DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
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As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.
It is pointed out that the increasing computation power and rapidly falling cost of microprocessors and memories have given an impetus to the development of distributed computing systems. There are a number of potential benefits. A number of issues have to be resolved, however, before the full potential of a distributed processing system can be realized. The present investigation is concerned with one such issue, taking into account the effectiveness of implementing recovery blocks (RB's) in backward error recovery for a set of cooperating processes. A quantitative evaluation of three different recovery blocks employed in backward error recovery for concurrent processing is performed.
Intelligent computer-aided training (ICAT) systems simulate the behavior of an experienced instructor observing a trainee, responding to help requests, diagnosing and remedying trainee errors, and proposing challenging new training scenarios. This paper presents a generic ICAT architecture that supports the efficient development of ICAT systems for varied tasks. In addition, details of ICAT projects, built with this architecture, that deliver specific training for Space Shuttle crew members, ground support personnel, and flight controllers are presented. Concurrently with the creation of specific ICAT applications, a general-purpose software development environment for ICAT systems is being built. The widespread use of such systems for both ground-based and on-orbit training will serve to preserve task and training expertise, support the training of large numbers of personnel in a distributed manner, and ensure the uniformity and verifiability of training experiences.
The mean sea-surface height and the gravitational geoid are presently estimated via near-surface velocity changes and concurrent sea-level changes along an ascending Geosat subtrack. The velocity measurements were made on three traverses, within ten days, of a Geosat subtrack, by means of an acoustic Doppler current profiler (ADCP). The mean sea-surface height was estimated as the difference between the instantaneous sea-surface height from ADCP and the Geosat residual sea level. In order to minimize mesoscale errors in the estimate, the along-track geoid estimate was computed as the difference between mean sea-level height from the Geosat Exact Repeat Mission and an estimate of the mean sea-surface height.
Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)
This investigation demonstrates the feasibility of mental workload assessment by rheoencephalographic (REG) and multichannel electroencephalographic (EEG) monitoring. During the performance of this research, unique testing, analytical and display procedures were developed for REG and EEG monitoring that extend the current state of the art and provide valuable tools for the study of cerebral circulatory and neural activity during cognition. REG records are analyzed to provide indices of the right and left hemisphere hemodynamic changes that take place during each test sequence. The EEG data are modeled using regression techniques and mathematically transformed to provide energy-density distributions of the scalp electrostatic field. These procedures permit concurrent REG/EEG cognitive testing not possible with current techniques. The introduction of a system for recording and analysis of cognitive REG/EEG test sequences facilitates the study of learning and memory disorders, dementia and other encephalopathies.
A pressure ratio to thermal environments (PRatTlE.pl) program is a Perl language code that estimates heating at requested body point locations by scaling the heating at a reference location times a pressure ratio factor. The pressure ratio factor is the ratio of the local pressure at the reference point and the requested point from CFD (computational fluid dynamics) solutions. This innovation provides pressure ratio-based thermal environments in an automated and traceable method. Previously, the pressure ratio methodology was implemented via a Microsoft Excel spreadsheet and macro scripts. PRatTlE is able to calculate heating environments for 150 body points in less than two minutes. PRatTlE is coded in Perl programming language, is command-line-driven, and has been successfully executed on both the HP and Linux platforms. It supports multiple concurrent runs. PRatTlE contains error trapping and input file format verification, which allows clear visibility into the input data structure and intermediate calculations.
The processing of biomass solids in a biorefinery consists of pretreatment, enzyme hydrolysis / concurrent fermentation of sugars to ethanol, product recovery, and drying. Sustainable operation requires a front end that transforms wet solids into a pumpable slurry. Otherwise the biorefinery will suffer unscheduled shut-downs and inefficient operation due to solids that obstruct pumps and other equipment and resist mixing in a bioreactor. Downtime in pioneer biorefineries due to interruptions from materials handling problems has been 50% or more, leading to unsustainable manufacturing processes. This work addresses new technology, predictive computational models, and definition of operational conditions that result in formation of slurries of corn stover at up to 300 g/L using low enzyme loadings (1 to 3 FPU cellulase/g) before the biomass (corn stover) enters the pretreatment step. A team of researchers from Purdue University, Idaho National Laboratory (INL), Forest Concepts, AdvanceBio, Argonne National Laboratory, and DOE BETO have combined their knowledge in agricultural and biological engineering, bioprocess engineering, mechanical engineering, chemical engineering, agricultural economics, materials engineering and enzyme and microbial technology to address the challenge of making lignocellulose flow. This team effort has resulted in the development and validation of conditions that employ low levels of commercial enzyme in an agitated bioreactor to which corn stover pellets are added resulting in formation of slurries at high solids loadings, before pretreatment. This approach overcomes challenges caused by handling of dry, particulate biomass materials at the front end of the biorefinery. The subsequent materials handling issues cause obstruction at pumps, pipes and valves. Formation of high loadings slurries with low yield stress, as reported here, significantly decreases the potential for process interruption and enhances plant operability. Key advances in the knowledge of how slurry formation occurs is reported here and in recently published journal papers. We found that pellets are needed to achieve high solids loading, and that commercial enzymes are effective in forming slurries of corn stover particles from pellets that have not been pretreated. Our work has resulted in models that predict solids behavior for formation of compressed solids and pellets that in turn facilitate slurries made of high concentrations of corn stover particles. A computational model was developed that gives mechanistic insights into properties of particles and mixing process that gives the slurry rheology needed to facilitate pumping. Hence, the corn stover may be pumped into a pretreatment reactor in place of auguring in solids against high pressure which is a root cause of interruptions at the front end of a biorefinery. Subsequent mixing in enzyme and microbial bioreactors results in conversion of lignocellulose to sugars in a biorefinery in agitated bioreactors, with flows in and out of the vessels being less likely to be interrupted due to plugging or materials handling problems. The obtained data coupled to process models, techno-economic assessment (TEA) and Life Cycle Analysis (LCA) were used to assess whether this approach is practical. These results are based on a foundation of laboratory characterization and pilot runs. The NREL biochemical sugar model was utilized to carry out techno-economic analysis of enzyme catalyzed liquefaction followed by enzyme hydrolysis. The minimum sugar selling price was between 17.5 and 18.3 ¢/pound or about the same as calculated by the NREL model for dilute acid pretreatment followed by enzyme hydrolysis. Life cycle analysis (LCA) based on Argonne’s Greet Model showed the enzyme catalyzed route had the lowest greenhouse gas emissions of the three combinations studied (i.e., enzyme, enzyme mimetic, and enzyme + mimetic combined). GHG emissions for enzyme-based corn stover liquefaction step, alone, were about 21 g CO 2 -equivalent/kg of liquefied slurry. We believe this approach will further enhance operability of a pioneer biorefinery, and bring large-scale conversion of lignocellulosic biomass to low carbon footprint biofuels closer to implementation.
Technologies developed through NASA's Energy Efficient Transport Program are described. The program was charged with research in advanced aerodynamics and active controls, with the goal of increasing the fuel efficiency of transport aircraft by 15 to 20 percent. Research in aerodynamics was directed toward the development of high-aspect-ratio supercritical wings, winglets, computational design methodology, high-lift devices, propulsion airframe integration, and surface coatings. The active control portion of the program investigated Wing Load Alleviation (WLA) through the use of active controls, drag reduction, and the effect of active pitch controls on fuel consumption. It was found that applying active control functions at the beginning of the aircraft design cycle brings the best benefit, and that if active control and advanced aerodynamic airframe configurations are applied to transport aircraft design concurrently with new lightweight materials, fuel consumption can be reduced by as much as 40 percent.
A new numerical algorithm for the solution of large-order eigenproblems typically encountered in linear elastic finite element systems is presented. The architecture of parallel processing is utilized in the algorithm to achieve increased speed and efficiency of calculations. The algorithm is based on the frontal technique for the solution of linear simultaneous equations and the modified subspace eigenanalysis method for the solution of the eigenproblem. Assembly, elimination and back-substitution of degrees of freedom are performed concurrently, using a number of fronts. All fronts converge to and diverge from a predefined global front during elimination and back-substitution, respectively. In the meantime, reduction of the stiffness and mass matrices required by the modified subspace method can be completed during the convergence/divergence cycle and an estimate of the required eigenpairs obtained. Successive cycles of convergence and divergence are repeated until the desired accuracy of calculations is achieved. The advantages of this new algorithm in parallel computer architecture are discussed.
The design of buildings has become a complex and multidisciplinary problem involving multiple conflicting objectives as architects and designers address competing technical, economic, environmental, and societal concerns. This has been driving research in Architecture, Engineering, and Construction (AEC) toward rigorous multidisciplinary decision-making frameworks that generate and evaluate numerous design alternatives using multi-objective optimization in concert with simulation and analysis models of varying fidelity and computational expense. While such frameworks are well known and widely employed in the aerospace and systems engineering domains, efforts by design professionals and researchers in the AEC field are scattered at best. In this paper, we provide a detailed review of recent developments in optimization frameworks in the AEC field and subsequently highlight how such developments are largely compartmentalized into separate domains such as structural, energy, daylighting, and other performance factors. Herein, we further discuss the technical challenges involved in concurrent coupled multidisciplinary design optimization (MDO) in the AEC field such as interoperability issues between Building Information Modeling (BIM) environments, analysis/simulation tools, and optimization frameworks. We conclude by outlining research needed for more unified modeling and simulation-based optimization frameworks to aid in complex and multidisciplinary building design processes. We also highlight the need for the identification and development of multi-fidelity simulation tools for use across multiple design phases. As such, this paper contributes a novel roadmap to leverage aerospace and systems engineering research in MDO into the field of AEC, along with a call for researchers in the MDO community to seek collaborations in AEC field.
Datagram Retransmission (DGR) is a computer program that, within certain limits, ensures the reception of each datagram transmitted under the User Datagram Protocol/Internet Protocol. [User Datagram Protocol (UDP) is considered unreliable because it does not involve a reliability-ensuring connection-initiation dialogue between sender and receiver. UDP is well suited to issuing of many small messages to many different receivers.] Unlike prior software for ensuring reception of UDP datagrams, DGR does not contribute to network congestion by retransmitting data more frequently as an ever-increasing number of messages and acknowledgements is lost. Instead, DGR does just the opposite: DGR includes an adaptive timeout-interval- computing component that provides maximum opportunity for reception of acknowledgements, minimizing retransmission. By monitoring changes in the rate at which message-transmission transactions are completed, DGR detects changes in the level of congestion and responds by imposing varying degrees of delay on the transmission of new messages. In addition, DGR maximizes throughput by not waiting for acknowledgement of a message before sending the next message. All DGR communication is asynchronous, to maximize efficient utilization of network connections. DGR manages multiple concurrent datagram transmission and acknowledgement conversations.
The sequential optimization of strength and corrosion resistance in conventional alloy design procedures often results in a tradeoff between environmental degradation and optimal mechanical properties. A concurrent optimization of these two material properties is essential to efficiently develop high temperature alloys that can withstand harsh environments increasingly required for carbon–neutral energy technologies. In this work, we demonstrate the feasibility of directed energy deposition (DED) to manufacture a dual-corrosion resistant Ni-based alloy (Hastelloy N and Haynes 282), that meets the high temperature operation requirements of a molten-salts\supercritical-CO 2 (sCO 2 ) heat exchanger. A combination of multiscale characterization techniques and computational thermodynamics was employed to evaluate the cracking susceptibilities during fabrication and predict the microstructural stability of the material. Very good agreement was achieved between the observed and predicted phases and phase fractions of the as-printed material. A careful characterization of the transition zone between the two terminal alloy chemistries revealed potential precipitation strengthening (γ') on the Hastelloy N side while columnar-shaped M 23 C 6 and γ' precipitates that formed at grain boundaries (GBs) of the transition zone likely minimized the local interfacial energies. Both these mechanisms are believed to increase the interfacial stability but their performance at high temperatures requires further investigation.
The initial part of the study was begun with a combination of literature searches, World Wide Web searches, and contacts with individuals and companies who were known to members of our team to have an interest in topics that seemed to be related to our study. There is a long list of such topics, such as concurrent engineering, design for manufacture, life-cycle engineering, systems engineering, systems integration, systems design, design systems, integrated product and process approaches, enterprise integration, integrated product realization, and similar terms. These all capture, at least in part, the flavor of what we describe here as integrated design systems. An inhibiting factor in this inquiry was the absence of agreed terminology for the study of integrated design systems. It is common for the term to be applied to what are essentially augmented Computer-Aided Design (CAD) systems, which are integrated only to the extent that agreements have been reached to attach proprietary extensions to proprietary CAD programs. It is also common for some to use the term integrated design systems to mean a system that applies only, or mainly, to the design phase of a product life cycle. It is likewise common for many of the terms listed earlier to be used as synonyms for integrated design systems. We tried to avoid this ambiguity by adopting the definition of integrated design systems that is implied in the introductory notes that we provided to our contacts, cited earlier. We thus arrived at this definition: Integrated Design Systems refers to the integration of the different tools and processes that comprise the engineering, of complex systems. It takes a broad view of the engineering of systems, to include consideration of the entire product realization process and the product life cycle. An important aspect of integrated design systems is the extent to which they integrate existing, "islands of automation" into a comprehensive design and product realization environment. As the study progressed, we relied increasingly upon a networking approach to lead us to new information. The departure point for such searches often was a government-sponsored project or a company initiative. The advantage of this approach was that short conversations with knowledgeable persons would usually cut through confusion over differences of terminology, thereby somewhat reducing the search space of the study. Even so, it was not until late in our eight-month inquiry that we began to see signs of convergence of the search, in the sense that a number of the latest inquiries began to turn up references to earlier contacts. As suggested above, this convergence often occurred with respect to particular government or company projects.
Power is becoming an increasingly scarce resource on the next generation of supercomputers, and should be used wisely to improve overall performance. One strategy for improving power usage is hardware overprovisioning, i.e., systems with more nodes than can be run at full power simultaneously without exceeding the system-wide power limit. With this study, we compare two strategies for allocating power throughout an overprovisioned system – adaptation and prediction – in the context of visualization workloads. While adaptation has been suitable for workloads with more regular execution behaviors, it may not be as suitable on visualization workloads, since they can have variable execution behaviors. This study considers a total of 104 experiments, which vary the rendering workload, power budget, allocation strategy, and node concurrency, including tests processing data sets up to 1 billion cells and using up to 18,432 cores across 512 nodes. Overall, we find that prediction is a superior strategy for this use case, improving performance up to 27% compared to an adaptive strategy.
Two experiments used a low fidelity multi-task simulation to investigate the effects of cue specificity on task preparation and performance. Subjects performed a continuous compensatory tracking task and were periodically prompted to perform one of several concurrent secondary tasks. The results provide strong evidence that subjects enacted a strategy to actively divert resources towards secondary task preparation only when they had specific information about an upcoming task to be performed. However, this strategy was not as much affected by the type of task cued (Experiment 1) or its difficulty level (Experiment 2). Overall, subjects seemed aware of both the costs (degraded primary task tracking) and benefits (improved secondary task performance) of cue information. Implications of the present results for computational human performance/workload models are discussed.