Animation is a technique to create illusion of motion, which is created by displaying a series of motionless pictures in sequence or using a spine/bones to create motion that looks real. All this time, the process of creating an animation still using traditional technique which requires special skills and take some time to finish a complicated animation which is used in movies or video games. Motion capture is an animation-making technique by tracking every part of body in order to find position and rotation of human’s joints which is generated by the image from the sensor. Motion capture has lots of method, such as marker base technique which use mark to track any motions. Motion capture markerless method that can capture or track motions without using any marks. Motion capture with markerless technique can be done by using RGB-Depth’s camera censor, which is by using Microsoft Kinect V2 with Kinect V2 SDK in order to make Kinect connected with computer, and using Unity Engine, a game engine that has already provided animation timeline and supports any 3D format which contains animation file that can be used in mostly other models which is using humanoid. In order to obtain motion data which will be changed into skeleton joint data, we will use connector OpenNI and 3D Collada model with (.dae) format because Collada is 3D format Open Source which is built using XML-based so that it can easily read and written back into 3D file as an output.
The work is devoted to the activity analysis of Kamchatka and the Kuril Islands volcanoes in 2019-2020.The activity of the volcanoes was estimated based on the processing of data from daily satellite monitoring carried out using the information system “Remote monitoring of Kamchatkan and the Kuriles volcanoes activity (VolSatView)”.The activity of the Kamchatka and the Kuril Islands volcanoes considered based on the analysis of their thermal anomalies. Analysis of the characteristics of thermal anomalies over volcanoes was carried out in KVERT IS. Analysis of the temperature of thermal anomalies of volcanoes in the Kuril-Kamchatka region in 2019-2020 shows a significantly higher activity of the Kamchatka volcanoes in comparison with the Kuril volcanoes.
Non-equilibrium flows of a reacting five-component air mixture consisting of N2, O2, NO, N, O behind shock waves at different altitudes from the earth’s surface at different speeds of the incoming flow are numerically investigated. One-temperature mathematical model of non-equilibrium air flows is applied. The distributions of flow quantities behind the shock wave fronts are obtained and analyzed. The relaxation lengths of flow quantities are compared for various Mach numbers and altitudes.
High-Performance Computing (HPC) is known for its use of massive concurrency. But it can be challenging for a parallel filesystem's control plane to utilize cores when every client process must globally synchronize and serialize its metadata mutations with those of other clients. We present DeltaFS, a new paradigm for distributed filesystem metadata. DeltaFS allows jobs to self-commit their namespace changes to logs, avoiding the cost of global synchronization. Followup jobs selectively merge logs produced by previous jobs as needed , a principle we term No Ground Truth which allows for efficient data sharing. By avoiding unnecessary synchronization of metadata operations, DeltaFS improves metadata operation throughput up to 98X leveraging parallelism on the nodes where job processes run. This speedup grows as job size increases. DeltaFS enables efficient inter-job communication, reducing overall workflow runtime by significantly improving client metadata operation latency up to 49X and resource usage up to 52X.
Traces of MPI communications are used by many performance analysis and visualization tools. Storing exhaustive traces of large scale MPI applications is infeasible, due to their large volume. Aggregated or lossy MPI traces are smaller, but provide much less information. In this paper, we present Pilgrim, a near lossless MPI tracing tool that incurs moderate overheads and generates small trace files at large scales, by using sophisticated compression techniques. Furthermore, for codes with regular communication patterns, Pilgrim can store their traces in constant space regardless of the problem size, the number of processors, and the number of iterations. In comparison with existing tools, Pilgrim preserves more information with less space in all the programs we tested.
The emergence of long-offset sparse stationary-recording surveys carried out with ocean bottom nodes (OBN) makes frequency-domain full waveform inversion (FWI) attractive to manage compact volume of data and perform attenuation imaging. One challenge of frequency-domain FWI is the forward problem, which requires the solution of large and sparse linear systems with multiple right-hand sides. While direct methods are suitable for dense acquisitions and problems involving less than 100 million unknowns, iterative solver are more suitable for large computational domains covered by sparse OBN surveys. Here, we solve these linear systems with a Krylov subspace method preconditioned with the two-level Optimized Restricted Additive Schwarz (ORAS) domain decomposition preconditioner, the prefix optimized referring to the use of absorbing conditions at the subdomain interfaces. We implement this method with finite differences on uniform grid and finite elements on unstructured tetrahedral meshes. A simulation in a model where the velocity linearly increases with depth allows us to validate the accuracy of the two schemes against an analytical solution while highlighting how their relative cost varies with the band of propagated wavelengths. A simulation in the overthrust model involving up to 2 billions of parameters allows us to tune the method and highlights its scalability.
This research was conducted to design a Decision Support System as a tool for decision makers in distributing the Bidik Misi Scholarship at the Politeknik Bisnis Indonesia. The selection of students who volunteered to become Bidik Misi Scholarship recipients used the Decision Support System (DSS) approach which applied the Simple Additive Weighting (SAW) method so that the decisions of Bidik Misi Scholarship recipients that had been subjective, non-transparent, and immeasurable could be overcome. The Simple Additive Weighting method is carried out by weighting the criteria and sub-criteria for each alternative for all attributes. The SAW method in the process is by normalizing the decision matrix (X) to a scale that can be compared with all existing alternative ratings. The criteria used in the SAW method in this study consisted of 2 (two) criteria and each of these criteria had Sub Criteria. The first criterion is Parents with Sub Criteria consisting of: Education, Income, The Number of Dependents. The second criterion is Students with Sub Criteria consisting of Age, Academic Potential, KIP Ownership. The output obtained from 5 data samples analyzed in this study obtained first rank NM1 with a value of 0.9, second rank NM3 with a value of 0.77, third rank NM5 with a value of 0.62, fourth rank NM4 with a value of 0.59, fifth rank NM2 with a value of 0.55. Based on the results of the tests conducted, it is concluded that the Bidik Misi Scholarship decision support system using the SAW method can make it easier and very helpful in solving the problems faced by the Politeknik Bisnis Indonesia.
In almost all technically relevant combustion applications, flames occur in turbulent flows. The interaction of turbulent flows with flames is still not fully understood due to the large range of time and length scales which govern combustion processes. One method of studying this interaction is by tracking thermo-physical trajectories of material points on flame surfaces. These trajectories give insight into the local flame dynamics and help to understand the influence of turbulence on flame properties. In this work, a Lagrangian tracking algorithm is presented which performs the tracking of material points on iso-surfaces. Because this tracking method is used in large-scale direct numerical simulations of combustion processes, the focus of the implementation lies on performance. By tracking the position of the Lagrangian particles in barycentric coordinates, efficient algorithms for spatial interpolation and the intersection of particle trajectories with iso-surfaces can be utilized. The code is written in a general way and not restricted to reacting flows but can be used to track any iso-surface. Additionally, the algorithm works by decomposing the computational cells into tetrahedra. This allows the tracking method to work on unstructured meshes with arbitrary cell shapes. The tracking method is implemented in OpenFOAM and applied to the direct numerical simulation of a 3D turbulent flame. The simulations are conducted with a custom solver which makes use of automatically generated, highly optimized code for the computation of chemical reaction rates, which performs up to 20 times faster than OpenFOAM’s implementation. For the turbulent flame, applying the tracking method increases simulation times by less than 5 %, so that the current implementation is well suited to be used during large scale simulations.
A new inflow boundary condition (BC) has been implemented into the open-source CFD code OpenFOAM, which generates synthetic turbulent fluctuations at the inlet boundary for 3D transient simulations. The method is based on convolution of digital random data series. The filter coefficients of the convolution process prescribe a two-point correlation function that possesses the basic properties of real turbulent flow. In this way, spatially and temporally correlated flow fields with specified bulk flow rate, turbulence intensity, turbulent length and time scales can be generated. Compared to previous implementations, the new turbulence generator is computationally more efficient by using coarse virtual grids and can be used for arbitrarily shaped inlets. Compared to OpenFOAM’s native turbulence generator, which shows some anomalies during parallel runs, the new implementation gives consistent results even for large-scale parallel simulations. The inlet BC has been applied to two turbulent combustion cases with Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS), using up to 8192 CPU cores on Hazel Hen at HLRS. The results reveal the significance of the inflow turbulence for reproducing the correct flame structure. A performance analysis of intra and inter-node performance on the Vulcan and Hawk clusters shows that the OpenFOAM solver is memory bound. Therefore, higher performance is reached when only half of the AMD CPU cores per node are utilized on Hawk because the L3 cache is shared by a core complex (CCX) and each core has a relatively low bandwidth. The simulation scales super-linearly on Hawk and reaches ideal speedup down to 8 000 computational cells per MPI rank, which is consistent with scaling results on the previous system Hazel Hen. The implementation of the BC is described in full detail.
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to inexpensively improve their raw prediction qualities. We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enable the model to account for non-linear relationships that are not captured by current numerical models or post-processing methods. Applied to the global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%. Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies. We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble. By using fewer trajectories, the computational costs of an ensemble prediction system can be reduced, allowing it to run at higher resolution and produce more accurate forecasts. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
Imagine entering Earth’s atmosphere after returning from the outer solar system. A heat shield less than 2 inches thick protects you from temperatures up to 2,900° Celsius (5,252° Fahrenheit). Such conditions were experienced by NASA’s Stardust capsule during reentry in 2006. The only materials capable of providing the necessary protection are composites with complex microstructures. Evaluating these materials is difficult, requiring precise knowledge of their properties. To this end, NASA scientists are developing research codes to compute material properties and simulate ablation at the microscale using agency supercomputers. Utilizing these tools, along with experiments, researchers are working to push the limits of spaceflight, allowing for greater flexibility in future space missions.
For over 35 years, the NASA Advanced Supercomputing (NAS) Division at Ames Research Center has housed and managed the U.S. space agency’s largest supercomputing assets. Focused on high-end computing technologies, efficient operations, and user success, the NAS Division has worked with industry to deploy a series of highly successful systems that enable scientific and engineering achievements across NASA. The complementary role of the High-End Computing Capability (HECC) project is evolving to meet NASA’s future challenges in returning to the Moon as a pathway to Mars, while continuing exciting research in aeronautics, space exploration, and Earth science.
One of NASA’s six Strategic Thrusts for aeronautics is “Innovation in Commercial Supersonic Aircraft,” with a vision of fast air travel widely available to the traveling public. Future supersonic aircraft will be efficient, affordable, and environmentally responsible, generating an acceptable level of en-route noise (sonic booms). The first major step is the ongoing construction of the new X-59 Quiet SuperSonic Technology X-plane to demonstrate technologies that reduce sonic booms to gentle thumps. By using highresolution Cart3D computational fluid dynamics simulations, the shape of the aircraft can be designed to control the non-linear interactions of shock waves to reduce the sonic boom noise on the ground to within outdoor ambient levels, thereby enabling supersonic overland flight.
Launching powerful space vehicles like the next-generation Space Launch System (SLS) creates extreme pressure waves that could damage the vehicle and the launch environment at NASA’s Kennedy Space Center (KSC). To ensure mission safety, the Ignition Overpressure Protection and Sound Suppression (IOP/SS) water deluge system suppresses the strong acoustic waves by delivering almost a half-million gallons of water to the mobile launcher and flame deflector. To better understand the effectiveness of this system at different operating conditions, the Launch Ascent and Vehicle Analysis (LAVA) code, developed at NASA’s Ames Research Center, is currently being modified and extended to incorporate the complex physics needed to model the IOP/SS system.
The market for new vertical takeoff and landing vehicles, including autonomous urban air taxis and drones for applications such as package delivery, imaging, and surveillance, is growing rapidly. However, aerodynamic noise continues to be the biggest roadblock to community acceptance and adoption. To predict the aerodynamic noise generated by an isolated quadcopter drone, derived from from first principles, we used the Lattice Boltzmann flow solver within NASA’s Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework. The solver’s computational efficiency, and the complete absence of labor-intensive manual volume mesh generation in the workflow, are key to making routine aeroacoustic analysis of urban air taxis and drones from first principles possible.
The Earth Observing System Data and Information System (EOSDIS) project at GSFC (Goddard Space Flight Center) maintains and operates a data and information system for NASA's Science Mission Directorate (SMD) and its Earth Science Division (ESD) to support multidisciplinary research in Earth science and public data access.
