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Results for “computational physics”
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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Equation of state (EOS) for computational multi-physics [Slides]
Abstract not provided.
Experimental Physics Software and Computing Infrastructure
EPSCI Group activities including Hydra, AIEC, AIOP, AIOPP, EJFAT, ERSAP, and JANA.
Uncertainty Quantification in Computational Models of Physical Systems
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Learning the capabilities of quantum computers using physics-informed neural networks
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Uncertainty Quantification in Computational Science: from Physical Models to Neural Networks
Tutorial at the upcoming BIRS workshop https://www.birs.ca/events/2025/5-day-workshops/25w5381/schedule
Emulating quantum computers with physics-aware neural networks
Poster for DOE/SC/ASCR PI's meeting
Experience gained from computer processing of physical experimental data during the restoration of measured values
The processing stage in which the restored values of the physical parameters are received is described. The following main steps are discussed: estimation of the state of the telemetry data, processing of the calibration data, and determination of the errors in the data; data decommutation and analysis of the structure of measurement cycles for each instrument; decoding, estimates of the reliability of the restored data, and their agreement with the models adopted for the measurement process; and analysis of errors due to deterministic and random factors. A block diagram of the method is presented.
A Multifunctional Aerospace Smart Skin Emerges From Computational Models and Physical Experiments
No abstract available
AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application
This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.
An Overview of the NASA Lift+Cruise eVTOL Crash Test
Introduction – NASA RVLT Project Impact Dynamics / Crash Safety Task - Task Objective: “To improve the crashworthiness and impact safety of Urban Air Mobility (UAM) vehicle and provide data to simplify the certification process. Efforts will include development of validated computational models of these vehicles, as well as other impacting bodies such as birds and drones. Efforts will also focus on developing and evaluating energy absorbing and crush properties of emerging and non-traditional composite materials and processes. Finally, occupant protection will be addressed using computational models and physical assets as it pertains to all rotorcraft environments.” - Problem Statement: “There currently is a lack of data for requirements regarding the crashworthy performance of UAM vehicles and impact loads generated by a bird strike. To address this technology gap, NASA will develop test guidelines, adopt modeling methodologies demonstrating capability for ‘certification by analysis’, acquire vehicle and occupant data on full-scale representative vehicles, and provide data/guidance to consensus standards organizations and the UAM community.” - 4 Main focus points - The investigation of occupant injury using physical and computational assets - The development of energy absorbing technology - The generation of data from sub- and full-scale crash test data - The execution of advanced finite element modelling techniques
Computational ESD Study: Physics- Based Simulations
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LHC@Home: a BOINC-based volunteer computing infrastructure for physics studies at CERN
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Developing physically-based micromechanical computational model to understand materials variability in structural applications.
Abstract not provided.
Uncertainty Quantification and Machine Learning Algorithms for Physical Models: Tackling Computational Expense and High-Dimensionality.
Abstract not provided.
Fundamental Physics of Reversible Computing--An Introduction.
Abstract not provided.