Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Active learning”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,477 records · Page 82

An adaptive learning control system for large flexible structures

The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

Thau, F. E.↗

Formation of Gullies on Mars: What Do We Learn from Earth?

The observation of gullies on Mars indicates the presence of liquid water in recent times [1]. They have been proposed to result of subsurface seepage of water [1], geothermal activity [2] or brines [3], near-surface ice melting at recent periods of high obliquity [4], snowmelt in more recent periods [5] or liquid CO2 breakout [6]. In this study, we describe how terrestrial studies help to understand better the formation of Martian gullies. We show that all characteristics of Martian gullies are consistent with some external process triggered by seasonal melting at high obliquity.

Mangold, N.↗

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗

Cognitive Engineering in Training: Monitoring and Pilot-Automation Coordination in Complex Environments

This paper reports our investigation of flight path monitoring in aviation. We interviewed experienced pilots to understand the knowledge and skills underlying effective monitoring and we developed an example learning environment to improve these skills. We explore how design of pilot training and learning, like the design of interfaces and of the underlying automation, benefits from cognitive engineering methods and perspective. In aviation, monitoring and managing flight path are critical activities. The influences on flight path are complex and come from the autoflight system, from control actions by the pilot, and from external factors, including weather and Air Traffic Control (ATC). Indeed, inadequate flight path monitoring is a current aviation concern as it has been implicated in accidents and incidents. Effective piloting depends on strategies for noticing, understanding, and anticipating these influences to monitor and manage flight path. Lack of such skills reduces pilots' ability to maintain safety margin and resilience. Although flightdeck automation is intended to aid pilot understanding and prediction, the Fight Management Systems (FMS) can mislead as well as aid the pilot's understanding and projection of what will happen. In dynamic conditions, FMS predictions may be based on old or incomplete information. Understanding such vulnerabilities is an important part of pilot-autoflight coordination. The learning environment we developed is designed to help pilots proactively monitor and manage flight path. We consider how a broad cognitive engineering approach might inform the "what" and "how" of learning in dynamic work domains.

pilot-monitoring↗

Machine learning at the edge to improve in-field safeguards inspections

Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term inspecta, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. Here this work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Active control of bearing preload using piezoelectric translators

In many spacecraft applications, mechanisms are required to perform precision pointing operations or to sometimes dither about or track a moving object. These mechanisms perform in a predictable and repeatable manner in benign temperature environments. Severe thermal gradients experienced in actual space applications however, cause assemblies to expand and contract around their bearings. This results in unpredictable changes in bearing preload, and hence bearing friction. This becomes a limitation for servos controlling pointing accuracy. Likewise, uncontrollable vibrations may couple into fixed preload (hence, fixed stiffness) mechanisms and limit pointing accuracy. Consequently, a complex problem faced today is how to design mechanisms that remain insensitive to changing thermal and vibrational spacecraft environments. Research presented involves the simplified modeling and test results of an actuator module that used piezoelectrically preload controlled bearings. The feasibility of actively controlling bearing preload was demonstrated. Because bearing friction is related to preload, a thermally active system designed with aluminum components and a 440 C bearing, was friction tested at temperatures ranging from 0 to 70 C (32 to 158 F). Effectiveness of the translators were demonstrated by mapping a controllable friction range throughout tested temperatures. It was learned that constant preload for this system could be maintained over an approximate 44 C (79 F) temperature span. From testing, it was also discovered that at the more deviate temperatures, expansions were so large that radial clearances were taken up and the duplex bearing became radially preloaded. Thus, active control of bearing preload is feasible but may be limited by inherent geometry constraints and materials used in the system.

Nye, Ted W.↗

Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States

The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. The analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems, which can inform planning activities, especially as the industry continues to expand into new geographic and climatic regions around the world.

14 SOLAR ENERGY↗

Pharmacological Inhibitors of the Proteosome in Atrophying Muscles

It is now clear that the marked loss of muscle mass that occurs with disuse, denervation or in many systemic diseases (cancer cachexia, sepsis, acidosis, various endocrine disorders) is due primarily to accelerated degradation of muscle proteins, especially myofibrillar components. Recent work primarily in Dr. Goldberg's laboratory had suggested that in these diverse conditions, the enhancement of muscle proteolysis results mainly from activation of the Ub-proteasome degradative pathway. In various experimental models of atrophy, rat muscles show a common series of changes indicative of activation of this pathway, including increases in MRNA for Ub and proteasome subunits, content of ubiquitinated proteins, and sensitivity to inhibitors of the proteasome. In order to understand the muscle atrophy seen in weightlessness, Dr. Goldberg's laboratory is collaborating with Dr. Baldwin in studies to define the changes in these parameters upon hind-limb suspension. Related experiments will explore the effects on this degradative system of exercise regimens and also of glucocorticoids, which are known to rise in space personnel and to promote muscle, especially in inactive muscles. The main goals will be: (A) to define the enzymatic changes leading to enhanced activity of the Ub-proteasome pathway in inactive muscles upon hind-limb suspension, and the effects on this system of exposure to glucocorticoids or exercise; and (B) to learn whether inhibitors of the Ub-proteasome pathway may be useful in retarding the excessive proteolysis in atrophying muscles. Using muscle extracts, Dr. Goldberg's group hopes to define the rate-limiting, enzymatic changes that lead to the accelerated Ub-conjugation and protein degradation. They have recently developed cell-free preparations from atrophying rat muscles, in which Ub-conjugation to muscle proteins is increased above control levels. Because these new preparations seem to reproduce the changes occurring in vivo, they will analyze in depth extracts from normal and atrophying muscles to compare the activities of the Ub-activating enzyme (El), the various LTh-carrier proteins (E2s), and Ub-protein ligases (E3s). Recent studies of other types of muscle wasting -suggest a very important role in muscle proteolysis of certain ubiquitination enzymes, E214k and E3-alpha(i.e. components of the "N-end pathway"). Future studies will focus in understanding their role and test whether they are in fact critical for muscle atrophy in vivo. Since weightlessness leads to a specific loss of contractile proteins and to a switching of myosin isotypes, Dr. Goldberg's group will attempt to identify the ubiquitination enzymes specifically involved in myosin degradation both in normal muscle and after hind-limb suspension.

Goldberg, Alfred↗

Improving and Assessing the Quality of Uncertainty Quantification in Deep Learning

Deep learning (DL) models have enjoyed increased attention in recent years because of their powerful predictive capabilities. While many successes have been achieved, standard deep learning methods suffer from a lack of uncertainty quantification (UQ). While the development of methods for producing UQ from DL models is an active area of current research, little attention has been given to the quality of the UQ produced by such methods. In order to deploy DL models to high-consequence applications, high-quality UQ is necessary. This report details the research and development conducted as part of a Laboratory Directed Research and Development (LDRD) project at Sandia National Laboratories. The focus of this project is to develop a framework of methods and metrics for the principled assessment of UQ quality in DL models. This report presents an overview of UQ quality assessment in traditional statistical modeling and describes why this approach is difficult to apply in DL contexts. An assessment on relatively simple simulated data is presented to demonstrate that UQ quality can differ greatly between DL models trained on the same data. A method for simulating image data that can then be used for UQ quality assessment is described. A general method for simulating realistic data for the purpose of assessing a model’s UQ quality is also presented. A Bayesian uncertainty framework for understanding uncertainty and existing metrics is described. Research that came out of collaborations with two university partners are discussed along with a software toolkit that is currently being developed to implement the UQ quality assessment framework as well as serve as a general guide to incorporating UQ into DL applications.

97 MATHEMATICS AND COMPUTING↗

The crisis in human capital

This overview discusses the current shortfalls in the U.S. national education system with attention to their effects on the aerospace industry and potential remedies. Student-achievement and literacy rates are examined to compare U.S. students to those of other countries, and the sociological and cultural phenomena are listed which can contribute to deficiencies in learning. Intervention programs for young children and for students in math and science are discussed, and corporate mechanisms are described which support scholarly activities. Also examined are teaching resources provided by government agencies that deal with science and technology. The general conclusion of the paper is that the efforts to date are insufficient because they do not address education levels across the country. It is suggested that corporations and communities rather than federal and state governments address the educational shortfalls.

Kersey, E. D. K.↗

MCTP Summer Research Internship Program. Research Presentation Day: Experience Mathematics and Science in the Real World

This paper presents the summaries of the MCTP Summer Research Internship Program. Technological areas discussed include: Mathematical curriculum development for real world problems; Rain effects on air-water gas exchange; multi-ring impact basins on mars; developing an interactive multimedia educational cd-rom on remote sensing; a pilot of an activity for for the globe program; fossils in maryland; developing children's programming for the american horticultural society at river farm; children's learning, educational programs of the national park service; a study of climate and student satisfaction in two summer programs for disadvantaged students interested in careers in mathematics and science; the maryland governor's academy, integrating technology into the classroom; stream sampling with the maryland biological stream survey (MBSS); the imaging system inspection software technology, the preparation and detection of nominal and faulted steel ingots; event-based science, the development of real-world science units; correlation between anxiety and past experiences; environmental education through summer nature camp; enhancing learning opportunities at the Salisbury zoo; plant growth experiment, a module for the middle school classroom; the effects of proxisome proliferators in Japanese medaka embryos; development of a chapter on birth control and contraceptive methodologies as part of an interactive computer-based education module on hiv and aids; excretion of gentamicin in toadfish and goldfish; the renaissance summer program; and Are field trips important to the regional math science center?

Source record↗

Superluminal Jets and Other Properties of Black Holes Binaries

Discoveries in the past few years of radio jets in Galactic black hole candidates have provided a link between active galactic nuclei (AGNS) and the compact stars in binary systems. The availability of binary systems relatively close by is an opportunity to learn about the jet production mechanism on a timescale a million times shorter than that of an AGN. Evidence is clearly seen of correlated high energy X-ray and gamma ray emission to radio emission from jets, linking the accretion and jet production mechanisms. objects such as GRS 1915+105, GRO J1655-40 and Cyg X-3 show striking properties which distinguish them from other black hole candidates. Our theoretical understanding of these systems is still in the formative stages. I review some of the most recent multiwavelength data and point out questions raised by these observations.

Harmon, Alan↗

Improving Organizational Productivity in NASA

Recognizing that NASA has traditionally been in the forefront of technological change, the NASA Administrator challenged the Agency in 1982 to also become a leader in developing and applying advanced technology and management practices to increase productivity. One of the activities undertaken by the Agency to support this ambitious productivity goal was participation in a 2-year experimental action research project devoted to learning more about improving and assessing the performance of professional organizations. Participating with a dozen private sector organizations, NASA explored the usefulness of a productivity improvement process that addressed all aspects of organizational performance. This experience has given NASA valuable insight into the enhancement of professional productivity. More importantly, it has provided the Agency with a specific management approach that managers and supervisors can effectively use to emphasize and implement continuous improvement. This report documents the experiences of the five different NASA installations participating in the project, describes the improvement process that was applied and refined, and offers recommendations for expanded application of that process. Of particular interest is the conclusion that measuring white collar productivity may be possible, and at a minimum, the measurement process itself is beneficial to management. Volume I of the report provides a project overview, significant findings, and recommendations. Volume II presents individual case studies of the NASA pilot projects that were part of the action research effort.

Source record↗

Reference Material Kydex(registered trademark)-100 Test Data Message for Flammability Testing

The Marshall Space Flight Center (MSFC) Materials and Processes Technical Information System (MAPTIS) database contains, as an engineering resource, a large amount of material test data carefully obtained and recorded over a number of years. Flammability test data obtained using Test 1 of NASA-STD-6001 is a significant component of this database. NASA-STD-6001 recommends that Kydex 100 be used as a reference material for testing certification and for comparison between test facilities in the round-robin certification testing that occurs every 2 years. As a result of these regular activities, a large volume of test data is recorded within the MAPTIS database. The activity described in this technical report was undertaken to mine the database, recover flammability (Test 1) Kydex 100 data, and review the lessons learned from analysis of these data.

Engel, Carl D.↗

Aerosol Profile Measurements from the NASA Langley Research Center Airborne High Spectral Resolution Lidar

Since achieving first light in December of 2005, the NASA Langley Research Center (LaRC) Airborne High Spectral Resolution Lidar (HSRL) has been involved in seven field campaigns, accumulating over 450 hours of science data across more than 120 flights. Data from the instrument have been used in a variety of studies including validation and comparison with the Cloud- Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite mission, aerosol property retrievals combining passive and active instrument measurements, aerosol type identification, aerosol-cloud interactions, and cloud top and planetary boundary layer (PBL) height determinations. Measurements and lessons learned from the HSRL are leading towards next-generation HSRL instrument designs that will enable even further studies of aerosol intensive and extensive parameters and the effects of aerosols on the climate system. This paper will highlight several of the areas in which the NASA Airborne HSRL is making contributions to climate science.

Obland, Michael D.↗

Flight Test Evaluation of an Unmanned Aircraft System Traffic Management (UTM) Concept for Multiple Beyond-Visual-Line-of-Sight (BVLOS) Operations

Many applications of small Unmanned Aircraft System (UAS) have been envisioned. These include surveillance of key assets such as pipelines, rail, or electric wires, deliveries, search and rescue, traffic monitoring, videography, and precision agriculture. These operations are likely to occur in the same airspace in the presence of many static and dynamic constraints such as airports, and high wind areas. Therefore, operations of small UAS need to be managed to ensure safety and operation efficiency is maintained. NASA has advanced a concept for UAS Traffic Management (UTM) and has initiated a research effort to refine that concept and develop operational and system requirements. A UTM research platform is in development and flight test activities to evaluate core functions and key assumptions focusing exclusively on UAS operations in different environments are underway. This seminar will present lessons learned from a recent flight test focused on enabling operations of multiple UAS in lower-risk environments within and beyond visual line of sight (BVLOS).

UTM↗