Results for “physics-based simulations” — Page 14

NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

NASA NTRSDate not supplied

Modeling of Multiscale Solar Dynamics for Understanding Drivers of Space Weather

Understanding the solar dynamics is critical for improving our capabilities to forecast the evolution of space weather conditions. We take advantage of currently available computational capabilities to model solar dynamics from the deep interior to the corona and investigate mechanisms that may drive space weather conditions. The simulations are performed using the 3D radiative MHD code StellarBox. Comparison of synthetic spectroscopic observables obtained from numerical simulations and actual observations allows us to uncover physical processes associated with observed phenomena. To facilitate a transition from modeling short-term physical phenomena to developing a reliable forecast-oriented model, we suggest using the data assimilation approach. It allows us to cross-analyze dynamo model solutions and observations and to consider possible uncertainties and errors. In this presentation, we briefly summarize current multi-scale modeling capabilities and results and discuss ongoing developments to build a reliable physics-based forecast-oriented model of solar activity.

Irina N Kitiashvili↗

NASA NTRSDate not supplied

Modeling of Multiscale Solar Dynamics for Understanding Drivers of Space Weather

Understanding the solar dynamics is critical for improving our capabilities to forecast the evolution of space weather conditions. We take advantage of currently available computational capabilities to model solar dynamics from the deep interior to the corona and investigate mechanisms that may drive space weather conditions. The simulations are performed using the 3D radiative MHD code StellarBox. Comparison of synthetic spectroscopic observables obtained from numerical simulations and actual observations allows us to uncover physical processes associated with observed phenomena. To facilitate a transition from modeling short-term physical phenomena to developing a reliable forecast-oriented model, we suggest using the data assimilation approach. It allows us to cross-analyze dynamo model solutions and observations and to consider possible uncertainties and errors. In this presentation, we briefly summarize current multi-scale modeling capabilities and results and discuss ongoing developments to build a reliable physics-based forecast-oriented model of solar activity

Irina N Kitiashvili↗

NASA NTRSDate not supplied

Modeling of Multiscale Solar Dynamics for Understanding Drivers of Space Weather

Understanding the solar dynamics is critical for improving our capabilities to forecast the evolution of space weather conditions. We take advantage of currently available computational capabilities to model solar dynamics from the deep interior to the corona and investigate mechanisms that may drive space weather conditions. The simulations are performed using the 3D radiative MHD code StellarBox. Comparison of synthetic spectroscopic observables obtained from numerical simulations and actual observations allows us to uncover physical processes associated with observed phenomena. To facilitate a transition from modeling short-term physical phenomena to developing a reliable forecast-oriented model, we suggest using the data assimilation approach. It allows us to cross-analyze dynamo model solutions and observations and to consider possible uncertainties and errors. In this presentation, we briefly summarize current multi-scale modeling capabilities and results and discuss ongoing developments to build a reliable physics-based forecast-oriented model of solar activity

Irina N. Kitiashvili↗

NASA NTRS2022

Mars 2020 Perseverance Rover SHERLOC Instrument Isolation System

The NASA Jet Propulsion Laboratory (JPL) successfully landed the Mars 2020 Perseverance Rover at Jezero Crater on the Martian surface. Perseverance’s main mission objective is to cache Martian rock and regolith samples in hermetically sealed tubes to be brought back to Earth by future missions. To achieve this goal the rover is equipped with a 2-meter-long Robotic Arm (RA) which manipulates the Turret to interact with the surface. The Turret is comprised of science instruments and tools that enable surface sampling and science. The backbone of the Turret hardware is the Rotary Percussive Coring Drill. The science instruments, which are directly mounted to the structural housing of this drill, must be able to withstand not only the launch and entry, descent and landing (EDL) loads of the vehicle but also the dynamic percussive environment, induced by the drill throughout surface operations. This paper discusses the technical hardware design, development and testing of a vibration isolation system to protect the Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) Instrument from these dynamic environments. The hardware design aspect of this problem was limited by both size and mass constraints. The Turret is tightly configured and the physical space in which hardware can reside is restricted due to multiple axes of surface interaction points. Another driving requirement is the extreme range of thermal non-operational environment from -135C to +90C. This, in addition to SHERLOC being a heavier instrument than previously integrated on a turret, made the use of isolation systems employed on previous rover missions problematic. The final design incorporates custom wire mesh springs preloaded within titanium hexapod struts equipped with flexure ends. Custom versions of commercially available wire mesh springs were designed and tested. These custom springs differed in dimension, density and stiffness from the off the shelf options. The newly designed springs went through various stages of flight acceptance testing. Detailed physics-based modeling to understand what loads the isolation system would experience was used to define the test conditions for the springs and entire isolation subsystem. The testing program developed around this was extensive to fully characterize the isolation performance over cleanliness levels, temperature and life. Another major aspect of this work was developing a method of replicating the drill’s percussive environment for performance and life testing of Turret mounted hardware. Attaching the instrument to an operating drill for testing was not a viable option. A method of using a Highly Accelerated Life Test (HALT) table was developed and applied across the project as the most representative and tunable option to physically simulate this new environment. This method, along with launch environment random vibration testing, was used to build an entire dynamic qualification and flight hardware test program. The results of the design, modeling, testing and analysis of the SHERLOC Isolation System is described throughout the following paper.

Krafchak, Todd↗

NASA NTRSDate not supplied

Predicting Melt Properties Using Atomistic Simulations With A Highly Accurate Physically Informed Neural Network Interatomic Potential

The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.

molecular dynamics↗