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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Approximating Trajectory Constraints With Machine Learning – Microgrid Islanding With Frequency Constraints

Here, we introduce deep earning aided constraint encoding to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Non-uniform active learning for Gaussian process models with applications to trajectory informed aerodynamic databases

The ability to non-uniformly weight the input space is desirable for many applications, and has been explored for space-filling approaches. Increased interests in linking models, such as in a digital twinning framework, increases the need for sampling emulators where they are most likely to be evaluated. In particular, here we apply non-uniform sampling methods for the construction of aerodynamic databases. This paper combines non-uniform weighting with active learning for Gaussian Processes (GPs) to develop a closed-form solution to a non-uniform active learning criterion. We accomplish this by utilizing a kernel density estimator as the weight function. We demonstrate the need and efficacy of this approach with an atmospheric entry example that accounts for both model uncertainty as well as the practical state space of the vehicle, as determined by forward modeling within the active learning loop.

42 ENGINEERING↗
DOE OSTI2024

Time-resolved photoelectron spectroscopy via trajectory surface hopping

Time-resolved photoelectron spectroscopy is a powerful pump-probe technique which can probe nonadiabatic dynamics in molecules. Interpretation of the experimental signals however requires input from theoretical simulations. Advances in electronic structure theory, nonadiabatic dynamics, and theory to calculate the ionization yields, have enabled accurate simulation of time-resolved photoelectron spectra leading to successful applications of the technique. Here, we review the basic theory and steps involved in calculating time-resolved photoelectron spectra, and highlight successful applications.

Chakraborty, Pratip↗
DOE OSTI2021

Bottomonium suppression in an open quantum system using the quantum trajectories method

We solve the Lindblad equation describing the Brownian motion of a Coulombic heavy quark-antiquark pair in a strongly coupled quark-gluon plasma using the highly efficient Monte Carlo wave-function method. The Lindblad equation has been derived in the framework of pNRQCD and fully accounts for the quantum and non-Abelian nature of the system. The hydrodynamics of the plasma is realistically implemented through a 3+1D dissipative hydrodynamics code. We compute the bottomonium nuclear modification factor and compare with the most recent LHC data. The computation does not rely on any free parameter, as it depends on two transport coefficients that have been evaluated independently in lattice QCD. Our final results, which include late-time feed down of excited states, agree well with the available data from LHC 5.02 TeV PbPb collisions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Trajectory of Additively Manufactured Titanium Alloys with Superior Mechanical Properties and Engineered Microstructures

Additive manufacturing (AM) plays a crucial role in the manufacturing and development of advanced materials including titanium alloys for engineering applications. During the last two decades, a great deal of effort has been devoted to print titanium parts with desired properties. However, due to presence of non-equilibrium/metastable phases, anisotropic microstructure, and various porosities in the printed titanium alloys, achieving an excellent combination of mechanical properties has been challenging. Here this review paper aims to provide a comprehensive overview of multiple post-process and in-process approaches to achieve enhanced strength-ductility combinations by microstructure engineering. Approaches such as addition of alloying elements/nucleants, multi-step post heat treatment, in-situ heat treatment, forced inter-pass cooling, in situ peening/rolling, hybridizing microstructure, high-intensity acoustic vibration, thermo-hydrogen refinement of microstructure, deliberately introducing lack of fusion defects with subsequent hot isostatic pressing are discussed in detail. Finally, we conclude by highlighting the challenges associated with each method and potential areas of improvement that can pave the way to develop reliable components with enhanced performance.

36 MATERIALS SCIENCE↗