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At least 307 records · Page 17

Intelligent Automation Approach for Improving Pilot Situational Awareness

Automation in the aviation domain has been increasing for the past two decades. Pilot reaction to automation varies from highly favorable to highly critical depending on both the pilot's background and how effectively the automation is implemented. We describe a user-centered approach for automation that considers the pilot's tasks and his needs related to accomplishing those tasks. Further, we augment rather than replace how the pilot currently fulfills his goals, relying on redundant displays that offer the pilot an opportunity to build trust in the automation. Our prototype system automates the interpretation of hydraulic system faults of the UH-60 helicopter. We describe the problem with the current system and our methodology for resolving it.

Spirkovska, Lilly↗

Controller Verification of a Smart-Grid Compatible 200 kHz Single-stage Photovoltaic Microinverter

This paper presents control system design, implementation, and experimental validation of a single-stage 400 W, 200 kHz solar photovoltaic (PV) microinverter using hardware-in-the-loop (HIL) and hardware testing. The selected circuit topology is based on a Gallium Nitride (GaN) direct-matrix based dual active bridge (DAB) converter with a low voltage active power decoupler (APD) circuit. Control performance is verified, smart-grid compatibility is tested, and circuit operation is confirmed. Controller HIL (CHIL) is shown to aid in a complex power electronics system design by 1) enabling detailed control development prior to hardware implementation, 2) expanding the use of automated testing, and 3) increasing confidence in control performance prior to prototype testing. Altogether, these factors make HIL a valuable tool in complex power electronic designs.

14 SOLAR ENERGY↗

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗

The Transactive Energy Network Template Metamodel

While transactive energy, which is defined as an allocation of electricity based on dynamically discovered values or prices, has been extensively studied, its uptake and use has been slow. This report describes a tool, the transactive network template, which should hasten the creation and uptake of transactive energy networks. Some basic principles of transactive energy are familiar from existing wholesale electricity markets. Locational prices are calculated today for zones within bulk electric transmission systems. Locational prices differ while accounting for the locational costs of electricity generation and the losses and constraints incurred when electricity is transmitted from generators and distributed to consumers. A transactive energy network might include these transmission zones. However, current research strives to apply transactive energy also in electricity distribution circuits, buildings, and even for individual generating and consuming devices. At the same time, researchers explore how to apply transactive energy in real time during increasingly shorter time intervals. Automated computational agents become necessary as transactive energy becomes applied to smaller circuit zones and at faster dynamic timescales. A transactive energy network is an example of a multi-agent system. Each zone in the network is represented by its transactive agent, which makes decisions for and acts on behalf of a business entity that is responsible for and manages one of the circuit regions. A transactive energy network is also an example of a decentralized, distributed control system. Control decisions and responsibilities are distributed among the network’s transactive agents. The transactive agents are independent; that is, there typically is no centralized authority or oversight function. Instead, transactive agents exchange transactive signals and thereby negotiate the prices and quantities of electricity that they will exchange. Initially, the circuit regions and responsibilities of transactive agents appear to be very dissimilar. Each circuit region may comprise transmission, distribution, or building-level circuits. Each has a unique position and electrical connectivity within the transactive energy network. Each possesses unique assets that either generate or consume electricity, and these (e.g., renewable energy generator, diesel generator, aggregate utility load, building load, space conditioning, refrigerator, etc.) may further differ in their price flexibility and in their strategies for responding to dynamic electricity prices. Given such diversity, an implementer’s first inclination might be to start from scratch to define all these devices and to engineer their seemingly unique interactions. Given that each implementer’s perspective may be narrow within a transactive energy network, it is unlikely that uniquely engineered systems would interact well. This is where the transactive network template is applicable. The transactive network template is a metamodel that has been developed to guide implementers as they configure their own transactive agent within a network of such agents. The object-oriented design of the transactive network template provides basic code object types that may be used and extended by implementers to represent each of the assets in their circuit region. These objects further facilitate the transactive agent’s necessary computations, which are divided among responsibilities to schedule power usage, balance electric supply and demand, and coordinate the exchange of electricity with the other transactive agents. This report addresses the conceptual transactive network template design. Implementers are directed to more formal design documents and reference implementations. A Python™-based1 reference implementation of the transactive network template has been coded, and three implementations have been configured to represent a national laboratory and two university campuses. Version 2 of the transactive node template generalizes the market class and its methods to facilitate multiple, and more diverse market coordination mechanisms than were facilitated by and demonstrated using Version 1. Version 3 includes new Appendix B, which addresses the designs of methods that would make dynamic prices track approved electricity rates. In the future, the author wishes to make the transactive network template more generally applicable to networks that require more accurate power flow. Development of the transactive network template is jointly funded by the U.S. Department of Energy (DOE) Energy Efficiency and Renewable Energy and the DOE Office of Electricity. In late 2015, one of the first projects to be funded by the DOE Grid Laboratory Modernization Laboratory Consortium was the Clean Energy and Transactive Campus project, led by Pacific Northwest National Laboratory. DOE funds were matched by an investment by the Washington Department of Commerce through its Clean Energy Fund. The transactive network template was developed to guide the implementation of transactive energy networks within this project’s scope.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.

60 APPLIED LIFE SCIENCES↗

The optimal control of merging aircraft-derivation of the hybrid air traffic controller.

The Federal Aviation Administration has initiated a program to automate its existing manual air traffic handling procedures. One procedure, whose automation is expected to increase controller efficiency by 2.6 to 2.9 times, is known as aircraft merging. This procedure is concerned with the merging of N aircraft, which are distributed among K feeder routes, into one or more final routes. The question of obtaining an exactly realizable and easily implementable automatic controller for merging is discussed. The optimal control of a linear system with state and control constraints is considered.

Schatz, J. G.↗

The role of manned extravehicular activity in reducing the cost of space payloads

Substantial cost savings and performance improvement will result by the use of Extravehicular Activity (EVA) to supplement or replace automation. Taking an all-pallet version of Langley Research Center's Advanced Technology Laboratory payload as an example, $54.5 million should be saved by EVA over automation, considering deployment and stowing only. Additional savings should accrue when reduced-reliability equipment (where permitted) is substituted for high reliability equipment and EVA is used for repairs. More comprehensively, launch and operation costs could also be reduced by elimination of the need to return to the ground for repairs; and production spending might be reduced when an entire vehicle was saved by manned EVA repair not feasible via automation. Potential disadvantages include increased cost due to development and manufacture of EVA equipment, payload provisions to enable EVA interfaces, training, orbiter modification, and prevention of EVA-caused contamination. Possible applications to the Space Shuttle missions are discussed.

Alton, L. R.↗

New Directions: 1982-2000

The major objective of the Deep Space Network in the period 1983-2000 is the fulfillment of the extremely diverse telecommunications requirements of the known and anticipated users. Deep space exploration projects will continue to occupy a dominant role, although in the mid-1980s, with the completion of the Networks Consolidation Program, high Earth orbiter projects will become substantial users of the Network. Also playing an increasingly important role in the Network of the next decade will be non-flight projects, such as Geodynamics, Radio Astronomy, Radar Astronomy, and the Search for Extraterrestrial Intelligence (SETI). The major challenge in meeting the primary Network objective of the next decade will be that of providing increased performance as required by users at costs which can be borne by NASA in an environment of limited resources. Emphasis will be on increased commonality, flexibility, and automation to reduce maintenance and operations costs, and lower mission costs.

Renzetti, N. A.↗

Operational requirements - system concepts

Nine research areas that are most critical to the issue of cockpits for the single pilot are discussed. Helicopter are addressed in this report. They are as follows: (1) automation priority issues; (2) increased complexity of systems; (3) cockpit workload highest in navigation; (4) auto hover and flight trim controls; (5) voice technology in integrated form; (6) systems must have visual and auditory declutter modes; (7) cockpit should be designed to be NBC resistant; and (8) considerations for spillover to civilian public service.

Voorhees, J. W.↗

Analysis of remote operating systems for space-based servicing operations. Volume 2: Study results

The developments in automation and robotics have increased the importance of applications for space based servicing using remotely operated systems. A study on three basic remote operating systems (teleoperation, telepresence and robotics) was performed in two phases. In phase one, requirements development, which consisted of one three-month task, a group of ten missions were selected. These included the servicing of user equipment on the station and the servicing of the station itself. In phase two, concepts development, which consisted of three tasks, overall system concepts were developed for the selected missions. These concepts, which include worksite servicing equipment, a carrier system, and payload handling equipment, were evaluated relative to the configurations of the overall worksite. It is found that the robotic/teleoperator concepts are appropriate for relatively simple structured tasks, while the telepresence/teleoperator concepts are applicable for missions that are complex, unstructured tasks.

Source record↗

Towards automatic Markov reliability modeling of computer architectures

The analysis and evaluation of reliability measures using time-varying Markov models is required for Processor-Memory-Switch (PMS) structures that have competing processes such as standby redundancy and repair, or renewal processes such as transient or intermittent faults. The task of generating these models is tedious and prone to human error due to the large number of states and transitions involved in any reasonable system. Therefore model formulation is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model formulation. This paper presents an overview of the Automated Reliability Modeling (ARM) program, under development at NASA Langley Research Center. ARM will accept as input a description of the PMS interconnection graph, the behavior of the PMS components, the fault-tolerant strategies, and the operational requirements. The output of ARM will be the reliability of availability Markov model formulated for direct use by evaluation programs. The advantages of such an approach are (a) utility to a large class of users, not necessarily expert in reliability analysis, and (b) a lower probability of human error in the computation.

Liceaga, C. A.↗

Space Station Freedom resource allocation accommodation of technology payload requirements

An overview of the Office of Aeronautics, Exploration, and Technology (OAET) Space Station Freedom Technology Payload Development Program is provided, and the OAET Station resource requirements are reviewed. The requirements are contrasted with current proposed resource allocations. A discussion of the issues and conclusions are provided. It is concluded that an overall 20 percent resource allocation is appropriate to support OAET's technology development program, that some resources are inadequate even at the 20 percent level, and that bartering resources among U.S. users and international partners and increasing the level of automation may be viable solutions to the resource constraint problem.

Avery, Don E.↗

Intelligent Processing Equipment Research and Development Programs of the Department of Commerce

The intelligence processing equipment (IPE) research and development (R&D) programs of the Department of Commerce are carried out within the National Institute of Standards and Technology (NIST). This institute has had work in support of industrial productivity as part of its mission since its founding in 1901. With the advent of factory automation these efforts have increasingly turned to R&D in IPE. The Manufacturing Engineering Laboratory (MEL) of NIST devotes a major fraction of its efforts to this end while other elements within the organization, notably the Material Science and Engineering Laboratory, have smaller but significant programs. An inventory of all such programs at NIST and a representative selection of projects that at least demonstrate the scope of the efforts are presented.

Simpson, J. A.↗

Space Station Freedom restructure impacts on technology experiment accommodation

This is a follow up to NASA Technical Memorandum (TM) 102766; it provides an overview of the Office of Aeronautics and Space Technology (OAST) Space Station Freedom (SSF) Technology Development Payload Program, reviews the OAST SSF resource requirements, and contrasts the requirements with the resources that are available to OAST since the restructure of SSF. A discussion of the issues as well as conclusions and recommendations, is provided. It is concluded that, even after adjustments to the OAST traffic model to reflect restructure, some resources will be inadequate even at the 20 percent allocation level. It is also concluded that bartering resources among U.S. users and international partners, and increasing the level of automation may be viable solutions to the resource constraint problem. The final conclusion is that, to facilitate the performance of technology experiments on SSF, OAST should fund SSF experiments and update its traffic model as soon as possible, and should provide technical and programmatic assistance to technology experiment developers.

Avery, Don E.↗

Automatic specification of reliability models for fault-tolerant computers

The calculation of reliability measures using Markov models is required for life-critical processor-memory-switch structures that have standby redundancy or that are subject to transient or intermittent faults or repair. The task of specifying these models is tedious and prone to human error because of the large number of states and transitions required in any reasonable system. Therefore, model specification is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model specification. Automation requires a general system description language (SDL). For practicality, this SDL should also provide a high level of abstraction and be easy to learn and use. The first attempt to define and implement an SDL with those characteristics is presented. A program named Automated Reliability Modeling (ARM) was constructed as a research vehicle. The ARM program uses a graphical interface as its SDL, and it outputs a Markov reliability model specification formulated for direct use by programs that generate and evaluate the model.

Liceaga, Carlos A.↗

SPIKE: Application for ASTRO-D mission planning

SPIKE is a mission planning software system developed by a team of programmers at the STScI for use with the Hubble Space Telescope (HST). SPIKE has been developed for the purpose of automating observatory scheduling to increase the effective utilization and ultimately, scientific return from orbiting telescopes. High-level scheduling strategies using both rule-based and neural network approaches have been incorporated. Graphical displays of activities, constraints, and schedules are an important feature of the system. Although SPIKE was originally developed for the HST, it can be used for other astronomy missions including ground-based observatories. One of the missions that has decided to use SPIKE is ASTRO-D, a Japanese X-ray satellite for which the U.S. is providing a part of the scientific payload. Scheduled to fly in Feb. 1993, its four telescopes will focus X-rays over a wide energy range onto CCD's and imaging gas proportional counters. ASTRO-D will be the first X-ray imaging mission operating over the 0.5-12 keV band with high energy resolution. This combination of capabilities will enable a varied and exciting program of astronomical research to be carried out. ASTRO-D is expected to observe 5 to 20 objects per day and a total of several thousands per year. This requires the implementation of an efficient planning and scheduling system which SPIKE can provide. Although the version of SPIKE that will be used for ASTRO-D mission is almost identical to that used for the HST, there are a few differences. For example, ASTRO-D will use two ground stations for data downlinks, instead of the TDRSS system for data transmission. As a consequence ASTRO-D is constrained by limited on-board data storage capacity to schedule high data-rate observations during periods of frequent high bit rate observations accordingly. We will demonstrate the ASTRO-D version of SPIKE to show what SPIKE can provide and how efficiently it creates an observational schedule.

Isobe, T.↗

Recent Productivity Improvements to the National Transonic Facility

Productivity gains have recently been made at the National Transonic Facility wind tunnel at NASA Langley Research Center. A team was assigned to assess and set productivity goals to achieve the desired operating cost and output of the facility. Simulations have been developed to show the sensitivity of selected process productivity improvements in critical areas to reduce overall test cycle times. The improvements consist of an expanded liquid nitrogen storage system, a new fan drive, a new tunnel vent stack heater, replacement of programmable logic controllers, an increased data communications speed, automated test sequencing, and a faster model changeout system. Where possible, quantifiable results of these improvements are presented. Results show that in most cases, improvements meet the productivity gains predicted by the simulations.

Popernack, Thomas G., Jr.↗

Langley Research Center's Unitary Plan Wind Tunnel: Testing Capabilities and Recent Modernization Activities

Description, capabilities, initiatives, and utilization of the NASA Langley Research Center's Unitary Plan Wind Tunnel are presented. A brief overview of the facility's operational capabilities and testing techniques is provided. A recent Construction of Facilities (Car) project to improve facility productivity and efficiency through facility automation has been completed and is discussed. Several new and maturing thrusts are underway that include systematic efforts to provide credible assessment for data quality, modifications to the new automation control system for increased compatibility with the Modern Design of Experiments (MDOE) testing methodology, and process improvements for better test coordination, planning, and execution.

Micol, John R.↗