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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Calculation of Thermodynamic Parameters and Degree of Ionization of Nitrogen and Its Mixtures with Argon in Typical Single-Bubble Sonoluminescence Conditions

The results of numerical simulation of the behavior of a system consisting of a spherical bubble filled with nitrogen or its mixtures with argon and surrounding water under external influence typical of experimental study of single-bubble sonoluminescence are presented. Comparison of the results of calculations and experiments shows that gas heated at the bubble compression stage cannot be regarded as the only source of radiation. This circumstance requires the presence of other, basic, sources. In the polarization model, this is the channel of electrical breakdown in a liquid. Possible electrical effects accompanying the liquid−solid phase transformation in water near the moment of the maximum compression of the bubble are assumed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

van der Waals bilayer energetics: Generalized stacking-fault energy of graphene, boron nitride, and graphene/boron nitride bilayers

The structure, thermodynamics, and band gaps in graphene/graphene, boron nitride/boron nitride, and graphene/boron nitride bilayers are determined using several different corrections to first-principles approaches to account for the dispersion interactions. While the density functional dispersion correction, van der Waals density functional, meta–generalized gradient approximation, and adiabatic fluctuation-dissipation theorem methods (ACFDT-RPA) all lead to qualitatively similar predictions, the best accuracy is obtained through the application of the computationally expensive ACFDT-RPA method. We present an accurate ACFDT-RPA-based method to determine bilayer structure, generalized stacking-fault energy (GSFE), and band gaps as a function of the relative translation states of the two layers. The GSFE data clearly identify all of the stable and metastable bilayer translations as well as the barriers between them. Furthermore, this is key for predicting the sliding, formation, and adhesion energies for homo- and hetero-bilayers, as well as for the determination of defects in such multilayer van der Waals systems. These, in turn, provide an accurate approach for determining and manipulating the spatial variation of electronic structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1991

Control system failure monitoring using generalized parity relations

Many applications require that a control system must be tolerant to the failure of its components. This is especially true for large space-based systems that must work unattended and with long periods between maintenance. Fault tolerance can be obtained by detecting the failure of the control system component, determining which component has failed, and reconfiguring the system so that the failed component is isolated from the controller. Component failure detection experiments that were conducted on an experimental space structure, the NASA Langley Mini-Mast are presented. Two methodologies for failure detection and isolation (FDI) exist that do not require the specification of failure modes and are applicable to both actuators and sensors. These methods are known as the Failure Detection Filter and the method of Generalized Parity Relations. The latter method was applied to three different sensor types on the Mini-Mast. Failures were simulated in input-output data that were recorded during operation of the Mini-Mast. Both single and double sensor parity relations were tested and the effect of several design parameters on the performance of these relations is discussed. The detection of actuator failures is also treated. It is shown that in all the cases it is possible to identify the parity relations directly from input-output data. Frequency domain analysis is used to explain the behavior of the parity relations.

Vanschalkwyk, Christiaan Mauritz↗
DOE OSTI2019

Bryan Mound InSAR Analysis U.S. Strategic Petroleum Reserve (2018 Addendum)

The U.S. Strategic Petroleum Reserve (SPR) is a stockpile of emergency crude oil to be tapped into if a disruption in the nation's oil supply occurs. The SPR comprises of four underground salt dome sites. Subsidence surveys have been conducted either annually or biennially at all four sites over the life of the program. Monitoring of surface behavior is a first line defense to detecting possible subsurface cavern integrity issues. Over the life of the Bryan Mound site, subsidence rates over abandoned Cavern 3 have continuously been the highest at the site. In an effort to try and understand the subsurface dynamics, specifically over Bryan Mound Cavern 3, interferometric synthetic aperture radar (InSAR) data has been collected since October 2015, which allows for the acquisition of a greater density of data over a higher frequency providing improved spatiotemporal resolution. Currently, satellite images are acquired from two orbit geometries allowing for a 2-D analysis, which provides both the true vertical and east-west horizontal displacement rates. This report serves as an addendum to the 2017 report, Bryan Mound InSAR Analysis, U.S. Strategic Petroleum Reserve, SAND 2017-6679. The latest data display an improvement in point density and precision, providing a higher confidence in the results. The results confirm, as seen in the previous analysis, that the fastest surface deformation is occurring over the southwest region of the site, where abandoned Cavern 3 is located. In addition, the horizontal displacement analyses suggest a geologic feature, such as a fault, may be contributing to the higher rates observed over Cavern 3. A loss in cavern integrity would significantly impact the site surface infrastructure.

02 PETROLEUM↗
DOE OSTI2013

The Gremlin Framework

The Gremlin sofrware package is a performance analysis approach targeted to support the Co-Design process for future systems. It consists of a series of modules that can be used to alter a machine's behavior with the goal of emulating future machine properties. The modules can be divided into several classes; the most significant ones are detailed below. PowGre is a series of modules that help explore the power consumption properties of applications and to determine the impact of power constraints on applications. Most of them use low-level processor interfaces to directly control voltage and frequency settings as well as per nodes, socket, or memory power bounds. MemGre are memory Gremlins and implement a new performance analysis technique that captures the application's effective use of the storage capacity of different levels of the memory hierarchy as well as the bandwidth between adjacent levels. The approach models various memory components as resources and measures how much of each resource the application uses from the application's own perspective. To the application a given amount of a resource is "used" if not having this amount will degrade the application's performance. This is in contrast to the hardware-centric perspective that considers "use" as any hardware action that utilizes the resource, even if it has no effect on performance. ResGre are Gremlins that use fault injection techniques to emulate higher fault rates than currently present in today's systems. Faults can be injected through various means, including network interposition, static analysis, and code modification, or direct application notification. ResGre also includes patches to previously released LLNL codes that can counteract and react to injected failures.

Source record↗
DOE OSTI2019

Microstructures and Impact Wear Behavior of Al-Alloyed High-Mn Austenitic Cast Steel After Aging Treatment

The microstructural evolution and impact wear behavior of Al-alloyed ultra-high-Mn austenitic cast steel were investigated after different heat treatments, including quenching (Q) and quenching + aging (Q + A). Another ultra-high-Mn austenitic cast steel without Al was compared under the same experimental condition. After Q + A, Al addition contributed to the precipitation of κ-carbides which improves the yield strength and wear resistance of the austenite matrix. However, the mechanical properties and wear resistance of the Al-free steel were strongly deteriorated due to the precipitation of plate-like M{sub 3}C-type carbides, as well as the worn surface of the Al-free steel had the feature of micro-scratches. After Q + A (2 h), the Al-alloyed steel obtained optimal wear resistance and the main wear feature was strain fatigue. But, excessive aging time (4 h) would lead to a large fragile peeling from the worn surface and the wear resistance was decreased. Also, the worn subsurface microstructures with different heat treatments were observed by transmission electron microscopy (TEM). These results show that the deformation microstructures of the Al-free steel contained stacking faults, dislocation tangle and deformation twins. The deformation character of Al-alloyed steel is dominated by planar glide, and with increase in the aging time, planar dislocation substructures gradually evolved, relating to dislocation pileups, Taylor lattices and micro-bands.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗
DOE OSTI2017

Big Data Meets HPC Log Analytics: Scalable Approach to Understanding Systems at Extreme Scale

Today's high-performance computing (HPC) systems are heavily instrumented, generating logs containing information about abnormal events, such as critical conditions, faults, errors and failures, system resource utilization, and about the resource usage of user applications. These logs, once fully analyzed and correlated, can produce detailed information about the system health, root causes of failures, and analyze an application's interactions with the system, providing valuable insights to domain scientists and system administrators. However, processing HPC logs requires a deep understanding of hardware and software components at multiple layers of the system stack. Moreover, most log data is unstructured and voluminous, making it more difficult for system users and administrators to manually inspect the data. With rapid increases in the scale and complexity of HPC systems, log data processing is becoming a big data challenge. This paper introduces a HPC log data analytics framework that is based on a distributed NoSQL database technology, which provides scalability and high availability, and the Apache Spark framework for rapid in-memory processing of the log data. The analytics framework enables the extraction of a range of information about the system so that system administrators and end users alike can obtain necessary insights for their specific needs. We describe our experience with using this framework to glean insights from the log data about system behavior from the Titan supercomputer at the Oak Ridge National Laboratory.

Park, Byung↗
NASA NTRS2008

Inductive System Monitors Tasks

The Inductive Monitoring System (IMS) software developed at Ames Research Center uses artificial intelligence and data mining techniques to build system-monitoring knowledge bases from archived or simulated sensor data. This information is then used to detect unusual or anomalous behavior that may indicate an impending system failure. Currently helping analyze data from systems that help fly and maintain the space shuttle and the International Space Station (ISS), the IMS has also been employed by data classes are then used to build a monitoring knowledge base. In real time, IMS performs monitoring functions: determining and displaying the degree of deviation from nominal performance. IMS trend analyses can detect conditions that may indicate a failure or required system maintenance. The development of IMS was motivated by the difficulty of producing detailed diagnostic models of some system components due to complexity or unavailability of design information. Successful applications have ranged from real-time monitoring of aircraft engine and control systems to anomaly detection in space shuttle and ISS data. IMS was used on shuttle missions STS-121, STS-115, and STS-116 to search the Wing Leading Edge Impact Detection System (WLEIDS) data for signs of possible damaging impacts during launch. It independently verified findings of the WLEIDS Mission Evaluation Room (MER) analysts and indicated additional points of interest that were subsequently investigated by the MER team. In support of the Exploration Systems Mission Directorate, IMS is being deployed as an anomaly detection tool on ISS mission control consoles in the Johnson Space Center Mission Operations Directorate. IMS has been trained to detect faults in the ISS Control Moment Gyroscope (CMG) systems. In laboratory tests, it has already detected several minor anomalies in real-time CMG data. When tested on archived data, IMS was able to detect precursors of the CMG1 failure nearly 15 hours in advance of the actual failure event. In the Aeronautics Research Mission Directorate, IMS successfully performed real-time engine health analysis. IMS was able to detect simulated failures and actual engine anomalies in an F/A-18 aircraft during the course of 25 test flights. IMS is also being used in colla

Source record↗
DOE OSTI2023

Global Characteristics of Observable Foreshocks for Large Earthquakes

Abstract Foreshocks are the only currently widely identified precursory seismic behavior, yet their utility and even identifiability are problematic, in part because of extreme variation in behavior. Here, we establish some global trends that help identify the expected frequency of foreshocks as well the type of earthquake most prone to foreshocks. We establish these tendencies using the global earthquake catalog of the U.S. Geological Survey National Earthquake Information Center with a completeness level of magnitude 5 and mainshocks with Mw≥7.0. Foreshocks are identified using three clustering algorithms to address the challenge of distinguishing foreshocks from background activity. The methods give a range of 15%–43% of large mainshocks having at least one foreshock but a narrower range of 13%–26% having at least one foreshock with magnitude within two units of the mainshock magnitude. These observed global foreshock rates are similar to regional values for a completeness level of magnitude 3 using the same detection conditions. The foreshock sequences have distinctive characteristics with the global composite population b-values being lower for foreshocks than for aftershocks, an attribute that is also manifested in synthetic catalogs computed by epidemic-type aftershock sequences, which intrinsically involves only cascading processes. Focal mechanism similarity of foreshocks relative to mainshocks is more pronounced than for aftershocks. Despite these distinguishing characteristics of foreshock sequences, the conditions that promote high foreshock productivity are similar to those that promote high aftershock productivity. For instance, a modestly higher percentage of interplate mainshocks have foreshocks than intraplate mainshocks, and reverse faulting events slightly more commonly have foreshocks than normal or strike-slip-faulting mainshocks. The western circum-Pacific is prone to having slightly more foreshock activity than the eastern circum-Pacific.

Geochemistry & Geophysics↗
DOE OSTI2018

FY2018 Discovery Through Situational Awareness: Anomalies, Oscillations, and Classification

This technical report is a summary of work performed under the DOE GMLC program under the task titled “Discovery through Situational Awareness”. The focus of this work was to apply big data, statistical techniques to PMU data to do the following: 1) baseline PMU data and find anomalies with a focus on phase angle pairs, 2) identify the frequencies of dominant modal oscillations between different parts of a power system, and 3) identify and classify frequency events in PMU data. This work was performed on PMU data from the Western Interconnect. Many of these algorithms will be applied to wide area phase angle pair data on the Eastern Interconnect in a tool called ESAMS (Eastern Interconnect Situational Awareness Monitoring System). ESAMS was built by EPG (Electric Power Group) and PNNL and is currently being installed on the Eastern Interconnect. The results discussed in this report were made from 14 PMUs from data ranging from Oct 2016 to May 2018. The PMU data were measured 60 times each second. Fourteen phase angle pairs were included, with reactive and active power being calculated at 14 sites. All results have been de-identified for this report. The methodologies applied in the ESAMS tool are described in Section 2. A baseline of PMU data is created using the last 120 days of data. This baseline describes typical behavior as understood from the data. The baseline is then applied to the next day’s data and anomalies from that baseline are identified. Plots are created to help describe what is anomalous during that specific moment in time. These algorithms are currently being installed on the Eastern Interconnect. FY19 work will include discussing the results and findings from the ESAMS tool. A new method of identifying the frequencies of dominant modal oscillations between different parts of a power system is described in Section 3. A list of 199 frequency events provided by Bonneville Power Administration were used to create algorithms to detect frequency events and to create classification rules that can be used to identify future frequency events. Section 4 discusses the results the performance of 7 different event detection, clustering-based methods. Accuracy percentages ranged from 93.59% to 99.16%, with most methods being very fast. GBM (Gradient Boosting Machine) performed well and quickly. These 7 different methods were also tested to determine how well each built classification rules on training data and then applied those rules to identify future frequency events. Accuracy percentages ranged from 89.09% to 99.9%, with all methods performing quickly. GLM (General Linear Model) was viewed as the top performer when considering accuracy and processing time. Because the list of events consisted of 80% of them as active power events and only 20% as faults, all methods struggled to identify events as faults. Small faults were especially difficult to identify. A separate algorithm was created to help with this. Additional work will be done in FY19 on event detection using more data and more events and refinements to the methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1991

Markov reward processes

Numerous applications in the area of computer system analysis can be effectively studied with Markov reward models. These models describe the behavior of the system with a continuous-time Markov chain, where a reward rate is associated with each state. In a reliability/availability model, upstates may have reward rate 1 and down states may have reward rate zero associated with them. In a queueing model, the number of jobs of certain type in a given state may be the reward rate attached to that state. In a combined model of performance and reliability, the reward rate of a state may be the computational capacity, or a related performance measure. Expected steady-state reward rate and expected instantaneous reward rate are clearly useful measures of the Markov reward model. More generally, the distribution of accumulated reward or time-averaged reward over a finite time interval may be determined from the solution of the Markov reward model. This information is of great practical significance in situations where the workload can be well characterized (deterministically, or by continuous functions e.g., distributions). The design process in the development of a computer system is an expensive and long term endeavor. For aerospace applications the reliability of the computer system is essential, as is the ability to complete critical workloads in a well defined real time interval. Consequently, effective modeling of such systems must take into account both performance and reliability. This fact motivates our use of Markov reward models to aid in the development and evaluation of fault tolerant computer systems.

Smith, R. M.↗
DOE OSTI2020

Nucleation of Dislocations in 3.9 nm Nanocrystals at High Pressure

As circuitry approaches single nanometer length scales, it has become important to predict the stability of single nanometer-sized metals. The behavior of metals at larger scales can be predicted based on the behavior of dislocations, but it is unclear if dislocations can form and be sustained at single nanometer dimensions. Here, we report the formation of dislocations within individual 3.9 nm Au nanocrystals under nonhydrostatic pressure in a diamond anvil cell. We used a combination of x-ray diffraction, optical absorbance spectroscopy, and molecular dynamics simulation to characterize the defects that are formed, which were found to be surface-nucleated partial dislocations. These results indicate that dislocations are still active at single nanometer length scales and can lead to permanent plasticity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Computational Study of Short-Pulse Laser-Induced Generation of Crystal Defects in Ni-Based Single-Phase Binary Solid–Solution Alloys

Single-phase concentrated solid–solution alloys exhibit enhanced mechanical characteristics and radiation damage resistance, making them promising candidate materials for applications involving an exposure to rapid localized energy deposition. We use large-scale atomistic modeling to investigate the mechanisms of the generation of vacancies, dislocations, stacking faults, and twin boundaries in Ni, Ni 50 Fe 50 , Ni 80 Fe 20 , and Ni 80 Cr 20 targets irradiated by short laser pulses in the regime of melting and resolidification. The decrease in the thermal conductivity and strengthening of the electron–phonon coupling due to the intrinsic chemical disorder in the solid-solution alloys are found to have important implications on localization of the energy deposition and generation of thermoelastic stresses. The interaction of the laser-induced stress waves with the melting front is found to play a key role in roughening of the crystal–liquid interface and generation of dislocations upon the solidification. A common feature revealed in the structural analysis of all irradiated targets is the presence of high vacancy concentrations exceeding the equilibrium values at the melting temperature by about an order of magnitude. On the basis of the results of molecular dynamics simulations of solidification occurring at fixed levels of undercooling, the generation of vacancies is correlated with the velocity of the solidification front, and the processes responsible for creating the strong vacancy supersaturation are revealed. The suppression of the vacancy generation in the solid–solution alloys is also revealed and related to combined effect of enhanced vacancy mobility and higher energy of the vacancy formation in the alloy systems. The analysis of the first atomic shells surrounding the vacancy sites in Ni–Fe alloys uncovers the preference for the vacancy sites to be surrounded by Fe atoms and suggests that atomic-scale chemical heterogeneities may play an important role in defining the behavior and properties of the single-phase concentrated solid-solution alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Pervasive symmetry-lowering nanoscale structural fluctuations in the cuprate La 2−𝑥 ⁢Sr 𝑥 ⁢Cu⁢O 4

The cuprate superconductors are among the most widely studied quantum materials, yet there remain fundamental open questions regarding their electronic properties and the role of structural degrees of freedom. Recent neutron and x-ray scattering measurements uncovered exponential scaling with temperature of the strength of orthorhombic fluctuations in the tetragonal phase of La 2−𝑥⁢ Sr 𝑥 ⁢Cu⁢O 4 and Tl 2 ⁢Ba 2 ⁢Cu⁢O 6+𝛿 , unusual behavior that closely resembles prior results for the emergence of superconducting fluctuations, and that points to a common origin rooted in inherent correlated structural inhomogeneity. Here, in this study, we use neutron and x-ray diffuse scattering to provide further insight into structural fluctuations in La 2−𝑥 ⁢Sr 𝑥 ⁢Cu⁢O 4 . We perform measurements up to temperatures that approach the melting point of the undoped parent compound, and we investigate the effects of in situ in-plane uniaxial elastic stress and plastic deformation. Our neutron scattering results for the parent compound La 2 ⁢Cu⁢O 4 reveal that short-range orthorhombic fluctuations persist to the maximum experimental temperature of nearly 1000 K, i.e., to a significant fraction of the crystallization temperature. At this temperature, the spatial characteristic length extracted from the momentum-space data is still about three lattice constants. The neutron scattering experiment enables quasistatic discrimination and reveals that the response is increasingly dynamic at higher temperatures. We also find that neither compressive elastic stress along the tetragonal [110] direction nor irreversible plastic deformation activating the ⟨100⟩⁢[001] slip system significantly alter the robust orthorhombic fluctuations. Overall, these results support the notion that these fluctuations couple to subtle, underlying inhomogeneity that underpins the cuprate phase diagram. Finally, in La 1.8⁢ Sr 0.2⁢ Cu⁢O 4 , we uncover low-energy structural fluctuations at a nominally forbidden reflection that are distinct from the orthorhombic distortions. While the origin of these fluctuations is not clear, they might be related to the presence of extended defects such as dislocations or stacking faults.

Spieker, R. J. [Univ. of Minnesota, Minneapolis, M↗
DOE OSTI2025

Exceptional radiation resistance of hardened amorphous SiC under high-temperature hydrogen ion implantation

This study provides a compelling comparison of the structural and mechanical responses of single-crystal silicon carbide (sc-SiC), nanocrystalline silicon carbide (nc-SiC), and amorphous silicon carbide (am-SiC) to hydrogen ion implantation at 650 °C across three distinct fluences: low, medium, and high (LF, MF, HF). While both sc-SiC and nc-SiC exhibit blistering, microcracking, and exfoliation, am-SiC remains free of blisters, demonstrating superior resilience. Notably, nc-SiC, with its high density of stacking faults (SFs), requires a higher fluence to initiate blistering compared to sc-SiC. At high fluences, hydrogen accumulation at grain boundaries (GBs) in nc-SiC elevates internal gas pressure, intensifying exfoliation. In sc-SiC, blistering leads to increased hardness, whereas in nc-SiC, the degradation of the SF structure results in a reduction in hardness. In contrast, am-SiC undergoes structural relaxation during irradiation, resulting in a significant increase in hardness while maintaining its structural integrity, with only the formation of nano-sized spherical bubbles observed. Furthermore, these findings highlight the exceptional suitability of am-SiC for nuclear applications, where resistance to radiation-induced microcracking is critical.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Cooperative Three-Robot System for Traversing Steep Slopes

Teamed Robots for Exploration and Science in Steep Areas (TRESSA) is a system of three autonomous mobile robots that cooperate with each other to enable scientific exploration of steep terrain (slope angles up to 90 ). Originally intended for use in exploring steep slopes on Mars that are not accessible to lone wheeled robots (Mars Exploration Rovers), TRESSA and systems like TRESSA could also be used on Earth for performing rescues on steep slopes and for exploring steep slopes that are too remote or too dangerous to be explored by humans. TRESSA is modeled on safe human climbing of steep slopes, two key features of which are teamwork and safety tethers. Two of the autonomous robots, denoted Anchorbots, remain at the top of a slope; the third robot, denoted the Cliffbot, traverses the slope. The Cliffbot drives over the cliff edge supported by tethers, which are payed out from the Anchorbots (see figure). The Anchorbots autonomously control the tension in the tethers to counter the gravitational force on the Cliffbot. The tethers are payed out and reeled in as needed, keeping the body of the Cliffbot oriented approximately parallel to the local terrain surface and preventing wheel slip by controlling the speed of descent or ascent, thereby enabling the Cliffbot to drive freely up, down, or across the slope. Due to the interactive nature of the three-robot system, the robots must be very tightly coupled. To provide for this tight coupling, the TRESSA software architecture is built on a combination of (1) the multi-robot layered behavior-coordination architecture reported in "An Architecture for Controlling Multiple Robots" (NPO-30345), NASA Tech Briefs, Vol. 28, No. 10 (October 2004), page 65, and (2) the real-time control architecture reported in "Robot Electronics Architecture" (NPO-41784), NASA Tech Briefs, Vol. 32, No. 1 (January 2008), page 28. The combination architecture makes it possible to keep the three robots synchronized and coordinated, to use data from all three robots for decision- making at each step, and to control the physical connections among the robots. In addition, TRESSA (as in prior systems that have utilized this architecture) , incorporates a capability for deterministic response to unanticipated situations from yet another architecture reported in Control Architecture for Robotic Agent Command and Sensing (NPO-43635), NASA Tech Briefs, Vol. 32, No. 10 (October 2008), page 40. Tether tension control is a major consideration in the design and operation of TRESSA. Tension is measured by force sensors connected to each tether at the Cliffbot. The direction of the tension (both azimuth and elevation) is also measured. The tension controller combines a controller to counter gravitational force and an optional velocity controller that anticipates the motion of the Cliffbot. The gravity controller estimates the slope angle from the inclination of the tethers. This angle and the weight of the Cliffbot determine the total tension needed to counteract the weight of the Cliffbot. The total needed tension is broken into components for each Anchorbot. The difference between this needed tension and the tension measured at the Cliffbot constitutes an error signal that is provided to the gravity controller. The velocity controller computes the tether speed needed to produce the desired motion of the Cliffbot. Another major consideration in the design and operation of TRESSA is detection of faults. Each robot in the TRESSA system monitors its own performance and the performance of its teammates in order to detect any system faults and prevent unsafe conditions. At startup, communication links are tested and if any robot is not communicating, the system refuses to execute any motion commands. Prior to motion, the Anchorbots attempt to set tensions in the tethers at optimal levels for counteracting the weight of the Cliffbot; if either Anchorbot fails to reach its optimal tension level within a specified time, it sends message to the other robots and the commanded motion is not executed. If any mechanical error (e.g., stalling of a motor) is detected, the affected robot sends a message triggering stoppage of the current motion. Lastly, messages are passed among the robots at each time step (10 Hz) to share sensor information during operations. If messages from any robot cease for more than an allowable time interval, the other robots detect the communication loss and initiate stoppage.

Stroupe, Ashley↗
DOE OSTI2017

XPRESS: eXascale PRogramming Environment and System Software

The XPRESS project is the research and development of a complete software architecture for Exascale computing. The four major R&D thrusts include: 1) an Exascale lightweight kernel operating system (LXK), based on the Kitten operating system (OS), to manage billion way hardware parallelism, management of faults and power, management of global virtual name space, and other features of future system architectures; 2) a runtime system (HPX-5), co-designed with LXK, based on the ParalleX execution model that supports dynamic resource management and task scheduling; 3) system interfaces for interoperability between the runtime system and both the OS and the runtime system (RIOS); 4) compilation strategies and systems to translate MPI and OpenMP legacy codes to a form that can be run with performance at least as good as a native code implementation. The software architecture development has been driven by performance evaluation of mini-applications developed at Department of Energy Co-Design Centers as well as ancillary in-house developed applications and benchmarks. Research on the ParalleX execution model has been driven by the development of formal semantics and model checkers for the execution model. The major components of the software architecture are: ParalleX execution model – guiding principles for co-design of components of the system stack with advances in locality management and task prioritization through introspection; HPX runtime system – support of application dynamic adaptive resource management, task scheduling, and introspective control policies; LXK operating system – lightweight kernel operating system for order-constant scalability and low/no noise to manage resources; RIOS – a realm of Exascale system operation unique in the XPRESS project that includes the relationship between the new generation of lightweight kernel operating systems and runtime system software; XPI advanced programming model – intermediate form and low-level (readable) programming interface reflecting the ParalleX model, providing a target for source-to- source high level parallel language translation, and supporting early direct programming experimentation and measurement; Performance models & metrics – providing parameters and their mutual sensitivities to guide co-design and quantify operational behavior; Legacy migration – ensuring seamless transition of legacy codes and programming methods to the future generation of ParalleX-based Exascale systems; . Experiments and evaluation – critical to determining degree of effectiveness and likelihood of ultimate success as well as guiding corrective design changes to achieve DOE objectives; and Applications – collaborations with Co-Design Centers and other mission critical codes. The exemplar implementation of the ParalleX execution model, HPX-5, has been released along with five fully ported applications (LULESH, HPCG, SSSP, WAMR, and DASHMM) and multiple microbenchmarks. Using these applications, the system architecture demonstrated dynamic load balancing through active migration, legacy code migration, and data-driven approaches to for graph-like applications. Crucially for Exascale and billion way parallelism, the needs and patterns of irregular applications in HPX-5 were addressed using local flow control rather than relying on globally synchronized data structures.

97 MATHEMATICS AND COMPUTING↗