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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Current Status of the Finite-Element Fluid Solver (COFFE) within HPCMP CREATE™-AV Kestrel

COFFE is the finite-element flow solver within HPCMP CREATE-AV™ Kestrel. Kestrel supports a range of flow solver fidelity options to support the DoD acquisition community, and COFFE targets the need for high-fidelity flow solutions. The COFFE solver, like all components of Kestrel, is under continual development as part of the CREATE-AV program. This paper documents the usage of COFFE on several workshop cases, which demonstrate new Kestrel capabilities and exercise features unique to COFFE. It concludes with a brief discussion of upcoming features in development for COFFE.

Holst, Kevin R.↗
DOE OSTI2025

NLR HPC Kestrel Jobs Data

Overview: Anonymized job-level records from the Kestrel HPC system at the National Laboratory of the Rockies (NLR). Each record represents a Slurm batch job with scheduling metadata, resource requests, utilization, energy estimates, and efficiency metrics. Sensitive fields (user, account, job name, submit line, working directory, submit script, and job type) are replaced with 7-character cryptographic hashes. System & Timeframe: Kestrel is located at the NLR campus. Standard compute nodes have 104 cores and 256 GB RAM; bigmem nodes have 2,000 GB. GPU nodes (gpu-h100 partition) use NVIDIA H100 GPUs. Data covers jobs submitted August 2023 through December 2025. Funding provided by the U.S. Department of Energy, EERE. Files: esif.hpc.kestrel.job-anon.zip — Anonymized job records (Hive-partitioned Parquet) datacard.md — Full dataset documentation ~11 million rows, 50 variables. Readable with PyArrow, pandas, DuckDB, Apache Spark, or any Parquet-compatible tool. Data Collection: Jobs collected via sacct with timezone-aware export (SLURM_TIME_FORMAT="%Y-%m-%dT%H:%M:%S%z"), loaded into PostgreSQL. Calculated columns updated via database triggers and batch functions. All timestamps use timestamptz and correctly handle DST transitions. Preprocessing: Anonymization of name, user, account, submit_line, work_dir, submit_script, and job_type via 7-char hex hashes Derived columns: queue_wait, cpu_eff, max/min/avg_mem_eff, energy estimates Simplified job state mapping (e.g., "CANCELLED by 132357" → "CANCELLED") Boolean flags: python_job, reframe_job Temporal decomposition: year, month, day, day_of_week, hour, minute from submit_time Shared node tracking: shared_job_count, nodes_shared, jobs_shared Key Variables: Scheduling: job_id, partition, state_simple, submit_time, start_time, end_time, queue_wait Resources: nodes_req/used, processors_req/used, memory_req, wallclock_req/used, gpus_requested Efficiency: cpu_eff, max/min/avg_mem_eff Energy: cpu_energy_tdp_estimated_max/used_watt_hours, consumed_energy_raw_joules, consumed_energy_raw_watt_hours Sharing: shared_job_count, nodes_shared, jobs_shared Partitions: short, standard, debug, gpu-h100 Job States: CANCELLED, COMPLETED, FAILED, PENDING, RUNNING QoS Levels: normal, high Important Notes: Timestamps include timezone offsets; DST transitions are handled correctly, though adding intervals across DST boundaries requires offset adjustment shared_job_count reflects physical node co-residency, not use of the shared partition Job step records and raw Slurm JSONB fields are excluded Do not attempt to re-identify individuals from hashed fields

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

Lateral static and dynamic aerodynamic parameters of the Kestrel aircraft (XV-6A) extracted from flight data

Flight test data have been used to extract the lateral static and dynamic aerodynamic parameters of the Kestrel aircraft. The aircraft configurations included thrust-jet angles of 0 deg, 15 deg, and 30 deg, and the test Mach numbers were 0.43, 0.62, and 0.82. The results showed that most of the parameters varied linearly with trim normal-force coefficient. The directional stability parameter showed a small increase with increasing trim normal-force coefficient and also with nozzle deflection. The effective-dihedral parameter, the damping-in-roll parameter, and damping-in-yaw parameter all increased (became more negative) with increasing trim normal-force coefficient. For the latter three parameters, the effect of nozzle deflection was dependent on the trim normal-force coefficient.

Suit, W. T.↗
NASA NTRS2019

Kestrel Results at Liftoff Conditions for a Space Launch System Configuration in Proximity to the Launch Tower

Aerodynamic data books for Space Launch System vehicles require databases for the integrated forces and moments and section loads during liftoff and transition to the ascent phase of flight. While the force and moment database can be generated from wind tunnel results, computational analyses are necessary to provide the extensive surface information required to generate proper lineloads. Of the two flight regimes, the liftoff problem is the more costly and complex situation to simulate, as it requires modeling of the vehicle in proximity to the launch tower. The effects of massive separation on the leeward pressure fields of both the tower and vehicle are not well captured with RANS methods, necessitating the use of more advanced methods, such as Delayed Detached Eddy Simulation, in conjunction with computational grids sufficiently refined to resolve the wakes. Details on the computational setup for employing the Kestrel flow solver to address the liftoff problem are presented. The methodology involves the use of independent unstructured near-body grids for the vehicle and the tower, overset by a solution adaptive Cartesian off-body grid. Results from the simulations are compared to experimental results from a test in the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel.

Computational fluid dynamics↗
NASA NTRS1972

A flight evaluation of a vectored thrust jet V/STOL airplane during simulated instrument approaches using the Kestrel (XV-6A) airplane

An in-flight investigation was made to determine the terminal-area operating problems of a vectored-thrust-jet vertical and short take-off landing (V/STOL) airplane under simulated instrument conditions. Handling-qualities data pertinent to the terminal-area approach and landing task are presented in the text, and additional documentation is included in the appendixes. Problems dealing with the cruise letdown to localizer capture, conversion to powered-lift flight, precise control of the glide slope, approach velocity or deceleration schedule, hover, and landing are discussed.

Morello, S. A.↗
NASA NTRS1972

Wind-tunnel investigation at low speeds of a model of the Kestrel (XV-6A) vectored-trust V/STOL airplane

Longitudinal and lateral stability data were obtained with the model out of and in ground effect over a moving ground plane for a range of model angles of attack and sideslip at various thrust coefficients. These data were taken primarily at thrust coefficients which simulate transition speeds on the airplane between hover and 200 knots. Some data, however, represent the effect of thrust deflection at speeds up to 350 knots. Also presented are the effects of control-surface deflections and interference between the jets and free stream.

Margason, R. J.↗
NASA NTRS1973

Longitudinal aerodynamic parameters of the Kestrel aircraft (XV-6A) extracted from flight data

Flight-test data have been used to extract the longitudinal aerodynamic parameters of a vectored-thrust aircraft. The results show that deflecting the thrust past 15 has an effect on the pitching-moment derivatives and tends to reduce the static stability. The trend toward reduction in the longitudinal stability also been noted by the pilots conducting the flight tests.

Suit, W. T.↗
NASA NTRSDate not supplied

Validation of Kestrel IDDES Simulations for SLS Transition Analysis

Complex computational simulations are needed to support the Space Launch System (SLS) program, and the fidelity of the computational results must be defended. More specifically, a number of databases are produced for the transition phase of flight, which occurs after the rocket clears the tower but before reaching transonic speeds. In an effort to reduce computational uncertainty, many of the computational parameters were altered to determine the sensitivity of the results to the value of the parameter and then updating the best practice procedures. The baseline routines were developed over many years through the maturation of the SLS program, and this paper delivers a detailed discussion of these baseline results. This section is followed by demonstration of perturbing some of the more significant parameters, including time step and turbulence model, and concluded with a summary of the herein determined best practices for such analysis.

CFD↗