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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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Lila: Optimal Dispatching in Probabilistic Temporal Networks using Monte Carlo Tree Search

Executing a Probabilistic Simple Temporal Network (PSTN) amounts at scheduling, i.e. \textit{dispatch}, a set of events under time uncertainty. This constitutes a NP-hard online optimization problem. The right execution time must be dynamically assigned to each event of the PSTN such that the temporal constraints are met, whereas activity durations are progressively observed as the execution unfolds. We propose a dispatching algorithm based on Monte Carlo Tree Search, called Lila, with the following characteristics: (i) it is an anytime algorithm, both offline and online, proven asymptotically optimal; (ii) it returns the current probability of success, either before or at any moment during operations; (iii) it handles any possible continuous or discrete, even non-parametric, probability distributions, as well as inter-dependencies between random variables, exogenous and endogenous uncertainty; and (iv) can be easily extended to handle probabilistic external events, PSTNs with resources, PSTNs with cutoff times and precondition chains, etc. Lila is universal in the sense that it can handle any dispatching protocol, simply by specifying it to the algorithm. It has the unlimited flexibility offered by the simulation paradigm, whilst it asymptotically converges to optimal decisions and/or robustness approximations.

Chien, Steve A.↗

Energy Intensity Baselining and Tracking Guidance

Each company joining the U.S. Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) commits to establishing an energy consumption and energy intensity (EI) baseline and to tracking its energy performance over a 10-year period against that baseline. The baseline must reflect a company’s energy consumption over a 12-month period, covering all its U.S.-based operations. Energy consumption is calculated by fuel type in terms of primary energy (also known as source energy). EI is broadly defined as the amount of energy consumed per unit of output produced. For this guidance document and for the program, the term energy performance represents an evaluation of a facility’s capacity to use energy efficiently. Metrics used to assess a facility’s energy performance can include EI, energy consumption, improvements in EI, etc. Establishing an energy baseline and tracking system is a critical first step in effectively managing energy use. Developing a baseline can help a company understand energy use within the corporation and give it a point of comparison to evaluate future efforts to improve energy performance. It can also support efforts to validate a company’s energy management activities, improve comparative analyses when using benchmarks, and help in predicting future energy needs. In addition, a company that normalizes its performance data can determine highly defensible measures of energy savings generated through implemented energy efficiency projects. Establishing a baseline and tracking energy performance is also a requirement for ISO 50001 certification. Although basic energy data can be collected through utility bills, most manufacturers will have to perform additional analyses to develop accurate and robust energy baselines and tracking systems. Energy is consumed in many ways within the manufacturing sector and can come from multiple sources. Energy is sometimes generated and sold to other parties or captured and reused on-site. External events can exert a significant impact on a facility or company’s energy use independent of any purposeful efforts to improve energy efficiency. Operational changes, such as production shifts—which may be inevitable for some companies over the 10-year period covered by the program—can also make a big difference in energy use. Since Better Plants asks companies to account for all their U.S.-based operations, mergers, acquisitions, and divestitures can also have significant implications for a company’s energy metrics. This document aims to demystify the sometimes complex baselining process. It devotes special attention to the task of normalizing and adjusting energy consumption to account for external factors, such as weather and production changes. A key recommendation is that companies use regression analysis to normalize their energy consumption data whenever possible. Regression analysis is a statistical technique that estimates the dependence of a variable (i.e., energy use in the context of Better Plants) on one or more independent variables such as ambient temperature, while controlling for the influence of other variables at the same time. A properly developed regression analysis can provide a reliable estimate of energy savings resulting from energy improvement strategies and projects by accounting for the effects of variables such as annual production levels and weather. DOE has developed a companion Energy Performance Indicator software tool (EnPI) to simplify the baselining process. This tool can run regression models, calculate changes in EI at the facility level, and automatically compile facility-level data into a corporate-wide metric. Note that although the relevant equations used to calculate EI are provided in this document, the EnPI tool will automatically perform most calculations for the user. Additionally, Better Plants Partners (Partners) can call on their Technical Account Manager (TAM) to help them establish a baseline and assist with the necessary calculations to track progress.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Common Cause Failures Dominate and Defeat Redundancy

Common cause failures occur when several malfunctions are produced by a single event or process. They are especially damaging when they eliminate an entire set of redundant systems and disable their intended function. Redundancy is used when the individual system failure probability is unacceptably high. Redundancy can improve the overall system failure probability if the failures are independent, but the reliability gain is limited if there are dependent failures having a common cause. No amount of redundancy can reduce the total failure probability below the common cause failure probability. Common cause failures defeat redundancy. Systems with high reliability requirements often use extensive redundancy. These highly redundant systems rarely fail unless all the redundant components providing a particular function fail. Complete failures of such highly redundant systems are then usually common cause failures. Common cause failures are prevalent in highly redundant, high reliability systems. Common cause failures dominate redundancy. Redundant systems may fail due to specification, design, manufacturing, operations, or maintenance problems that disable all the identical redundant systems. Common cause failures typically account for one tenth of all failures. If the failure probability is relatively low and common cause failures are significant, adding more than two or three redundant identical units usually gives little added reliability improvement. Common cause failures can be reduced by using diverse components with different technologies and manufacturers, by separating and shielding subsystems, and by avoiding shared control, power, or location. External events and shared vulnerabilities may still cause common cause failures.

common cause failures↗

Common Cause Failures Dominate and Defeat Redundancy

Common cause failures occur when several malfunctions are produced by a single event or process. They are especially damaging when they eliminate an entire set of redundant systems and disable their intended function. Redundancy is used when the individual system failure probability is unacceptably high. Redundancy can improve the overall system failure probability if the failures are independent, but the reliability gain is limited if there are dependent failures having a common cause. No amount of redundancy can reduce the total failure probability below the common cause failure probability. Common cause failures defeat redundancy. Systems with high reliability requirements often use extensive redundancy. These highly redundant systems rarely fail unless all the redundant components providing a particular function fail. Complete failures of such highly redundant systems are then usually common cause failures. Common cause failures are prevalent in highly redundant, high reliability systems. Common cause failures dominate redundancy. Redundant systems may fail due to specification, design, manufacturing, operations, or maintenance problems that disable all the identical redundant systems. Common cause failures typically account for one tenth of all failures. If the failure probability is relatively low and common cause failures are significant, adding more than two or three redundant identical units usually gives little added reliability improvement. Common cause failures can be reduced by using diverse components with different technologies and manufacturers, by separating and shielding subsystems, and by avoiding shared control, power, or location. External events and shared vulnerabilities may still cause common cause failures.

common cause failures↗

Simultaneous optimal system and controller design for multibody systems with joint friction using direct sensitivities

Abstract Real-world multibody systems are often subject to phenomena like friction, joint clearances, and external events. These phenomena can significantly impact the optimal design of the system and its controller. This work addresses the gradient-based optimization methodology for multibody dynamic systems with joint friction using a direct sensitivity approach. The Brown–McPhee model has been used to characterize the joint friction in the system. This model is suitable for the study due to its accuracy for dynamic simulation and its compatibility with sensitivity analysis. This novel methodology supports codesign of the multibody system and its controller, which is especially relevant for applications like robotics and servo-mechanical systems, where the actuation and design are highly dependent on each other. Numerical results are obtained using a software package written in Julia with state-of-the-art libraries for automatic differentiation and differential equations. Three case studies are provided to demonstrate the attractive properties of simultaneous optimal design and control approach for certain applications.

Verulkar, Adwait↗

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Technology-Neutral, Accident Containment-Based Path to Reduce Nuclear Power Costs

Cost remains a substantial impediment to nuclear power plant (NPP) deployment. The origin of the multifold cost difference between NPPs and other industrial facilities is in their unique potential for societally disruptive accidents. Consequently, one means to substantially reduce NPP costs would be to provide a transparent means of demonstrating that a plant’s potential for land-contamination accidents is so remote to amount to a practical certainty that no significant quantity of radionuclides would ever reach the public. The necessary confidence can be developed through employing a containment capable of withstanding both a complete internal energy release accident along with credible external events with ample margin. This paper provides an overview of a path forward to develop a technology-neutral, containment-based safety-adequacy demonstration method.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Scheduling real-time, periodic jobs using imprecise results

A process is called a monotone process if the accuracy of its intermediate results is non-decreasing as more time is spent to obtain the result. The result produced by a monotone process upon its normal termination is the desired result; the error in this result is zero. External events such as timeouts or crashes may cause the process to terminate prematurely. If the intermediate result produced by the process upon its premature termination is saved and made available, the application may still find the result unusable and, hence, acceptable; such a result is said to be an imprecise one. The error in an imprecise result is nonzero. The problem of scheduling periodic jobs to meet deadlines on a system that provides the necessary programming language primitives and run-time support for processes to return imprecise results is discussed. This problem differs from the traditional scheduling problems since the scheduler may choose to terminate a task before it is completed, causing it to produce an acceptable but imprecise result. Consequently, the amounts of processor time assigned to tasks in a valid schedule can be less than the amounts of time required to complete the tasks. A meaningful formulation of this problem taking into account the quality of the overall result is discussed. Three algorithms for scheduling jobs for which the effects of errors in results produced in different periods are not cumulative are described, and their relative merits are evaluated.

Liu, Jane W. S.↗

Report on the formal specification and partial verification of the VIPER microprocessor

The VIPER microprocessor chip is partitioned into four levels of abstractions. At the highest level, VIPER is described with decreasingly abstract sets of functions in LCF-LSM. At the lowest level are the gate-level models in proprietary CAD languages. The block-level and gate-level specifications are also given in the ELLA simulation language. Among VIPER's deficiencies are the fact that there is no notion of external events in the top-level specification, and it is impossible to use the top-level specifications to prove abstract properties of programs running on VIPER computers. There is no complete proof that the gate-level specifications implement the top-level specifications. Cohn's proof that the major-state machine correctly implements the top-level specifications has no formal connection with any of the other proof attempts. None of the latter address resetting the machine, memory timeout, forced error, or single step modes.

Brock, Bishop↗

NASA TileWorld manual (system version 2.2)

The commands are documented of the NASA TileWorld simulator, as well as providing information about how to run it and extend it. The simulator, implemented in Common Lisp with Common Windows, encodes a particular range in a spectrum of domains, for controllable research experiments. TileWorld consists of a two dimensional grid of cells, a set of polygonal tiles, and a single agent which can grasp and move tiles. In addition to agent executable actions, there is an external event over which the agent has not control; this event correspond to a 'gust of wind'.

Philips, Andrew B.↗

Signal detection theory and methods for evaluating human performance in decision tasks

Signal Detection Theory (SDT) can be used to assess decision making performance in tasks that are not commonly thought of as perceptual. SDT takes into account both the sensitivity and biases in responding when explaining the detection of external events. In the standard SDT tasks, stimuli are selected in order to reveal the sensory capabilities of the observer. SDT can also be used to describe performance when decisions must be made as to the classification of easily and reliably sensed stimuli. Numbers are stimuli that are minimally affected by sensory processing and can belong to meaningful categories that overlap. Multiple studies have shown that the task of categorizing numbers from overlapping normal distributions produces performance predictable by SDT. These findings are particularly interesting in view of the similarity between the task of the categorizing numbers and that of determining the status of a mechanical system based on numerical values that represent sensor readings. Examples of the use of SDT to evaluate performance in decision tasks are reviewed. The methods and assumptions of SDT are shown to be effective in the measurement, evaluation, and prediction of human performance in such tasks.

Obrien, Kevin↗

Overview on METEOSAT geometrical image data processing

Digital Images acquired from the geostationary METEOSAT satellites are processed and disseminated at ESA's European Space Operations Centre in Darmstadt, Germany. Their scientific value is mainly dependent on their radiometric quality and geometric stability. This paper will give an overview on the image processing activities performed at ESOC, concentrating on the geometrical restoration and quality evaluation. The performance of the rectification process for the various satellites over the past years will be presented and the impacts of external events as for instance the Pinatubo eruption in 1991 will be explained. Special developments both in hard and software, necessary to cope with demanding tasks as new image resampling or to correct for spacecraft anomalies, are presented as well. The rotating lens of MET-5 causing severe geometrical image distortions is an example for the latter.

Diekmann, Frank J.↗

Life support technology investment strategies for flight programs: An application of decision analysis

Applied research and technology development (R&TD) is often characterized by uncertainty, risk, and significant delays before tangible returns are obtained. Given the increased awareness of limitations in resources, effective R&TD today needs a method for up-front assessment of competing technologies to help guide technology investment decisions. Such an assessment approach must account for uncertainties in system performance parameters, mission requirements and architectures, and internal and external events influencing a development program. The methodology known as decision analysis has the potential to address these issues. It was evaluated by performing a case study assessment of alternative carbon dioxide removal technologies for NASA's proposed First Lunar Outpost program. An approach was developed that accounts for the uncertainties in each technology's cost and performance parameters as well as programmatic uncertainties such as mission architecture. Life cycle cost savings relative to a baseline, adjusted for the cost of money, was used as a figure of merit to evaluate each of the alternative carbon dioxide removal technology candidates. The methodology was found to provide a consistent decision-making strategy for development of new life support technology. The case study results provided insight that was not possible from more traditional analysis approaches.

Schlater, Nelson J.↗

Life support technology investment strategies for flight programs: An application of decision analysis

Applied research and technology development (R&TD) is often characterized by uncertainty, risk, and significant delays before tangible returns are obtained. Given the increased awareness of limitations in resources, effective R&TD today needs a method for up-front assessment of competing technologies to help guide technology investment decisions. Such an assessment approach must account for uncertainties in system performance parameters, mission requirements and architectures, and internal and external events influencing a development program. The methodology known as decision analysis has the potential to address these issues. It was evaluated by performing a case study assessment of alternative carbon dioxide removal technologies for NASA"s proposed First Lunar Outpost program. An approach was developed that accounts for the uncertainties in each technology's cost and performance parameters as well as programmatic uncertainties such as mission architecture. Life cycle cost savings relative to a baseline, adjusted for the cost of money, was used as a figure of merit to evaluate each of the alternative carbon dioxide removal technology candidates. The methodology was found to provide a consistent decision-making strategy for the develpoment of new life support technology. The case study results provided insight that was not possible from more traditional analysis approaches.

Schlater, Nelson J.↗

Multiple Core Galaxies

Nuclei of galaxies often show complicated density structures and perplexing kinematic signatures. In the past we have reported numerical experiments indicating a natural tendency for galaxies to show nuclei offset with respect to nearby isophotes and for the nucleus to have a radial velocity different from the galaxy's systemic velocity. Other experiments show normal mode oscillations in galaxies with large amplitudes. These oscillations do not damp appreciably over a Hubble time. The common thread running through all these is that galaxies often show evidence of ringing, bouncing, or sloshing around in unexpected ways, even though they have not been disturbed by any external event. Recent observational evidence shows yet another phenomenon indicating the dynamical complexity of central regions of galaxies: multiple cores (M31, Markarian 315 and 463 for example). These systems can hardly be static. We noted long-lived multiple core systems in galaxies in numerical experiments some years ago, and we have more recently followed up with a series of experiments on multiple core galaxies, starting with two cores. The relevant parameters are the energy in the orbiting clumps, their relative.masses, the (local) strength of the potential well representing the parent galaxy, and the number of cores. We have studied the dependence of the merger rates and the nature of the final merger product on these parameters. Individual cores survive much longer in stronger background potentials. Cores can survive for a substantial fraction of a Hubble time if they travel on reasonable orbits.

Miller, R.H.↗

Testing for Software Safety

This research focuses on testing whether or not the hazardous conditions identified by design-level fault tree analysis will occur in the target implementation. Part 1: Integrate fault tree models into functional specifications so as to identify testable interactions between intended behaviors and hazardous conditions. Part 2: Develop a test generator that produces not only functional tests but also safety tests for a target implementation in a cost-effective way. Part 3: Develop a testing environment for executing generated functional and safety tests and evaluating test results against expected behaviors or hazardous conditions. It includes a test harness as well as an environment simulation of external events and conditions.

Chen, Ken↗

Dynamic Positioning System (DPS) Risk Analysis Using Probabilistic Risk Assessment (PRA)

The National Aeronautics and Space Administration (NASA) Safety & Mission Assurance (S&MA) directorate at the Johnson Space Center (JSC) has applied its knowledge and experience with Probabilistic Risk Assessment (PRA) to projects in industries ranging from spacecraft to nuclear power plants. PRA is a comprehensive and structured process for analyzing risk in complex engineered systems and/or processes. The PRA process enables the user to identify potential risk contributors such as, hardware and software failure, human error, and external events. Recent developments in the oil and gas industry have presented opportunities for NASA to lend their PRA expertise to both ongoing and developmental projects within the industry. This paper provides an overview of the PRA process and demonstrates how this process was applied in estimating the probability that a Mobile Offshore Drilling Unit (MODU) operating in the Gulf of Mexico and equipped with a generically configured Dynamic Positioning System (DPS) loses location and needs to initiate an emergency disconnect. The PRA described in this paper is intended to be generic such that the vessel meets the general requirements of an International Maritime Organization (IMO) Maritime Safety Committee (MSC)/Circ. 645 Class 3 dynamically positioned vessel. The results of this analysis are not intended to be applied to any specific drilling vessel, although provisions were made to allow the analysis to be configured to a specific vessel if required.

Thigpen, Eric B.↗

Dynamic Positioning System (DPS) Risk Analysis Using Probabilistic Risk Assessment (PRA)

The National Aeronautics and Space Administration (NASA) Safety & Mission Assurance (S&MA) directorate at the Johnson Space Center (JSC) has applied its knowledge and experience with Probabilistic Risk Assessment (PRA) to projects in industries ranging from spacecraft to nuclear power plants. PRA is a comprehensive and structured process for analyzing risk in complex engineered systems and/or processes. The PRA process enables the user to identify potential risk contributors such as, hardware and software failure, human error, and external events. Recent developments in the oil and gas industry have presented opportunities for NASA to lend their PRA expertise to both ongoing and developmental projects within the industry. This paper provides an overview of the PRA process and demonstrates how this process was applied in estimating the probability that a Mobile Offshore Drilling Unit (MODU) operating in the Gulf of Mexico and equipped with a generically configured Dynamic Positioning System (DPS) loses location and needs to initiate an emergency disconnect. The PRA described in this paper is intended to be generic such that the vessel meets the general requirements of an International Maritime Organization (IMO) Maritime Safety Committee (MSC)/Circ. 645 Class 3 dynamically positioned vessel. The results of this analysis are not intended to be applied to any specific drilling vessel, although provisions were made to allow the analysis to be configured to a specific vessel if required.

Thigpen, Eric B.↗