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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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Revolutionary Vertical Lift Technology (Rvlt) Side-By-Side Hybrid Powertrain Dynamic Model
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Dynamic Modeling, Simulation, and Analysis of Orbiting Sample Capture for Potential Mars Sample Return
The current notional architecture for the Mars Sample Return (MSR) campaign would require autonomous on-orbit rendezvous and capture of a sample container after it has been delivered to Mars orbit by a preceding MSR mission. As part of the complete orbital payload known as the Capture, Contain, and Return System (CCRS), a concept has been developed for autonomously sensing and capturing this Orbiting Sample (OS) container. The concept, comprised of a capture mechanism and multiple optical break-beam arrays, would detect and enclose the OS into CCRS, and engage a dust-tight seal to prevent the escape of the OS or other unsterilized particles. A challenging goal for the system is to close the lid prior to the OS coming into physical contact after entering CCRS in order to reduce the risk of unsterilized Mars particles on the surface of the OS from contaminating the outside of the spacecraft. The mechanical and sensor performance requirements were driven by the broader architecture of CCRS, OS rendezvous parameters, and Planetary Protection requirements regarding the interaction and handling of the OS. The mechanism and break-beam array concepts were designed accordingly, and kinematic simulations of the mechanism’s behavior, combined with a Monte-Carlo simulation of OS rendezvous/collision behaviors and corresponding breakbeam sensor responses verify compliance.
X-57 Ground Dynamics Modeling and Analysis
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Potential seasonal predictability: Comparison between empirical and dynamical model estimates
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Dynamic Modeling and Optimization of Advanced Energy Systems.
Abstract not provided.
Dynamic Modeling and Simulation of a Large Scale Direct-Fired Supercritical CO2 Power Cycle for Off-Design Operating Conditions
7th International sCO2 Power Cycles Symposium, Virtual, February 21-24, 2022
Computational Fluid Dynamics Modeling of Solar Thermal Dry Reforming of Methane in a Parabolic Trough (paper).
Abstract not provided.
Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems
In recent years, the field of data-driven neural network-based machine learning (ML) algorithms has grown significantly and spurred research in its applicability to instrumentation and control systems. While they are promising in operational contexts, the trustworthiness of such algorithms is not adequately assessed. Failures of ML-integrated systems are poorly understood; the lack of comprehensive risk modeling can degrade the trustworthiness of these systems. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption and will play a vital role in intelligent systems' safe and accountable operation. Thus, in this work, we demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset. It is well documented that ML algorithms excel at interpolation (or near-interpolation) tasks but significantly degrade at extrapolation. This occurs when new samples are "far" from training samples. The method, referred to as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets, which is used to calculate a prediction's relative reliability. LADDR is demonstrated on a feedforward neural network-based model used to predict safety significant factors during different loss-of-flow transients. LADDR is intended as a "data supervisor" and determines the appropriateness of well-trained ML models in the context of operational conditions. Ultimately, LADDR illustrates how training data can be used as evidence to support the trustworthiness of ML predictions when utilized for conventional interpolation tasks.
Dynamic Modeling of Nuclear Thermal Propulsion Systems Under Drum Fault Conditions
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Dynamic Modelling of HiveWind Floating Wind Substructure in OpenFAST
Current aero-elastic modes tend to consider the wind turbine floating substructures as rigid. However, the new generation of offshore wind platforms are leaner, less rigid. The OpenFAST code has been updated to consider the flexibility of the floating substructure. This presentation shows how a numerical model of the HiveWind design was created within OpenFAST as well as some verifications performed. When comparing the flexible model to a simplified rigid model (previous state-of-the-art), there are some significant differences in key performance indicators. The new flexibility capability within OpenFAST allows to calculate ultimate and fatigue loads within the floating substructure, allowing for the development of more economical designs.
Dynamic Modeling of Power Conversion Stages for an Exascale Supercomputer
In this paper a power conversion and energy consumption model for an exascale supercomputer is investigated. Power consumption, energy loss and efficiency are derived for the 27.2 MW liquid-cooled, centralized, High Performance Computing (HPC) power system, which is supplied directly from the 480 V three-phase mains. Two energy conversion stages are analyzed, measured and modeled. The model is developed in order to be adapted and implemented in a digital twin platform utilizing a Resource Allocator and Power Simulator (RAPS) module. RAPS enables estimation of potential energy savings in the direct AC power supply architecture via both conventional rectifier load sharing (commonly used in HPC systems), as well as smart rectifier load sharing. Moreover, besides the direct AC supply architecture analysis, the full direct DC supply architecture with with 1 kV DC bus were also studied. Comparison of 10 hour time frame operation of the system, with direct 480 V AC voltage supply with conventional and smart load sharing and medium dc voltage supply were done. For the direct AC supply architecture, with conventional and smart load sharing the predicted power loss was approximately 840 kW and 820 kW, respectively and the predicted total system efficiency was 92.87% and 93.05%, respectively. For the direct DC supply architecture with the 1000 V DC supply bus power loss was approximately 340 kW and the predicted total system efficiency was 97.02%.
Multi-Tethered Space-Based Interferometers: Particle System Model
Dynamics models are presented for a class of space-based interferometers comprised of multiple component bodies, interconnected in various arrangements, by low-mass flexible tethers of variable length. The tethered constellations are to perform coordinated rotational scanning accompanied by baseline dimensional changes, as well as spin axis realignments and spin-up/spin-down maneuvers. The mechanical idealization is a system of N point masses interconnected by massless tethers of variable length. Both extensible and inextensible tethers are considered. Expressions for system angular and linear momenta are developed. The unrestricted nonlinear motion equations are derived via Lagranges equations. Rheonomic constraints are introduced to allow prescribed motion of any degrees of freedom, and the associated physical forces are determined. The linearized equations of motion are obtained for the steady rotation of a system with extensible tethers of constant unstrained length.
Long and Short Term Geomagnetic Prediction (Chapter 19)
Prediction of geomagnetic variability depends on the accuracy of geomagnetic field modeling, dynamical modeling of source regions that contribute to geomagnetic signals, and advanced assimilation algorithms that combine effectively the results of geomagnetic field and dynamic models to make accurate estimates of the dynamic states of the sources and, therefore, accurate forecast of geomagnetic variations. In this chapter we provide an overview of recent research efforts in these three research areas, focusing primarily on geomagnetic variations from the dynamic outer core and from solar and lunar tidal effects, but also including a review of relevant research results and developments. Prediction of weak but periodic tidal phenomena, and of strong but chaotic secular variation (SV) showcases two very important new developments which will lead to new opportunities in geomagnetic research and application.
Dynamic stall modeling and correlation with experimental data on airfoils and rotors
Two methods for modeling dynamic stall have been developed. The alpha, A, B method generates lift and pitching moments as functions of angle of attack and its first two time derivatives. The coefficients are derived from experimental data for oscillating airfoils. The Time Delay Method generates the coefficients from steady state airfoil characteristics and an associated time delay in stall beyond the steady state stall angle. Correlation with three types of test data shows that the alpha, A, B method is somewhat better for use in predicting helicopter rotor response in forward flight. Correlation with lift and moment hysteresis loops generated for oscillating airfoils was good for both models.