Results for “Power Iteration” — Page 5

DOE OSTI2025

First simultaneous observation of co- and counter-current fast-ion losses in the ASDEX Upgrade tokamak

In ITER and future fusion power plants, the source of the fusion born alpha particles is almost isotropic in pitch angle, thus having co- and counter-current populations. For trapped ions, the co-current side of the orbit corresponds to its outer leg, while the counter-current side corresponds to the inner leg. Understanding the mechanisms responsible for the fast-ion losses (FILs) is critical for future magnetically confined fusion power plants. To further study the interplay of fast ions with plasma instabilities, a double pinhole collimator has been developed for a Fast-Ion Loss Detector (FILD) in the ASDEX Upgrade tokamak (AUG). This new FILD opens the operational window to simultaneous measurements of the co- and counter-current ion velocity-space. In this paper, the first results for the AUG double collimator FILD detector are shown. The commissioning of this new probe is carried out in H-mode plasmas with an on-axis magnetic field $B_0 = -2.5$ T, and a plasma current $I_{\textrm{p}} = 0.7$ MA. Simultaneous co- and counter-current FILs have been measured. Both have shown a similar dependence on Ion Cyclotron Resonance Heating (ICRH) power, where the main difference is the intensity of the losses, with the co-losses being an order of magnitude larger. Toroidal Alfvén eigenmode-coherent ICRH-only losses have been identified for the co-current ions. Additionally, the presence of Edge Localized Modes during the discharge were shown to increment Neutral Beam Injection prompt losses, while partially mitigating ICRH-driven losses on both co- and counter- sides of the velocity-space. Finally, a very trapped and high gyroradius losses, with an unclear origin, have been measured in the co- and counter-current velocity-space. The computed ion trajectories show that these ions remain permanently near the vessel wall, suggesting that they are accelerated within the scrape-off layer.

ELM↗

DOE OSTI2026

Impact of Resonant Magnetic Perturbations on the Toroidal Location of the Runaway Electron Final Loss Strike Point

It is demonstrated that the peak toroidal impact phase of the post-disruption runaway electrons (REs) can be varied shot-to-shot by means of applied static non-axisymmetric (3D) magnetic fields, or resonant magnetic perturbations (RMPs). In the experiments, high-current (500 kA), purged RE plateaus (i.e. low impurity post-disruption plasmas with current carried completely by REs) were terminated on the DIII-D tokamak center post (CP). The global toroidal impact peak of the REs appears to be dominantly set by the toroidal phase of a wall-locked tearing mode, typically (m/n) = (2/1), but sometimes (m/n) = (3/2). In the absence of external RMP, the locked mode reliably locks close to a preferred toroidal phase Φ ≃ 75◦ probably set by intrinsic error fields. With applied RMP, the locked mode and RE impact phase is pushed to a new location. When the tearing mode is (m/n) = (2/1), the phase is well controlled and tracks the applied RMP phase, but in the case of an (m/n) = (3/2) tearing mode, the new phase location is not well controlled. These results could have application in ITER and other fusion power plant (FPP) - sized tokamaks as a method for spreading disruption first wall damage from REs

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1988

Analysis of spectra using correlation functions

A novel method is presented for the quantitative analysis of spectra based on the properties of the cross correlation between a real spectrum and either a numerical synthesis or laboratory simulation. A new goodness-of-fit criterion called the heteromorphic coefficient H is proposed that has the property of being zero when a fit is achieved and varying smoothly through zero as the iteration proceeds, providing a powerful tool for automatic or near-automatic analysis. It is also shown that H can be rendered substantially noise-immune, permitting the analysis of very weak spectra well below the apparent noise level and, as a byproduct, providing Doppler shift and radial velocity information with excellent precision. The technique is in regular use in the Atmospheric Trace Molecule Spectroscopy (ATMOS) project and operates in an interactive, realtime computing environment with turn-around times of a few seconds or less.

Beer, Reinhard↗

NASA NTRSDate not supplied

Microbial Optical Data Processing: A Key Step in the Metabolic Assessment of Lunar Explorer Instrument for Space Biology Applications (LEIA) and Biosentinel’s Payload Data

The BioSensor payload platform on BioSentinel and LEIA autonomously collects optical data from microbial model organisms in liquid culture. The BioSensor is designed to monitor metabolic activity using absorbance measurements of cell density and alamarBlue, a readily available colorimetric redox indicator dye. BioSentinel, a pioneering NASA CubeSat, uses yeast to study deep space radiation. LEIA investigates radiation and lunar gravity response. The experimental setup includes 16 wells equipped with three LEDs (570, 630, and 850 nm) and their corresponding photodetectors. One well is a calibration control without biology while the rest have desiccated cultures. Autonomous rehydration initiates the experiment. Data from the BioSensor are received from the flight and ground units, enabling comparison to uncover location-based metabolic rate variations. This study presents a Python Jupyter notebook developed for efficient data processing of multiple CSV files containing date and time columns, temperature, and well illumination data. It offers a user-friendly interface while maintaining computational power, automatically recognizing and iteratively processing data files in a user-input path. A Hampel filter with a short window eliminates outlier artifacts from sensor dropout. Because absorbance is a relative measurement, conversion from raw illumination requires defining a “blank” value, so the first data points are averaged to provide the necessary denominator. A cube-root function correction mitigates undesired drift caused by air pockets during the fluidic card filling phase, maintaining optical path length consistency. Beer-Lambert's law is applied to further convert absorbance values to cell and dye form concentrations, the desired science parameters. The processed data are saved and visualized as SVG plots. Future plans include extracting specific science parameters from the processed data like growth rate and metabolic rate, and identification of features corresponding to metabolic and phenotypic shifts such as starvation, shifts from aerobic to anaerobic growth, and osmotic stresses.

Space biology↗

DOE OSTI2023

Power system restoration incorporating diverse distributed energy resources

An example system includes an aggregator configured to receive a service collaboration request and iteratively determine, based on minimum and maximum power values for DERs under its management, an optimized operation schedule. The aggregator may also be configured to iteratively determine, based on the optimized operation schedule, an estimated flexibility range for devices under its management and output an indication thereof. The system may also include a power management unit (PMU) configured to iteratively receive the indication and determine, based on a network model that includes the estimated flexibility range, a reconfiguration plan and an overall optimized operation schedule for the network. The PMU may also be configured to iteratively cause reconfiguration of the network based on the plan. The PMU and aggregator may also be configured to iteratively, at a fast timescale, cause energy resources under their management to modify operation based on the overall optimized operation schedule.

Ding, Fei↗

DOE OSTI2022

Power balance analysis at the L-H transition in JET-ILW NBI-heated deuterium plasmas

The understanding of the physics underlying the L-H transition has strong implications for ITER experimental reactor and demonstration power plant (DEMO). In many tokamaks, including JET, it has been observed that, at a particular plasma density, n e,min , the power necessary to access H-mode P L-H is minimum. In the present work, L-H transitions of JET deuterium plasmas heated by neutral beam injection (NBI) are studied for the first time by means of a power balance analysis to characterize the main contributions in the transition, through integrated transport modelling. In the pulses analysed, we do observe a minimum of the L-H power threshold in density, indicating the presence of density branches and of n e,min . Electron and ion heat fluxes at the transition are estimated separately. The electron/ion equipartition power results in favour of the ions, as shown by QuaLiKiz quasilinear gyrokinetic simulations, which predict a larger ion transport that causes T e > T i . The resulting edge ion heat flux also shows a clear change of slope below n e,min , similarly to ASDEX-Upgrade (AUG) NBI pulses (Ryter et al 2014 Nucl. Fusion 54 083003). JET NBI data are compared to radio-frequency heated AUG and Alcator C-mod pulses (Schmidtmayr et al 2018 Nucl. Fusion 58 056003), showing a different trend of the power, coupled to ions at the L-H transition with respect to the linearity observed in the radio-frequency heated plasmas. Finally, the presence of n e,min and the role of the ion heat flux is discussed in the paper, although it seems it is not possible to explain the presence of a P L-H minimum in density by a critical ion heat flux and by the equipartition power for the JET NBI-heated plasmas analysed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Predicting core transport in ITER baseline discharges with neon injections

Achieving self-consistent performance predictions for ITER requires integrated modeling of core transport and divertor power exhaust under realistic impurity conditions. We present results from a systematic power-flow and impurity-content study for the ITER 15 MA baseline scenario constrained directly by existing SOLPS-ITER neon-seeded divertor solutions. Using the OMFIT STEP workflow, stationary temperature and density profiles are predicted with TGYRO for $1.5 \unicode{x2A7D} Z_\textrm{eff} \unicode{x2A7D} 2.5$, and the corresponding power crossing the separatrix $P_\textrm{sep}$ is evaluated. We find that $P_\textrm{sep}$ varies by more than a factor of 1.7 across this scan and matches the ${\sim}100$ MW SOLPS-ITER prediction when $Z_\textrm{eff} \simeq 1.6$ or when auxiliary heating is reduced to ${\sim}75\%$ of nominal. Rotation-sensitivity studies show that plausible variations in toroidal flow magnitude modify $P_\textrm{sep}$ by $\lesssim 20\%$, while AURORA modeling confirms that charge-exchange radiation inside the separatrix is dynamically negligible under predicted ITER neutral densities. These results identify a restricted compatibility window, $Z_\textrm{eff} \approx 1.6$ –1.75 and $0.75 \lesssim f_{P_\textrm{aux}} \unicode{x2A7D} 1.0$, in which core transport predictions remain aligned with neon-seeded divertor protection targets. This self-consistent, model-constrained framework provides actionable guidance for impurity control and auxiliary-heating scheduling in early ITER operation and supports future whole-device scenario optimization.

ITER↗

NASA NTRS1985

The Solid Rocket Booster Auxiliary Power Unit: Meeting the Challenge

The thrust vector control systems of the solid rocket boosters are turbine-powered, electrically controlled hydraulic systems which function through hydraulic actuators to gimbal the nozzles of the solid rocket boosters and provide vehicle steering for the Space Shuttle. Turbine power for the thrust vector control systems is provided through hydrazine fueled auxiliary power units which drive the hydraulic pumps. The solid rocket booster auxiliary power unit resulted from trade studies which indicated significant advantages would result if an existing engine could be found to meet the program goal of 20 missions reusability and adapted to meet the seawater environments associated with ocean landings. During its maturation, the auxiliary power unit underwent many design iterations and provided its flight worthiness through full qualification programs both as a component and as part of the thrust vector control system. More significant, the auxiliary power unit has successfully completed six Shuttle missions.

Hughes, R. W.↗

NASA NTRS2022

NDARC - NASA Design and Analysis of Rotorcraft: Input and Data Structures - Vol 2

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. Figure 1-1 illustrates the tasks. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description (figure 1-1) consists of all the information, input and derived, that defines the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions (indicated by light arrows). The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission (burned plus reserve) equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for antitorque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude. The aircraft must be trimmed, by solving for the controls and motion that produce equilibrium in the specified flight state. Different trim solution definitions are required for various flight states. Evaluating the rotor hub forces may require solution of the blade flap equations of motion.

NDARC↗

NASA NTRS2022

NDARC - NASA Design and Analysis of Rotorcraft: Input - Vol 3

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. Figure 1-1 illustrates the tasks. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description (figure 1-1) consists of all the information, input and derived, that defines the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions (indicated by light arrows). The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission (burned plus reserve) equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for antitorque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude. The aircraft must be trimmed, by solving for the controls and motion that produce equilibrium in the specified flight state. Different trim solution definitions are required for various flight states. Evaluating the rotor hub forces may require solution of the blade flap equations of motion.

NDARC↗

NASA NTRS2023

NDARC - NASA Design and Analysis of Rotorcraft: Input - Appendix 3

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. Figure 1-1 illustrates the tasks. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description (figure 1-1) consists of all the information, input and derived, that defines the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions (indicated by light arrows). The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission (burned plus reserve) equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for antitorque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude. The aircraft must be trimmed, by solving for the controls and motion that produce equilibrium in the specified flight state. Different trim solution definitions are required for various flight states. Evaluating the rotor hub forces may require solution of the blade flap equations of motion.

NDARC↗

NASA NTRS2016

NDARC NASA Design and Analysis of Rotorcraft - Input, Appendix 4

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description consists of all the information, input and derived, that denes the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions. The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for anti-torque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude.

Rotorcraft Input↗

NASA NTRS2014

Doppler Lidar System Design via Interdisciplinary Design Concept at NASA Langley Research Center - Part II

Optimized designs of the Navigation Doppler Lidar (NDL) instrument for Autonomous Landing Hazard Avoidance Technology (ALHAT) were accomplished via Interdisciplinary Design Concept (IDEC) at NASA Langley Research Center during the summer of 2013. Three branches in the Engineering Directorate and three students were involved in this joint task through the NASA Langley Aerospace Research Summer Scholars (LARSS) Program. The Laser Remote Sensing Branch (LRSB), Mechanical Systems Branch (MSB), and Structural and Thermal Systems Branch (STSB) were engaged to achieve optimal designs through iterative and interactive collaborative design processes. A preliminary design iteration was able to reduce the power consumption, mass, and footprint by removing redundant components and replacing inefficient components with more efficient ones. A second design iteration reduced volume and mass by replacing bulky components with excessive performance with smaller components custom-designed for the power system. Thermal modeling software was used to run steady state thermal analyses, which were used to both validate the designs and recommend further changes. Analyses were run on each redesign, as well as the original system. Thermal Desktop was used to run trade studies to account for uncertainty and assumptions about fan performance and boundary conditions. The studies suggested that, even if the assumptions were significantly wrong, the redesigned systems would remain within operating temperature limits.

Crasner, Aaron I.↗

NASA NTRS2013

Doppler Lidar System Design via Interdisciplinary Design Concept at NASA Langley Research Center - Part I

Optimized designs of the Navigation Doppler Lidar (NDL) instrument for Autonomous Landing Hazard Avoidance Technology (ALHAT) were accomplished via Interdisciplinary Design Concept (IDEC) at NASA Langley Research Center during the summer of 2013. Three branches in the Engineering Directorate and three students were involved in this joint task through the NASA Langley Aerospace Research Summer Scholars (LARSS) Program. The Laser Remote Sensing Branch (LRSB), Mechanical Systems Branch (MSB), and Structural and Thermal Systems Branch (STSB) were engaged to achieve optimal designs through iterative and interactive collaborative design processes. A preliminary design iteration was able to reduce the power consumption, mass, and footprint by removing redundant components and replacing inefficient components with more efficient ones. A second design iteration reduced volume and mass by replacing bulky components with excessive performance with smaller components custom-designed for the power system. Mechanical placement collaboration reduced potential electromagnetic interference (EMI). Through application of newly selected electrical components and thermal analysis data, a total electronic chassis redesign was accomplished. Use of an innovative forced convection tunnel heat sink was employed to meet and exceed project requirements for cooling, mass reduction, and volume reduction. Functionality was a key concern to make efficient use of airflow, and accessibility was also imperative to allow for servicing of chassis internals. The collaborative process provided for accelerated design maturation with substantiated function.

Boyer, Charles M.↗

DOE OSTI2025

Impact of Time Dependent Reactor and Sensor Physics on Core Power Synthesis

Online synthesis of the power distribution is critical in the operation and control of nuclear power reactors to ensure that the core is operating within safety margins, and to provide essential knowledge associated with the burnup of the fuel. In light water reactors (LWRs), power synthesis is achieved by using some a priori knowledge of the state of the reactor core and updating based on the signals coming from in-core sensors—namely, self-powered neutron detectors (SPNDs). This report aims to study the effects of fuel burnup and sensor degradation on the ability to accurately synthesize the power distribution in a LWR. Several modeling tools were used to simulate power synthesis based on the responses of SPNDs, with emitters made out of Rh or V. A representative pressurized water reactor low-enriched uranium (LEU) core was modeled using the Polaris/Purdue Advanced Reactor Core Simulator (PARCS) approach. The Monte Carlo N-Particle Transport 6 (MCNP6) code was used, as well, to calculate response functions between different segments of fuel to individual SPNDs; this is a crucial parameter for power synthesis. The Oak Ridge Isotope GENeration (ORIGEN) package in the Standardized Computer Analyses for Licensing Evaluation (SCALE) code was used to model the time-dependent isotopic transmutation in the SPND emitters. All these data were fed into a custom code that enacted the point-based iterative (PBI) method to simulate power synthesis. Developmental work was also performed on high-fidelity SPND models in the GEometry ANd Tracking 4 (Geant4) code, which enables higher-accuracy modeling of the current responses from SPNDs. In this work, five sets of time-dependent power synthesis test cases were conducted. In these test cases, systematic changes in the input conditions enabled an analysis of the effect of (1) slightly inaccurate a priori power distribution assumptions with respect to fuel burnup, (2) highly inaccurate a priori assumptions with respect to fuel burnup (such that burnup is not included in the a priori assumed distribution), and (3) differences between Rh and V SPNDs in terms of downstream consequences of the transmutation in the emitters. The authors discovered that one may permissibly have slightly inaccurate a priori assumptions of the fuel burnup (such that the level of burnup may be slightly under- or over-approximated by the accumulated burnup in approximately 9.3 full power days), but to not account for burnup at all in the a priori assumption leads to severe levels of error, approaching 25% at maximum. The authors also discovered that V SPNDs are extraordinarily robust in the low-enriched uranium fuel cycle considered in this modeling work, whereas Rh SPNDs undergo significant transmutation that can result in large errors in the synthesized power distribution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRSDate not supplied

System Modeling of a Lunar Molten Regolith Electrolysis Plant

Introduction: In-Situ Resource Utilization (ISRU) is the process of extracting local resources to produce commodities for propulsion, life support systems, and off-planet construction rather than transporting consumables from Earth. Molten Regolith Electrolysis (MRE) is a novel ISRU method of extracting oxygen gas and metal alloy from lunar regolith. The MRE process involves placing lunar regolith between two electrodes, through which current is passed, to melt the regolith and reduce the metal oxide constituents by direct electrolysis (e.g. FeO, SiO2, MgO, Al2O3) into oxygen gas and metal alloys. The oxygen is liquefied and used as propellant for landers, while the metals (e.g. Ferro-alloys) are further processed and used in structural building materials and parts manufacturing. A system model was developed that accounted for the major processes of an MRE plant (from excavation of raw materials to storage of products) to assess the feasibility of a lunar MRE plant. The System Engineering and Integration (SE&I) ISRU Modeling and Analysis (SIMA) team utilized its previously documented system sizing model, the Mission Analysis and Integration Tool (MAIT) [1] as framework of the system model. MAIT uses MATLAB/Simulink to integrate subsystem models into a complete system model of the MRE plant. Total mass, volume, and power requirements were computed for numerous iterations of a MRE plant. System Model: Figure 1: MRE Plant Block Diagram The regolith excavation model determines the mass and power needed to excavate sufficient regolith. The preheating auger initiates the regolith heating process before regolith enters the MRE re-actor to reduce the energy required to turn the solid into a molten liquid. The MRE reactor is modeled in COMSOL Multiphysics and based on the research by Dominguez, Sibille, and Schreiner [2, 3, 4]. This preliminary reactor model provides an accurate calculation of thermal equilibrium during electrochemical operation of the reactor system to assess the optimal mass and power required to process the inlet flow of regolith. The model also computes the outlet flowrates of oxygen and molten products. For this analysis, the primary components of the metal alloy considered were iron and silicon. The oxygen is then purified using an Yttrium Stabilized Zirconia (YSZ) electrode, followed by liquefaction using a 90K cryocooler to be stored as liquid oxygen in insulated cylindrical tanks. In future iterations of the system model, the molten metal tapped from the MRE reactor will undergo additional processing or refinement. However, downstream handling of metals is currently a technology gap that is missing a high TRL subsystem model. Therefore, for this analysis, the accumulated metal alloy stream terminates after leaving the MRE reactor. Study Goals: This analysis investigates multiple input variables to the system to determine the sensitivity of a (near) complete plant at full-scale. This preliminary investigation ran parametric sweeps on the MRE reactor geometry, electrical current supply, layers of multi-layer insulation (MLI) on the reactor, size of the electrodes in the oxygen purification model, and regolith composition (based on landing site location). Three production targets of oxygen (1,000, 10,000, and 50,000 kg/yr) were investigated for this analysis. The parametric sweeps conducted in this analysis provide valuable insight into the expected impact of the various model inputs on plant size. This information can be used to identify the most critical components of the plant and guide future decisions on allocating funding for research and development, providing subsystem developers with appropriate interfaces with downstream and upstream processes, and assessing the overall feasibility of MRE when compared to other ISRU plants. References: [1] Carlson, A. et al. (2024) ICES, ICES-2024-53. [2] Dominguez, D.A., and Sibille, L. (2011) AIAA, AIAA-2011-700. [3] Schreiner, S.S. (2015) MIT, Dissertation. [4] Schreiner, S.S. et al. (2016) ASR, 57(7), pp.1585-1603.

ISRU↗

DOE OSTI2022

Convergence Analysis of Fixed Point Chance Constrained Optimal Power Flow Problems

For optimal power flow problems with chance constraints, a particularly effective method is based on a fixed point iteration applied to a sequence of deterministic power flow problems. However, a priori, the convergence of such an approach is not necessarily guaranteed. Here this article analyses the convergence conditions for this fixed point approach, and reports numerical experiments including for large IEEE networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Self-consistent core-pedestal ITER scenario modeling

The purpose of these integrated ITER simulations is to identify dependencies that can impact the performance of ITER. The dependence of fusion power production, temperature and density pedestals, and core profiles on varying magnetic-q, edge density fueling strength, neoclassical transport, magnetic field strength, and alpha heating, are examined. It is noted that ion modes usually dominate the interior, while electron modes dominate the barrier. The electron mode without flow shear is substantially stronger. The q increase tends to cause more kinetic ballooning modes at the edge and the q decrease means that the peeling is getting worse, but the ballooning is getting better. The large edge q tends to provide hollow density while small edge q gives normal density profile. This is consistent with the observation that the density of particles is inversely proportional to the density of the plasma current. The rise in the source of the edge particle increases the density of the edge and the reaction rate close to the edge increases temperature fluxes, resulting in weak pedestal barriers. When neoclassical transport is turned off or the strength of B-field is increased, the pronounced edge barriers are identified. In addition, a distinct effect on the magnetohydrodynamic (MHD) modes is observed due to the increased B-field. The interesting aspect is that the barrier collapses about q = 2 as the stability of MHD has decreased to replace the barrier with a continuous slope but with roughly the same fusion power. This would actually be good for a reactor. The slope of the H-mode pedestal is found to be reduced due to the alpha heating. It is expected that the problem of edge-localized modes causing damage to the first wall will be eased. The effects of circular, elongated, and general geometry on plasma profiles are also compared and contrasted. The circular geometry has a hollow density profile and no edge or internal thermal transport barrier (ETB or ITB). However, both ETBs and ITBs can be found in the elongated geometry. In general geometry, there is a weaker ETB, comparable ITBs, but a higher edge particle transport barrier than in elongated geometry. Higher density near the edge, on the other hand, causes more wall erosion, so elongated geometry might be the best option.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗