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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

14 SOLAR ENERGY↗
DOE OSTI2020

A Pilot-Scale Evaluation of Natural Gas-Based Foam at Elevated Pressure and Temperature Conditions

Abstract Nitrogen (N2) and Carbon Dioxide (CO2) foams have been used as hydraulic fracturing fluids for several decades to reduce water usage and minimize damage in water-sensitive reservoirs. These foam treatments require gases to be liquefied and transported to site. An alternative approach would be to use natural gas (NG) that is readily available from nearby wells, pipelines, and processing facilities as the internal, gaseous phase to create a NG-based foam. Hydraulic fracturing with NG foam is a relatively inexpensive option, makes use of an abundant and often wasted resource, and may even provide production benefits in certain reservoirs. As part of an ongoing development project sponsored by the Department of Energy (DOE), the surface process to create NG foam is being developed and the properties of NG foam are being explored. This paper presents recent results from a rigorous pilot-scale demonstration of NG foam over a range of operating scenarios relevant to surface and bottomhole conditions with a variety of base-fluid mixtures. The Pilot-scale Foam Test Facility (PFTF) used in these investigations is first described. The PFTF is capable of generating foamed fluids at pressures up to 7,500 psig and at temperatures in excess of 300°F. Then, results from several investigations aimed at proving NG foam at conditions relevant to the field are presented. NG foam was characterized using rheology measurements and flow visualization techniques. Experiments were performed to investigate the texture and stability of NG foam generated by two different mixing methods: one using a custom designed tee to match mixing velocities in the field where the gas phase is jetted into the aqueous stream, and another to ensure comprehensive mixing for laboratory analysis. Parametric studies were conducted to explore the effects of flow rate, foam quality, and temperature on the stability of NG foam. Moreover, different fluid preparations were used to investigate the effect of base fluid and additive concentrations on the stability of NG foams. Additional laboratory studies that investigated foam stability with produced water and multicomponent NG mixtures are also reported. The NG foams explored in these investigations exhibited typical, shear-thinning behavior observed in rheological studies of N2- and CO2-based foams. The measured viscosity and observed stability indicate that NG foams are well suited for fracturing applications. Like other foams, NG foam exhibits sensitivity to operating temperature characterized by a decrease in apparent viscosity as temperature increases. Rapid foam breakdown was observed at significantly elevated temperatures exceeding 290°F. In addition to fluid characterization, these investigations also yielded several key lessons that should be applied to future field demonstrations of NG foam.

Beck, Griffin↗
DOE OSTI2021

Acetonitrile Destruction and Fate of Organics in the Reverse Osmosis System at the ETF

The Hanford Site Effluent Treatment Facility (ETF) currently treats aqueous waste streams that include condensates from the 242-A evaporator, leachate from the Environmental Restoration Disposal Facility (ERDF), as well as laboratory wastes and, in the future, will treat liquid effluents from the Hanford Tank Waste Treatment and Immobilization Plant (WTP) and Integrated Disposal Facility (IDF) leachate. Liquid effluents from the WTP will have significant concentrations of acetonitrile. Acetonitrile is formed by reaction of nitrates and sugar in the WTP low activity waste (LAW) melters and is prevalent in the submerged bed scrubber (SBS) and wet electrostatic precipitator (WESP) liquid effluents from WTP off-gas treatment. When these liquids are concentrated in the WTP Effluent Management Facility (EMF) evaporator in the direct feed low activity waste (DFLAW) flow-sheet, testing has shown that the majority of the acetonitrile partitions to the evaporator condensate. Since the evaporator condensate is directed to the ETF, this creates a potential issue with the ETF waste acceptance criteria. Consequently, there is a need to validate flow-sheet assumptions on the fate of acetonitrile and other organics within the ETF. The present plan includes the addition of a steam stripper to the ETF to remove acetonitrile. There is, therefore, also a need to determine a suitable method to destroy acetonitrile in the overhead condensate stream from the new steam stripper. Washington River Protection Solutions, LLC (WRPS) previously contracted with Atkins and the Vitreous State Laboratory (VSL) of The Catholic University of America (CUA) to perform development and testing work to evaluate potential methods for destruction of acetonitrile in WTP secondary liquid effluents. Based on the results of that work, WRPS requested that follow-on testing be conducted to further evaluate acetonitrile destruction in the steam stripper condensate using ultraviolet oxidation (UV/OX) with persulfate. WRPS also requested testing to assess the rejection rate of organics in the reverse osmosis (RO) system installed in the ETF. This report presents the results from testing to address those needs.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

CSB-RNN: A Faster-Than-Realtime RNN Acceleration Framework with Compressed Structured Blocks

Recurrent Neural Networks (RNN) is widely applied to temporal sequence analysis, where real-time performance is usually in demand. However, RNN suffers a heavy computational workload as the model comes with a large weight matrix. To alleviate the pain, model compression (pruning) schemes have been proposed for RNN that pruning the redundant (near-zero) weight-values. On the one hand, the non-structured pruning methods achieve a considerable pruning rate while bringing the computational irregularity, which is un-friendly to parallel-hardware. On the other hand, the existing structured pruning methods consider the hardware parallelism; However, they suffer a poor pruning rate due to the restrict constraints on pruning structure. This paper presents CSB-RNN, an optimized full-stack RNN framework with the novel compressed structured block (CSB) technique. The CSB-pruned RNN model comes with both fine-granularity that benefits the pruning rate and regular structure that facilitates the hardware-parallelism. Further, we propose a novel hardware architecture for inferencing the CSB-pruned model. Different from conventional parallel hardware, this architecture solves the block-workload imbalance issue and achieves an over 95% hardware utilization. With the experiments on 10 RNN models in 5 application domains, the CSB-RNN realizes 7×-20× lossless compression and up to 50× acceptable lossy-compression, which is 2×-7× to the prior art. With the addition of the novel hardware, the compressed-RNN inference reaches a super real-time latency of 10-400µs with FPGA implementation.

Shi, Runbin↗
DOE OSTI2020

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

SU-F-T-550: Radiochromic Plastic Thin Sheet Dosimeter: Initial Performance

Purpose: Thin sheets, of a high sensitivity formulation of radiochromic dosimeter, Presage were prepared and evaluated for optical readout. Methods: Sheets of radiochromic polyurethane, 12 cm long, 10 cm wide and 0.2 cm thick were prepared with leuco crystal violet as the reporter molecule. Sample transmission was evaluated at a wavelength of 590 nm with in-house constructed instruments: optical cone beam laser CT scanner, fixed and scanning spot densitometers. Sample sequential irradiations to a total dose of 40 Gy were conducted with a modified, Theratron 60, cobalt radiotherapy machine at dose rates of 1 or 0.25 Gy per minute. Exposure to ambient and readout light was minimized to limit background photochromic signals. Samples were stored at 4°C. Optical activity was assessed from linearly polarized transmission images. Comparison sensitivity measurements with EBT3 film were conducted. Results: Samples were transparent, smooth and pale purple before irradiation. Radiochromic reaction was completed in less than 5 minutes. A linear dose response with a sensitivity of 0.5 cm-1Gy-1 was observed. Micrometer measurements found sheet thickness variations up to 20%. Uniform dose, 2 Gy attenuation images, correlated with local sheet thicknesses. Comparable measurements with EBT3 film were 3 times more sensitive at 1 Gy but above 15 Gy, EBT3 film had lower sensitivity than 0.2 cm thick Presage sheet dosimeter due to its non-linear response. Conclusion: Dose sensitivity provided a 10% decrease in transmission for a 1 Gy dose. Improvements in mold design are expected to allow production of sheets with less than 5% variation in thickness. Above, 10 Gy, Presage sheet dosimeter performance expected to exceed EBT3 film based on linearity, sensitivity, transparency and smoothness of samples. J Adamovics is owner of Heuris Inc.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Analysis of a Wave-Powered, Reverse-Osmosis System and Its Economic Availability in the United States: Preprint

A wave energy converter (WEC) system has the potential to convert the wave energy resource directly into the high-pressure flow that is needed by the desalination system to permeate saltwater through the reverse-osmosis membrane to generate clean water. In this study, a wave-to-water numerical model was developed to investigate the potential use of a wave-powered desalination system (WPDS) for water production in the United States. The model was developed by coupling a time-domain radiation-and-diffraction-method-based numerical tool (WEC-Sim) for predicting the hydrodynamic performance of WECs with a solution-diffusion model that was used to simulate the reverse-osmosis process. To evaluate the feasibility of the WPDS, the wave-to-water numerical model was applied to simulate a desalination system that used an oscillating surge WEC device to pump seawater through the system. The annual water production was estimated based on the wave resource at a reference site on the coast of northern California to investigate the potential cost of water in that area, where the cost of water and electricity is high compared to other regions. In the scenario evaluated, for a 100-unit utility-scale electricity-producing array, the estimated levelized cost of energy for these WECs is about 3-6 times the U.S.'s current, unsubsidized electricity rates. However, with clean water as an end product and by directly producing pressurized water with WECs, rather than electricity as an intermediary, it is presently only 12 percent greater than typical water cost in California. This study suggests that a WEC array that produces water may be a viable, near-term solution to the nation's water supply, and the niche application of the WPDS may also provide developers with new opportunities to further develop technologies that benefit both the electric and drinking water markets.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

Layered Li{sub 1+x}(Ni{sub 0.33}Co{sub 0.33}Mn{sub 0.33})O{sub 2} cathode material prepared by microwave assisted solvothermal method for lithium ion batteries

Highlights: • Microwave assisted solvothermal method. • Effect of (0.05–0.2) mol% of excess lithium in layered Li{sub 1+x}(Ni{sub 0.33}Co{sub 0.33}Mn{sub 0.33})O{sub 2}. • Different calcination temperature. • Voltage-cut off optimization. • Conductivity and electrochemical performance correlation. - Abstract: Layered hexagonal structure Li{sub 1+x}(Ni{sub 0.33}Co{sub 0.33}Mn{sub 0.33})O{sub 2} (LNCM) (x = 0, 0.05, 0.1, 0.15 and 0.2 mol%) compounds were prepared by microwave assisted solvothermal method at two different calcination temperature of 850 and 950 °C. The phase purity of the LNCM compounds was confirmed by Rietveld refined XRD pattern, Raman, and HRTEM studies. The average particle sizes were in the submicron range as confirmed by SEM and TEM analysis. Galvanostatic cycling studies were made in various voltage range, 2.5 to 4.4/4.6/4.8 V at a current rate of 0.2 C. Among all the compounds, LNCM prepared at 950 °C (HS3) exhibited an initial discharge capacity of 197 mAh/g and maintained a capacity of 180 mAh/g at the end of 40{sup th} cycle in 2.5–4.6 V window, indicating an excellent capacity retention. HS3 shows the electrical conductivity of 9.31 × 10{sup −6} S/cm, which was higher than all the other compounds and discussed the reason for the improved electrochemical performance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Smart Ventilation Controls for Occupancy and Auxiliary Fan Use Across U.S. Climates

Smart Ventilation has been developed as a way to reduce the energy associated with ventilation by changing when ventilation happens and how much ventilation occurs at any given time. In high performance buildings with low envelope and appliance related loads, ventilation is becoming a bigger part of the total building energy use and needs to be addressed if performance targets are to be met. This work explores the development and performance of smart ventilation controls based on occupancy and auxiliary fan operation that provide annual dwelling unit ventilation equivalence to ASHRAE Standard 62.2-2016. A prototype high performance home compliant with U.S. DOE Building America Zero Energy Ready program requirements was simulated using the REGCAP tool with and without smart controls across 15 U.S. DOE climate zones. Balanced and unbalanced IAQ fans were independently simulated, and all smart controlled fans had airflows double the reference 62.2-2016 airflow. Three idealized occupancy patterns were examined: 1st shift (a typical daily work/school absence), an extended 1st shift with more time spent out of the home evenings and weekends and 3rd shift (night work). The Occupancy controller turns the IAQ fan off during unoccupied periods, and it resumes ventilation upon their return. While unoccupied, contaminants are allowed to accumulate in the space, because occupants are not exposed to these contaminants. When occupants return home, they are exposed to these higher contaminant concentrations, and the controller increases the ventilation rate sufficiently to ensure equivalence with a continuous IAQ fan. Accounting for pollutant emissions that occur during unoccupied periods (as required by ASHRAE 62.2-2016), sharply distinguishes our occupancy controls from past Demand Controlled Ventilation systems. This new accounting method results in equivalent contaminant exposure, as well as lower reductions in average ventilation rates and lower energy savings. Smart controls were demonstrated that saved HVAC energy (i.e., avoided heating/cooling load, as well as fan energy)—averaging between 6 and 46% of ventilation-related energy use depending on the control strategy and occupancy pattern assumptions. The greatest savings were in the combined Auxiliary Fan + Occupancy control. Energy savings increased with climate heating demand and longer unoccupied time periods. The 3rd shift occupancy pattern had better performance, due to the thermal benefit of reducing the ventilation rate during the cold nighttime hours. This same effect provided a cooling energy benefit in hotter locations for the 1st shift. Overall, savings from occupancy-based smart controls were low, because of the recovery period required after occupants return home, during which the airflow is double the 62.2 reference. This recovery is required to maintain equivalence with the ASHRAE standard. Occupancy-based control performance was improved when combined with sensing auxiliary fans and when providing a pre-occupancy flush out of one- or two-hours. Performance was similarly improved if the ASHRAE Standard were to recognize that pollutant emissions are lower during unoccupied periods, iii allowing a lower target ventilation rate during unoccupied periods (not currently in the standard) (see Full vs. Half AEQ in this report). Finally, over-sized unbalanced fans that are cycled on-and-off by a smart controller (or timer) were found to substantially increase annual average air exchange and energy use relative to a continuous unbalanced fan due to the effects of superposition with natural infiltration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Understanding Fission Gas Bubble Distribution and Zirconium Redistribution in Neutron-irradiated U-Zr Metallic Fuel Using Machine Learning

U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Currently, Idaho National Laboratory (INL) has been the leading national laboratory for research, development, and demonstration (RD&D) on metallic fuel. Advanced post-irradiation characterization will help to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. Characterization capabilities ranging from sub-nanometer to micrometer, such as scanning electron microscopy (SEM), focused ion beam (FIB) sampling, transmission electron microscopy (TEM) characterization, and local thermal conductivity microscopy (TCM), have been utilized recently on irradiated U-10Zr fuel samples to gain a better understanding of nuclear fuel microstructure and property evolution inside a reactor. The FIB/SEM coupled with energy dispersive X-ray spectroscopy (EDS) can capture the essential information to achieve better understanding of fuel behaviors. Inside a nuclear reactor, the phase and microstructure of U-10Zr is constantly changing under neutron bombardment. For example, the gaseous fission product atoms have a limited solubility inside fuel matrix and tend to precipitate out in bubble form, which not only contribute to fuel thermal conductivity degradation but also provide a shortcut for movement of fission products, i.e. lanthanides. The resultant deposition of lanthanides at the cladding inner surface will potentially trigger a chemical reaction/interaction between nuclear fuel and cladding at reactor operational conditions, threatening fuel integrity and safety. FIB/SEM coupled with EDS can provide the fission bubble information as well as probe into phase separation or Zr redistribution, which is fundamental to predict the fuel performance. With high velocity image data generating method, such as FIB/SEM, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to generate a bubble classifier and to categorize bubbles into three categories: isolated bubble, connected without lanthanides, and connected with lanthanides bubbles[3]. This work presents a showcase of this approach on six regions of a fuel cross-section along the radial temperature gradient. We obtained distributions of bubble categories and porosity rates along the six regions. Moreover, a secondary phase U-Zr2 was determined and found on regions 5 and 6. The secondary phase fraction was increasing from 15.61% in region 5 to 34.79% in region 6 based on this approach . This quantitative data offers insights into the lanthanide migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Clinical Implementation of a Model-Based In Vivo Dose Verification System for Stereotactic Body Radiation Therapy–Volumetric Modulated Arc Therapy Treatments Using the Electronic Portal Imaging Device

Purpose: To report findings from an in vivo dosimetry program implemented for all stereotactic body radiation therapy patients over a 31-month period and discuss the value and challenges of utilizing in vivo electronic portal imaging device (EPID) dosimetry clinically. Methods and Materials: From December 2013 to July 2016, 117 stereotactic body radiation therapy–volumetric modulated arc therapy patients (100 lung, 15 spine, and 2 liver) underwent 602 EPID-based in vivo dose verification events. A developed model-based dose reconstruction algorithm calculates the 3-dimensional dose distribution to the patient by back-projecting the primary fluence measured by the EPID during treatment. The EPID frame-averaging was optimized in June 2015. For each treatment, a 3%/3-mm γ comparison between our EPID-derived dose and the Eclipse AcurosXB–predicted dose to the planning target volume (PTV) and the ≥20% isodose volume were performed. Alert levels were defined as γ pass rates <85% (lung and liver) and <80% (spine). Investigations were carried out for all fractions exceeding the alert level and were classified as follows: EPID-related, algorithmic, patient setup, anatomic change, or unknown/unidentified errors. Results: The percentages of fractions exceeding the alert levels were 22.6% for lung before frame-average optimization and 8.0% for lung, 20.0% for spine, and 10.0% for liver after frame-average optimization. Overall, mean (± standard deviation) planning target volume γ pass rates were 90.7% ± 9.2%, 87.0% ± 9.3%, and 91.2% ± 3.4% for the lung, spine, and liver patients, respectively. Conclusions: Results from the clinical implementation of our model-based in vivo dose verification method using on-treatment EPID images is reported. The method is demonstrated to be valuable for routine clinical use for verifying delivered dose as well as for detecting errors.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Realistic operation of two residential cordwood-fired outdoor hydronic heater appliances—Part 3: Optical properties of black and brown carbon emissions

Residential biomass combustion is a source of carbonaceous aerosol. Inefficient combustion, particularly of solid fuels produces large quantities of black and brown carbon (BC and BrC). These particle types are important as they have noted effects on climate forcing and human health. One method of measuring these quantities is by measurement of aerosol light-absorption and scattering, which can be performed using an aethalometer and nephelometer, respectively. These instruments are widely deployed in the study of ambient air and are frequently used in air quality modeling and source apportionment studies. In this study, we will describe (1) a method for measuring primary BC and BrC emissions from two residential log-fired wood hydronic heaters and (2) the BC and BrC emission from these devices over a wide range of operating conditions, such as cold-starts, warm-starts, four different levels of output ranging from 15% to 100% maximum rated output, and periods of repeated cycling. The range in flue-gas BC concentrations, measured using an aethalometer at the 880 nanometer (nm) wavelength, were between 5.09 × 10 2 and 2.24 × 10 4 micrograms per cubic meter (µg/m 3 ) while the scattering coefficient of the flue-gas, measured by a nephelometer at 880 nm, ranged between 2.20 × 10 3 and 8.56 × 10 5 inverse megameters (Mm –1 ). The BrC concentrations, measured using the 370 nm wavelength of an aethalometer, were between 9.10 × 10 1 and 3.56 × 10 4 µg/m 3 . The calculated Angstrom Absorption Exponent (AAE) of the flue-gas aerosol ranged between 1.54 and 3.63. Performing a comparison between the measured BC concentration and an external particulate matter (PM) concentration showed that overall BC makes up roughly a quarter of the PM emitted by either of the two appliances. Further for both appliances, the cold-start and the test phase immediately following it had the highest BC and BrC concentrations, the highest measured scattering coefficient, as well as a low AAE.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Extended abdominopelvic MRI versus CT at the time of adnexal mass characterization for assessing radiologic peritoneal cancer index (PCI) prior to cytoreductive surgery

Purpose: To evaluate whether extending the MRI scan to include the abdomen at the time of adnexal mass characterization could replace additional CT for peritoneal cancer index (PCI) assessment. Methods: After institutional review board approval for this prospective study, 36 consecutive females with ovarian and FT malignancies were included. All patients signed an informed consent. Patients underwent preoperative CT (32 patients) and MRI (36 patients). Images were interpreted by 2 independent observers. Surgical data were available in 27 patients. Region-by-region analysis was performed for detection rates of peritoneal carcinomatosis (PC). Inter-observer agreement for each region was evaluated by kappa statistics. Radiologic PCI was calculated by CT and MRI independently and inter-observer agreement for CT and MRI as well as agreement between radiologic and surgical PCI were evaluated by weighted-kappa statistics. Results: On region-by-region analysis, the highest detection rates of PC were noted at the central abdomen and pelvis. Detection rates were higher by MRI than CT, mainly in bowel serosal surface, pelvis, and right upper abdomen regions. Inter-observer agreement of MRI was higher than CT in most regions. The median PCI by CT was 5 and 4 for the first and second observers (range 0–21 for both observers), respectively. The median PCI by MRI was 6 (range 0–23 for both observers). The inter-observer agreement of PCI was excellent by both CT and MRI (k = 0.876 and k = 0.912, respectively). The agreement between CT and surgical PCI was 0.660 and 0.590 for the first and second observers, respectively. The agreement between MRI and surgical PCI was 0.797 and 0.798 for the first and second observers, respectively. Conclusions: Extending MRI scan to include the abdomen at the time of adnexal mass characterization allows accurate estimation of PC, with better results than CT, obviating the need for dedicated CT scan of abdomen and pelvis for imaging of PC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Corrosion Performance of Fe-Based Alloys in Simulated Oxy-Fuel Environment

The long-term corrosion of Fe-based alloys in simulated oxy-fuel environment at 1023 K (750 A degrees C) was studied. Detailed results are presented on weight change, scale thickness, internal penetration, microstructural characteristics of the corrosion products, and the cracking of scales for the alloys after exposure at 1023 K (750 A degrees C) for up to 3600 hours. An incubation period during which the corrosion rate was low was observed for the alloys. After the incubation period, the corrosion accelerated, and the corrosion process followed linear kinetics. Effects of alloy, CaO-containing ash, and gas composition on the corrosion rate were also studied. In addition, synchrotron nanobeam X-ray analysis was employed to determine the phase and chemical composition of the oxide layers on the alloy surface. Results from these studies are being used to address the long-term corrosion performance of Fe-based alloys in various coal-ash combustion environments and to develop methods to mitigate high-temperature ash corrosion.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bayesian calibration of bubble size dynamics applied to CO 2 gas fermenters

To accelerate the scale-up of gaseous CO 2 fermentation reactors, computational models need to predict gas-to-liquid mass transfer which requires capturing the bubble size dynamics, i.e. bubble breakup and coalescence. However, the applicability of existing models beyond air–water mixtures remains to be established. Here, an inverse modeling approach, accelerated with a neural network surrogate, calibrates the breakup and coalescence closure models, that are used in class methods for population balance modeling (PBM). The calibration is performed based on experimental results obtained in a CO 2 -air–water-coflowing bubble column reactor. Bayesian inference is used to account for noise in the experimental dataset and bias in the simulation results. To accurately capture gas holdup and interphase mass transfer, the results show that the breakage rate needs to be increased by one order of magnitude. In conclusion, the inferred model parameters are then used on a separate configuration and shown to also improve bubble size distribution predictions.

09 BIOMASS FUELS↗
DOE OSTI2016

Long-Term Outcomes After Proton Beam Therapy for Sinonasal Squamous Cell Carcinoma

Purpose: Squamous cell carcinoma (SCC) is the most common sinonasal cancer and is associated with one of the poor outcomes. Proton therapy allows excellent target coverage with maximal sparing of adjacent normal tissues. We evaluated the long-term outcomes in patients with sinonasal SCC treated with proton therapy. Methods and Materials: Between 1991 and 2008, 54 patients with Stage III and IV SCC of the nasal cavity and paranasal sinus received proton beam therapy at our institution to a median dose of 72.8 Gy(RBE). Sixty-nine percent underwent prior surgical resection, and 74% received elective nodal radiation. Locoregional control and survival probabilities were estimated with the Kaplan-Meier method. Multivariate analyses were performed using the Cox proportional-hazards model. Treatment toxicity was scored using the Common Terminology Criteria for Adverse Events version 4.0. Results: With a median follow-up time of 82 months in surviving patients, there were 10 local, 7 regional, and 11 distant failures. The 2-year and 5-year actuarial local control rate was 80%. The 2-year and 5-year rates of overall survival were 67% and 47%, respectively. Only smoking status was predictive for worse locoregional control, with current smokers having a 5-year rate of 23% compared with 83% for noncurrent smokers (P=.004). Karnofsky performance status ≤80 was the most significant factor predictive for worse overall survival in multivariate analysis (adjusted hazard ratio 4.5, 95% confidence interval 1.6-12.5, P=.004). There were nine grade 3 and six grade 4 toxicities, and no grade 5 toxicity. Wound adverse events constituted the most common grade 3-4 toxicity. Conclusions: Our long-term results show that proton radiation therapy is well tolerated and yields good locoregional control for SCC of the nasal cavity and paranasal sinus. Current smokers and patients with poor performance status had inferior outcomes. Prospective study is necessary to compare IMRT with proton therapy in the treatment of sinonasal malignancy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm 2 aqueous organic redox flow battery

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗
DOE OSTI2022

Novel textured surfaces for superior corrosion mitigation in molten carbonate salts for concentrating solar power

Corrosion of metals in contact with molten salts used for thermal energy storage and heat transport at high temperatures is a serious impediment to the development of next-generation concentrating solar thermal systems. Toward addressing this limitation, novel multiscale fractal-textured Ni coatings on a variety of substrate alloys are presented in this study for exceptional corrosion mitigation in molten carbonate salts at a high temperature. Strongly adherent, durable, single-layer, and double-layer multiscale fractal coatings were fabricated using the electrodeposition method. Corrosion performance of the coatings on SS310, SS316, SS347, and In800H was studied in molten carbonate salt (32% Li 2 CO 3 +33% Na 2 CO 3 +35% K 2 CO 3 ) at 750 °C. Single-layer coatings are stable up to 300 h immersion, whereas double-layer coatings are stable beyond 750 h. The corrosion rate of double-layer Ni coatings on ferrous alloys was reduced by as much as 60% from that of uncoated surfaces and was about 18% below that of higher cost, high Ni content Ha230. The study represents the first-ever report of corrosion characteristics of alloys in carbonate salts at 750 °C and the first demonstration of a means of dramatically reducing corrosion in carbonate salts at a high temperature. The article is significant in that it provides a viable approach for low-cost structural alloys to be corrosion-resistant to molten carbonate heat transfer fluids and storage media in high-temperature concentrating solar thermal applications. Furthermore, the corrosion-resistant coatings provide opportunities for the use of low-cost, ferritic alloys instead of the expensive nickel-based alloys in practice.

14 SOLAR ENERGY↗