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Outline - Background and Motivation - CATNLF Concept Development - Computational Study - Wind Tunnel Test - Flight Test - Current and Future Work - Summary
A high Reynolds number wind tunnel test of the NATO AVT-298 Swept Wing Flow Test (SWiFT) configuration was conducted in the National Transonic Facility at the NASA Langley Research Center during the summer of 2023. The SWiFT research model geometry has relevance to both blended/hybrid wing body (BWB/HWB) and unmanned combat aerial vehicle (UCAV) configurations, and the test campaign was the culmination of an international collaboration under the NATO AVT-298 research task group. The main objectives of the test were to investigate Reynolds number scaling effects on low-speed stability & control characteristics and to examine the onset and progression of flow separation on the wings particularly near the wing crank. Force & moment and surface pressure data were acquired at Mach numbers from 0.2 to 0.8, Reynolds numbers from 2.5 to 34 million, angles of attack from -3 to 20 degrees, and sideslip angles from -10 to 10 degrees. Boundary layer transition detection techniques utilizing static pressure taps, unsteady pressure transducers, and sublimating chemicals were used on the model in a free/natural transition state or a forced transition state using trip dots. Pressure sensitive paint was used to obtain a global surface pressure profile on the model and an advanced laser velocimetry technique was used to obtain velocity measurements in the wake downstream of the wing crank. The results from the test showed clear Reynolds number scaling effects on the maximum lift coefficient and pitching moment coefficient at Mach 0.2, while also capturing significant hysteresis effects. The data acquired from the test will ultimately help improve computational aerodynamic analysis and design tools for application to future BWB-type vehicle configurations.
A high Reynolds number wind tunnel test of the Swept Wing Flow Test (SWiFT) configuration was conducted in the National Transonic Facility at the NASA Langley Research Center during the summer of 2023. The test campaign was the culmination of an international collaboration under the NATO AVT-298 research task group. The main objective of the test was to investigate Reynolds number scaling effects on the low-speed stability & control characteristics of the SWiFT model. Surface pressure results corroborate the force & moment results and help to provide more insight into the observed flow behavior. The effect of angle of attack and Reynolds number on the low-speed surface pressure results are analyzed particularly around the leading edge of the model. The onset and progression of flow separation on the wings particularly near the wing crank is also investigated, as well as the cause of an observed double-stall behavior. Finally, the transonic results showed a slight forward movement of the shock location with decreased shock strength as Reynolds number was increased. The data acquired from the test will ultimately help improve computational aerodynamic analysis and design tools for application to future BWB-type vehicle configurations.
A high Reynolds number wind tunnel test of the NATO AVT-298 Swept Wing Flow Test (SWiFT) configuration was conducted in the National Transonic Facility at the NASA Langley Research Center during the summer of 2023. The SWiFT research model geometry has relevance to both blended/hybrid wing body (BWB/HWB) and unmanned combat aerial vehicle (UCAV) configurations, and the test campaign was the culmination of an international collaboration under the NATO AVT-298 research task group. The main objectives of the test were to investigate Reynolds number scaling effects on low-speed stability & control characteristics and to examine the onset and progression of flow separation on the wings particularly near the wing crank. Force & moment and surface pressure data were acquired at Mach numbers from 0.2 to 0.8, Reynolds numbers from 2.5 to 34 million, angles of attack from -3 to 20 degrees, and sideslip angles from -10 to 10 degrees. Boundary layer transition detection techniques utilizing static pressure taps, unsteady pressure transducers, and sublimating chemicals were used on the model in a free/natural transition state or a forced transition state using trip dots. Pressure sensitive paint was used to obtain a global surface pressure profile on the model and an advanced laser velocimetry technique was used to obtain velocity measurements in the wake downstream of the wing crank. The results from the test showed clear Reynolds number scaling effects on the maximum lift coefficient and pitching moment coefficient at Mach 0.2, while also capturing significant hysteresis effects. The data acquired from the test will ultimately help improve computational aerodynamic analysis and design tools for application to future BWB-type vehicle configurations.
A high Reynolds number wind tunnel test of the Swept Wing Flow Test (SWiFT) configuration was conducted in the National Transonic Facility at the NASA Langley Research Center during the summer of 2023. The test campaign was the culmination of an international collaboration under the NATO AVT-298 research task group. The main objective of the test was to investigate Reynolds number scaling effects on the low-speed stability & control characteristics of the SWiFT model. Surface pressure results corroborate the force & moment results and help to provide more insight into the observed flow behavior. The effect of angle of attack and Reynolds number on the low-speed surface pressure results are analyzed particularly around the leading edge of the model. The onset and progression of flow separation on the wings particularly near the wing crank is also investigated, as well as the cause of an observed double-stall behavior. Finally, the transonic results showed a slight forward movement of the shock location with decreased shock strength as Reynolds number was increased. The data acquired from the test will ultimately help improve computational aerodynamic analysis and design tools for application to future BWB-type vehicle configurations.
Population-based central cancer registries collect valuable structured and unstructured data primarily for cancer surveillance and research, enhancing insights into clinical features associated with cancer occurrence, cancer treatment, and cancer outcomes to guide interventions which reduce the cancer burden. Cancer registries primarily collect data on (1) cancer type (case or tumor); (2) patient demographics such as age, gender, and residential address at time of diagnosis; (3) planned first course of treatment; and (4) date of last contact, vital status, and cause of death. Cancer registry data is dynamic, structured data, which is extracted from many unstructured sources such as electronic healthcare records, and consolidated for reporting and other purposes. While available advanced analytic tools such as SEER*Stat have the ability to build SAS queries, we, however, explore an innovative knowledge graph approach to organizing cancer registry data for advanced analytics and visualization, which has unique advantages over approaches of existing tools. This innovative knowledge graph approach semantically enriches the data and easily enables linkage with third-party data, which can better explain variation in outcomes. We have developed a prototype knowledge graph based on data from the Louisiana Tumor Registry and other publicly available datasets including Behavioral Risk Factor Surveillance System, Clinical Trials, DBpedia, GeoNames, Rural-Urban Continuum Codes, and Semantic MEDLINE. The resource description framework (RDF) data model was selected to represent our knowledge graph, which contains more than 25 billion triples and is ~4TB in storage size. To exhibit the benefits of the knowledge graph approach, we used scenario specific queries, which find the relationships between cancer treatment sequences and outcomes. To illustrate its ease of use in iterative analysis, the knowledge graph was linked to external datasets for performing complex queries across multiple datasets. In addition, we used knowledge graphs to identify data discrepancies and to handle schema changes. Finally, we visualized the knowledge graph to discover data patterns. Our results demonstrate this graph-based solution enables cancer researchers to execute complex queries and more easily perform iterative analyses to improve understanding of cancer registry data. In the future, we would like to use high-performance computing (HPC) resources for faster-generating hypotheses with clinical potential from our knowledge graph.
Mobility in America is changing fast. To understand where it’s headed, Argonne caught up with Michael Berube, the acting deputy assistant secretary for transportation in the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy. While at the laboratory, Berube took time to highlight the ways in which technology and consumer demand are shaping transportation, and the insights national laboratories like Argonne can provide about the future.
Today the LHC offline computing relies heavily on CPU resources, despite the interest in compute accelerators, such as GPUs, for the longer term future. The number of cores per CPU socket has continued to increase steadily, reaching the levels of 64 cores (128 threads) with recent AMD EPYC processors, and 128 cores on Ampere Altra Max ARM processors. Over the course of the past decade, the CMS data processing framework, CMSSW, has been transformed from a single-threaded framework into a highly concurrent one. The first multithreaded version was brought into production by the start of the LHC Run 2 in 2015. Since then, the framework's threading efficiency has gradually been improved by adding more levels of concurrency and reducing the amount of serial code paths. The latest addition was support for concurrent Runs. In this work we review the concurrency model of the CMSSW, and measure its scalability with real CMS applications, such as simulation and reconstruction, on mode rn many-core machines. We show metrics such as event processing throughput and application memory usage with and without the contribution of I/O, as I/O has been the major scaling limitation for the CMS applications.
The Mobile Plutonium Facility (MPF) is a portable module design facility that can be deployed anywhere in the world for the safe recovery, packaging, and shipment of a nations legacy, or excess plutonium stockpile. All of the modules of MPF have specific purposes for operating in a mission if one were to ever come up. For my past four internships here at SRNL I have been working on the support of the X-Ray module for MPF. This year I installed new equipment I had purchased last year for the X-Ray Module and made sure the module was mission ready for the chance that the team deploys in the future for plutonium recovery. Using digital radiography (DR) and computed tomography (CT) provides the MPF team the ability to verify the contents of containers that would be holding material. This also ensures the safety of the team members in the glovebox facilities by verifying that there are no sharps, liquids, or other items that may harm the workers or glovebox operations when working with radioactive material. X-Ray imaging can also pick up low density objects like plastic bags and tape. Note how the DR image hides the puncture in the inner can while the CT image shows the damage. New DR and CT computers with upgraded software, New imaging panel, NOVO Portable X-Ray System. Because of the new upgrades to the X-Ray module system containers can be scanned and evaluated quicker than before. With the last system the 3D model reconstruction alone would take anywhere from 10-12 minutes, now with the new system that time has been brought down to just shy of 1 minute 30 seconds. The new portable system also can 1,2 or 4 images of an object and stitch them together into a mosaic for scanning of larger containers.
In a quasiclassical trajectory simulation, the vibrational modes are initialised with quantised vibrational energies, but vibrational phases are sampled by Monte Carlo. This requires an algorithm to assign coordinates and momenta to the various atoms. In this work, we present two methods for implementing this for nonrotating polyatomic molecules, namely, fixed-energy vibrational-state-selected initial conditions and thermal initial conditions. We also present a method for initiating classical trajectories with a ground-state Wigner distribution. These vibrational treatments are sufficient to initialise trajectories for unimolecular processes, and we also show how they can be applied to simulate bimolecular collision processes. The treatments of unimolecular and bimolecular collision processes are available in two Python codes called wigner_state_selected.py and bimolecular_collision.py, respectively, which will generate initial condition files that are recognisable by the SHARC and SHARC-MN computer programs for dynamics calculations. Both codes are available as standalone programs, as well as being included in SHARC-MN, and they will be included in future versions of SHARC. Here, the methods implemented in these codes are mostly also available in the ANT computer program, and those that are not available in ANT will be incorporated in future versions of ANT.
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.
This paper presents a computational framework for uncertainty quantification in prognostics in the context of condition-based monitoring of aerospace systems. The different sources of uncertainty and the various uncertainty quantification activities in condition-based prognostics are outlined in detail, and it is demonstrated that the Bayesian subjective approach is suitable for interpreting uncertainty in online monitoring. A state-space model-based framework for prognostics, that can rigorously account for the various sources of uncertainty, is presented. Prognostics consists of two important steps. First, the state of the system is estimated using Bayesian tracking, and then, the future states of the system are predicted until failure, thereby computing the remaining useful life of the system. The proposed framework is illustrated using the power system of a planetary rover test-bed, which is being developed and studied at NASA Ames Research Center.
The Natural Environments Branch of the Engineering Directorate at Marshall Space Flight Center (MSFC) provides solar cycle forecasts for NASA space flight programs and the aerospace community. These forecasts provide future statistical estimates of sunspot number, solar radio 10.7 cm flux (F10.7), and the geomagnetic planetary index, Ap, for input to various space environment models. For example, many thermosphere density computer models used in spacecraft operations, orbital lifetime analysis, and the planning of future spacecraft missions require as inputs the F10.7 and Ap. The solar forecast is updated each month by executing MSAFE using historical and the latest month's observed solar indices to provide estimates for the balance of the current solar cycle. The forecasted solar indices represent the 13-month smoothed values consisting of a best estimate value stated as a 50 percentile value along with approximate +/- 2 sigma values stated as 95 and 5 percentile statistical values. This presentation will give an overview of the MSAFE model and the forecast for the current solar cycle.
The Natural Environments Branch of the Engineering Directorate at Marshall Space Flight Center (MSFC) provides solar cycle forecasts for NASA space flight programs and the aerospace community. These forecasts provide future statistical estimates of sunspot number, solar radio 10.7 cm flux (F10.7), and the geomagnetic planetary index, Ap, for input to various space environment models. For example, many thermosphere density computer models used in spacecraft operations, orbital lifetime analysis, and the planning of future spacecraft missions require as inputs the F10.7 and Ap. The solar forecast is updated each month by executing MSAFE using historical and the latest month's observed solar indices to provide estimates for the balance of the current solar cycle. The forecasted solar indices represent the 13-month smoothed values consisting of a best estimate value stated as a 50 percentile value along with approximate +/- 2 sigma values stated as 95 and 5 percentile statistical values. This presentation will give an overview of the MSAFE model and the forecast for the current solar cycle.
The Natural Environments Branch of the Engineering Directorate at Marshall Space Flight Center (MSFC) provides solar cycle forecasts for NASA space flight programs and the aerospace community. These forecasts provide future statistical estimates of sunspot number, solar radio 10.7 cm flux (F10.7), and the geomagnetic planetary index, Ap, for input to various space environment models. For example, many thermosphere density computer models used in spacecraft operations, orbital lifetime analysis, and the planning of future spacecraft missions require as inputs the F10.7 and Ap. The solar forecast is updated each month by executing MSAFE using historical and the latest month's observed solar indices to provide estimates for the balance of the current solar cycle. The forecasted solar indices represent the 13-month smoothed values consisting of a best estimate value stated as a 50 percentile value along with approximate +/- 2 sigma values stated as 95 and 5 percentile statistical values. This presentation will give an overview of the MSAFE model and the forecast for the current solar cycle.
The final review for the FY21 Advanced Simulation and Computing (ASC) Computational Systems and Software Environments (CSSE) L2 Milestone #7840 was conducted on August 25th, 2021 at Sandia National Laboratories in Albuquerque, New Mexico. The review committee/panel unanimously agreed that the milestone has been successfully completed, exceeding expectations on several of the key deliverables.
Computer programming, data processing, and a correlation study that employed data collected in the first phase test were used to demonstrate that standard test procedures and equipment could be used to collect a significant number of transfer functions from tests of the Lunar Module test article LTA-11. The testing consisted of suspending the vehicle from the apex fittings of the outrigger trusses through a set of air springs to simulate the free-free state. Impulsive loadings were delivered, one at a time, at each of the landing gear's attachment points, in three mutually perpendicular directions; thus a total of 36 impulses were applied to the vehicle. Time histories of each pulse were recorded on magnetic tape along with 40 channels of strain gage response and 28 channels of accelerometer response. Since an automated data processing system was not available, oscillograph playbacks were made of all 2400 time histories as a check on the validity of the data taken. In addition, one channel of instrumentation was processed to determine its response to a set of forcing functions from a prior LTA-11 drop test. This prediction was compared with drop test results as a first measure of accuracy.
Although the purpose of this research is to illustrate the importance of computers to the public, particularly the IBM PC, present examinations will include computers developed before the IBM PC was brought into use. IBM, as well as other computing facilities, began serving the public years ago, and is continuing to find ways to enhance the existence of man. With new developments in supercomputers like the Cray-2, and the recent advances in artificial intelligence programming, the human race is gaining knowledge at a rapid pace. All have benefited from the development of computers in the world; not only have they brought new assets to life, but have made life more and more of a challenge everyday.