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The artdaq toolkit is a data-acquisition framework designed for high-energy physics experiments. It provides a flexible, reliable backbone for data transfers and has several locations where users can perform custom analysis tasks using the art framework.
Managing the flow of water, nutrients, and contaminants in watersheds is vital to addressing pressing issues related to water scarcity, access to clean drinking water, energy production, resilience to natural and anthropogenic perturbations, and ecological restoration. Decisions about the management of watersheds critically depend on the accuracy with which the flow of water and chemicals through the watershed can be predicted by computer models. Prediction uncertainty can be reduced by matching the model to data, which are collected in the field at great expense. The contribution of watershed characterization data to reducing uncertainty of relevant model predictions can be evaluated in a so-called data-worth analysis, which provides transparent, quantitative metrics about a data set’s value for the support of relevant watershed management objectives. To achieve this goal, we developed a software package that implements the data-worth analysis approach for use with state-of-the-art watershed models. The purpose of the proposed data-worth analysis is to help decision-makers allocate resources for watershed characterization such that the uncertainty in model predictions can be significantly reduced, which leads to better, more effective management decisions. At the same time, watershed characterization costs can be reduced. The specific technical objectives of this SBIR/STTR Phase II project were to develop a framework and associated software toolsets that implement the uncertainty quantification and data-worth analysis approach for use with state-of-the-art watershed models. This goal was achieved by (A) developing a user-friendly, robust software package that is accessible to a wide audience, including watershed managers, policy-makers, and public stakeholders; (B) by demonstrating application of the prototype on several use cases that are representative of complex watershed management challenges spanning a range of scales and that consider different open-source, DOE-based codes and other modeling platforms; and (C) by gathering information about the needs and requirements from potential users to help guide future developments, ensuring that the final product will be commercially viable. The developed software consists of a graphical user interface that guides the user through a sequence of analysis steps, supported by toolsets that leverage state-of-the-art computational simulation-optimization capabilities. A prototype of the software runs on multiple platforms (PC, Mac, multi-processor Linux environment), is linked to diverse watershed simulators (e.g., ECOSYS, TOUGH2, TOUGHREACT, Amanzi-ATS), performs multiple analysis tasks (predictive simulations, sensitivity analysis, uncertainty analysis, automatic parameter estimation, and data-worth analysis, multicomponent geothermometry), and is readily extensible to include external simulators and analysis tools. The software is being commercialized and will be continually updated to address user needs.
Technical and economic engine improvement concepts selected for subsequent development include: (1) fan improvement; (2) short core exhaust; (3) HP turbine aerodynamic improvement; (4) HP turbine roundness control; (5) HP turbine active clearance control; and (6) cabin air recirculation. The fuel savings for the selected engine modification concepts for the CF6 fleet are estimated.
Volume II contains thirty two complete manuscripts and five abstracts. The topics covered include the application of event-related brain potential analysis to operational problems, the subjective evaluation of workload, mental models, training, crew interaction analysis, multiple task performance, and the measurement of workload and performance in simulation.
In a planned experiment, male subjects between the age of 18 and 50 will be required to produce speech while performing various tasks. Analysis of the speech produced should reveal which aspects of voice prosody are associated with increased workloads. Preliminary results with two female subjects suggest a possible trend for voice frequency and amplitude to be higher and the variance of the voice frequency to be lower in the high workload condition.
Abstract not provided.
The Integrated Analysis task in the Agile BioFoundry (ABF) conducts techno-economic analysis (TEA) and life cycle assessment (LCA) to quantify the economics and environmental impacts of bioprocesses under development. The team from Argonne National Laboratory (ANL) and the National Renewable Energy Laboratory (NREL) develops TEA and LCA models of selected compound targets and hosts of interest to the ABF in an effort to provide an analysis-based foundation to the R&D. To date, the Integrated Analysis task has performed process modeling and analyses on multiple pairings of bioproducts and hosts, centered around major metabolic "beachheads" and "exemplar" molecules chosen for each of the metabolic pathways of interest. This presentation provides an overview of the approach used for TEA and LCA in the ABF and of the progress, outcomes, and impacts of TEA and LCA to support ABF goals, with a focus towards developing bio-based products that are both environmentally and economically viable.
The increased use of automation in the cockpits of commercial planes has dramatically decreased the workload requirements of pilots, enabling them to function more efficiently and with a higher degree of safety. Unfortunately, advances in technology have led to an unexpected problem: the decreased demands on pilots have increased the probability of inducing 'hazardous states of awareness.' A hazardous state of awareness is defined as a decreased level of alertness or arousal which makes an individual less capable of reacting to unique or emergency types of situations. These states tend to be induced when an individual is not actively processing information. Under such conditions a person is likely to let his/her mind wander, either to internal states or to irrelevant external conditions. As a result, they are less capable of reacting quickly to emergency situations. Since emergencies are relatively rare, and since the high automated cockpit requires progressively decreasing levels of engagement, the probability of being seduced into a lowered state of awareness is increasing. This further decreases the readiness of the pilot to react to unique circumstances such as system failures. The HEM Lab at NASA-Langley Research Center has been studying how these states of awareness are induced and what the physiological correlates of these different states are. Specifically, they have been interested in studying electroencephalographic (EEG) measures of different states of alertness to determine if such states can be identified and, hopefully, avoided. The project worked on this summer involved analyzing the EEG and the event related potentials (ERP) data collected while subjects performed under two conditions. Each condition required subjects to perform a relatively boring vigilance task. The purpose of using these tasks was to induce a decreased state of awareness while still requiring the subject to process information. Each task involved identifying an infrequently presented target stimulus. In addition to the task requirements, irrelevant tones were presented in the background. Research has shown that even though these stimuli are not attended, ERP's to them can still be elicited. The amplitude of the ERP waves has been shown to change as a function of a person's level of alertness. ERP's were also collected and analyzed for the target stimuli for each task. Brain maps were produced based on the ERP voltages for the different stimuli. In addition to the ERP's, a quantitative EEG (QEEG) was performed on the data using a fast Fourier technique to produce a power spectral analysis of the EEG. This analysis was conducted on the continuous EEG while the subjects were performing the tasks. Finally, a QEEG was performed on periods during the task when subjects indicated that they were in an altered state of awareness. During the tasks, subjects were asked to indicate by pressing a button when they realized their level of task awareness had changed. EEG epochs were collected for times just before and just after subjects made this reponse. The purpose of this final analysis was to determine whether or not subjective indices of level of awareness could be correlated with different patterns of EEG.
Tasks were performed in two areas: (1) systems analysis and (2) algorithmic development. The major effort in the systems analysis task was the development of a recommended approach to the monitoring of resource utilization data for the Large Area Crop Inventory Experiment (LACIE). Other efforts included participation in various studies concerning the LACIE Project Plan, the utility of the GE Image 100, and the specifications for a special purpose processor to be used in the LACIE. In the second task, the major effort was the development of improved algorithms for estimating proportions of unclassified remotely sensed data. Also, work was performed on optimal feature extraction and optimal feature extraction for proportion estimation.
Surface electromyograms (EMG) taken from three upper torso muscles during a push-pull task were analyzed by a power spectral density technique to determine the utility of the spectral analysis for identifying changes in the EMG caused by muscular fatigue. The results confirmed the value of the frequency analysis for identifying fatigue producing muscular performance. Data revealed reliable differences between muscles in fatigue induced responses to various locations in the reach envelope at which the subjects were required to perform the push-pull exercise, and the differential sensitivity of individual muscles to the various reach positions; i.e., certain reach positions imposed more fatigue related shifts in EMG power than did others. It was found that a pressurized space suit changed the pattern of normal shirtsleeve muscle fatigue responses in all three of the muscles.
The performance and cost of moderate technology coal-fired open cycle MHD/steam power plant designs which can be expected to require a shorter development time and have a lower development cost than previously considered mature OCMHD/steam plants were determined. Three base cases were considered: an indirectly-fired high temperature air heater (HTAH) subsystem delivering air at 2700 F, fired by a state of the art atmospheric pressure gasifier, and the HTAH subsystem was deleted and oxygen enrichment was used to obtain requisite MHD combustion temperature. Coal pile to bus bar efficiencies in ease case 1 ranged from 41.4% to 42.9%, and cost of electricity (COE) was highest of the three base cases. For base case 2 the efficiency range was 42.0% to 45.6%, and COE was lowest. For base case 3 the efficiency range was 42.9% to 44.4%, and COE was intermediate. The best parametric cases in bases cases 2 and 3 are recommended for conceptual design. Eventual choice between these approaches is dependent on further evaluation of the tradeoffs among HTAH development risk, O2 plant integration, and further refinements of comparative costs.
The primary tasks performed were the continued development of inverse design procedures for the TAWFIVE code, the development of corresponding relofting and trailing edge closure procedures, and the testing of the methods for a variety of cases. The period from July 1, 1986 through December 31, 1986 is covered.
A new paradigm was developed to examine prospective memory performance in a visual-spatial task that resembles some aspects of the work of air traffic controllers. Two experiments examined the role of workload (number of aeroplanes that participants directed), delay (between receipt of prospective instructions and execution), and phonological rehearsal. High workload increased prospective memory errors but increasing delay from 1-3 or 5 minutes had no effect. Shadowing aurally presented text reduced prospective memory performance, presumably because it prevented verbal rehearsal of the prospective instructions. However, performance on the foreground task of directing aeroplanes to routine destinations was affected only by workload and not by opportunity for rehearsal. Our results suggest that ability to maintain performance on a routine foreground task while performing a secondary task--perhaps analogous to conversation--does not predict ability to retrieve a prospective intention to deviate from the routine.
As a part of the task of performing preliminary engineering analysis of modular payload subelement/host vehicle interfaces, a subsystem interface analysis was performed to establish the integrity of the modular approach to the equipment design and integration. Salient areas that were selected for analysis were power and power conditioning, heat rejection and electromagnetic capability (EMC). The equipment and load profiles for twelve representative experiments were identified. Two of the twelve experiments were chosen as being representative of the group and have been described in greater detail to illustrate the evaluations used in the analysis. The shuttle orbiter will provide electrical power from its three fuel cells in support of the orbiter and the Spacelab operations. One of the three shuttle orbiter fuel cells will be dedicated to the Spacelab electrical power requirements during normal shuttle operation. This power supplies the Spacelab subsystems and the excess will be available to the payload. The current Spacelab sybsystem requirements result in a payload allocation of 4.0 to 4.8 kW average (24 hour/day) and 9.0 kW peak for 15 minutes.
Traditional filter data structures, such as Bloom filters, do not offer necessary features that modern high-performance data analytics applications need in order to efficiently perform complex data analysis tasks. For example, MetaHipMer, a de novo metagenome assembler, can use filters to weed out singleton k-mers and reduce memory usage by 30%-70%. However, the filter needs the ability to associate values with k-mers in order to perform the analysis in a single communication pass. Bloom filters do not support value associations and cause the application to perform an extra communication pass, thereby increasing the run time. Therefore, MetaHipMer faces a trade off between memory and speed due to the limited capabilities of traditional filters. In this paper, we overcome the memory and speed trade off in MetaHipMer by integrating a GPU-based feature-rich filter, the Two-Choice filter (TCF), in the MetaHipMer pipeline. The TCF uses key-value association to approximately store k-mers with extensions. This allows MetaHipMer to perform k-mer analysis on the GPUs in a single communication pass. Our empirical analysis shows a 50% reduction in memory usage in k-mer analysis on each node in MetaHipMer without any effect on the overall run time or assembly quality. The memory reduction in turn results in a 43% reduction in the number of nodes required to assemble datasets and enables MetaHipMer to scale to much larger datasets.
While new versions of automated control systems such as flight guidance systems are introduced at a rapid pace, it is widely recognized that user interaction with these machines is increasingly problematic. One cause for this difficulty that is commonly cited in the literature, is the discrepancy between the machine's behavior and the operator's (e.g., pilot) expectations. This paper discusses a formal approach to the analysis of operator's interaction with complex automated control systems. We focus attention on the issue of interface correctness; that is, on the question whether the display provides adequate information about the machine's configurations (states, modes, and associated parameters) and transitions, so as to enable the operator to successfully perform the specified set of tasks. To perform the analysis several assumptions are made: (1) A complete formal model of the machine's behavior is available (e.g., as a state transition system, or as a hybrid-machine); (2) A specification of operator's tasks is available and can be formally described (e.g., the reliable and predictable transition between activities involved in executing a climb to a new altitude); (3) The pilot is well trained and has a correct 'mental' model of the machine's response-map. By 'comparing' the machine's model with the set of operator's tasks we formally (i.e., mathematically) evaluate two questions: 1) does the machine's output interface (display) enable the operator to determine, unambiguously, what the current configuration (e.g., mode) of the machine is, and 2) does the display enable the operator to determine, unambiguously, what the next configuration of the machine will be, in response to a specified interaction by the operator (e.g., engaging a mode or changing a parameter such as a speed or target altitude). This paper describes a methodology for conducting such an evaluation using examples from automated flight control systems of modem 'glass cockpit' jetliners. Taxonomy of the different types of discrepancies that lead to pilot inability to resolve the current and next configuration of the machine is suggested. Data from incident reports involving 'mode confusion' is used to corroborate these discrepancies. Finally, means for compensating, either by augmenting the display and/or the operator's 'mental model' are briefly mentioned.
During the four year period of performance for NASA contract, NASB-39095, ERC has performed a wide variety of tasks to support the design and continued development of new and existing solid rocket motors and the resolution of operational problems associated with existing solid rocket motor's at NASA MSFC. This report summarizes the support provided to NASA MSFC during the contractual period of performance. The report is divided into three main sections. The first section presents summaries for the major tasks performed. These tasks are grouped into three major categories: full scale motor analysis, subscale motor analysis and cold flow analysis. The second section includes summaries describing the computational fluid dynamics (CFD) tasks performed. The third section, the appendices of the report, presents detailed descriptions of the analysis efforts as well as published papers, memoranda and final reports associated with specific tasks. These appendices are referenced in the summaries. The subsection numbers for the three sections correspond to the same topics for direct cross referencing.
Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) systems that excel in managing distributed workloads. Because data is the main fuel for AI applications, the performance of the storage and I/O subsystem of HPC systems is critical. In the past, HPC applications accessed large portions of data written by simulations or experiments or ingested data for visualizations or analysis tasks. ML workloads perform small reads spread across a large number of random files. This shift of I/O access patterns poses several challenges to modern parallel storage systems. In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024. We define the scope of the survey, provide an overview of the common phases of ML, review available profilers and benchmarks, examine the I/O patterns encountered during offline data preparation, training, and inference, and explore I/O optimizations utilized in modern ML frameworks and proposed in recent literature. Lastly, we seek to expose research gaps that could spawn further R&D.