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At least 1,459 records · Page 81

Adaptive Control in the Presence of Simultaneous Sensor Bias and Actuator Failures

The problem of simultaneously accommodating unknown sensor biases and unknown actuator failures in uncertain systems is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor biases and actuator faults may be present at the outset or may occur at unknown instants of time during operation. A modified MRAC law is proposed, which combines sensor bias estimation with control gain adaptation for accommodation of sensor biases and actuator failures. This control law is shown to provide signal boundedness in the resulting system. For the case when an external asymptotically stable sensor bias estimator is available, an MRAC law is developed to accomplish asymptotic state tracking and signal boundedness. For a special case wherein biases are only present in the rate measurements and bias-free position measurements are available, an MRAC law is developed using a model-independent bias estimator, and is shown to provide asymptotic state tracking with signal boundedness.

Joshi, Suresh M.↗

Shakeup and shakeoff spectra in the electron capture decay of atomic 7 Be

The most stringent laboratory-based experimental limits on the existence of submegaelectronvolt sterile neutrinos are currently set by decay spectroscopy of radioactive 7 Be embedded into superconducting sensors. The systematic uncertainties are dominated by the modeling of the electron shakeup and shakeoff spectra that are not based on state-of-the-art atomic theory and do not include electron correlations or relativistic effects. We have used the multiconfiguration Dirac-Fock formalism to obtain correlated wave functions ab initio and compute all single and double shake processes in the electron capture decay of atomic 7 Be . The simulations can explain some but not all of the observed spectral features, likely because the wave functions are modified by the Ta sensor material that the 7 Be is embedded into. The new models also show that the 𝐿/𝐾 electron capture ratio of 7 Be in Ta has previously been slightly underestimated revising the previous value of 0.070(7) to a new value of 0.0756(20).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Attitude Sensor Pseudonoise

Even assuming perfect attitude sensors and gyros, sensor measurements on a vibrating spacecraft have apparent errors. These apparent sensor errors, referred to as pseudonoise, arise because gyro and sensor measurements are performed at discrete times. This paper explains the concept of pseudonoise, quantifies its behavior, and discusses the effect of vibrations that are nearly commensurate with measurement periods. Although pseudonoise does not usually affect attitude determination it does affect sensor performance evaluation. Attitude rates are usually computed from differences between pairs of accumulated angle measurements at different times and are considered constant in the periods between measurements. Propagation using these rates does not reproduce exact instantaneous spacecraft attitudes except at the gyro measurement times. Exact sensor measurements will therefore be inconsistent with estimates based on the propagated attitude. This inconsistency produces pseudonoise. The characteristics of pseudonoise were determined using a simple, one-dimensional model of spacecraft vibration. The statistical properties of the deviations of measurements from model truth were determined using this model and a range of different periods of sensor and rate measurements. This analysis indicates that the magnitude of pseudonoise depends on the ratio of the spacecraft vibration period to the time between gyro measurements and can be as much as twice the amplitude of the vibration. In cases where the vibration period and gyro or sensor measurement period are nearly commensurate, unexpected changes in pseudonoise occur.

Hashmall, Joseph A.↗

Formation of doughnut and super-Gaussian intensity distributions of laser radiation in the far field using a bimorph mirror

We consider the methods for modelling doughnut and super-Gaussian intensity distributions in the far field by means of deformable bimorph mirrors. A method for the rapid formation of a specified intensity distribution using a Shack – Hartmann sensor is proposed, and the results of the modelling of doughnut and super-Gaussian intensity distributions are presented. (laser beams)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

20 Meter Solar Sail Analysis and Correlation

This paper describes finite element analyses and correlation studies to predict deformations and vibration modes/frequencies of a 20-meter solar sail system developed by ATK Space Systems. Under the programmatic leadership of NASA Marshall Space Flight Center's In-Space Propulsion activity, the 20-meter solar sail program objectives were to verify the design, to assess structural responses of the sail system, to implement lessons learned from a previous 10-meter quadrant system analysis and test program, and to mature solar sail technology to a technology readiness level (TRL) of 5. For this 20 meter sail system, static and ground vibration tests were conducted in NASA Glenn Research Center's 100 meter diameter vacuum chamber at Plum Brook station. Prior to testing, a preliminary analysis was performed to evaluate test conditions and to determine sensor and actuator locations. After testing was completed, an analysis of each test configuration was performed. Post-test model refinements included updated properties to account for the mass of sensors, wiring, and other components used for testing. This paper describes the development of finite element models (FEM) for sail membranes and masts in each of four quadrants at both the component and system levels, as well as an optimization procedure for the static test/analyses correlation.

Taleghani, B. K.↗

Shake-up and shake-off spectra in the electron capture decay of atomic $^7$Be

The most stringent laboratory-based experimental limits on the existence of sub-MeV sterile neutrinos are currently set by decay spectroscopy of radioactive $^7$Be embedded into superconducting sensors. The systematic uncertainties are dominated by the modeling of the electron shake-up and shake-off spectra that are not based on state-of-the-art atomic theory and do not include electron correlations or relativistic effects. We have used the multiconfiguration Dirac-Fock formalism to obtain correlated wavefunctions ab initio and compute all single and double shake processes in the electron capture decay of atomic $^7$Be. The simulations can explain some but not all of the observed spectral features, likely because the wave functions are modified by the Ta sensor material that the $^7$Be is embedded into. The new models also show that the L/K electron capture ratio of $^7$Be in Ta has previously been slightly underestimated revising the previous value of 0.070(7) to a new value of 0.0756(20).

Atomic Physics (physics.atom-ph)↗

Transfer learning for probabilistic localization of hidden cracks in concrete structures

Abstract The utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.

Miele, S.↗

Accelerometer Modeling in the State-Space

Generically, accelerometers are sensors that measure the accelerating force applied to an object. Their operation is based on a simple mechanical system and D’Alembert’s force principle along with Newton’s laws of motion. This device is typically mounted to the body of the test object under investigation that is subjected to an accelerating force. The displacement of the seismic or “proof” mass due to the acceleration is sensed by an embedded component from which the acceleration is determined. Interestingly enough, the resulting the resulting mathematical relations governing the dynamical motion is based on the underlying physics, sensor materials, geometry and fabrication. Mathematically, the various designs satisfy the underlying Newtonian equations of motion, but their type and performance differ significantly.

42 ENGINEERING↗

Shock Tube Characterization of Laser Absorption Sensors for Combustion Measurements of Green Propellants

Chemical kinetic models for green spacecraft propulsion systems are needed to calculate efficiency, estimate toxic products, and mitigate plume impingement risks. Laser absorption spectroscopy (LAS) is a common technique used to measure time-resolved mole fraction for model validation. LAS was used to measure species-time history nitrous oxide (N2O) and carbon monoxide (CO) behind reflected shock waves. The diagnostics were implemented at the University of Central Florida high-pressure shock tube facility. A mixture of ethylene (0.002) and N2O (0.008) was diluted with argon gas (0.99) and shock heated 12 bar and temperatures from 1400 to 1600 K.

Marley A. Albright↗

Online Energy-optimal Routing for Electric Vehicles with Combinatorial Multi-arm Semi-Bandit

We have seen a rapid growth in the adoption of electric vehicle in today’s commercial mobility service market, from carrying passengers to the delivery of goods. One cardinal issue concerning the operation of EVs is the optimal routing of EV fleets with limited battery capacity. In this study, we investigate the energy-optimal online routing problem for the fleet of EVs, which focuses on identifying real-time minimum electricity consumption paths (MECP) for multiple OD pairs with limited information. We develop a multi-OD combinatorial multi-arm semi-bandit model (MCMAB) that uses the fleet of EVs as sensors in the transportation network and promotes the utilization of common information shared by different OD pairs. We further enrich the model with the path elimination policy to obtain MECP of high confidence while significantly reducing the number of learning iterations and the number of explorations needed. We demonstrate the effectiveness of the MCMAB and the efficiency of the path elimination policy with comprehensive numerical experiments in Manhattan, NYC. The results show that the proposed online routing algorithms can achieve near-optimal MECPs efficiently, and the quality of the solutions is significantly better than using the shortest travel time paths as approximate MECPs.

Chen, Xiaowei↗

An inverse approach to solving zone air infiltration rate and people count using indoor environmental sensor data

Physics-based simulation of energy use in buildings is typically widely used in building design and performance rating, controls design and operations. However, various challenges exist in the modeling process. Model parameters such as people count and air infiltration rate are usually highly uncertain, yet they have significant impacts on the simulation accuracy. With the increasing availability and affordability of sensors and meters in buildings, a large amount of measured data has been collected including indoor environmental parameters, such as room air dry-bulb temperature, humidity ratio, and CO 2 concentration levels. Fusing these sensor data with traditional energy modeling poses new opportunities to improve simulation accuracy. This study develops a set of physics-based inverse algorithms which can solve the highly uncertain and hard-to-measure building parameters such as zone-level people count and air infiltration rate. A simulation-based case study is conducted to verify the inverse algorithms implemented in EnergyPlus covering various sensor measurement scenarios and different modeling use cases. The developed inverse models can solve the zone people count and air infiltration at sub-hourly resolution using the measured zone air temperature, humidity and/or CO 2 concentration given other easy-to-measure model parameters are known.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Hybrid Analytics Solution to Improve Coal Power Plant Operations

This project focused on developing advanced methods for thermal performance monitoring of a coal-fueled power plant. The specific goal was to develop and demonstrate a new thermal performance monitoring approach using a hybrid model that integrates a physics-based heat balance model with a machine learning-based pattern recognition model. The hybrid model enables increased accuracy and scope of the thermal analysis and an improved ability to monitor and detect changes in plant operation. This new approach takes full advantage of the individual model capabilities and creates an important new set of capabilities not previously possible using the two types of models separately. Using the heat balance model, a rich set of derived parameters (virtual sensors) are calculated from the measured plant operating data at each time point. The combined measured and derived data values are used by machine learning algorithms to create pattern recognition models over the range of normal unit operation. To create the monitoring models, historical data from normal operation of the plant is first processed by the heat balance model to compute the derived parameter data. The result is a greatly expanded set of normal operating data that can be used as input to create the pattern recognition model. Once the models are calibrated for normal operation, the hybrid model is suitable for use in continuous online monitoring. During online monitoring, new plant operating data is processed first by the heat balance model and then by the pattern recognition model. Results from the pattern recognition model quantify the deviation of each measured or derived parameter from its expected value in normal operation. The hybrid models can detect abnormal changes in plant operating data with very high accuracy and sensitivity. When abnormal behavior is detected, alerts are generated automatically for evaluation by the plant monitoring staff. The new hybrid solution product was developed and verified in the performance of the project. The hybrid solution was tested first in a simulation environment that mimicked the plant data systems and infrastructure used by U.S. power generating plants and utilities. The hybrid solution was then deployed for real-time, online monitoring of an operating coal-fueled power plant at a field test site. Field testing demonstrated that all hybrid solution development objectives were accomplished. The project work was based on combining the capabilities of two existing software products to create the new hybrid solution product. One of these was the existing MapEx® heat balance product and the other was the existing SureSense® advanced pattern recognition product. Each of these separate products was assessed to be at a Technology Readiness Level (TRL) of 9 at the start of the effort. The hybrid solution product was assessed to be at a TRL of 2 at the start of the project based on early feasibility work by the project team. At completion of the field testing performed in the project, the hybrid solution product was assessed to be at a TRL of 7. The project team expects that the hybrid solution product will be deployed commercially and will achieve a TRL of 9 within one year after completion of the project.

01 COAL, LIGNITE, AND PEAT↗

Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs)

Concentrations of airborne chemical and biological agents from a hazardous release are not spread uniformly. Instead, there are regions of higher concentration, in part due to local atmospheric flow conditions which can attract agents. We equipped a ground station and two rotary-wing unmanned aircraft systems (UASs) with ultrasonic anemometers. Flights reported here were conducted 10 to 15 m above ground level (AGL) at the Leach Airfield in the San Luis Valley, Colorado as part of the Lower Atmospheric Process Studies at Elevation—a Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) campaign in 2018. The ultrasonic anemometers were used to collect simultaneous measurements of wind speed, wind direction, and temperature in a fixed triangle pattern; each sensor was located at one apex of a triangle with ∼100 to 200 m on each side, depending on the experiment. A WRF-LES model was used to determine the wind field across the sampling domain. Data from the ground-based sensors and the two UASs were used to detect attracting regions (also known as Lagrangian Coherent Structures, or LCSs), which have the potential to transport high concentrations of agents. This unique framework for detection of high concentration regions is based on estimates of the horizontal wind gradient tensor. To our knowledge, our work represents the first direct measurement of an LCS indicator in the atmosphere using a team of sensors. Our ultimate goal is to use environmental data from swarms of sensors to drive transport models of hazardous agents that can lead to real-time proper decisions regarding rapid emergency responses. The integration of real-time data from unmanned assets, advanced mathematical techniques for transport analysis, and predictive models can help assist in emergency response decisions in the future.

54 ENVIRONMENTAL SCIENCES↗

Uniform and Half‐Space Models for Magnetic Dipole Sources in a Conducting Medium: I. Theory and Results

Underground nuclear events result in electromagnetic signals near the event location. Of particular interest, because its signal can be potentially enhanced by the use of large cavities to decouple the seismic signal, is the generation of an EMP signal within the nuclear detonation (NUDET) cavity, from the formation of a bubble in the local magnetic field. This mechanism is equivalent to a buried time varying magnetic dipole. In order to model such signals Los Alamos National Laboratory is developing a detailed model for the propagation of RF signals from buried sources to nearby sensors, with a focus on sources that are magnetic dipole in character. In order to validate this code, models for simple geometries involving magnetic dipole sources have been implemented. These models represent the fields from vertical and horizontal dipoles in cylindrical coordinates, either in a uniform linear isotropic electrical medium, or in a space consisting of two half spaces interfacing at the plane z=0, with the upper half space having the electrical properties of free space and the lower half space having arbitrary linear conductivity and electrical permittivity. While primarily intended for the validation of the more detailed model, the shorter turnaround time of these models facilitates the study of the general properties of geomagnetic bubble signals. This report summarizes the theory and example results for those models. The use of the model for model validation is illustrated with the near and surface fields for dipoles with Gaussian time dependence. Also examined are the surface fields expected for the geomagnetic bubble predicted by the numerical model of Kovalenko et al for a 1 kt nuclear detonation. The model of Kovalenko et al was also rescaled to allow comparison with Zablocki’s electric field observations for Bilby, Event III, and Event V. While the rescaled Kovalenko et al model had approximately the right time scale for the duration of the main peak, and a slower fall than rise also in qualitative agreement with the observations, the predicted signals had very different polarizations, were too low by more than an order of magnitude, and had less temporal structure. A simple comparison with the vertical B field data of Sweeney, where the detailed event geometries and yields are classified, but the sensor range and bounds on the yields are given, suggest that the scaled Kovalenko et al model gives too small a peak field compared to observations for that data set as well, except perhaps for the Borate event. A companion report will give a detailed discussion of the implementation and code.

58 GEOSCIENCES↗

Building Simulation Modelers are we big-data ready?

Recent advances in computing and sensor technologies have pushed the amount of data we collect or generate to limits previously unheard of. Sub-minute resolution data from dozens of channels is becoming increasingly common and is expected to increase with the prevalence of non-intrusive load monitoring. Experts are running larger building simulation experiments and are faced with an increasingly complex data set to analyze and derive meaningful insight. This paper focuses on the data management challenges that building modeling experts may face in data collected from a large array of sensors, or generated from running a large number of building energy/performance simulations. The paper highlights the technical difficulties that were encountered and overcome in order to run 3.5 million EnergyPlus simulations on supercomputers and generating over 200 TBs of simulation output. This extreme case involved development of technologies and insights that will be beneficial to modelers in the immediate future. The paper discusses different database technologies (including relational databases, columnar storage, and schema-less Hadoop) in order to contrast the advantages and disadvantages of employing each for storage of EnergyPlus output. Scalability, analysis requirements, and the adaptability of these database technologies are discussed. Additionally, unique attributes of EnergyPlus output are highlighted which make data-entry non-trivial for multiple simulations. Practical experience regarding cost-effective strategies for big-data storage is provided. The paper also discusses network performance issues when transferring large amounts of data across a network to different computing devices. Practical issues involving lag, bandwidth, and methods for synchronizing or transferring logical portions of the data are presented. A cornerstone of big-data is its use for analytics; data is useless unless information can be meaningfully derived from it. In addition to technical aspects of managing big data, the paper details design of experiments in anticipation of large volumes of data. The cost of re-reading output into an analysis program is elaborated and analysis techniques that perform analysis in-situ with the simulations as they are run are discussed. The paper concludes with an example and elaboration of the tipping point where it becomes more expensive to store the output than re-running a set of simulations.

Sanyal, Jibonananda↗

U.S. Domestic Security-by-Design: On Site Response Force Strategies

U.S. nuclear power facilities face increasing challenges in meeting dynamic security requirements caused by evolving and expanding threats while keeping cost reasonable to make nuclear energy competitive. The past approach has often included implementing security features after a facility has been designed and without attention to optimization, which can lead to cost overruns. Incorporating security in the design process can provide robust, cost- effective, and sufficient physical protection systems. The purpose of this work is both to develop a framework for the integration of security into the design phase of High Temperature Gas Reactors (HTGRs) that utilize pebble-based fuels and microreactors. Specifically, this effort focuses on integrating security into the design phase of a model HTGR and microreactor that meets current Nuclear Regulatory Commission (NRC) physical protection requirements and providing advanced solutions to improve physical protection and decrease costs. A suite of tools, including SCRIBE3D©, PATHTRACE© and Blender© were used to model a hypothetical, generic domestic HTGR facility and microreactor facility. Physical protection elements such as sensors, cameras, barriers, and onsite response forces were added to the model based on best practices for physical protection systems. Multiple outsider sabotage scenarios were examined with four-to-eight adversaries to determine security metrics. The results of this work will influence physical protection system designs and facility designs for U.S. domestic HTGRs and microreactors. This work will also demonstrate how a series of experimental and modeling capabilities across the Department of Energy (DOE) Complex can lead to efficient security systems that utilize an onsite response force. The conclusions and recommendations in this document may be applicable to all SMR designs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA Airborne Observations in Support of Wildfire Science and Applications

The NASA Science Mission Directorate Earth Science Division Airborne Science Program enables access to modified aircraft in support of NASA projects. The Program supports researchers and engineers by enabling testing of new instruments, calibration of on-orbit satellite instruments with airborne sensors, and to support process studies and model development. This talk will provide an overview of the Program capabilities, review past NASA flight projects that have focused on wildfires, and discuss how the Program is poised to support the FireSense project and interagency efforts to incorporate new technologies and observations into the wildfire management community. Past missions including the Western States Fire Mission and FIREX-AQ will be discussed, an overview of relevant NASA sensors will be provided, and an introduction to upcoming flight activity will be presented. Data telemetry, communications, and data standards will also be discussed towards improving timely distribution of data products to land managers.

Matthew M Fladeland↗