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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Slow control and data acquisition development in the Mu2e experiment

The muon campus program at Fermilab includes the Mu2e experiment that will search for a charged-lepton flavor violating processes where a negative muon converts into an electron in the field of an aluminum nucleus, improving by four orders of magnitude the search sensitivity reached so far.Mu2e’s Trigger and Data Acquisition System (TDAQ) uses {\it otsdaq} solution. Developed at Fermilab, {\it otsdaq} uses the {\it artdaq} DAQ framework and {\it art} analysis framework, for event transfer, filtering, and processing.{\it otsdaq} is an online DAQ software suite with a focus on flexibility and scalability, and provides a multi-user interface accessible through a web browser.A Detector Control System (DCS) for monitoring, controlling, alarming, and archiving has been developed using the Experimental Physics and Industrial Control System (EPICS) open source Platform. The DCS System has also been integrated into {\it otsdaq}, providing a GUI multi-user, web-based control, and monitoring dashboard.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Application of Recursive Estimation to Heat Tracing for Groundwater/Surface‐Water Exchange

Abstract We present and demonstrate a recursive‐estimation framework to infer groundwater/surface‐water exchange based on temperature time series collected at different vertical depths below the sediment/water interface. We formulate the heat‐transport problem as a state‐space model (SSM), in which the spatial derivatives in the convection/conduction equation are approximated using finite differences. The SSM is calibrated to estimate time‐varying specific discharge using the Extended Kalman Filter (EKF) and Extended Rauch‐Tung‐Striebel Smoother (ERTSS). Whereas the EKF is suited to real‐time (“online”) applications and uses only the past and current measurements for estimation (filtering), the ERTSS is intended for near‐real time or batch‐processing (“offline”) applications and uses a window of data for batch estimation (smoothing). The two algorithms are demonstrated with synthetic and field‐experimental data and are shown to be efficient and rapid for the estimation of time‐varying flux over seasonal periods; further, the recursive approaches are effective in the presence of rapidly changing flux and (or) nonperiodic thermal boundary conditions, both of which are problematic for existing approaches to heat tracing of time‐varying groundwater/surface‐water exchange.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evolution of the ATLAS TDAQ online software framework towards Phase-II upgrade: Use of Kubernetes as an orchestrator of the ATLAS Event Filter computing farm

The ATLAS experiment at the LHC at CERN continuously evolves its TDAQ system to meet the challenges of new physics goals and technological advancements. As ATLAS prepares for the Phase-II Run 4 of the LHC, significant enhancements in the TDAQ Controls and Configuration (TDAQ-CC) tools have been designed to ensure efficient data collection, processing, and management. This abstract presents the evolution of ATLAS TDAQ-CC system leading up to Phase-II Run 4. As part of the evolution towards Phase-II, Kubernetes has been chosen to orchestrate the Event Filter (EF) farm. By leveraging Kubernetes, ATLAS can dynamically allocate computing resources, scale processing capacity in response to changing data taking conditions and ensure high availability of data processing services. The integration of the Kubernetes with the TDAQ Run Control framework enables perfect synchronisation between the experiment’s data acquisition components and the computing infrastructure. We will discuss the architectural considerations and implementation challenges involved in Kubernetes integration with the ATLAS TDAQ-CC system. We will highlight the benefits of using Kubernetes as an EF farm orchestrator, including improved resource utilization, enhanced fault tolerance, and simplified deployment and management of data processing workflows. In addition, we will report on the extensive testing of Kubernetes that was conducted using a farm of 2500 servers within the experiment data taking environment, demonstrating its scalability and robustness in handling the demands of the ATLAS TDAQ system for Phase-II. The adoption of Kubernetes represents a significant step forward in the evolution of ATLAS TDAQ-CC system, aligning with industry best practices in container orchestration.

Corso Radu, Alina [Univ. of California, Irvine, CA↗
NASA NTRS1974

Digital adaptive model following flight control

Simple mechanical linkages are often unable to cope with the many control problems associated with high performance aircraft maneuvering over a wide flight envelope. One procedure for retaining uniform handling qualities over such an envelope is to implement a digital adaptive controller. Towards such an implementation an explicit adaptive controller, which makes direct use of online parameter identification, has been developed and applied to the linearized equations of motion for a typical fighter aircraft. The system is composed of an online weighted least squares identifier, a Kalman state filter, and a single stage real model following control law. The corresponding control gains are readily adjustable in accordance with parameter changes to ensure asymptotic stability if the conditions for perfect model following are satisfied and stability in the sense of boundedness otherwise.

Alag, G. S.↗
NASA NTRS1987

Spacecraft attitude determination using a second-order nonlinear filter

The stringent attitude determination accuracy and faster slew maneuver requirements demanded by present-day spacecraft control systems motivate the development of recursive nonlinear filters for attitude estimation. This paper presents the second-order filter development for the estimation of attitude quaternion using three-axis gyro and star tracker measurement data. Performance comparisons have been made by computer simulation of system models and filter mechanization. It is shown that the second-order filter consistently performs better than the extended Kalman filter when the performance index of the root sum square estimation error of the quaternion vector is compared. The second-order filter identifies the gyro drift rates faster than the extended Kalman filter. The uniqueness of this algorithm is the online generation of the time-varying process and measurement noise covariance matrices, derived as a function or the process and measurement nonlinearity, respectively.

Vathsal, S.↗
NASA NTRS1975

Development of a digital adaptive optimal linear regulator flight controller

Digital adaptive controllers have been proposed as a means for retaining uniform handling qualities over the flight envelope of a high-performance aircraft. Towards such an implementation, an explicit adaptive controller, which makes direct use of online parameter identification, has been developed and applied to the linearized lateral equations of motion for a typical fighter aircraft. The system is composed of an online weighted least-squares parameter identifier, a Kalman state filter, and a model following control law designed using optimal linear regulator theory. Simulation experiments with realistic measurement noise indicate that the proposed adaptive system has the potential for onboard implementation.

Berry, P.↗
NASA NTRS2000

New Technology-Large-Area Three- Dimensional Surface Profiling Using Only Focused Air-Coupled Ultrasound-Given 1999 R&D 100 Award

Surface topography, which significantly affects the performance of many industrial components, is normally measured with diamond-tip profilometry over small areas or with optical scattering methods over larger areas. To develop air-coupled surface profilometry, the NASA Glenn Research Center at Lewis Field initiated a Space Act Agreement with Sonix, Inc., through two Glenn programs, the Advanced High Temperature Engine Materials Program (HITEMP) and COMMTECH. The work resulted in quantitative surface topography profiles obtained using only high-frequency, focused ultrasonic pulses in air. The method is nondestructive, noninvasive, and noncontact, and it does not require light-reflective surfaces. Air surface profiling may be desirable when diamond-tip or laserbased methods are impractical, such as over large areas, when a significant depth range is required, or for curved surfaces. When the configuration is optimized, the method is reasonably rapid and all the quantitative analysis facilities are online, including two- and three-dimensional visualization, extreme value filtering (for faulty data), and leveling.

Roth, Don J.↗
NASA NTRS1977

Application of Kalman filtering to spacecraft range residual prediction

One function of the Deep Space Network is validation of the range data that they receive. In this paper we present an automated online sequential range predictor which shows promise of significantly reducing computational and manpower expenditures. The proposed algorithm, a U-D covariance factored Kalman filter, is demonstrated by processing a four-month record of Viking spacecraft data taken enroute to Mars.

Madrid, G. A.↗
DOE OSTI2025

String instability mitigation of adaptive cruise control without modifying control laws: trajectory shaper and parameter estimation

Vehicle automation technologies equip vehicles with adaptive cruise control (ACC) systems, which relieve driving fatigue. However, recent studies have shown that the current ACC systems are string-unstable (i.e., exacerbate traffic congestion). To achieve string stability, most existing studies directly modify the control algorithms of ACC systems. Alternatively, this study proposes a trajectory shaper (TS)-based method, which only modifies the trajectory information of the predecessor vehicle, so that the ego vehicle driven by a string-unstable ACC system leverages the modified trajectory information to achieve string stability. To devise the TS-based method, an offline-online parameter estimation method integrating batch optimization and an extended Kalman filter is applied to estimate the parameters of an ACC system. The proposed TS-based method is cost-effective during implementation, as it avoids modifying existing ACC control algorithms (which entails a complex analysis of control systems and parameter tuning). In conclusion, the effectiveness of the proposed TS-based method is validated through extensive numerical experiments.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Vertical Aerosol Profiling during SAIL (VAPS) Field Campaign Report

To more fully address the science goals of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s Surface Atmosphere Integrated Field Laboratory (SAIL) campaign, particularly the key science question of “How strongly do aerosols affect the surface energy and water balance by altering clouds, precipitation, and surface albedo, and how do these impacts vary seasonally?”, our campaign added instrumentation to planned tethered balloon system (TBS) flights in May and July of 2022. A key aspect of aerosol-cloud interactions is understanding cloud condensation nuclei (CCN) and ice nucleating particle (INP) populations, and how they vary seasonally and spatially. Because the interactions between aerosols and clouds is the target of this work, vertical distributions are particularly important to evaluate. Measurements of aerosol at ground level, below-cloud, and in-cloud will allow a better understanding of the airmasses that are feeding cloud-active particles into low- and mid-level clouds. Measurements of INP and aerosol populations often differ between the ground and aloft, with relationships that likely depend on regional and seasonal. Because of this, collocated measurements of aerosol, CCN, and INP, across different seasons, are crucial to reducing uncertainties in relationships between ground-level and cloud-base aerosol populations. Our campaign added online measurements of CCN using a compact CCN counter and offline filter collections for measurement of INP populations to the SAIL campaign TBS flights in May and July of 2022. The CCN instrument, the CloudPuck, flew and successfully collected data for a total of eight flights across both campaign periods. The IcePuck flew for most flights during the same period. Both instruments have continued to be used for other campaign requests in the SAIL domain. Analysis of this data was proposed through a concurrent DOE Atmospheric System Research (ASR) project submission, which was not funded in 2022. A similar proposal, which would involve analysis of the samples and data collected from this campaign, has been resubmitted in 2023 but a funding decision has not yet been made. If the proposal is again declined, we will seek other resources to ensure that this data can be analyzed.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Bilinear modeling and nonlinear estimation

New methods are illustrated for online nonlinear estimation applied to the lateral deflection of an elastic beam on board measurements of angular rates and angular accelerations. The development of the filter equations, together with practical issues of their numerical solution as developed from global linearization by nonlinear output injection are contrasted with the usual method of the extended Kalman filter (EKF). It is shown how nonlinear estimation due to gyroscopic coupling can be implemented as an adaptive covariance filter using off-the-shelf Kalman filter algorithms. The effect of the global linearization by nonlinear output injection is to introduce a change of coordinates in which only the process noise covariance is to be updated in online implementation. This is in contrast to the computational approach which arises in EKF methods arising by local linearization with respect to the current conditional mean. Processing refinements for nonlinear estimation based on optimal, nonlinear interpolation between observations are also highlighted. In these methods the extrapolation of the process dynamics between measurement updates is obtained by replacing a transition matrix with an operator spline that is optimized off-line from responses to selected test inputs.

Dwyer, Thomas A. W., III↗
DOE OSTI2024

Convergence Analysis for an Online Data-Driven Feedback Control Algorithm

This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system’s state via indirect observations, alongside an efficient stochastic maximum principle-type optimal control solver. By integrating weak convergence techniques for the particle filter with convergence analysis for the stochastic maximum principle control solver, we derive a weak convergence result for the optimization procedure in search of optimal data-driven feedback control. Numerical experiments are performed to validate the theoretical findings.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

On-line implementation of nonlinear parameter estimation for the Space Shuttle main engine

We investigate the performance of a nonlinear estimation scheme applied to the estimation of several parameters in a performance model of the Space Shuttle Main Engine. The nonlinear estimator is based upon the extended Kalman filter which has been augmented to provide estimates of several key performance variables. The estimated parameters are directly related to the efficiency of both the low pressure and high pressure fuel turbopumps. Decreases in the parameter estimates may be interpreted as degradations in turbine and/or pump efficiencies which can be useful measures for an online health monitoring algorithm. This paper extends previous work which has focused on off-line parameter estimation by investigating the filter's on-line potential from a computational standpoint. ln addition, we examine the robustness of the algorithm to unmodeled dynamics. The filter uses a reduced-order model of the engine that includes only fuel-side dynamics. The on-line results produced during this study are comparable to off-line results generated previously. The results show that the parameter estimates are sensitive to dynamics not included in the filter model. Off-line results using an extended Kalman filter with a full order engine model to address the robustness problems of the reduced-order model are also presented.

Buckland, Julia H.↗
DOE OSTI2020

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF). We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulation-based optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real- time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Enhancements and Deployment of the TDAQ System for the Mu2e Experiment

The Real Time Processing Systems Division at Fermilab has deployed new features to the Off-The-Shelf Data Acquisition framework (otsdaq) for the Mu2e experiment. The Mu2e experiment will search for the coherent neutrino-less conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement of 10,000 times over existing limits. Such a charged lepton flavor-violating reaction probes new physics at a scale unavailable at present or planned high-energy colliders. The Mu2e Trigger and Data Acquisition (TDAQ) system uses otsdaq as its online Data Acquisition System (DAQ) framework. otsdaq integrates the artdaq and art frameworks for event transfer, filtering, and processing. otsdaq is a web-based DAQ software suite focusing on flexibility and scalability and provides a multi-user interface accessible through a web browser. artdaq handles the entire data stream, which is read over the peripheral component interconnect express (PCIe) bus to a software filter algorithm that selects events combined with the data flux coming from a cosmic-ray veto (CRV) system. Detector front-ends are configured through the PCIe bus by customized otsdaq plugins. The otsdaq slow controls infrastructure has been further developed using the experimental physics and industrial control system (EPICS) open-source platform for monitoring, controlling, alarming, and archiving. The detector control system (DCS) for Mu2e has been integrated into otsdaq. The production TDAQ and DCS system has been deployed at the experimental hall and is being debugged and optimized for experiment operations. We report on the feature enhancements and deployment of otsdaq for Mu2e.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1983

A Review of System Identification Methods Applied to Aircraft

Airplane identification, equation error method, maximum likelihood method, parameter estimation in frequency domain, extended Kalman filter, aircraft equations of motion, aerodynamic model equations, criteria for the selection of a parsimonious model, and online aircraft identification are addressed.

Klein, V.↗
NASA NTRS2012

Anomaly Detection in Test Equipment via Sliding Mode Observers

Nonlinear observers were originally developed based on the ideas of variable structure control, and for the purpose of detecting disturbances in complex systems. In this anomaly detection application, these observers were designed for estimating the distributed state of fluid flow in a pipe described by a class of advection equations. The observer algorithm uses collected data in a piping system to estimate the distributed system state (pressure and velocity along a pipe containing liquid gas propellant flow) using only boundary measurements. These estimates are then used to further estimate and localize possible anomalies such as leaks or foreign objects, and instrumentation metering problems such as incorrect flow meter orifice plate size. The observer algorithm has the following parts: a mathematical model of the fluid flow, observer control algorithm, and an anomaly identification algorithm. The main functional operation of the algorithm is in creating the sliding mode in the observer system implemented as software. Once the sliding mode starts in the system, the equivalent value of the discontinuous function in sliding mode can be obtained by filtering out the high-frequency chattering component. In control theory, "observers" are dynamic algorithms for the online estimation of the current state of a dynamic system by measurements of an output of the system. Classical linear observers can provide optimal estimates of a system state in case of uncertainty modeled by white noise. For nonlinear cases, the theory of nonlinear observers has been developed and its success is mainly due to the sliding mode approach. Using the mathematical theory of variable structure systems with sliding modes, the observer algorithm is designed in such a way that it steers the output of the model to the output of the system obtained via a variety of sensors, in spite of possible mismatches between the assumed model and actual system. The unique properties of sliding mode control allow not only control of the model internal states to the states of the real-life system, but also identification of the disturbance or anomaly that may occur.

Solano, Wanda M.↗