High Performance Scientific Computing in Applications with Direct Finite Element Simulation

To predict separated flow including stall of a full aircraft with Computational Fluid Dynamics (CFD) is considered one of the problems of the grand challenges to be solved by 2030, according to NASA [1]. The nonlinear Navier- Stokes equations provide the mathematical formulation for fluid flow in 3- dimensional spaces. However, classical solutions, existence, and uniqueness are still missing. Since brute-force computation is intractable, to perform predictive simulation for a full aircraft, one can use Direct Numerical Simulation (DNS); however, it is prohibitively expensive as it needs to resolve the turbulent scales of order Re4 . Considering other methods such as statistical average Reynolds’s Average Navier Stokes (RANS), spatial average Large Eddy Simulation (LES), and hybrid Detached Eddy Simulation (DES), which require less number of degrees of freedom. All of these methods have to be tuned to benchmark problems, and moreover, near the walls, the mesh has to be very fine to resolve boundary layers (which means the computational cost is very expensive). Above all, the results are sensitive to, e.g. explicit parameters in the method, the mesh, etc. As a resolution to the challenge, here we present the adaptive time- resolved Direct FEM Solution (DFS) methodology with numerical tripping, as a predictive, parameter-free family of methods for turbulent flow. We solved the JAXA Standard Model (JSM) aircraft model at realistic Reynolds number, presented as part of the High Lift Prediction Workshop 3. We predicted lift Cl within 5% error vs. experiment, drag Cd within 10% error and stall 1◦ within the angle of attack. The workshop identified a likely experimental error of order 10% for the drag results. The simulation is 10 times faster and cheaper when compared to traditional or existing CFD approaches. The efficiency mainly comes from the slip boundary condition that allows coarse meshes near walls, goal-oriented adaptive error control that refines the mesh only where needed and large time steps using a Schur-type fixed-point iteration method, without compromising the accuracy of the simulation results. As a follow-up, we were invited to the Fifth High Order CFD Workshop, where the approach was validated for a tandem sphere problem (low Reynolds number turbulent flow) wherein a second sphere is placed a certain distance downstream from a first sphere. The results capture the expected slipstream phenomenon, with appx. 2% error. A comparison with the higher-order frameworks Nek500 and PyFR was done. The PyFR framework has demonstrated high effectiveness for GPUs with an unstructured mesh, which is a hard problem in this field. This is achieved by an explicit time-stepping approach. Our study showed that our large time step approach enabled appx. 3 orders of magnitude larger time steps than the explicit time steps in PyFR, which made our method more effective for solving the whole problem. We also presented a generalization of DFS to variable density and validated against the well-established MARIN benchmark problem. The results show good agreement with experimental results in the form of pressure sensors. Later, we used this methodology to solve two applications in multiphase flow problems. One has to do with a flash rainwater storage tank (Bilbao water consortium), and the second is about designing a nozzle for 3D printing. In the flash rainwater storage tank, we predicted that the water height in the tank has a significant influence on how the flow behaves downstream of the tank door (valve). For the 3D printing, we developed an efficient design with the focused jet flow to prevent oxidation and heating at the tip of the nozzle during a melting process. Finally, we presented here the parallelism on multiple GPUs and the embedded system Kalray architecture. Almost all supercomputers today have heterogeneous architectures, such as CPU+GPU or other accelerators, and it is, therefore, essential to develop computational frameworks to take advantage of them. For multiple GPUs, we developed a stencil computation, applied to geological folds simulation. We explored halo computation and used CUDA streams to optimize computation and communication time. The resulting performance gain was 23% for four GPUs with Fermi architecture, and the corresponding improvement obtained on four Kepler GPUs were 47%. The Kalray architecture is designed to have low energy consumption. Here we tested the Jacobi method with different communication strategies. Additionally, visualization is a crucial area when we do scientific simulations. We developed an automated visualization framework, where we could see that task parallelization is more than 10 times faster than data parallelization. We have also used our DFS in the cloud computing setting to validate the simulation against the local cluster simulation. Finally, we recommend the easy pre-processing tool to support DFS simulation.La Caixa 201

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Development of a grid service for multi-objective design optimisation

The emerging grid technology is receiving great attention from researchers and applications that need computational and data capabilities to enhance performance and efficiency. Multi-Objective Design Optimisation (MODO) is computationally and data challenging. The challenges become even more with the emergence of evolutionary computing (EC) techniques which produce multiple solutions in a single simulation run. Other challenges are the complexity in mathematical models and multidisciplinary involvement of experts, thus making MODO collaborative and interactive in nature. These challenges call for a problem solving environment (P SE) that can provide computational and optimisation resources to MODO experts as services. Current PSEs provide only the technical specifications of the services which is used by programmers and do not have service specifications for designers that use the system to support design optimisation as services. There is need for PSEs to have service specification document that describes how the services are provided to the end users. Additionally, providing MODO resources as services enabled designers to share resources that they do not have through service subscription. The aim of this research is to develop specifications and architecture of a grid service for MODO. The specifications provide the service use cases that are used to build MODO services. A service specification document is proposed and this enables service providers to follow a process for providing services to end users. In this research, literature was reviewed and industry survey conducted. This was followed by the design, development, case study and validation. The research studied related PSEs in literature and industry to come up with a service specification document that captures the process for grid service definition. This specification was used to develop a framework for MODO applications. An architecture based on this framework was proposed and implemented as DECGrid (Decision Engineering Centre Grid) prototype. Three real-life case studies were used to validate the prototype. The results obtained compared favourably with the results in literature. Different scenarios for using the services among distributed design experts demonstrated the computational synergy and efficiency in collaboration. The mathematical model building service and optimisation service enabled designers to collaboratively build models using the collaboration service. This helps designers without optimisation knowledge to perform optimisation. The key contributions in this research are the service specifications that support MODO, the framework developed which provides the process for definining the services and the architecture used to implement the framework. The key limitations of the research are the use of only engineering design optimisation case studies and the prototype is not tested in industry.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development of robust building energy demand-side control strategy under uncertainty

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.Ph.D.Committee Chair: Augenbroe, Gofried; Committee Member: Brown, Jason; Committee Member: Jeter, Sheldon; Committee Member: Paredis,Christiaan; Committee Member: Sastry, Chellur

SOFTENG 2023: the ninth international conference on advances and trends in software engineering

The Ninth International Conference on Advances and Trends in Software Engineering (SOFTENG 2023), held between April 24th and April 28th, 2023, continued a series of events focusing on these challenging aspects for software development and deployment, across the whole life-cycle. Software engineering exhibits challenging dimensions in the light of new applications, devices, and services. Mobility, user-centric development, smart-devices, e-services, ambient environments, e-health and wearable/implantable devices pose specific challenges for specifying software requirements and developing reliable and safe software. Specific software interfaces, agile organization and software dependability require particular approaches for software security, maintainability, and sustainability. We take here the opportunity to warmly thank all the members of the SOFTENG 2023 technical program committee, as well as all the reviewers. The creation of such a high-quality conference program would not have been possible without their involvement. We also kindly thank all the authors who dedicated much of their time and effort to contribute to SOFTENG 2023. We truly believe that, thanks to all these efforts, the final conference program consisted of top-quality contributions. We also thank the members of the SOFTENG 2023 organizing committee for their help in handling the logistics of this event. We hope that SOFTENG 2023 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in the field of software engineering

Libro de Actas JCC&BD 2018 : VI Jornadas de Cloud Computing & Big Data

Se recopilan las ponencias presentadas en las VI Jornadas de Cloud Computing & Big Data (JCC&BD), realizadas entre el 25 al 29 de junio de 2018 en la Facultad de Informática de la Universidad Nacional de La Plata.Universidad Nacional de La Plata (UNLP) - Facultad de Informátic