the researchers in detecting minuscule changes in surface texture. A 3D optical profiler extracts topographical data from the surface. The sessile drop method incorporates a contact angle goniometer to measure the contact angle. The response surface method builds a second-order polynomial model for the contact angle and obtains optimized parameters to maximize the contact angle. This research shows that laser surface texturing can generate a wide range of surface profiles and roughness values with geometric features ranging from hundreds of μm to submicron. All three laser parameters (pulse energy, pulse duration, repetition rate) affect surface roughness and contact angle to some degree. To quantify the relationship between the contact angle, pulse energy, and pulse repetition rate, a response surface model for Al 6061 is identified and used to find the optimal conditions of E=214 μJ, tp=10 ps, and fp=2427 Hz with a predicted maximum contact angle of 161°. A confirmation experiment produces a contact angle of 168°, in good agreement with the predicted value.

Angell, Dakota↗