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At least 793 records · Page 44

Solar Coronal Structure Study

The subject of this investigation is to study the physics of the solar corona through the analysis of the EUV and UV data produced by two flights (12 May 1992 and 25 April 1994) of the Lockheed Solar Plasma Diagnostics Experiment (SPDE) sounding rocket payload, in combination with Yohkoh and ground-based data. Each rocket flight produced both spectral and imaging data. These joint datasets are useful for understanding the physical state of various features in the solar atmosphere at different heights ranging from the photosphere to the corona at the time of the, rocket flights, which took place during the declining phase of a solar cycle, 2-4 years before the minimum. The investigation is narrowly focused on comparing the physics of small- and medium-scale strong-field structures with that of large-scale, weak fields. As we close th is investigation, we have to recall that our present position in the understanding of basic solar physics problems (such as coronal heating) is much different from that in 1995 (when we proposed this investigation), due largely to the great success of SOHO and TRACE. In other words, several topics and techniques we proposed can now be better realized with data from these missions. For this reason, at some point of our work, we started concentrating on the 1992 data, which are more unique and have more supporting data. As a result, we discontinued the investigation on small-scale structures, i.e., bright points, since high-resolution TRACE images have addressed more important physics than SPDE EUV images could do. In the final year, we still spent long time calibrating the 1992 data. The work was complicated because of the old-fashioned film, which had problems not encountered with more modern CCD detectors. After our considerable effort on calibration, we were able to focus on several scientific topics, relying heavily on the SPDE UV images. They include the relation between filaments and filament channels, the identification of hot loops, and the physical conditions of such loops especially at their foot-points. A total of four papers were completed from this contract which are listed in the last section.

Nitta, Nariaki↗

Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties

Abstract Identification of a heterogeneous conductivity field and reconstruction of a contaminant release history are key aspects of subsurface remediation. These two goals are achieved by combining model predictions with sparse and noisy hydraulic head and concentration measurements. Solution of this inverse problem is notoriously difficult due to, in part, high dimensionality of the parameter space and high computational cost of repeated forward solves. We use a convolutional adversarial autoencoder (CAAE) to parameterize a heterogeneous non‐Gaussian conductivity field via a low‐dimensional latent representation. A three‐dimensional dense convolutional encoder‐decoder (DenseED) network serves as a forward surrogate of the flow and transport model. The CAAE‐DenseED surrogate is fed into the ensemble smoother with multiple data assimilation (ESMDA) algorithm to sample from the Bayesian posterior distribution of the unknown parameters, forming a CAAE‐DenseED‐ESMDA inversion framework. The resulting CAAE‐DenseED‐ESMDA inversion strategy is used to identify a three‐dimensional contaminant source and conductivity field. A comparison of the inversion results from CAAE‐ESMDA with physical flow and transport simulator and from CAAE‐DenseED‐ESMDA shows that the latter yields accurate reconstruction results at the fraction of the computational cost of the former.

Zhou, Zitong↗

System Guidelines for EMC Safety-Critical Circuits: Design, Selection, and Margin Demonstration

Demonstration of required safety margins on critical electrical/electronic circuits in large complex systems has become an implementation and cost problem. These margins are the difference between the activation level of the circuit and the electrical noise on the circuit in the actual operating environment. This document discusses the origin of the requirement and gives a detailed process flow for the identification of the system electromagnetic compatibility (EMC) critical circuit list. The process flow discusses the roles of engineering disciplines such as systems engineering, safety, and EMC. Design and analysis guidelines are provided to assist the designer in assuring the system design has a high probability of meeting the margin requirements. Examples of approaches used on actual programs (Skylab and Space Shuttle Solid Rocket Booster) are provided to show how variations of the approach can be used successfully.

Lawton, R. M.↗

Model Refinement Using Eigensystem Assignment

IA novel approach for the refinement of finite-element-based analytical models of flexible structures is presented. The proposed approach models the possible refinements in the mass, damping, and stiffness matrices of the finite element model in the form of a constant gain feedback with acceleration, velocity, and displacement measurements, respectively. Once the free elements of the structural matrices have been defined, the problem of model refinement reduces to obtaining position, velocity, and acceleration gain matrices with appropriate sparsity that reassign a desired subset of the eigenvalues of the model, along with partial mode shapes, from their baseline values to those obtained from system identification test data. A sequential procedure is used to assign one conjugate pair of eigenvalues at each step using symmetric output feedback gain matrices, and the eigenvectors are partially assigned, while ensuring that the eigenvalues assigned in the previous steps are not disturbed. The procedure can also impose that gain matrices be dissipative to guarantee the stability of the refined model. A numerical example, involving finite element model refinement for a structural testbed at NASA Langley Research Center (Controls-Structures-Interaction Evolutionary model) is presented to demonstrate the feasibility of the proposed approach.

Maghami, Peiman G.↗

$\text{GPLaSDI}$: Gaussian Process-based interpretable Latent Space Dynamics Identification through deep autoencoder

Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the ROM predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. Here we demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.

97 MATHEMATICS AND COMPUTING↗

Identification of a pathway for electron uptake in Shewanella oneidensis

Extracellular electron transfer (EET) could enable electron uptake into microbial metabolism for the synthesis of complex, energy dense organic molecules from CO 2 and renewable electricity. Theoretically EET could do this with an efficiency comparable to H 2 -oxidation but without the need for a volatile intermediate and the problems it causes for scale up. However, significant gaps remain in understanding the mechanism and genetics of electron uptake. For example, studies of electron uptake in electroactive microbes have shown a role for the Mtr EET complex in the electroactive microbe Shewanella oneidensis MR-1, though there is substantial variation in the magnitude of effect deletion of these genes has depending on the terminal electron acceptor used. This speaks to the potential for previously uncharacterized and/or differentially utilized genes involved in electron uptake. To address this, we screened gene disruption mutants for 3667 genes, representing ≈99% of all nonessential genes, from the S. oneidensis whole genome knockout collection using a redox dye oxidation assay. Confirmation of electron uptake using electrochemical testing allowed us to identify five genes from S. oneidensis that are indispensable for electron uptake from a cathode. Knockout of each gene eliminates extracellular electron uptake, yet in four of the five cases produces no significant defect in electron donation to an anode. This result highlights both distinct electron uptake components and an electronic connection between aerobic and anaerobic electron transport chains that allow electrons from the reversible EET machinery to be coupled to different respiratory processes in S. oneidensis. Homologs to these genes across many different genera suggesting that electron uptake by EET coupled to respiration could be widespread. These gene discoveries provide a foundation for: studying this phenotype in exotic metal-oxidizing microbes, genetic optimization of electron uptake in S. oneidensis; and genetically engineering electron uptake into a highly tractable host like E. coli to complement recent advances in synthetic CO2 fixation.

59 BASIC BIOLOGICAL SCIENCES↗

Towards optimal sensor placement for inverse problems in spaces of measures

The objective of this work is to quantify the reconstruction error in sparse inverse problems with measures and stochastic noise, motivated by optimal sensor placement. To be useful in this context, the error quantities must be explicit in the sensor configuration and robust with respect to the source, yet relatively easy to compute in practice, compared to a direct evaluation of the error by a large number of samples. In particular, we consider the identification of a measure consisting of an unknown linear combination of point sources from a finite number of measurements contaminated by Gaussian noise. The statistical framework for recovery relies on two main ingredients: first, a convex but non-smooth variational Tikhonov point estimator over the space of Radon measures and, second, a suitable mean-squared error based on its Hellinger–Kantorovich distance to the ground truth. To quantify the error, we employ a non-degenerate source condition as well as careful linearization arguments to derive a computable upper bound. This leads to asymptotically sharp error estimates in expectation that are explicit in the sensor configuration. Thus they can be used to estimate the expected reconstruction error for a given sensor configuration and guide the placement of sensors in sparse inverse problems.

97 MATHEMATICS AND COMPUTING↗

Time dependent supervisory control update with FARM using rolling window

This report describes improvements to the Feasible Actuator Range Modifier (FARM) component of the RAVEN-based HYBRID framework for analysis of Integrated Energy Systems (IES). FARM supports the HERON plug-in that solves the power dispatch problem. The solution to the dispatch problem involves economically optimal dispatches that satisfy limits on production variables and their rates of variation (explicit constraints) as well as process variables tied to the service life of equipment (implicit constraints). FARM serves to validate or confirm that a HERON solution for explicit constraints also satisfies the implicit constraints. FARM-alpha was released by Argonne National Laboratory in January 2021 followed by FARM-Beta in January 2022 with the latter providing increased flexibility for the user. In this report, FARM-Gamma, the latest version of the code, is described. The major improvement is the implementation of a system identification algorithm based on the Dynamic Mode Decomposition with Control (DMDc) coupled with a “Rolling Window” scheme that allows obtaining linear time-varying state-space models. This feature equips FARM with the most accurate approximation of system dynamics, and it relieves the user from the burden of performing an exhaustive off-line characterization of the dynamics. FARM-Gamma capabilities are assessed by solving the power dispatch problem for a representative IES unit. The simulation times corresponding to the different releases are estimated and compared. These values capture the increasing computational burden of the successively higher fidelity state-space models adopted by FARM-Alpha, FARM-Beta and FARM-Gamma. The code implementation provides significant flexibility, i.e., the user can always select the most suitable version of FARM according to the problem to be solved and the available computational resources. It is anticipated that FARM will play a role in addressing several future IES applications. We outline how it can support the coordinated management and safe operation of a nuclear plant coupled to industrial processes to produce hydrogen and synfuels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

A Critical Review of High Burnup Fuel Fragmentation, Relocation, and Dispersal under Loss-Of-Coolant Accident Conditions

High burnup fuel in excess of ~6268 GWd/MTU has been observed to severely fragment when subjected to temperature transient conditions as in a loss-of-coolant accident. In the event of cladding failure, the severely fragmented fuel can relocate into the balloon region and potentially disperse through the burst opening into the reactor’s primary system. This observation has led to implementation of several independent test programs designed to focus on various aspects of fuel fragmentation, relocation, and dispersal. The purpose of this review is to perform a critical, holistic assessment of fuel fragmentation, relocation, and dispersal under loss-of-coolant conditions to identify data gaps in the experimental data base. Phenomena have been identified and well defined through the various testing programs, and general agreement regarding the governing parameters (temperature, burnup, heating rate, cladding deformation, etc.) has been established. However, there is a significant data gap connecting research to commercial application. Furthermore, previous research attempted to evaluate the problem holistically, but has yet to provide a comprehensive understanding of the problem. Major identified data gaps consist of (1) a comprehensive understanding of pretransient fuel rod conditions (fuel temperature, fuel stress, rod internal pressures, microstructure, etc.), (2) definition of prototypic loss-of-coolant accident conditions (heating rate, fuel temperatures, fuel stress), (3) identification of the differences between nuclear and electrical integral loss-of-coolant accident tests, and connection of fuel rod performance (cladding ballooning, burst opening, potential relocation, fragmentation susceptibility, etc.) to fuel fragmentation, relocation, and dispersal severity. Finally, the content of this manuscript provides a clear path for connecting testing and data acquisition to commercial application by providing concrete means for filling data gaps and prioritized testing regimes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Case studies in Aircraft Parameter Identification

Three case studies in Aircraft Parameter Identification using simulated data for X-22 VTOL aircraft and flight test data for HL-10 and M2/F3 lifting bodies. After a brief discussion of the previous techniques and their limitations, a technique based on the Maximum Likelihood criterion is described. The problems of identifiability and uniqueness in determining the Stability and Control derivatives from flight test data are discussed and several methods for alleviating these problems are presented. The flight test data is analyzed in several different ways for obtaining physically meaningful estimates for the aircraft parameters.

Mehra, R. K.↗

Yaw rate control of an air bearing vehicle

The results of a 6 week project which focused on the problem of controlling the yaw (rotational) rate the air bearing vehicle used on NASA's flat floor facility are summarized. Contained within is a listing of the equipment available for task completion and an evaluation of the suitability of this equipment. The identification (modeling) process of the air bearing vehicle is detailed as well as the subsequent closed-loop control strategy. The effectiveness of the solution is discussed and further recommendations are included.

Walcott, Bruce L.↗

Dynamic Control of Aerodynamic Instabilities in Gas Turbine Engines

This lecture discusses the use of closed loop control at the component level to enhance the performance of gas turbine engines. The general theme is the suppression of flow instabilities (rotating stall and surge) through use of feedback, either actively or by means of the aeromechanical coupling provided by tailored structures. The basic concepts that underlie active control of turbomachinery instability, and their experimental demonstration, are first described for a centrifugal compressor. It is shown that the mechanism for stabilization is associated with damping of unsteady perturbations in the compression system, and the steady-state performance can thus remain virtually unaltered. Control of instability using a tailored structure is then discussed, along with experimental results illustrating the flow range extension achievable using this technique. A considerably more complex problem is presented by active control or rotating stall where the multi-dimensional features mean that distributed sensing and actuation are required. In addition, there are basic questions concerning unsteady fluid mechanics; these imply the need to resolve issues connected with identification of suitable signals as well as with definition of appropriate wave launchers for implementing the feedback. These issues are discussed and the results of initial successful demonstrations of active control of rotating stall in a single-stage and a three-stage axial compressor are presented. The lecture concludes with suggestions for future research on dynamic control of gas turbine engines.

E M Greitzer↗

Learning characteristics of a space-time neural network as a tether skiprope observer

The Software Technology Laboratory at the Johnson Space Center is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

Lea, Robert N.↗

Learning characteristics of a space-time neural network as a tether skiprope observer

The Software Technology Laboratory at JSC is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

Lea, Robert N.↗

Stakeholder-Engaged Structured Decision Making for the Los Alamos Legacy Cleanup Mission - 20501

The Los Alamos National Laboratory (LANL) environmental legacy cleanup program requires decisions to be made for environmental remediation, decommissioning and disposal or management of radioactive waste. This legacy cleanup program was established to address groundwater contamination, material disposal areas (MDAs) that have been used to dispose of radioactive and other waste material, and 'aggregate areas' that might produce radioactive or other chemical waste as a result of remediation activities. The LANL site is regulated for environmental concerns under the Resource Conservation and Recovery Act (RCRA). However, some parts of LANL, such as material disposal area G (MDA G), have disposed of radioactive waste under DOE Order 435.1, and are subject to other regulations. For example, decommissioning the remote-handled TRU material stored in 33 shafts at MDA G falls under EPA's 40 CFR 191. Collectively, the regulations are all aimed in the same direction of finding the best solution, either through constructs such as 'as low as reasonably achievable' (ALARA), considering balancing factors as opposed to only cost and human health risk, and, under EPA regulations such as RCRA and NEPA, evaluating impact from all chemicals and both human health and ecological endpoints. Despite the basic goals and objectives of the regulations or their guidance, the main challenge is in their implementation. Arguably perhaps, but really in principle, all of these (and similar) regulations are asking for a decision analysis to be performed. Implementation challenges encountered have included lack of understanding of decision analysis in the industry, lack of effective stakeholder engagement in the decision analysis process, and lack of appreciation of the need to separate value judgments from science, the latter leading to developing conservative, or protective, science-based models. Conservative models lead to poor solutions, lack of effective stakeholder engagement leads to long drawn out protracted approaches to finding a solution (which also might never be found with this approach), and lack of understanding of decision analysis and Bayesian statistics causes poor models to be built, which creates unfortunate situations of 'garbage in, garbage out' becoming the basis for decision making. Stakeholder-engaged structured decision making (SDM) is an approach to solving problems that relies on the theory of decision science to involve stakeholders in the decision-making process. This approach incorporates stakeholder values using a scientifically rigorous methodology that separates value judgments from science in a way that helps avoid the pitfalls of biased, protective, or conservative modeling. This approach has its foundation in Keeney's 1992 treatise on value-focused thinking [1]. Keeney advocated a paradigm shift in decision making based on the idea that the standard way of thinking about decisions is backwards. The standard approach of focusing first on identifying alternatives rather than on articulating values results in a reactive approach with the emphasis on mechanics and fixed choices instead of the core values that have meaning to stakeholders. This paradigm shift effectively engages all stakeholders in the decision-making process while using a values focused thinking approach that can lead to the identification of decision opportunities and the creation of better alternatives. The intent is to be proactive and generate solutions that are related directly to values and objectives as identified by stakeholders. There are, perhaps, two overarching reasons why SDM can be used to benefit LANL's environmental legacy cleanup. Some of LANL's remaining waste management and environmental management problems are challenging and complex (for example, the Cr and RDX plumes, and MDAs) and while the traditional approach has, arguably, worked well for relatively simple risk-based problems, it cannot, or should not, be applied to more complex problems if the most effective and efficient solutions are desired. The second reason is cost. This has perhaps become more critical since publication of the Government Accountability Office (GAO) reports that DoE's environmental liability is considered a high-risk concern for the nation [2]. The focus of SDM is on structuring solutions to decision risk problems by first addressing stakeholder and decision maker values and subsequently developing decision objectives and ways to measure those objectives, preference weighting across objectives, identifying decision alternatives that best achieve those values, and characterizing uncertainty in predictions of the measures. Because a complete decision model is created using SDM, it can be evaluated to find the main elements of the model that drive, or predict, the best solution. This approach creates complete decision models that are transparent, traceable, reproducible and technically defensible. The science behind SDM, or decision analysis, is well founded, yet it is not unusual to see ad hoc approaches to decision making implemented under various environmental regulations that are pertinent to the LANL site, including NEPA, RCRA and DOE Order 435.1. Such ad hoc approaches are often not transparent or traceable, and lack reproducibility and technical defensibility. The LANL legacy cleanup program has embarked on using SDM to address the complex problems that remain. Stakeholder meetings have been held, and a prototype version of the stakeholder value system has been developed. Further meetings are expected in the future to address specific project needs. This is a long-term endeavor considering the complex environmental problems faced by DOE EM in Los Alamos (EM-LA), and careful planning, consideration of stakeholder value systems, and engagement with stakeholders throughout the SDM process is expected to lead to a successful endpoint. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Eleventh Annual Conference on Manual Control

This volume contains a compilation of written versions of papers presented at the Eleventh Annual Conference on Manual Control in a meeting held at NASA-Ames Research Center, May 21-23, 1975. Five main topics were discussed during this three-day conference. These were covered in sessions on Concurrent Tasks, Physiological Systems, Controls and Displays, Performance/Workload Evaluation, and System Identification. Forty-nine papers are included with presentations on results of analytical studies to develop and evaluate human operator models for a range of control task, vehicle dynamics and display situations; results of tests of physiological control systems and applications to medical problems; and on results of simulator and flight tests to determine display, control and dynamics effects on operator performance and workload for aircraft, automobile and remote control systems.

Source record↗

An Empirical Bayes Approach to Spatial Analysis

Multi-channel LANDSAT data are collected in several passes over agricultural areas during the growing season. How empirical Bayes modeling can be used to develop crop identification and discrimination techniques that account for spatial correlation in such data is considered. The approach models the unobservable parameters and the data separately, hoping to take advantage of the fact that the bulk of spatial correlation lies in the parameter process. The problem is then framed in terms of estimating posterior probabilities of crop types for each spatial area. Some empirical Bayes spatial estimation methods are used to estimate the logits of these probabilities.

Morris, C. N.↗

Precision pointing and control of flexible spacecraft

The problem and long term objectives for the precision pointing and control of flexible spacecraft are given. The four basic objectives are stated in terms of two principle tasks. Under Task 1, robust low order controllers, improved structural modeling methods for control applications and identification methods for structural dynamics are being developed. Under Task 2, a lab test experiment for verification of control laws and system identification algorithms is being developed. For Task 1, work has focused on robust low order controller design and some initial considerations for structural modeling in control applications. For Task 2, work has focused on experiment design and fabrication, along with sensor selection and initial digital controller implementation. Conclusions are given.

Bantell, M. H., Jr.↗