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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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NASA NTRS1992

Clustering methods for removing outliers from vision-based range estimates

The present approach to the automation of helicopter low-altitude flight uses one or more passive imaging sensors to extract environmental obstacle information; this is then processed via computer-vision techniques to yield a time-varying map of range to obstacles in the sensor's field of view along the vehicle's flight path. Attention is given to two related techniques which can eliminate outliers from a sparse range map, clustering sparse range-map information into different spatial classes that rely on a segmented and labeled image to aid in spatial classification within the image plane.

Hussien, B.↗

NASA NTRS2004

Automated Analysis, Classification, and Display of Waveforms

A computer program partly automates the analysis, classification, and display of waveforms represented by digital samples. In the original application for which the program was developed, the raw waveform data to be analyzed by the program are acquired from space-shuttle auxiliary power units (APUs) at a sampling rate of 100 Hz. The program could also be modified for application to other waveforms -- for example, electrocardiograms. The program begins by performing principal-component analysis (PCA) of 50 normal-mode APU waveforms. Each waveform is segmented. A covariance matrix is formed by use of the segmented waveforms. Three eigenvectors corresponding to three principal components are calculated. To generate features, each waveform is then projected onto the eigenvectors. These features are displayed on a three-dimensional diagram, facilitating the visualization of the trend of APU operations.

Kwan, Chiman↗

NASA NTRS2004

Trajectory Specification for High-Capacity Air Traffic Control

In the current air traffic management system, the fundamental limitation on airspace capacity is the cognitive ability of human air traffic controllers to maintain safe separation with high reliability. The doubling or tripling of airspace capacity that will be needed over the next couple of decades will require that tactical separation be at least partially automated. Standardized conflict-free four-dimensional trajectory assignment will be needed to accomplish that objective. A trajectory specification format based on the Extensible Markup Language is proposed for that purpose. This format can be used to downlink a trajectory request, which can then be checked on the ground for conflicts and approved or modified, if necessary, then uplinked as the assigned trajectory. The horizontal path is specified as a series of geodetic waypoints connected by great circles, and the great-circle segments are connected by turns of specified radius. Vertical profiles for climb and descent are specified as low-order polynomial functions of along-track position, which is itself specified as a function of time. Flight technical error tolerances in the along-track, cross-track, and vertical axes define a bounding space around the reference trajectory, and conformance will guarantee the required separation for a period of time known as the conflict time horizon. An important safety benefit of this regimen is that the traffic will be able to fly free of conflicts for at least several minutes even if all ground systems and the entire communication infrastructure fail. Periodic updates in the along-track axis will adjust for errors in the predicted along-track winds.

Paielli, Russell A.↗

NASA NTRS2006

NASA Tech Briefs, April 2006

The topics covered include: 1) Replaceable Sensor System for Bioreactor Monitoring; 2) Unitary Shaft-Angle and Shaft-Speed Sensor Assemblies; 3) Arrays of Nano Tunnel Junctions as Infrared Image Sensors; 4) Catalytic-Metal/PdO(sub x)/SiC Schottky-Diode Gas Sensors; 5) Compact, Precise Inertial Rotation Sensors for Spacecraft; 6) Universal Controller for Spacecraft Mechanisms; 7) The Flostation - an Immersive Cyberspace System; 8) Algorithm for Aligning an Array of Receiving Radio Antennas; 9) Single-Chip T/R Module for 1.2 GHz; 10) Quantum Entanglement Molecular Absorption Spectrum Simulator; 11) FuzzObserver; 12) Internet Distribution of Spacecraft Telemetry Data; 13) Semi-Automated Identification of Rocks in Images; 14) Pattern-Recognition Algorithm for Locking Laser Frequency; 15) Designing Cure Cycles for Matrix/Fiber Composite Parts; 16) Controlling Herds of Cooperative Robots; 17) Modification of a Limbed Robot to Favor Climbing; 18) Vacuum-Assisted, Constant-Force Exercise Device; 19) Production of Tuber-Inducing Factor; 20) Quantum-Dot Laser for Wavelengths of 1.8 to 2.3 micron; 21) Tunable Filter Made From Three Coupled WGM Resonators; and 22) Dynamic Pupil Masking for Phasing Telescope Mirror Segments.

Source record↗

DOE OSTI2026

A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction

In many university and healthcare projects, models are built for very different data types such as tables, institutional time series, and medical images, but they are deployed as separate applications. In this work, that separation made testing and maintenance difficult because each module had its own pipeline and runtime requirements. This paper presents an integrated AI lakehouse-style implementation that runs three model pipelines inside one containerized backend. For medical imaging, we used MRI datasets from IEEE DataPort: a four-class classification set with 7012 images (5708 train/1304 test) and a segmentation set with 3063 image–mask pairs. The classification model (ResNet50 transfer learning) is evaluated using a proper train–validation–test protocol across multiple splits (80/10/10, 70/10/20, 60/10/30, and 10/30/60), achieving a test accuracy of 99.00% under the standard 80/10/10 split. Additionally, a patient-level evaluation is conducted using an external glioma dataset to provide a more realistic assessment without data leakage. The segmentation model (DeepLabV3-ResNet50) achieved 83.09% validation mIoU and 88.79% Dice score. For university KPI forecasting, we used annual IPEDS and NSF HERD data from 2010 to 2023 for three universities (BSU, EOU, and UAB). To examine the effect of preprocessing on forecasting performance, two case studies are conducted. In the first case, linear interpolation is applied to generate semester-level data. In the second case, the original annual data is used directly without interpolation. Random Forest regression and ARIMA models are evaluated using MAE, RMSE, MAPE, and R 2 . The results showed that interpolation improved apparent forecasting performance due to smoothing, while evaluation on the original annual data provided a more realistic assessment of model behavior. To further validate the framework on a larger dataset, an additional case study is conducted using a student dropout dataset. For water potability, we trained and compared multiple tabular classifiers on a large dataset (1,048,575 samples). A Random Forest model (100 trees, max depth 10) achieved 85.86% test accuracy and high recall for unsafe samples (0.8447). All modules are served via FastAPI and deployed together using Docker, with workflow automation routing requests to the correct endpoint. System-level benchmarking indicates that the backend maintains stable throughput and latency under concurrent requests.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Tropical leaf phenology-particularly its variability at the tree-crown scale-dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red-green-blue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. Here, we first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Fuel Efficient Speed Optimization for Real-World Highway Cruising

This paper introduces an eco-driving highway cruising algorithm based on optimal control theory that is applied to a conventionally-powered connected and automated vehicle. Thanks to connectivity to the cloud and/or to infrastructure, speed limit and slope along the future route can be known with accuracy. This can in turn be used to compute the control variable trajectory that will minimize energy consumption without significantly impacting travel time. Automated driving is necessary to the implementation of this concept, because the chosen control variables (e.g., torque and gear) impact vehicle speed. An optimal control problem is built up where quadratic models are used for the powertrain. The optimization is solved by applying Pontryagin’s minimum principle, which reduces the problem to the minimization of a cost function with parameters called co-states. The proposed algorithm assumes piecewise constant slopes and speed limits, and it solves the transition vehicle speeds for each junction of such segments. In addition, a method to extract route information from digital maps is presented, which complies with the proposed optimization algorithm. The algorithms are evaluated in simulation, using Autonomie, a simulation tool for vehicle energy consumption modeling. The optimal control trajectory (e.g., torque, gear) is first computed offline. It is then applied in an open-loop fashion in online simulation, using a forward-looking model of the vehicle in Autonomie. Simulation results show over 8% fuel saving for two selected real-world routes.

Simulation and modeling, Optimization, Vehicle net↗

NASA NTRS2004

A Correction for the IRI Topside Electron Density Model Based on Alouette/ISIS Topside Sounder Data

The topside segment of the International Reference Ionosphere (IRI) electron density model (and also of the Bent model) is based on the limited amount of topside data available at the time (40,OOO Alouette 1 profiles). Being established from such a small database it is therefore not surprising that the models have well-known shortcomings, for example, at high solar activities. Meanwhile a large data base of close to 200,000 topside profiles from Alouette 1,2, and ISIS I, 2 has become available online. A program of automated scaling and inversion of a large volume of digitized ionograms adds continuously to this data pool. We have used the currently available ISIs/Alouette topside profiles to evaluate the IRI topside model and to investigate ways of improving the model. The IRI model performs generally well at middle latitudes and shows discrepancies at low and high latitudes and these discrepancies are largest during high solar activity. In the upper topside IRI consistently overestimates the measurements. Based on averages of the data-model ratios we have established correction factors for the IRI model. These factors vary with altitude, modified dip latitude, and local time.

Bilitza, D.↗

DOE OSTI2024

Digital twins and deep learning segmentation of defects in monolayer MX 2 phases

Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX 2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remain a challenge even as high-throughput scanning transmission electron microscopy methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX 2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX 2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. Further, this digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings and potentially chart paths forward for automated discovery of materials defect phases in experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks

Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.

Gonzalez, Emmanuel M.↗

NASA NTRS1993

Modal characterization of the ASCIE segmented optics testbed: New algorithms and experimental results

New frequency response measurement procedures, on-line modal tuning techniques, and off-line modal identification algorithms are developed and applied to the modal identification of the Advanced Structures/Controls Integrated Experiment (ASCIE), a generic segmented optics telescope test-bed representative of future complex space structures. The frequency response measurement procedure uses all the actuators simultaneously to excite the structure and all the sensors to measure the structural response so that all the transfer functions are measured simultaneously. Structural responses to sinusoidal excitations are measured and analyzed to calculate spectral responses. The spectral responses in turn are analyzed as the spectral data become available and, which is new, the results are used to maintain high quality measurements. Data acquisition, processing, and checking procedures are fully automated. As the acquisition of the frequency response progresses, an on-line algorithm keeps track of the actuator force distribution that maximizes the structural response to automatically tune to a structural mode when approaching a resonant frequency. This tuning is insensitive to delays, ill-conditioning, and nonproportional damping. Experimental results show that is useful for modal surveys even in high modal density regions. For thorough modeling, a constructive procedure is proposed to identify the dynamics of a complex system from its frequency response with the minimization of a least-squares cost function as a desirable objective. This procedure relies on off-line modal separation algorithms to extract modal information and on least-squares parameter subset optimization to combine the modal results and globally fit the modal parameters to the measured data. The modal separation algorithms resolved modal density of 5 modes/Hz in the ASCIE experiment. They promise to be useful in many challenging applications.

Carrier, Alain C.↗

NASA NTRS2022

Digital Flight: A New Cooperative Operating Mode to Complement VFR and IFR

Digital Flight is a proposed new operating mode for all airspace users, complementing and adding to the existing operating modes of visual and instrument flight rules (VFR and IFR) and providing for cooperative integration in controlled airspace. Under new regulations (Digital Flight Rules, DFR) that set requirements for its sustained use, qualified operators employ Digital Flight to enhance their airspace access and operational flexibility in all visibility conditions and eventually all airspace classes without requiring segregation from incumbent operations. Enabled by connected digital information and technologies, Digital Flight operators employ cooperative practices and self-separation to ensure flight path safety in lieu of visual procedures or receiving separation services from Air Traffic Control. Its distributed structure and automated functions enable traffic densities and operational tempos not achievable with the existing operating modes. Various ongoing and emerging industry initiatives for enabling new entrants (e.g., unmanned aircraft systems, urban air taxis) and enhanced use of underserved airspace (e.g., ultra-high altitude), are pursuing various alternative operating modes with significant similarities, creating a unique and time-limited opportunity for harmonization and convergence. Digital Flight is proposed to serve that harmonizing role, creating a common new paradigm for airspace operations. Such convergence would not only bring together these emerging market segments but will also bolster the existing operators with access to this new operating mode and its advantages. Digital Flight provides an opportunity to focus regulatory development and benefit the aviation industry. This paper describes Digital Flight in sufficient detail to initiate community engagement and deliberation on a common new operating mode. It describes the essential elements, principal capabilities, and operational integration of DFR in shared airspace. It describes the value proposition from multiple perspectives and presents initial thoughts on the path forward.

Digital flight↗

DOE OSTI2023

Aero‐servo‐elastic co‐optimization of large wind turbine blades with distributed aerodynamic control devices

Abstract This work introduces automated wind turbine optimization techniques based on full aero‐servo‐elastic models and investigates the potential of trailing edge flaps to reduce the levelized cost of energy (LCOE) of wind turbines. The Wind Energy with Integrated Servo‐control (WEIS) framework is improved to conduct the presented research. Novel methods for the generic implementation and tuning of trailing edge flap devices and their controller are also introduced. Primary flap and controller parameters are optimized to demonstrate potential maximum blade tip deflection reductions of 21 % . Concurrent design optimization (i.e., co‐design) of a novel segmented wind turbine blade with trailing edge flaps and its controller is then conducted to demonstrate blade cost savings of 5 % . Additionally, rotor diameter co‐design optimization is demonstrated to reduce the LCOE by 1.3 % without significant load increases to the tower. These results demonstrate the efficacy of control co‐design optimization using trailing edge flaps, and the entirety of this work provides a foundation for numerous control co‐design‐oriented studies for distributed aerodynamic control devices.

17 WIND ENERGY↗

NASA NTRS1996

The smart highway project: Smart highways, smart vehicles, smart engineering

The Smart Highway project is a six mile, limited access roadway being built between Interstate 81 and Blacksburg, Virginia. The initial construction segment will be two miles long and is designed to serve as a test bed and test track for Intelligent Transportation Systems (ITS) research. The Center for Transportation Research (CTR) at Virginia Tech is developing three evaluation tools for its ITS research including DYNAVIMTS (a software framework), and the FLASH Lab (a 1/15th scale model highway and vehicle system). The Smart Highway rounds out the Center's evaluation methodology by allowing full scale operational tests, evaluations, and research under both experimental and conventional traffic conditions. Currently under development is a concept for a fully automated highway using a 'Cooperative Infrastructure Managed System' which involves ultra wide band communication beacons installed in the infrastructure with appropriate sensors, receivers and processors on board the vehicles. The project is part of the research program funded by the National Automated Highway System Consortium. The CTR hopes to develop the automated concept to prototype status by 1997. Other smart transportation and smart engineering concepts are proposed. This presentation will address the goals and objectives of the Smart Highway project, overview its status and importance to the region, and identify some of the transportation technology now under development and planned in the future.

Pethtel, Ray D.↗

NASA NTRS2013

Orion MPCV Touchdown Detection Threshold Development and Testing

A robust method of detecting Orion Multi ]Purpose Crew Vehicle (MPCV) splashdown is necessary to ensure crew and hardware safety during descent and after touchdown. The proposed method uses a triple redundant system to inhibit Reaction Control System (RCS) thruster firings, detach parachute risers from the vehicle, and transition to the post ]landing segment of the Flight Software (FSW). The vehicle crew is the prime input for touchdown detection, followed by an autonomous FSW algorithm, and finally a strictly time based backup timer. RCS thrusters must be inhibited before submersion in water to protect against possible damage due to firing these jets under water. In addition, neglecting to declare touchdown will not allow the vehicle to transition to post ]landing activities such as activating the Crew Module Up ]righting System (CMUS), resulting in possible loss of communication and difficult recovery. A previous AIAA paper gAssessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module h concluded that a strictly Inertial Measurement Unit (IMU) based detection method using an acceleration spike algorithm had the highest safety margins and shortest detection times of other methods considered. That study utilized finite element simulations of vehicle splashdown, generated by LS ]DYNA, which were expanded to a larger set of results using a Kriging surface fit. The study also used the Decelerator Systems Simulation (DSS) to generate flight dynamics during vehicle descent under parachutes. Proto ]type IMU and FSW MATLAB models provided the basis for initial algorithm development and testing. This paper documents an in ]depth trade study, using the same dynamics data and MATLAB simulations as the earlier work, to further develop the acceleration detection method. By studying the combined effects of data rate, filtering on the rotational acceleration correction, data persistence limits and values of acceleration thresholds, an optimal configuration was determined. The lever arm calculation, which removes the centripetal acceleration caused by vehicle rotation, requires that the vehicle angular acceleration be derived from vehicle body rates, necessitating the addition of a 2nd order filter to smooth the data. It was determined that using 200 Hz data directly from the vehicle IMU outperforms the 40 Hz FSW data rate. Data persistence counter values and acceleration thresholds were balanced in order to meet desired safety and performance. The algorithm proved to exhibit ample safety margin against early detection while under parachutes, and adequate performance upon vehicle splashdown. Fall times from algorithm initiation were also studied, and a backup timer length was chosen to provide a large safety margin, yet still trigger detection before CMUS inflation. This timer serves as a backup to the primary acceleration detection method. Additionally, these parameters were tested for safety on actual flight test data, demonstrating expected safety margins.

Daum, Jared↗

DOE OSTI2019

Future integrated mobility-energy systems: A modeling perspective

After over a century of petroleum dominance, the transportation sector is on the verge of radical transformations driven by rapid technology advancement of alternative fuels, automation, information technologies that create new mobility options and business models, and policies at all levels of government. While the technologies and fuels that will move people and goods in the future remain uncertain, the future transportation system will be more integrated with smart buildings, the electric grid, renewables, and information ecosystems, allowing for great opportunities to exploit these interconnections. Modeling tools for analyzing integrated mobility-energy systems require a deep understanding of these interconnections, of the infrastructure required to support alternative fuel vehicles, and a more nuanced understanding of transportation energy needs across multiple segments and spatiotemporal scales. In this paper, we assess the landscape of existing tools used to represent and model future mobility systems and their interactions with other energy systems. Here, we conclude that (a) out-of-sample extrapolation of emerging trends and future anticipated developments is more important than ever due to the plethora of factors driving disruptive change in mobility systems; (b) understanding adoption opportunities for alternative fuel light-duty vehicles requires modeling intra-household decisions affecting travel demand and mode choice; (c) mobility and energy systems need to be modeled as an integrated continuum, breaking the traditional approach in which dynamic energy supply models use relatively simple transportation demand and vice-versa; and (d) increased spatiotemporal fidelity and scalability are required to dynamically couple transportation/mobility and energy supply models and capitalize on these unprecedented interconnection opportunities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

A Gamma Assay System for Characterizing Excavated Soil with High {sup 137}Cs and {sup 90}Sr Contamination - 19460

We report on the recent development of a remote gamma-assay system for the measurement and characterization of high-activity soil excavated as part of an ongoing remediation project. The soil below a hot cell in our customer's facility has been contaminated with high concentrations of radionuclides {sup 137}Cs and {sup 90}Sr over the past several decades. The contaminated soil is to be remotely excavated from beneath the hot cell floor, to a depth of several feet, and placed in steel-walled waste bins for assay and disposition. The project requires measuring the {sup 137}Cs activity to a minimum detectable concentration (MDC) of 1 micro curie per gram. The high dose-rate of the material and the expectation of high Bremsstrahlung background, due to an unknown ratio of {sup 90}Sr admixture with the {sup 137}Cs, present unique measurement challenges. A novel assay system was designed to make measurements through a small access port in a 4-foot thick concrete wall isolating operators from the high-dose excavation area. Waste bins filled with soil are placed by a remotely operated crane onto an automated turntable, and initial dose measurements are taken for all bins. When excavation has proceeded to the point that dose rates below 20 Rad/hr on contact are reached, gamma spectroscopy will be used to characterize the {sup 137}Cs content. Gamma assay is performed with a 1 cm{sup 3} CZT detector in a collimated tungsten shield, which is extended through the access port toward the side of the bin by means of a seven foot long aluminum pipe. Each of the four sides of the bin is assayed in a discrete measurement, and the individual segments and summed result are reported. Canberra's NDA2000 software is used to collect and analyze the spectroscopic data and to control the turntable movement during the assay. The system is expected to meet the required MDC limits within a forty minute assay time under the most adverse conditions anticipated, with total measurement uncertainty of less than 25%. We describe in this paper the design and construction of the assay system, its calibration using the ISOCS software, and the evaluation of performance estimates. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

SE2-ANL Subchannel Analysis with New Heating Calculation Based on VARIANT Transport Code

The multi-assembly, steady-state sub-channel analysis code SE2-ANL has been widely used in thermal-hydraulic analysis of sodium cooled fast reactors (SFR). It is used to compute the core-wide temperature profiles for a given flow distribution and to determine the assembly flow rates yielding a desired (assembly-wise) peak temperature distribution. It is a modified version of SUPERENERGY-2 coupled with the DIF3D/GAMSOR/DIF3D procedure for coupled neutron and gamma heating calculations, developed at Argonne National Laboratory (ANL). In the current version of SE2-ANL, the neutron and gamma fluxes are calculated using the finite-difference diffusion theory option of the DIF3D code with six triangular meshes per hexagonal assembly. In addition, the pin power distributions are estimated by assuming that the power distribution within an assembly is separable in the radial and axial directions. Furthermore, the total heat generation rate in an assembly duct wall is simply determined by a user-specified fraction of the total gamma heating within that assembly. In order to determine the pin power distributions more accurately by eliminating these limitations, a new coupled heating calculation procedure has recently developed based on the neutron and gamma transport calculations with the VARIANT code. For this, the GAMSOR code has been extended to generate the intra-nodal gamma source distribution to be consistent with the trial functions of VARIANT. Using the computed neutron and gamma flux distributions, the power distribution in each fuel pin and duct wall is determined using a new utility code CURVE, which has been developed to evaluate the VARIANT flux solution at the center of each fuel pin and along the duct mid-wall. For each pin segment contained in a VARIANT node, the axial power shape is represented as a quadratic profile. The SE2-ANL code has been modified to interface with the new, coupled neutron and gamma heating calculation scheme. In addition, several improvements have been made on the computational methods and models of SE2-ANL. To eliminate the limit on the axial mesh size due to the numerical instability problem of the current explicit scheme, the numerical scheme for discretizing the energy equation in the axial direction has been changed to the {theta}-method of the SLTHEN code. An automated flow allocation scheme has also been implemented in SE2-ANL to determine assembly flow rates such that the peak cladding mid-wall temperatures of individual orifice zones are equalized over the burn cycle. The impacts of the new heating calculation scheme based on VARIANT transport solutions on the cladding and fuel temperatures have been tested against the existing method of SE2-ANL. Using the existing and new heating calculation schemes, full-core subchannel analyses were performed for the metal core design of the 1000 MWt Advanced Burner Reactor (ABR-1000), and the resulting coolant, cladding and fuel temperatures were compared. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗