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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
Analyzing high energy physics data using database computing: Preliminary report
A proof of concept system is described for analyzing high energy physics (HEP) data using data base computing. The system is designed to scale up to the size required for HEP experiments at the Superconducting SuperCollider (SSC) lab. These experiments will require collecting and analyzing approximately 10 to 100 million 'events' per year during proton colliding beam collisions. Each 'event' consists of a set of vectors with a total length of approx. one megabyte. This represents an increase of approx. 2 to 3 orders of magnitude in the amount of data accumulated by present HEP experiments. The system is called the HEPDBC System (High Energy Physics Database Computing System). At present, the Mark 0 HEPDBC System is completed, and can produce analysis of HEP experimental data approx. an order of magnitude faster than current production software on data sets of approx. 1 GB. The Mark 1 HEPDBC System is currently undergoing testing and is designed to analyze data sets 10 to 100 times larger.
Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks
Abstract Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach’s efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.
Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging
Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth’s subsurface, acoustic imaging and nondestructive testing (NDT) in material science, and ultrasound computed tomography (USCT) in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning (ML)-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review discusses how contemporary scientific ML techniques, and deep neural networks in particular, have been developed to enhance and integrate with traditional physics-based methods for solving CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic.
On the stability constants of metal–nitrate complexes in aqueous solutions
Stability constants of simple reactions involving addition of the NO 3 − ion to hydrated metal complexes, [M(H 2 O) x ] n + are calculated with a computational workflow developed using cloud computing resources.
Some physical and numerical aspects of computing the effects of viscosity on fluid flow
Some physical, analytical, and computational aspects of viscous flow are examined with reference to examples of computed flows. The discussion of the physical aspects covers the development of important scales used to reference flow phenomena in laminar and turbulent shear layers; the usefulness of the concepts of circulation and vorticity; and some relatively large-scale organized structures that have recently been identified in transitional and fully developed turbulent flows. Among the analytical aspects discussed are a compact presentation of the compressible Navier-Stokes equations, the Reynolds-averaged form of these equations, and a simplified description of some forms of turbulent models. Finally, results of a simulation of the onset of transition, direct turbulence simulations, and large-eddy simulations are given.
NASA Tech Briefs, February 1999
Topics: Test and Measurement; Electronic Components and Circuits; Electronic Systems; Materials; Computer Software; Mechanics; Machinery/Automation; Physical Sciences; Computers and Peripherals.
Recent advances in numerical simulation of space-plasma-physics problems
Computer simulations have become an increasingly popular, important and insightful tool for studying space plasmas. This review describes MHD and particle simulations, both of which treat the plasma and the electromagnetic field in which it moves in a self consistent fashion but on drastically different spatial and temporal scales. The complementary roles of simulation, observations and theory are stressed. Several examples of simulations being carried out in the area of magnetospheric plasma physics are described to illustrate the power, potential and limitations of the approach.
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
Physics-informed deep operator networks (DeepONets) have emerged as a promising approach toward numerically approximating the solution of partial differential equations (PDEs). In this work, we aim to develop further understanding of what is being learned by physics-informed DeepONets by assessing the universality of the extracted basis functions and demonstrating their potential toward model reduction with spectral methods. Results provide clarity about measuring the performance of a physics-informed DeepONet through the decays of singular values and expansion coefficients. In addition, we propose a transfer learning approach for improving training for physics-informed DeepONets between parameters of the same PDE as well as across different, but related, PDEs where these models struggle to train well. This approach results in significant error reduction and learned basis functions that are more effective in representing the solution of a PDE.
Elastic and resonance structures of the nucleon from the hadronic tensor in lattice QCD: Implications for neutrino-nucleon scattering and hadron physics
We compute the Euclidean hadronic tensor from charge density operators and extract elastic and resonance structures by employing exponential fits to the four-point function correlator, as well as a Bayesian reconstruction inverse algorithm to obtain the corresponding spectral density for qualitative comparison. We present the determination of the nucleon’s Sachs electric form factor using the hadronic tensor formalism and verify that it is consistent with that from the conventional three-point function calculation. Beyond the elastic peak, we observe a structure located approximately 0.5–0.7 GeV above the nucleon mass in the Bayesian reconstruction. This structure is interpreted as a mixture of the Roper resonance [𝑁(1440)], and states with both positive and negative parities in this mass region, as well as multihadron states. Assuming the observed structure is dominated by 𝐽 𝑃 = 1/2 ± states, we extract the transition electric form factor 𝐺$^*_𝐸$(𝑄 2 ) and the corresponding longitudinal helicity amplitude 𝑆 1/2 (𝑄 2 ), and compare them with those determined from the CLAS experimental data of nucleon-to-Roper transition. Although fitting to the four-point correlation function or using the inverse algorithm does not resolve individual resonances, it nevertheless enables the determination of total inclusive lepton–nucleon scattering cross sections in appropriate energy bins. This lattice QCD calculation presents the first major step toward studying the inclusive 𝑁 → 𝑋 contributions within the hadronic tensor formalism.
On-line computer system for use with low- energy nuclear physics experiments is reported
Computer program handles data from low-energy nuclear physics experiments which utilize the ND-160 pulse-height analyzer and the PHYLIS computing system. The program allows experimenters to choose from about 50 different basic data-handling functions and to prescribe the order in which these functions will be performed.
Masking failures of multidimensional sensors (extended abstract)
When a computer monitors a physical process, the computer uses sensors to determine the values of the physical variables that represent the state of the process. A sensor can sometimes fail, however, and in the worst case report a value completely unrelated to the true physical value. The work described is motivated by a methodology for transforming a process control program that can not tolerate sensor failure into one that can. In this methodology, a reliable abstract sensor is created by combining information from several real sensors that measure the same physical value. To be useful, an abstract sensor must deliver reasonably accurate information at reasonable computational cost. Sensors are considered that deliver multidimensional values (e.g., location or velocity in three dimensions, or both temperature and pressure). Geometric techniques are used to derive upper bounds on abstract sensor accuracy and to develop efficient algorithms for implementing abstract sensors.
Marshall system for aerospace system simulation (MARSYAS), user's manual
The capabilities of the Marshall system for aerospace system simulation (MARSYAS) and how to use it are described. MARSYAS is a software system that allows easy setup and control of the simulation of the dynamics of large physical systems on a digital computer. The physical systems are modeled in the form of block diagrams or equations. The blocks can have multiple inputs and multiple outputs, and they can be nested to form hierarchies. The block diagrams can contain transfer functions, nonlinear and logical functions, equations, analog computer elements and FORTRAN programs. The input format of the equations can be combinations of nonlinear, time-varying differential equations and algebraic equations in their original format. MARSYAS could also serve as a storage and retrieval system for models as a basis for a model configuration control system on a central time-shared computer. The outputs of the simulation system can be not only time-responses but also other analysis data such as frequency response, power spectrum and stability parameters. The MARSYAS translator is written in FORTRAN running on the Univac 1108 computer under the EXEC 8 operating system.
A breakthrough for experiencing and understanding simulated physics
The use of computer simulation in physics research is discussed, focusing on improvements to graphic workstations. Simulation capabilities and applications of enhanced visualization tools are outlined. The elements of an ideal computer simulation are presented and the potential for improving various simulation elements is examined. The interface between the human and the computer and simulation models are considered. Recommendations are made for changes in computer simulation practices and applications of simulation technology in education.
Improve SSME power balance model
Effort was dedicated to development and testing of a formal strategy for reconciling uncertain test data with physically limited computational prediction. Specific weaknesses in the logical structure of the current Power Balance Model (PBM) version are described with emphasis given to the main routing subroutines BAL and DATRED. Selected results from a variational analysis of PBM predictions are compared to Technology Test Bed (TTB) variational study results to assess PBM predictive capability. The motivation for systematic integration of uncertain test data with computational predictions based on limited physical models is provided. The theoretical foundation for the reconciliation strategy developed in this effort is presented, and results of a reconciliation analysis of the Space Shuttle Main Engine (SSME) high pressure fuel side turbopump subsystem are examined.