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Topics include:a technology focus on computers, electronic components and systems, software, materials, mechanics,physical sciences machinery, manufacturing/fabrication, mathematics, book and reports, motion control tech briefs and a special section on Photonics Tech Briefs.
Topics covered include: Cryogenic Temperature-Gradient Foam/Substrate Tensile Tester; Flight Test of an Intelligent Flight-Control System; Slat Heater Boxes for Thermal Vacuum Testing; System for Testing Thermal Insulation of Pipes; Electrical-Impedance-Based Ice-Thickness Gauges; Simulation System for Training in Laparoscopic Surgery; Flasher Powered by Photovoltaic Cells and Ultracapacitors; Improved Autoassociative Neural Networks; Toroidal-Core Microinductors Biased by Permanent Magnets; Using Correlated Photons to Suppress Background Noise; Atmospheric-Fade-Tolerant Tracking and Pointing in Wireless Optical Communication; Curved Focal-Plane Arrays Using Back-Illuminated High-Purity Photodetectors; Software for Displaying Data from Planetary Rovers; Software for Refining or Coarsening Computational Grids; Software for Diagnosis of Multiple Coordinated Spacecraft; Software Helps Retrieve Information Relevant to the User; Software for Simulating a Complex Robot; Software for Planning Scientific Activities on Mars; Software for Training in Pre-College Mathematics; Switching and Rectification in Carbon-Nanotube Junctions; Scandia-and-Yttria-Stabilized Zirconia for Thermal Barriers; Environmentally Safer, Less Toxic Fire-Extinguishing Agents; Multiaxial Temperature- and Time-Dependent Failure Model; Cloverleaf Vibratory Microgyroscope with Integrated Post; Single-Vector Calibration of Wind-Tunnel Force Balances; Microgyroscope with Vibrating Post as Rotation Transducer; Continuous Tuning and Calibration of Vibratory Gyroscopes; Compact, Pneumatically Actuated Filter Shuttle; Improved Bearingless Switched-Reluctance Motor; Fluorescent Quantum Dots for Biological Labeling; Growing Three-Dimensional Corneal Tissue in a Bioreactor; Scanning Tunneling Optical Resonance Microscopy; The Micro-Arcsecond Metrology Testbed; Detecting Moving Targets by Use of Soliton Resonances; and Finite-Element Methods for Real-Time Simulation of Surgery.
The development of models to represent human characteristics and behaviors in human factors is broad and general. The term "model" can refer to any metaphor to represent any aspect of the human; it is generally used in research to mean a mathematical tool for the simulation (often in software, which makes the simulation digital) of some aspect of human performance and for the prediction of future outcomes. This section is restricted to the application of human models in physical design, e.g., in human factors engineering. This design effort is typically human interface design, and the digital models used are anthropometric. That is, they are visual models that are the physical shape of humans and that have the capabilities and constraints of humans of a selected population. They are distinct from the avatars used in the entertainment industry (movies, video games, and the like) in precisely that regard: as models, they are created through the application of data on humans, and they are used to predict human response; body stresses workspaces. DHM enable iterative evaluation of a large number of concepts and support rapid analysis, as compared with use of physical mockups. They can be used to evaluate feasibility of escape of a suited astronaut from a damaged vehicle, before launch or after an abort (England, et al., 2012). Throughout most of human spaceflight, little attention has been paid to worksite design for ground workers. As a result of repeated damage to the Space Shuttle which adversely affected flight safety, DHM analyses of ground assembly and maintenance have been developed over the last five years for the design of new flight systems (Stambolian, 2012, Dischinger and Dunn Jackson, 2014). The intent of these analyses is to assure the design supports the work of the ground crew personnel and thereby protect the launch vehicle. They help the analyst address basic human factors engineering questions: can a worker reach the task site from the work platform provided; can she or he see the task site; can she or he control tools, which, if dropped, might damage the system? Figure 7.3.1 provides an example of such analysis for a future NASA launch vehicle. [figure 7.3.1 here] In-space systems for operation by astronauts have long been targets for DHM analysis, given the focus on mission success and concerns for astronaut safety. Figure 7.3.2 illustrates the analysis of the design to support astronaut tasks for an International Space Station glovebox. [Figure 7.3.2 here] Use by
This presentation touches on the Radiation Safety Information Computational Center (RSICC). This Lecture provides general mission, statistics, and strategies. A general background is provided of the tool. Licensee agreements and export control agreements are discussed. The presentation finishes with a look at the most in demand and requested software packages.
The goal of the new NASA Aviation Safety Program (AvSP) is to reduce the civil aviation fatal accident rate by 80% in ten years and 90% in twenty years. This program is being driven by the accident data with a focus on the most recent history. Pilot error is the most commonly cited cause for fatal accidents (up to 70%) and obviously must be given major consideration in this program. While the greatest source of pilot error is the loss of situation awareness , mode confusion is increasingly becoming a major contributor as well. The January 30, 1995 issue of Aviation Week lists 184 incidents and accidents involving mode awareness including the Bangalore A320 crash 2/14/90, the Strasbourg A320 crash 1/20/92, the Mulhouse-Habsheim A320 crash 6/26/88, and the Toulouse A330 crash 6/30/94. These incidents and accidents reveal that pilots sometimes become confused about what the cockpit automation is doing. Consequently, human factors research is an obvious investment area. However, even a cursory look at the accident data reveals that the mode confusion problem is much deeper than just training deficiencies and a lack of human-oriented design. This is readily acknowledged by human factors experts. It seems that further progress in human factors must come through a deeper scrutiny of the internals of the automation. It is in this arena that formal methods can contribute. Formal methods refers to the use of techniques from logic and discrete mathematics in the specification, design, and verification of computer systems, both hardware and software. The fundamental goal of formal methods is to capture requirements, designs and implementations in a mathematically based model that can be analyzed in a rigorous manner. Research in formal methods is aimed at automating this analysis as much as possible. By capturing the internal behavior of a flight deck in a rigorous and detailed formal model, the dark corners of a design can be analyzed. This paper will explore how formal models and analyses can be used to help eliminate mode confusion from flight deck designs and at the same time increase our confidence in the safety of the implementation. The paper is based upon interim results from a new project involving NASA Langley and Rockwell Collins in applying formal methods to a realistic business jet Flight Guidance System (FGS).
An automated environment with hardware-in-the-loop has been developed by Rocketdyne Huntsville for test of a real-time control system. The target system of application is the man-rated real-time system which controls the Space Shuttle Main Engines (SSME). The primary use of the environment is software verification and validation, but it is also useful for evaluation and analysis of SSME avionics hardware and mathematical engine models. It provides a test bed for the integration of software and hardware. The principles and skills upon which it operates may be applied to other target systems, such as those requiring hardware-in-the-loop simulation and control system development. Potential applications are in problem domains demanding highly reliable software systems requiring testing to formal requirements and verifying successful transition to/from off-nominal system states.
Collection of computer programs constitutes software tool for simulation of medium- to large-scale software-development projects. Necessary to include easily identifiable and more-readily quantifiable characteristics like costs, times, and numbers of errors. Mathematical model incorporating these and other factors of dynamics of software-development process implemented in the Software Life Cycle Simulator (SLICS) computer program. Simulates dynamics of software-development process. In combination with input and output expert software systems and knowledge-based management software system, develops information for use in managing large software-development project. Intended to aid managers in planning, managing, and controlling software-development processes by reducing uncertainties in budgets, required personnel, and schedules.
Formal methods research begins to produce methods which will enable mathematic modeling of the physical behavior of digital hardware and software systems. The development of these methods directly supports the NASA mission of increasing the scope and effectiveness of flight system modeling capabilities. The conventional, continuous mathematics that is used extensively in modeling flight systems is not adequate for accurate modeling of digital systems. Therefore, the current practice of digital flight control system design has not had the benefits of extensive mathematical modeling which are common in other parts of flight system engineering. Formal methods research shows that by using discrete mathematics, very accurate modeling of digital systems is possible. These discrete modeling methods will bring the traditional benefits of modeling to digital hardware and hardware design. Sound reasoning about accurate mathematical models of flight control systems can be an important part of reducing risk of unsafe flight control.
A multiwavelength technique was developed and applied to measure the temperatures of nongray surfaces. The instruments required are a spectral radiometer, a dedicated auxiliary radiation source, and a computer. In general, three radiation spectra are recorded: (1) spectrum S sub 0 of the auxiliary radiation source; (2) spectrum S sub 1 of the surface-emitted radiation; and (3) spectrum S sub 2, the sum of the radiation of S sub 1 plus the reflected radiation due to the incidence of the auxiliary radiation source on the surface. Subtracting spectrum S sub 1 from spectrum Sub 2 yields the reflection spectrum resulting from the incidence radiation. From these spectra, a quantity z(lambda) is derived and is related to the reflectivity r(lambda) by r(lambda) = z(lambda)/f, where f is a constant. Spectrum S sub 1 is represented mathematically as the product of a wavelength-dependent emissivity obtained from Kirchhoff's law and a Planck function of temperature T. Application of two-variable (lambda and z), nonlinear, least-squares curve-fitting computer software to fit spectrum S sub 1 to this mathematical expression yielded the surface temperature. This technique also measured the spectral reflectivity and emissivity of the surface. Instrumentation necessary to extend measurement to elevated temperatures and in the presence of reflective interference is discussed.
The goals of this teaching workshop are for the learners to leave with a broad view of University curriculum, successes and limitations, and the state of industry; an awareness of grand challenges in software engineering; and an awareness of current approaches to address those challenges.
The primary purpose of TPG interface (TPGITF) software is to create test patterns, according to a mathematical model, for a real device. In order for the device actually to be tested or to be accurately simulated, a means is required to transform the TPGITF generated test into forms acceptable as input to logic test equipment or to a software logic simulator. The MACRODATA-200 Logic Test System and the LOGSIM logic simulator are the particular systems supported by the TPGITF software. A description is presented of the TPGITF software processor. It is assumed that the reader is familiar with the TPG, LOGSIM, and TOIL systems. Flow charts are given.
The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify if the software is reliable (or unreliable) while still leveraging the strength of the existing methods.
The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify software reliability (or unreliability) while still leveraging the strength of the existing methods.
Writing software is difficult. However, writing complex, well tested and designed, and functionally correct software is incredibly difficult. An entire field of study is devoted to the validation and verification of software to address this problem, and in this paper we analyze the landscape of currently available third party software. We have divided our analyses into three separate subsections with regards to software validation: formal methods, static analysis, and test generation. Formal verification is the most complex method in which to validate software correctness, but also the most thorough as it truly validates the mathematical validity of the source code. Static analysis generally is relegated to abstract syntax tree traversal techniques to find errors related to faulty software such as memory leaks or stack overflow issues. Automatic test generation is similar in implementation to static analysis, but pushes a bit further in verifying the boundedness of function inputs and outputs with regards to annotated or parsed criteria. The crux of this report is to analyze and describe the software tools that implement these techniques to validate and verify software. Pros and cons related to installation, utilization, and capabilities of the frameworks are described, and reproducible examples are provided with a focus on usability. The initial survey concluded that the most interesting tools of note are Z3, Isabelle/HOL, and TLA+ with regards to formal verification; and Infer, Frama-C, and SonarQube with regards to static analysis. With these tools in mind, a final conjecture is provided that describes future avenues of utilizing these tools for developing a verification framework to assist in validating existing software at Sandia National Laboratories.
Cyber-physical systems (CPS) are engineered systems that rely on the smooth integration of computational algorithms and physical elements. This integration presents new challenges for verifying that systems will behave as expected. The goal of this presentation is to present current challenges and potential solutions for the formal verification of cyber-physical systems. For cyber systems, formal methods refer to systematically rigorous mathematical techniques employed in the specification, development, analysis, and verification of both software and hardware systems. Recent advancements in computer science have yielded sophisticated tools specifically designed to address challenges associated with formal methods in complex systems. These tools leverage various foundational concepts such as logic, formal languages, program semantics, type systems, type theory, and automata theory. A notable achievement in the application of formal methods is the seL4 microkernel, claimed to be the first general-purpose operating-system kernel to be verified. Its proof implies the absence of bugs and guarantees that the kernel meets specifications. For physical systems, dynamic and control theory has a history of using rigorous analytic techniques to prove functional correctness. Lyapunov, optimal, classical, modern, and robust control theories all provide rigorous mathematical methods both to analyze system performance and to design controller that can be guaranteed to meet certain objectives. Recent computational techniques like level set theory and reachability analysis provide assertions that a system's state will avoid unsafe regions. Even though success has been independently achieved for cyber systems and physical systems, the integration of such systems creates new challenges. In particular, there is an obvious discrepancy between finite-state machines and infinite-state systems, resulting in different approaches for modeling and analyzing these system. While it is possible to simulate hybrid systems, this provides only a demonstration of a performance and not proof. For hybrid systems, current formal methods and system analysis approaches typically require a workarounds to work on hybrid systems like CPS. This paper will outline the state of the art and limits of current practice for formally verifying CPS and will identify possible research directions that require attention.
We are surrounded by all kinds of technology. Scientists and engineers have created amazing tools and resources, paved the way for multi-functional devices, and have put useful information at our finger tips. Software technology has enabled unprecedented levels of automation which has revolutionized our world and daily lives. Every year increasingly more data is collected and stored. Are we on the right track? This is an introduction to the new department on software technology.
4D scanning transmission electron microscopy (STEM) is experiencing a revolution both in terms of data size, and data veracity, as are many other areas of experimental data acquisition. This presents enormous opportunities to make new scientific discoveries, but is also hamstrung by aging software that is no longer fit for purpose. Experimental data acquisition platforms must make the same investments in software infrastructure that the simulation community made decades ago to take advantage of high-performance computing (HPC) if they hope to realize the full potential of the latest advances made in detector technology. This requires rethinking the design and deployment of hardware, moving away from small, fixed commodity computing capabilities with closed interfaces to open, modular, scalable systems designed for parallelism and throughput. The technical approach will leverage best-in-class open source technologies, and develop new approaches strategically to build an open source platform for experimental data acquisition targeting high data rate electron microscopy. The project will develop a software platform that uses the power and performance of modern HPC resources to ingest the large volume of data produced by electron microscopes using direct electron detectors. A set of configurable pipelines will be used to enable the processing to be tailored to the problem being investigated with builtin visualization of data at interactive rates. This project will leverage investments made by the DOE and other agencies to provide a powerful software platform for the experimental data community working with most powerful electron microscopes, such as 4D STEM detectors. It will create a platform that serves as a reference for the community. Permissive open source licensing will make the platform available to all, with a simple deployment strategy offering the ability to keep data close to where it is generated. This advanced software platform will require customization for each deployment, creating opportunities for software services that are typically offered to extend open source software platforms.
ParaView has been deployed as a visualization tool at Sandia for almost 20 years. ParaView of the talk title refers to a collection of software.