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NASA NTRS2013

Solutions of the Taylor-Green Vortex Problem Using High-Resolution Explicit Finite Difference Methods

A computational fluid dynamics code that solves the compressible Navier-Stokes equations was applied to the Taylor-Green vortex problem to examine the code s ability to accurately simulate the vortex decay and subsequent turbulence. The code, WRLES (Wave Resolving Large-Eddy Simulation), uses explicit central-differencing to compute the spatial derivatives and explicit Low Dispersion Runge-Kutta methods for the temporal discretization. The flow was first studied and characterized using Bogey & Bailley s 13-point dispersion relation preserving (DRP) scheme. The kinetic energy dissipation rate, computed both directly and from the enstrophy field, vorticity contours, and the energy spectra are examined. Results are in excellent agreement with a reference solution obtained using a spectral method and provide insight into computations of turbulent flows. In addition the following studies were performed: a comparison of 4th-, 8th-, 12th- and DRP spatial differencing schemes, the effect of the solution filtering on the results, the effect of large-eddy simulation sub-grid scale models, and the effect of high-order discretization of the viscous terms.

DeBonis, James R.↗

NASA NTRSDate not supplied

Evaluation of Aerobic Standards for Lunar Surface Extravehicular Activities

Introduction: As NASA prepares to return to the Moon, astronauts will need to be physically primed to successfully execute Extravehicular Activities (EVA) on the Lunar surface. Compared to past Apollo missions, Artemis missions will include EVAs of increased physical demand, frequency, intensity, and duration, thus requiring adequate fitness to successfully and safely complete mission objectives. The physical demand associated with partial gravity (g) EVAs on the Moon is expected to be greater compared to microgravity EVAs based on initial workload estimation. Currently, aerobic fitness standards for partial g EVAs are not well supported by high-fidelity data and require further research for establishing standards to protect crew health and performance during Lunar surface missions. Therefore, the aim of this investigation is to characterize metabolic data from Lunar analog simulations and in-flight crew population aerobic capacity data to validate the current NASA 3001 standard for celestial partial g aerobic fitness (aerobic capacity (VO_2pk) ≥36.5ml/kg/min). Methods: In order to evaluate aerobic fitness requirements for Lunar EVAs, the following were performed: 1) preliminary analysis of long-duration (6 hr) EVA analog simulations in the Neutral Buoyancy Laboratory (NBL) and the Active Response Gravity Offload System (ARGOS) to evaluate expected metabolic rates for 1/6 g EVAs (NBL: n=1 female; ARGOS: n=1 male) and 2) assessment of the current NASA 3001 celestial surface EVA aerobic standard (aerobic capacity (VO_2pk) ≥36.5ml/kg/min) with data from an ISS astronaut population (n=30 male + 13 female) captured before and during space flight (flight day 15). Preliminary Results: Average fractional aerobic capacity during simulated EVAs were 33%±7% VO_2pk and 23.3±7% VO_2pk in the NBL and ARGOS, respectively. This was within a previously predicted 30–40% sustainable work rate. Average metabolic rates for some tasks performed in the NBL, such as traverse (40.4% VO_2pk) and ingress (47.1% VO_2pk) were higher than the predicted sustainable work range. In ARGOS, the tasks with the greatest metabolic rates were object relocation (34.2% VO_2pk) and incapacitated crew rescue (27.0% VO_2pk). Characterization of ISS crewmember aerobic capacity determined that the average preflight VO_2pk was 42.1±5.4 ml/kg/min for females and 37.5±5.4 ml/kg/min for males. At preflight, 21.5% of crewmembers were below the 36.5 ml/kg/min in-mission aerobic standard for celestial surface EVA as outlined in NASA-STD-3001. In-flight, both female and male crewmembers experienced reductions in VO_2pk (11.7% and 10.9%, respectively), such that, during the mission, 62% of crewmembers were below the standard aerobic capacity level for celestial surface EVAs. Conclusions: Our preliminary data suggest that while average metabolic rates for simulated Lunar EVA fall within the 30–40% sustainable work range, task specific metabolic rates exceed this range and may indicate that greater fitness is necessary for more strenuous tasks expected to be performed on the Lunar surface. Additionally, deconditioning due to space flight results in most crewmembers falling below the current celestial partial g EVA standard, which may increase risk to crew health and performance and completing mission objectives for surface missions. Further research is necessary in Artemis-specific analog environments to validate the current NASA-3001 aerobic standard for celestial EVAs. Additionally, work is ongoing to validate the current NASA-3001 strength standard for celestial EVAs.

N.C. Strock↗

DOE OSTI2021

Modeling of Thermal Decomposition of TATB-Based Explosive for Safety Analysis

We investigate and model the cook-off behavior of LX-17 to understand the response of explosive systems in abnormal thermal environments. Decomposition has been explored via conventional ODTX (One-Dimensional Time-to-eXplosion), PODTX (ODTX with pressure-measurement), TGA (Thermo-Gravimetric Analysis), and DSC (Differential Scanning Calorimetry) experiments under isothermal and ramped temperature profiles. The data were used to fit reaction rate parameters for proposed schemes in an ALE3D computational model. This model includes chemical reactions, thermo- and hydro-dynamics, and material properties, including thermal expansion, compressibility, and strength. These parameterizations were carried out utilizing a Python evolutionary optimization method on LLNL’s high-performance computing clusters. Additional experiments are being developed to further characterize and monitor decomposition intermediates to improve the model. Once experimentally validated, this model will be scalable to several applications involving LX-17. Furthermore, the optimization methodology developed herein should be applicable to other high explosive materials.

Chemistry - Chemical explosives↗

DOE OSTI2022

Fault Characterization and Diagnostics Supporting Condition-Based Operation and Maintenance of Gas Turbine Engines

Condition-Based Operation and Maintenance (CBOM) is the state-of-the-art in maintenance approaches for gas turbine engines. CBOM applies engine sensor information to the optimization of future operation and maintenance procedures; this technique reduces costs for engine operators by minimizing unplanned outages and catastrophic engine degradation. However, due to the complexities of gas turbine operation and the lack of available engine sensors, further development is required to fully realize the benefits of CBOM. In the turbine section, rotating components interact with high temperature flows, which creates intense thermal and mechanical stresses. As a result, there are numerous mechanisms of component degradation in the turbine section. Furthermore, turbine components – like stator vanes and rotor blades – are among the most expensive in the engine because they are complex to design and manufacture. For these reasons, this dissertation addresses two main questions: (i) which parameters or faults within the turbine section are most important to monitor, and (ii) how can these parameters or faults be monitored in an engine-relevant environment? Although many turbine parameters and faults have been investigated in the open literature, there are some faults that are still not well understood. Rotor-casing eccentricity, which causes a non-constant blade tip clearance around the annulus, has not been investigated in terms of its effects on turbine efficiency. Therefore, the first study in this dissertation quantifies overall and local turbine efficiency for varying levels of rotor-casing eccentricity. Results showed negligible variations to overall turbine efficiency, meaning rotor-casing eccentricity only becomes relevant to CBOM when its severity causes rotordynamic issues. Purge flow is critical to turbine hardware longevity because it prevents ingestion of hot main gas path (MGP) flow into the under-platform regions. Despite its importance, there are currently no methods for monitoring purge flow performance in an engine environment. Therefore, the second study in this dissertation develops a predictive model for sealing effectiveness using inputs from two fast-response pressure sensors. Results exhibited low prediction errors across a full range of purge flow rates, which supports the viability of the modelling approach for CBOM. The final two studies in this dissertation address blade coolant flow monitoring. This cooling flow is responsible for protecting the turbine blades from the MGP flow, which exits the combustor at temperatures greater than the blade melting point. These studies showed that temperature measurements on the blade surface can be used to accurately predict blade coolant flow rate, and that defining the candidate features relative to the coolant trajectory is important for maintaining accuracy as coolant flow rate degradation occurs. This work enables blade coolant flow monitoring, which is currently not possible through existing condition monitoring techniques.

condition-based, gas turbines, diagnostics,↗

NASA NTRS2008

Estimating the Need for Medical Intervention due to Sleep Disruption on the International Space Station

During ISS and shuttle missions, difficulties with sleep affect more than half of all US crews. Mitigation strategies to help astronauts cope with the challenges of disrupted sleep patterns can negatively impact both mission planning and vehicle design. The methods for addressing known detrimental impacts for some mission scenarios may have a substantial impact on vehicle specific consumable mass or volume or on the mission timeline. As part of the Integrated Medical Model (IMM) task, NASA Glenn Research Center is leading the development of a Monte Carlo based forecasting tool designed to determine the consumables required to address risks related to sleep disruption. The model currently focuses on the International Space Station and uses an algorithm that assembles representative mission schedules and feeds this into a well validated model that predicts relative levels of performance, and need for sleep (SAFTE Model, IBR Inc). Correlation of the resulting output to self-diagnosed needs for hypnotics, stimulants, and other pharmaceutical countermeasures, allows prediction of pharmaceutical use and the uncertainty of the specified prediction. This paper outlines a conceptual model for determining a rate of pharmaceutical utilization that can be used in the IMM model for comparison and optimization of mitigation methods with respect to all other significant medical needs and interventions.

Myers, Jerry G.↗

NASA NTRS2009

Return to Flying Duties Following Centrifuge or Vibration Exposures

Introduction: In an effort to determine the human performance limits for vibration in spacecraft being developed by NASA, astronauts were evaluated during a simulated launch profile in a centrifuge/vibration environment and separate vibration-only simulation. Current USAF and Army standards for return to flight following centrifuge exposures require 12-24 hours to pass before a crewmember may return to flying duties. There are no standards on vibration exposures and return to flying duties. Based on direct observation and provocative neurological testing of the astronauts, a new standard for return to flying duties following centrifuge and/or vibration exposures was established. Methods: 13 astronaut participants were exposed to simulated launch profiles in a + 3.5 Gx bias centrifuge/vibration environment and separately on a vibration table at the NASA-Ames Research Center. Each subject had complete neurological evaluations pre- and post-exposure for the centrifuge/vibration runs with the NASA neurological function rating scale (NFRS). Subjects who participated in the vibration-only exposures had video oculography performed with provocative maneuvers in addition to the NFRS. NFRS evaluations occurred immediately following each exposure and at 1 hour post-run. Astronauts who remained symptomatic at 1 hour had repeat NFRS performed at 1 hour intervals until the crewmember was asymptomatic. Results: Astronauts in the centrifuge/vibration study averaged a 3-5 point increase in NFRS scores immediately following exposure but returned to baseline 3 hours post-run. Subjects exposed to the vibration-only simulation had a 1-3 point increase following exposure and returned to baseline within 1-2 hours. Pre- and post- vibration exposure video oculography did not reveal any persistent ocular findings with provocative testing 1 hour post-exposure. Discussion: Based on direct observations and objective measurement of neurological function in astronauts following simulated launch profiles, asymptomatic individuals are allowed to return to flying duties within 3 hours following centrifuge/vibration and 2 hours after vibration-only exposures.

Scheuring, Richard A.↗

DOE OSTI2021

CTRAMER: An open-source software package for correlating interfacial charge transfer rate constants with donor/acceptor geometries in organic photovoltaic materials

In this paper, we present CTRAMER (Charge-Transfer RAtes from Molecular dynamics, Electronic structure, and Rate theory)—an opensource software package for calculating interfacial charge-transfer (CT) rate constants in organic photovoltaic (OPV) materials based on ab initio calculations and molecular dynamics simulations. The software is based on identifying representative donor/acceptor geometries within interfacial structures obtained from molecular dynamics simulation of donor/acceptor blends and calculating the corresponding Fermi’s golden rule CT rate constants within the framework of the linearized-semiclassical approximation. While the methods used are well established, the integration of these state-of-the-art tools originating from different disciplines to study photoinduced CT processes with explicit treatment of the environment, in our opinion, makes this package unique and innovative. The software also provides tools for investigating other observables of interest. After outlining the features and implementation details, the usage and performance of the software are demonstrated with results from an example OPV system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST

In this paper, we report on disruption prediction using a shallow machinelearning method known as Random Forests, trained on large databases containingonly plasma parameters that are available in real-time on Alcator C-Mod, DIII-D, and EAST. The database for each tokamak contains parameters sampled ~10 6 times throughout ~10 4 discharges (disruptive and non-disruptive) over the last 4years of operation. It is found that a number of parameters (e.g.P rad /P input ,l i ,n/n G ,B n=1 /B 0 ) exhibit changes in aggregate as a disruption is approached on one ormore of these tokamaks. However, for each machine, the most useful parameters,as well as the details of their precursor behaviors, are markedly different. When the prediction problem is framed using a binary classification scheme to discriminate between time slices "close to disruption" and "far from disruption," it is found that the prediction algorithms differ substantially in performance among the three machines ona time slice-by-time slice basis, but have similar disruption detection rates (~80-90%) on a shot-by-shot basis after appropriate optimisation. This could have important implications for disruption prediction and avoidance on ITER, for which developmentof a training database of disruptions may be infeasible. The algorithm's output is interpretable using a method that identifies the most strongly contributing inputsignals, which may have implications for avoiding disruptive scenarios. Lastly, to further support its real-time capability, successful applications in inter-shot and real-time environments on EAST and DIII-D are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Efficient and assured reinforcement learning-based building HVAC control with heterogeneous expert-guided training

Abstract Building heating, ventilation, and air conditioning (HVAC) systems account for nearly half of building energy consumption and $$20\%$$ of total energy consumption in the US. Their operation is also crucial for ensuring the physical and mental health of building occupants. Compared with traditional model-based HVAC control methods, the recent model-free deep reinforcement learning (DRL) based methods have shown good performance while do not require the development of detailed and costly physical models. However, these model-free DRL approaches often suffer from long training time to reach a good performance, which is a major obstacle for their practical deployment. In this work, we present a systematic approach to accelerate online reinforcement learning for HVAC control by taking full advantage of the knowledge from domain experts in various forms . Specifically, the algorithm stages include learning expert functions from existing abstract physical models and from historical data via offline reinforcement learning, integrating the expert functions with rule-based guidelines, conducting training guided by the integrated expert function and performing policy initialization from distilled expert function. Moreover, to ensure that the learned DRL-based HVAC controller can effectively keep room temperature within the comfortable range for occupants, we design a runtime shielding framework to reduce the temperature violation rate and incorporate the learned controller into it. Experimental results demonstrate up to 8.8 X speedup in DRL training from our approach over previous methods, with low temperature violation rate.

Xu, Shichao↗

DOE OSTI2017

FY2016 ILAW Glass Corrosion Testing with the Single-Pass Flow-Through Method

The inventory of immobilized low-activity waste (ILAW) produced at the Hanford Tank Waste Treatment and Immobilization Plant (WTP) will be disposed of at the near-surface, on-site Integrated Disposal Facility (IDF). When groundwater comes into contact with the waste form, the glass will corrode and radionuclides will be released into the near-field environment. Because the release of the radionuclides is dependent on the dissolution rate of the glass, it is important that the performance assessment (PA) model accounts for the dissolution rate of the glass as a function of various chemical conditions. To accomplish this, an IDF PA model based on Transition State Theory (TST) can be employed. The model is able to account for changes in temperature, exposed surface area, and pH of the contacting solution as well as the effect of silicon concentrations in solution, specifically the activity of orthosilicic acid (H4SiO4), whose concentration is directly linked to the glass dissolution rate. In addition, the IDF PA model accounts for the alkali-ion exchange process as sodium is leached from the glass and into solution. The effect of temperature, pH, H4SiO4 activity, and the rate of ion-exchange can be parameterized and implemented directly into the PA rate law model. The rate law parameters are derived from laboratory tests with the single-pass flow-through (SPFT) method. To date, rate law parameters have been determined for seven ILAW glass compositions, thus additional rate law parameters on a wider range of compositions will supplement the existing body of data for PA maintenance activities. The data provided in this report can be used by ILAW glass scientists to further the understanding of ILAW glass behavior, by IDF PA modelers to use the rate law parameters in PA modeling efforts, and by Department of Energy (DOE) contractors and decision makers as they assess the IDF PA program.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

An In Situ , multi-electrode electrochemical method to assess the open circuit potential corrosion of Cr in unpurified molten FLiNaK

An in situ electrochemical method to investigate the time-dependent spontaneous corrosion of pure Cr in an unpurified LiF-NaF-KF eutectic salt at 600 °C was developed. A multi-electrode electrochemical cell and a dual-electrode method were utilized to detect Cr(II) and Cr(III) ions on a platinum working electrode using cyclic voltammetry as a function of exposure time and compensating for the distance from the Cr electrode. XRD was performed to characterize the crystalline composition of the exposed FLiNaK and the salt films formed on the working electrodes. The fate of all electro-oxidized Cr must be accounted for to quantify corrosion rates. The concentrations of Cr(II) and Cr(III) species obtained were compared with the gravimetric mass change and ICP-OES analysis of the residual salt. Finally, outcomes, difficulties, and limitations are discussed.

ICP-OES↗

DOE OSTI2017

Carbon nanotube supported PdAg nanoparticles for electrocatalytic oxidation of glycerol in anion exchange membrane fuel cells

Electro-oxidation of alcohol is the key reaction occurring at the anode of a direct alcohol fuel cell (DAFC), in which both reaction kinetics (rate) and selectivity (to deep oxidation products) need improvement to obtain higher power density and fuel utilization for a more efficient DAFC. We recently found that a PdAg bimetallic nanoparticle catalyst is more efficient than Pd for alcohol oxidation: Pd can facilitate deprotonation of alcohol in a base electrolyte, while Ag can promote intermediate aldehyde oxidation and cleavage of C-single bondC bond of C 3 species to C 2 species. Furthermore, a combination of the two active sites (Pd and Ag) with two different functions, can simultaneously improve the reaction rates and deeper oxidation products of alcohols. In this continuing work, Pd, Ag mono, and bimetallic nanoparticles supported on carbon nanotubes (Ag/CNT, Pd/CNT, Pd 1 Ag 1 /CNT, and Pd 1 Ag 3 /CNT) were prepared using an aqueous-phase reduction method; they served as working catalysts for studying electrocatalytic oxidation of glycerol in an anion-exchange membrane-based direct glycerol fuel cell. Combined XRD, TEM, and HAADF-STEM analyses performed to fully characterize as-prepared catalysts suggested that they have small particle sizes: 2.0 nm for Pd/CNT, 2.3 nm for PdAg/CNT, 2.4 nm for PdAg 3 /CNT, and 13.9 nm for Ag/CNT. XPS further shows that alloying with Ag results in more metal state Pd presented on the surface, and this may be related to their higher direct glycerol fuel cell (DGFC) performances. Single DGFC performance and product analysis results show that PdAg bimetallic nanoparticles can not only improve the glycerol reaction rate so that higher power output can be achieved, but also facilitate deep oxidation of glycerol so that a higher faradaic efficiency and fuel utilization can be achieved along with optimal reaction conditions (increased base-to-fuel ratio). Half-cell electrocatalytic activity measurement and single fuel cell product analysis of different glycerol oxidation intermediates, including C 3 : glycerate, tartronate, mesoxalate, and lactate; C 2 : glycolate and oxalate, over PdAg/CNT catalyst was further conducted and produced deeper insight into the synergistic effects and reaction pathways of bimetallic PdAg catalysts in glycerol electrocatalytic oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data

This report presents the results from the 2016 ABRF Proteome Informatics Research Group (iPRG) study on proteoform inference and FDR estimation from bottom-up proteomics data. For this study, three replicate Q Exactive Orbitrap LC-MS/MS datasets were generated from each of four E. coli samples spiked with different equimolar mixtures of small recombinant proteins selected to mimic pairs of homologous proteins. Participants were given raw data and a sequence file, and asked to identify the proteins and provide estimates on the false discovery rate at the proteoform level. As part of this study, we tested a new submission system with a format validator running on a virtual private server (VPS) and allowed methods to be provided as executable R Markdown or IPython Notebooks. The task was perceived as difficult, and only eight unique submissions were received, though those who participated did well, with no one method performing best on all samples. However, none of the submissions included a complete Markdown or Notebook, even though examples were provided. Future iPRG studies need to be more successful in promoting and encouraging participation. The VPS and submission validator easily scale to much larger numbers of participants in these types of studies. The unique “ground-truth” dataset for proteoform identification generated for this study is now available to the research community, as are the server-side scripts for validating and managing submissions.

Proteomics, proteoforms, inference, false discover↗

DOE OSTI2018

Potential Experimental Topics for EGS Collab Experiment 3

To facilitate the success of FORGE, the DOE GTO has initiated a new research effort, the EGS Collab project, which will utilize readily accessible underground facilities that can refine our understanding of rock mass response to stimulation and provide a test bed at intermediate (~10 m) scale for the validation of thermal-hydrological-mechanical-chemical modeling approaches as well as novel monitoring tools. The first two EGS Experiments 1 and 2 are planned be performed under different stress/fracture conditions, and will evaluate different stimulation processes: Experiment 1 will focus on hydrofracturing of a competent rock mass, while Experiment 2 will concentrate on hydroshearing of a rock mass that contains natural fractures. Experiment 3 is scheduled to begin in 2019 will build off the lessons learned in Experiments 1 and 2 and will investigate alternate stimulation and operation methods to improve heat extraction in an EGS reservoir. This paper evaluates potential experiments that could potentially be conducted in Experiment 3. The two technical parameters defining energy extracted from EGS reservoirs with the highest economic uncertainty and risk are the production well flow rates and the reservoir thermal drawdown rate. A review of historical and currently on-going EGS studies has identified that over ½ of the projects have identified heat extraction challenges during their operation associated with these two parameters as well as some additional secondary issues. At present, no EGS reservoir has continuously produced flow rates on the order of 80 kg/s. Short circuiting (i.e. early thermal breakthrough) has been identified in numerous cases. In addition, working fluid loss (i.e. the difference between the injected fluid mass and the extracted fluid mass as compared to the injected mass) has been as high as 90%. Finally, the engineering aspects of operating a true EGS multi-fracture reservoir such as repairing/modifying fractures and controlling working fluid fluxes within multiple fractures for the effective EGS fracture management has not been sufficiently studied. To examine issues such as these, EGS Collab Experiment 3 may be conducted in the testbeds prepared for Experiments 1 and 2 by improving the previously performed stimulations, or conducted at a new site performing new stimulations with alternate method. Potential experiments may include using different stimulation and working fluids, evaluating different stimulation methods, using proppants to enhance permeability, and other high-risk high-reward methods that can be evaluated at the 10-m scale environment.

15 GEOTHERMAL ENERGY↗

DOE OSTI2018

Potential Experimental Topics for EGS Collab Experiment 3

To facilitate the success of FORGE, the DOE GTO has initiated a new research effort, the EGS Collab project, which will utilize readily accessible underground facilities that can refine our understanding of rock mass response to stimulation and provide a test bed at intermediate (~10 m) scale for the validation of thermal-hydrological-mechanical-chemical modeling approaches as well as novel monitoring tools. The first two EGS Experiments 1 and 2 are planned be performed under different stress/fracture conditions, and will evaluate different stimulation processes: Experiment 1 will focus on hydrofracturing of a competent rock mass, while Experiment 2 will concentrate on hydroshearing of a rock mass that contains natural fractures. Experiment 3 is scheduled to begin in 2019 will build off the lessons learned in Experiments 1 and 2 and will investigate alternate stimulation and operation methods to improve heat extraction in an EGS reservoir. This paper evaluates potential experiments that could potentially be conducted in Experiment 3. The two technical parameters defining energy extracted from EGS reservoirs with the highest economic uncertainty and risk are the production well flow rates and the reservoir thermal drawdown rate. A review of historical and currently on-going EGS studies has identified that over 1/2 of the projects have identified heat extraction challenges during their operation associated with these two parameters as well as some additional secondary issues. At present, no EGS reservoir has continuously produced flow rates on the order of 80 kg/s. Short circuiting (i.e. early thermal breakthrough) has been identified in numerous cases. In addition, working fluid loss (i.e. the difference between the injected fluid mass and the extracted fluid mass as compared to the injected mass) has been as high as 90%. Finally, the engineering aspects of operating a true EGS multi-fracture reservoir such as repairing/modifying fractures and controlling working fluid fluxes within multiple fractures for the effective EGS fracture management has not been sufficiently studied. To examine issues such as these, EGS Collab Experiment 3 may be conducted in the testbeds prepared for Experiments 1 and 2 by improving the previously performed stimulations, or conducted at a new site performing new stimulations with alternate method. Potential experiments may include using different stimulation and working fluids, evaluating different stimulation methods, using proppants to enhance permeability, and other high-risk high-reward methods that can be evaluated at the 10-m scale environment.

15 GEOTHERMAL ENERGY↗

DOE OSTI2016

SU-F-T-36: Dosimetric Comparison of Point Based Vs. Target Based Prescription for Intracavitary Brachytherapy in Cancer of the Cervix

Purpose: Improved patient imaging used for planning the treatment of cervical cancer with Tandem and Ovoid (T&O) Intracavitary high-dose-rate brachytherapy (HDR) now allows for 3D delineation of target volumes and organs-at-risk. However, historical data relies on the conventional point A-based planning technique. A comparative dosimetric study was performed by generating both target-based (TBP) and point-based (PBP) plans for ten clinical patients. Methods: Treatment plans created using Elekta Oncentra v. 4.3 for ten consecutive cervical cancer patients were analyzed. All patients were treated with HDR using the Utrecht T&O applicator. Both CT and MRI imaging modalities were utilized to delineate clinical target volume (CTV) and organs-at-risk (rectum, sigmoid, bladder, and small bowel). Point A (left and right), vaginal mucosa, and ICRU rectum and bladder points were defined on CT. Two plans were generated for each patient using two prescription methods (PBP and TBP). 7Gy was prescribed to each point A for each PBP plan and to the target D90% for each TBP plan. Target V90%, V100%, and V200% were evaluated. In addition, D0.1cc and D2cc were analyzed for each organ-at-risk. Differences were assessed for statistical significance (p<0.05) by use of Student’s t-test. Results: Target coverage was comparable for both planning methods, with each method providing adequate target coverage. TBP showed lower absolute dose to the target volume than PBP (D90% = 7.0Gy vs. 7.4Gy, p=0.028), (V200% = 10.9cc vs. 12.8cc, p=0.014), (ALeft = 6.4Gy vs. 7Gy, p=0.009), and (ARight = 6.4Gy vs. 7Gy, p=0.013). TBP also showed a statistically significant reduction in bladder, rectum, small bowel, and sigmoid doses compared to PBP. There was no statistically significant difference in vaginal mucosa or ICRU-defined rectum and bladder dose. Conclusion: Target based prescription resulted in substantially lower dose to delineated organs-at-risk compared to point based prescription, while maintaining similar target coverage.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Particle Mass Flow Control for High-Temperature Concentrating Solar Receivers

This report summarizes the results of a two-year project funded by the U.S. Department of Energy's Solar Energy Technologies Office (SuNLaMP 1506) to evaluate the performance of high-temperature (>700 °C) particle receivers for concentrating solar power (see Appendix A for project information). In the first year, novel particle release patterns were designed and tested to increase the effective solar absorptance of the particle curtain. Modeling results showed that increasing the magnitude and frequency of different wave-like patterns increased the effective absorptance and thermal efficiency by several percentage points, depending on the mass flow rate. Tests showed that triangular-wave, square-wave, and parallel-curtain particle release patterns could be implemented and maintained at flow rates of ~10 kg/s/m. The second year of the project focused on the development and testing of particle mass-flow control and measurement methods. An automated slide gate controlled by the outlet temperature of the particles was designed and tested. Testing demonstrated that the resolution accuracy of the slide-gate positioning was less than ~1 mm, and the speed of the slide gate enabled rapid adjustments to accommodate changes in the irradiance to maintain a desired outlet temperature range. Different in-situ particle mass-flow measurement techniques were investigated, and two were tested. The in-situ microwave sensor was found to be unreliable and sensitive to variations in particle flow patterns. However, the in-situ weigh hopper using load cells was found to provide reliable and repeatable measurements of real-time in-situ particle mass flow. On-sun tests were performed to determine the thermal efficiency of the receiver as a function of mass flow rate, particle temperature, and irradiance. Models of the tests were also developed and compared to the tests.

14 SOLAR ENERGY↗

DOE OSTI2020

Deep Learning for Intelligent Bubble Size Detection in the Spallation Neutron Source Visual Target

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) will undergo proton power upgrade (PPU), increasing the proton beam power from 1.4 MW to 2.8 MW. From 2.8 MW, 2.0 MW will go to the current First Target Station and the rest will go to the future Second Target Station (STS). The First Target Station uses a liquid mercury target that is contained in a 316L stainless steel vessel. The proton beam is pulsed at 60 Hz, with a pulse of about 0.7μs. When the proton beam hits the target, the intense energy deposition leads to a rapid rise in temperature in the mercury. This temperature rise creates pressure waves that propagate through the mercury and cause cavitation erosion. The power upgrade will cause stronger pressure waves that will further increase damage because of cavitation. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at 1.4 MW. However, at higher power, additional mitigation is necessary. Therefore, the 2 MW target vessel will be equipped with swirl bubblers and an additional gas injection port near the nose to inject more gas in the target. To develop a gas injection strategy and design, flow visualization in water with a transparent prototypical target (“visual target”) was performed. Bubble sizes and their spatial distribution in the flow loop are crucial to understanding the effectiveness of the bubbles in mitigating pressure waves. Bubbles were generated in the visual target under varied conditions of input pressures with helium and air. Images were captured using a high-speed camera at varied frame rates at different positions away from the swirl bubbler and different depths in the flow loop under varying lighting conditions. Initially, methods such as circular Hough transforms were applied after a series of images processing to obtain a general distribution of bubble sizes. Bubbles smaller than 500 μm are preferred to effectively mitigate the effect of pressure waves, which demands an accurate bubble detection and sizing system. Intelligent detection and identification of bubble sizes alleviate misdetection and improves accuracies. Employing neural networks, intelligent detection of bubble sizes and their distribution was developed and provides a robust alternative to traditional techniques. Human intervention was employed to label in-focus and out-of-focus bubbles in the set of training images. An object detection network using a pretrained convolutional neural network was created that extracted the features from the training images. Data augmentation was used to improve network accuracy through a random transformation of the original data.

Rasheed, Fayaz↗