DEVELOPMENT AND USE OF MARK SENSE RECORD CARDS FOR RECORDING MEDICAL DATA ON PILOTS SUBJECTED TO ACCELERATION STRESS
Mark sense record cards for recording medical data on pilots subjected to acceleration stress
PHYSIOLOGICAL ACCELERATION↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform
The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Accelerating Nuclear-Integrated Data Centers in the USA: SWOT Analysis, Power-Thermal Management Strategies, and Industrial-Scale Demonstration and Potential Deployment
Driven by the growth in digital services, cloud computing, AI, and manufacturing, data centers face rising energy demands that challenge traditional power sources and cooling efficiency. This study explores using nuclear power to meet these demands, focusing on accelerated reactor technology deployment and highlighting needs such as N+1/N+2 power supplies and integrated power-thermal management. A SWOT analysis addresses grid connectivity, reactors, and site selection, particularly DOE sites. Reactor technology demonstration and deployment could be accelerated by leveraging test facilities such as MARVEL, MAGNET, TED, FAS, DOME, LOTUS, ATR, Energy System Proving Grounds, and upcoming Energy Launch Pads, along with modeling and simulation tools such as RELAP5, MOOSE, VERA, RAVEN, and FORCE. The potential power and thermal management options, including various cooling technologies, waste-heat utilization, and an industrial-scale demonstration plan, aim to accelerate the integration of nuclear power and data centers in the USA, while emphasizing community and stakeholder engagement and synergistic efforts.
21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
Acceleration effects observed in optical data taken in Spacelab 3 FES
Optical instrumentation in the Fluids Experiment System (FES) is briefly described. Samples of the data produced by the schlieren and holography systems during the Spacelab 3 flight are then presented with some of the holographic interferometry data being presented for the first time. Acceleration effects that can be observed in these data are discussed and the potential for using them as a basis for measurement is explored. This includes the tracking of deliberately introduced tracer particles and density gradients in the FES, the analysis of the existing concentration gradients, and a new fiber optic G-meter concept. Finally, some of the plans for acceleration measurement in the upcoming International Microgravity-1/FES are described.
Trolinger, James↗
Rock Valley Accelerated Weight Drop Seismic Data Processing and Picking of P-wave and S-wave Arrival Times
Rock Valley, in the southern end of the Nevada National Security Site, hosts a fault system that was responsible for a shallow (< 3 km below surface ) magnitude 3.7 earthquake in May 1993. In order to better understand this system, seismic properties of the shallow subsurface need to be better constrained. In April and May of 2021, accelerated weight drop (AWD) active-source seismic data were recorded in order to measure P- and S-wave travel-times for the area. This report describes the processing and phase picking of the recorded seismic waveforms. In total, we picked 7,982 P-wave arrivals at offsets up to ~2500 m, and 4,369 S-wave arrivals at offsets up to ~2200 m. These travel-time picks can be inverted for shallow P-wave and S-wave velocity structure in future studies.
58 GEOSCIENCES↗
The High Resolution Accelerometer Package (HiRAP) flight experiment summary for the first 10 flights
The High Resolution Accelerometer Package (HiRAP) instrument is a triaxial, orthogonal system of gas damped accelerometers with a resolution of 1 x 10(exp -6) g (1 micro-g). The purpose of HiRAP is to measure the low frequency component of the total acceleration along the orbiter vehicle (OV) body axes while the OV descends through the rarefied flow flight regime. Two HiRAP instruments have flown on a total of 10 Space Transport System (STS) missions. The aerodynamic component of the acceleration measurements was separated from the total acceleration. Instrument bias and orbiter mechanical system acceleration effects were incorporated into one bulk bias. The bulk bias was subtracted from the acceleration measurements to produce aerodynamic descent data sets for all 10 flights. The aerodynamic acceleration data sets were input to an aerodynamic coefficient model. The aerodynamic acceleration data and coefficient model were used to estimate the atmospheric density for the altitude range of 140 to 60 km and a downrange distance of 600 km. For 8 of 10 flights results from this model agree with expected results. For the results that do not agree with expected results, a variety of error sources have been explored.
Blanchard, Robert C.↗
Effect of constant acceleration on the information content of a single pass of Doppler data
Effect of constant acceleration on information content of single pass of Doppler data
DATA RETRIEVAL↗
Dayside auroral particle acceleration mechanisms derived from Dynamics Explorer data
The characteristics of dayside electron acceleration regions, or inverted V's, and the nature of the field-aligned currents flowing in their vicinity are studied by using data from Dynamics Explorer hot-plasma and magnetic-field instruments. It is shown that dayside inverted-V events are common features of the mid-altitude extension of the boundary layer, which lies equatorward of the cusp. Upward-accelerated ionospheric electron beams are found to be carriers of downward region-1 currents even in regions of downward electron acceleration. It is not clear, however, how the upward-accelerated cold electron beams can exist above an apparent upward parallel electric field, since such a potential different would tend to confine the cold ionospheric electrons to low altitudes.
Burch, J. L.↗
Applying Machine‐Learning Methods to Laser Acceleration of Protons: Lessons Learned From Synthetic Data
ABSTRACT In this study, we consider three different machine‐learning methods—a three‐hidden‐layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine‐learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus on both the accuracy of the machine‐learning methods and the performance on one GPU including memory consumption. Although it is arguably the least sophisticated machine‐learning model we considered, support vector regression performed very well in our tests.
Desai, Ronak↗
Accelerating scientific discoveries through data-driven innovations
Developing artificial intelligence (AI) and machine learning (ML) methods that can accelerate scientific discoveries and advance science has become one of the important research directions for the AI/ML research community. It has been gaining increasing attention from researchers in diverse scientific areas, including biomedical science, materials science, climate science, physics, chemistry, and many others. Data-driven AI/ML innovations to enable reliable predictions and optimal decision making for scientific discoveries face several critical challenges, among which are high system complexity, large search space, incomplete knowledge, and small data, all of which demand novel strategies to effectively address them. Meeting these challenges and thereby accelerating scientific discoveries and industrial innovations, calls for research that can take full advantage of the latest advances in AI/ML to integrate data-driven techniques with scientific knowledge and is able to execute them in modern high-performance computing (HPC) environments at scale. This Patterns special collection "Accelerating scientific discoveries through data-driven innovations" features articles that showcase the promising roles of AI/ML and data-driven modeling in accelerating scientific discoveries and may inspire the next wave of data-driven innovations in various scientific domains.
97 MATHEMATICS AND COMPUTING↗
Smart CO2 Transport-Route Planning Tool: Providing Data and Insights for Accelerating Carbon Transport & Storage Deployment
Overview presentation given at the 2024 FECM / NETL Carbon Management Research Project Review Meeting on NETL's Bipartisan Infrastructure Law-funded Smart CO2 Transport-Route Planning Tool and associated geodatabase. This machine learning informed, data-driven public resource was designed to inform regulators, industry, and researchers plan and develop safe and efficient transport routes across the country.
Romeo, Lucy↗
Measurement and Data Distribution for Microgravity Accelerations on the International Space Station
Two accelerometer systems will be available on the International Space Station to support microgravity payloads with information about the quasi-steady and vibratory acceleration environment of the research facilities. The Microgravity Acceleration Measurement System will record contributions to the quasi-steady microgravity environment, including the influences of aerodynamic drag, vehicle rotation, and venting effects. The Space Acceleration Measurement System-II will measure vibratory disturbances on-board due to vehicle, crew, and equipment disturbances. Due to the dynamic nature of the microgravity environment and its potential to influence sensitive experiments, NASA's Principal Investigator Microgravity Services project has initiated a plan through which the data from these instruments will be distributed to researchers in a timely and meaningful fashion. Beyond the obvious benefit of correlation between accelerations and the scientific phenomena being studied, such information is also useful for hardware developers who can gain qualitative and quantitative feedback about their facility acceleration output to station.
McPherson, Kevin↗
Development and Use of Mark Sense Record Cards for Recording Medical Data on Pilots Subjected to Acceleration Stress
A TIME-HONORED system of recording medical histories and the data obtained on physical and laboratory examination has been that of writing the information on record sheets that go into a folder for each patient. In order to have information which would be more readily retrieved, 'a program was initiated in 1952 by the U. S. Naval School of Aviation Medicine in connection with their "Care of the Flyer" study to place this information on machine record cards. In 1958, a machine record card method was developed for recording medical data in connection with the astronaut selection program. Machine record cards were also developed by the Aero Medical Laboratory, Wright-Patterson AFB, Ohio, and the Aviation Medical Acceleration Laboratory, Naval Air Development Center, Johnsville, Pennsylvania, for use in connection with a variety of tests including acceleration stress.1 Therefore, a variety of systems resulted in which data of a medical nature could easily be recalled. During the NASA, Ames Research Center centrifuge studies/'S the pilot subjects were interviewed after each centrifuge run, or series of runs, and subjective information was recorded in a log book by the usual history taking methods referred to above. After the methods Were reviewed, it' was recognized that a card system would be very useful in recording data from our pilots after they had been exposed to acceleration stress. Since the acceleration stress cards already developed did not meet our requirements, it was decided a different card was needed.
Smedal, Harald A.↗
ARENA: Asynchronous Reconfigurable Accelerator Ring to Enable Data-Centric Parallel Computing
The next generation HPC and data centers are likely to be reconfigurable and data-centric due to the trend of hardware specialization and the emergence of data-driven applications. In this work, we propose ARENA – an asynchronous reconfigurable accelerator ring architecture as a potential scenario on how the future HPC and data centers will be like. Despite using the coarse-grained reconfigurable arrays (CGRAs) as the substrate platform, our key contribution is not only the CGRA-cluster design itself, but also the ensemble of a new architecture and programming model that enables asynchronous tasking across a cluster of reconfigurable nodes, so as to bring specialized computation to the data rather than the reverse. We presume distributed data storage without asserting any prior knowledge on the data distribution. Hardware specialization occurs at runtime when a task finds the majority of data it requires are available at the present node. In other words, we dynamically generate specialized CGRA accelerators where the data reside. The asynchronous tasking for bringing computation to data is achieved by circulating the task token, which describes the dataflow graphs to be executed for a task, among the CGRA cluster connected by a fast ring network. Evaluations on a set of HPC and data-driven applications across different domains show that ARENA can provide better parallel scalability with reduced data movement (53.9 percent). Compared with contemporary compute-centric parallel models, ARENA can bring on average 4.37× speedup. The synthesized CGRAs and their task-dispatchers only occupy 2.93mm 2 chip area under 45nm process technology and can run at 800MHz with on average 759.8mW power consumption. ARENA also supports the concurrent execution of multi-applications, offering ideal architectural support for future high-performance parallel computing and data analytics systems.
97 MATHEMATICS AND COMPUTING↗
Data-Driven Strategies for Accelerated Materials Design
The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.
36 MATERIALS SCIENCE↗
Summary Report of Mission Acceleration Measurements for STS-78. Launched June 20, 1996
The microgravity environment of the Space Shuttle Columbia was measured during the STS-78 mission using accelerometers from three different instruments: the Orbital Acceleration Research Experiment, the Space Acceleration Measurement System and the Microgravity Measurement Assembly. The quasi-steady environment was also calculated in near real-time during the mission by the Microgravity Analysis Workstation. The Orbital Acceleration Research Experiment provided investigators with real-time quasi-steady acceleration measurements. The Space Acceleration Measurement System recorded higher frequency data on-board for post-mission analysis. The Microgravity Measurement Assembly provided investigators with real-time quasi-steady and higher frequency acceleration measurements. The Microgravity Analysis Workstation provided calculation of the quasi-steady environment. This calculation was presented to the science teams in real-time during the mission. The microgravity environment related to several different Orbiter, crew and experiment operations is presented and interpreted in this report. A radiator deploy, the Flight Control System checkout, and a vernier reaction control system reboost demonstration had minimal effects on the acceleration environment, with excitation of frequencies in the 0.01 to 10 Hz range. Flash Evaporator System venting had no noticeable effect on the environment while supply and waste water dumps caused excursions of 2 x lO(exp -6) to 4 x 10(exp -6) g in the Y(sub b) and Z(sub b) directions. Crew sleep and ergometer exercise periods can be clearly seen in the acceleration data, as expected. Accelerations related to the two Life Science Laboratory Equipment Refrigerator/Freezers were apparent in the data as are accelerations caused by the Johnson Space Center Projects Centrifuge. As on previous microgravity missions, several signals are present in the acceleration data for which a source has not been identified. The causes of these accelerations are under investigation.
Hakimzadeh, Roshanak↗
Real-Time Data from the Orbital Acceleration Research Experiment (OARE)
The objective of the Orbital Acceleration Research Experiment (OARE) is to measure, with high accuracy, the low-frequency, low-magnitude acceleration levels onboard the space shuttle. The shuttle experiences acceleration from atmospheric drag, gravity gradient forces, shuttle rotations, crew activities, water/waste dumps, and shuttle attitude thrusters. The OARE instrument has successfully flown on five past shuttle missions and is scheduled for five upcoming microgravity science missions. The data collected by OARE will be utilized by microgravity scientists to better predict and analyze the influence and effects of the shuttle's on-orbit microgravity environment on experiments in materials, combustion, and fluids research.
Source record↗
Accelerated alloy discovery using synthetic data generation and data mining
The search for new alloys with improved properties is never ending with infinite combinations and amounts of alloying elements in the alloy. Advancements in machine learning have made navigating this enormous search space feasible. However, training the machine learning models and tuning their hyper-parameters to make accurate predictions can be time-consuming and often require high-performance computing resources. Furthermore, the quality of the predictions depend on the availability of sufficient training data. Here, we present a generic approach to accelerate alloy discovery by coupling high throughput CALPHAD calculations, synthetic data generation, and data mining. Finally, as a demonstration of the approach, we design super bainitic steels that form bainite at 200 C in lower transformation times.
36 MATERIALS SCIENCE↗