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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Electronic collaboration: Some effects of telecommunication media and machine intelligence on team performance

Both NASA and DoD have had a long standing interest in teamwork, distributed decision making, and automation. While research on these topics has been pursued independently, it is becoming increasingly clear that the integration of social, cognitive, and human factors engineering principles will be necessary to meet the challenges of highly sophisticated scientific and military programs of the future. Images of human/intelligent-machine electronic collaboration were drawn from NASA and Air Force reports as well as from other sources. Here, areas of common concern are highlighted. A description of the author's research program testing a 'psychological distancing' model of electronic media effects and human/expert system collaboration is given.

Wellens, A. Rodney↗
NASA NTRS1993

Presenting the Rain-Sea Interaction Facility

The new Rain-Sea Interaction Facility (RSIF) was established at GSFC/WFF and the first finds are presented. The unique feature of this laboratory is the ability to systematically study microwave scattering from a water surface roughened by artificial rain, for which the droplets are at terminal velocity. The fundamental instruments and systems (e.g., the rain simulator, scatterometers, and surface elevation probes) were installed and evaluated during these first experiments - so the majority of the data were obtained with the rain simulator at 1 m above the water tank. From these initial experiments, three new models were proposed: the square-root function for NCS vs. R, the log Gaussian model for ring-wave elevation frequency spectrum, and the Erland probability density distribution for back scattered power. Rain rate is the main input for these models, although the coefficients may be dependent upon other factors (drop-size distribution, fall velocity, radar configuration, etc.). The facility is functional and we foresee collaborative studies with investigators who are engaged in measuring and modeling rain-sea interaction processes.

Bliven, Larry F.↗
NASA NTRS1994

Analysis of plasma measurements for the Geotail mission

The CPI plasma measurements from the Geotail spacecraft are currently used by a number of scientists in support of varied research projects. Measurements from the CPI hot plasma analyzer have been processed to compute one-minute averages of plasma densities, temperatures, and velocities for the period 1 Oct. 1992 - 31 July 1993. These parameters are used in a preliminary survey of the magnetotail for distances from earth of 10 to 210 earth radii. The cold plasmas within the magnetotail drift towards the midplane and are thought to be a principal source for the hot plasma sheet. A remarkable result from Geotail is the observation of cold ion beams coexisting as distinct components in the presence of hot plasma-sheet plasmas. The development and evolution of plasmoids is a topic of considerable interest in studies of the magnetotail and magnetospheric substorms. A number of possible plasmoids have been identified in the Geotail data set based upon reversals of the Z component of the magnetic field as observed with the MGF instrumentation. An intensive study of the electron and ion velocity distributions and the plasma parameters for several of these events reveals unexpected features. Collaborative work with several Geotail instrument teams and other researchers is also ongoing.

Frank, Louis A.↗
NASA NTRS2010

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Smelyanskiy, Vadim N.↗
NASA NTRS2003

Organizing Diverse, Distributed Project Information

SemanticOrganizer is a software application designed to organize and integrate information generated within a distributed organization or as part of a project that involves multiple, geographically dispersed collaborators. SemanticOrganizer incorporates the capabilities of database storage, document sharing, hypermedia navigation, and semantic-interlinking into a system that can be customized to satisfy the specific information-management needs of different user communities. The program provides a centralized repository of information that is both secure and accessible to project collaborators via the World Wide Web. SemanticOrganizer's repository can be used to collect diverse information (including forms, documents, notes, data, spreadsheets, images, and sounds) from computers at collaborators work sites. The program organizes the information using a unique network-structured conceptual framework, wherein each node represents a data record that contains not only the original information but also metadata (in effect, standardized data that characterize the information). Links among nodes express semantic relationships among the data records. The program features a Web interface through which users enter, interlink, and/or search for information in the repository. By use of this repository, the collaborators have immediate access to the most recent project information, as well as to archived information. A key advantage to SemanticOrganizer is its ability to interlink information together in a natural fashion using customized terminology and concepts that are familiar to a user community.

Keller, Richard M.↗
NASA NTRS2003

NASA Tech Briefs, June 2003

Topics covered include: Nulling Infrared Radiometer for Measuring Temperature; The Ames Power Monitoring System; Hot Films on Ceramic Substrates for Measuring Skin Friction; Probe Without Moving Parts Measures Flow Angle; Detecting Conductive Liquid Leaking from Nonconductive Pipe; Adaptive Suppression of Noise in Voice Communications; High-Performance Solid-State W-Band Power Amplifiers; Microbatteries for Combinatorial Studies of Conventional Lithium-Ion Batteries; Correcting for Beam Aberrations in a Beam-Waveguide Antenna; Advanced Rainbow Solar Photovoltaic Arrays; Metal Side Reflectors for Trapping Light in QWIPs; Software for Collaborative Engineering of Launch Rockets; Software Assists in Extensive Environmental Auditing; Software Supports Distributed Operations via the Internet; Software Estimates Costs of Testing Rocket Engines; yourSky: Custom Sky-Image Mosaics via the Internet; Software for Managing Inventory of Flight Hardware; Lower-Conductivity Thermal-Barrier Coatings; Process for Smoothing an Si Substrate after Etching of SiO2; Flexible Composite-Material Pressure Vessel; Treatment to Destroy Chlorohydrocarbon Liquids in the Ground; Noncircular Cross Sections Could Enhance Mixing in Sprays; Small, Untethered, Mobile Roots for Inspecting Gas Pipes; Paint-Overspray Catcher; Preparation of Regular Specimens for Atom Probes; Inverse Tomo-Lithography for Making Microscopic 3D Parts; Predicting and Preventing Incipient Flameout in Combustors; MEMS-Based Piezoelectric/Electrostatic Inchworm Actuator; Metallized Capillaries as Probes for Raman Spectroscopy; Adaptation of Mesoscale Weather Models to Local Forecasting; Aerodynamic Design using Neural Networks; Combining Multiple Gyroscope Outputs for Increased Accuracy; and Improved Collision-Detection Method for Robotic Manipulator.

Source record↗
NASA NTRS2015

AIRS Mission Support from GES DISC

This talk will describe the support and distribution of AIRS (Atmospheric Infra Red Sounding) data products that are archived and distributed from the Goddard Earth Sciences Data and Information Services Center. Along with data stewardship, an important mission of GES DISC is to enhance the usability of data and broaden the user base. We will provide a brief summary of the current online archive and distribution metrics for the AIRS v5 and v6 products. We will also describe collaborative data sets and services (e.g., visualization and potential science applications) and solicit feedback for potential future services.

version↗
NASA NTRS2022

Yonkers Urban Development II: Leveraging NASA Earth Observations to Support Modeling Urban Cooling Interventions and Urban Heat Vulnerability in Yonkers, New York

The City of Yonkers, New York, located in Westchester County, is experiencing rising temperatures which are a growing threat to the health and safety of its residents. Furthermore, the risk of heat-related illnesses and mortality disproportionately affects neighborhoods in Yonkers historically subjected to race-based housing segregation. To better understand these inequities, Groundwork Hudson Valley and NASA DEVELOP collaborated for a second term to evaluate community-level heat vulnerability, landcover distribution, street-level thermal comfort, and modeled urban cooling interventions. This team applied 2019 5-year American Community Survey (ACS) data and social and biophysical heat vulnerability variables established by the New York State Department of Health (NYSDOH), along with land surface temperature (LST) data collected from Landsat 8 Thermal Infrared Sensor (TIRS), and ISS ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to identify communities in Yonkers in need of prioritized cooling intervention at the census tract level. Data from the Real-Time Mesoscale Analysis (RTMA) provided relevant meteorological data for the ENVI-met model to conduct street-level thermal observations and model tree canopy cooling interventions in the Yonkers neighborhoods of Kimball and Old 7th Ward. The project results will support the prioritization and equitable distribution of cooling infrastructure in identified neighborhoods. Additionally, Groundwork Hudson Valley will use the analyses as a heat literacy tool to improve advocacy efforts and inform both residents and officials about how investment in deliberate modification to tree canopy cover improves the city’s thermal environment and helps mitigate extreme heat.

Tamara Barbakova↗
DOE OSTI2024

Scaling Decarbonization Development Innovation with Emerging Community-Based Developers: Preprint

The building real estate development industry is being asked to lead in delivering building decarbonization solutions across the United States. However, creating decarbonized best practices and development innovation while increasing developer diversity and community ownership is often not a primary focus of this sector. To address these issues, we have created an incubator and support ecosystem specifically for the innovation scaling we have found in a leading group of emerging, small-scale developers. These leaders are not just working at the forefront of decarbonized development but addressing diversity and bolstering community generational wealth. We have found that small development firms are the ones who often take risks to innovate and demonstrate despite lacking a specific set of resources or support ecosystems focused on emerging decarbonization developers. Those with the fewest resources are being asked to lead our decarbonization innovation efforts. This paper documents the necessity of a cohort and incubator program to provide a decarbonization-specific ecosystem to support small developers and scale innovation. The incubator creates connections, fosters innovative strategies to access incentives and alternative funding, and assembles resources for small and emerging minority developers with the goal of sustainability and affordability. For instance, collaborative efforts with leading developers and utilities can enable the seamless integration of distributed energy resources through optimal metering and interconnection and significantly reduce the utility cost of electrification, benefiting the utility, the developer, and the tenant economically. The process described in this paper will result in case studies and how-to resources to provide tangible examples of innovation within the emerging development field.

carbon↗
NASA NTRS2011

Application of ESE Data and Tools to Air Quality Management: Services for Helping the Air Quality Community use ESE Data (SHAirED)

The goal of this REASoN applications and technology project is to deliver and use Earth Science Enterprise (ESE) data and tools in support of air quality management. Its scope falls within the domain of air quality management and aims to develop a federated air quality information sharing network that includes data from NASA, EPA, US States and others. Project goals were achieved through a access of satellite and ground observation data, web services information technology, interoperability standards, and air quality community collaboration. In contributing to a network of NASA ESE data in support of particulate air quality management, the project will develop access to distributed data, build Web infrastructure, and create tools for data processing and analysis. The key technologies used in the project include emerging web services for developing self describing and modular data access and processing tools, and service oriented architecture for chaining web services together to assemble customized air quality management applications. The technology and tools required for this project were developed within DataFed.net, a shared infrastructure that supports collaborative atmospheric data sharing and processing web services. Much of the collaboration was facilitated through community interactions through the Federation of Earth Science Information Partners (ESIP) Air Quality Workgroup. The main activities during the project that successfully advanced DataFed, enabled air quality applications and established community-oriented infrastructures were: develop access to distributed data (surface and satellite), build Web infrastructure to support data access, processing and analysis create tools for data processing and analysis foster air quality community collaboration and interoperability.

Falke, Stefan↗
NASA NTRS2020

Strategic Deconfliction Performance: Results and Analysis from the NASA UTM Technical Capability Level 4 Demonstration

Unmanned Aircraft System (UAS) Traffic Management (UTM) refers to the service-based, cooperative approach to the management of small UAS in the National Airspace System that is safe, scalable, and fair. UTM provides the means to manage the airspace in a complementary manner that does not burden the current air traffic control workforce or infrastructure but allows the Air Navigation Service Provider to maintain its regulatory and operational authority of the airspace. A key feature of UTM is the ability to provide operators the means to strategically deconflict operations from others in the airspace through the digital exchange of information via supporting services. Through this approach, the four-dimensional operation volumes that encompass the intent of operators in a given area are discoverable and can be used for airspace awareness as well as planning conflict free operations that account for and avoid other operations. In certain cases, it is also possible to negotiate volume intersections for shared airspace use without the need to re-plan. In the NASA UTM concept, strategic deconfliction is the first layer of three in the overall conflict management model. The three layers of the conflict management model, which follow the International Civil Aviation Organization’s scheme [ICAO 2005] are: strategic conflict management, separate provision, and collision avoidance. In UTM, the strategic layer mostly occurs prior to departure, but is applicable to en route operations with sufficient planning horizon. The initial requirements for a strategic deconfliction capability within UTM are defined in a NASA publication [Rios 2018]. Within the concept and implementation of service-provided strategic deconfliction is the notion of priority. It is understood that there are instances in which an operation requires a priority designation within the UTM system and special handling accordingly to provide situation awareness and facilitate appropriate responses from other airspace users. Examples of situations requiring priority designation include: when an operator declares an emergency due to problems with the vehicle or its immediate surroundings; operations that are in support of certain organizations (e.g., public safety and first responders); or special missions that also require priority use of airspace (e.g., emergency medical deliveries). UAS Volume Reservations (UVRs) also relate to the topic of priority in the sense that the airspace that the volume encompasses has a different status or classification in which unassociated operations must vacate if inside, or avoid if outside, through strategic deconfliction with the volume. Operations that are specially permitted to access the UVR area are typically assigned priority status given the nature of their mission and their associated credentials. The ability to perform strategic deconfliction, handle certain operations with a priority distinction, and establish UVRs that are communicated throughout the UTM system, is predicated on an architecture that has been established through an evolutionary process in response to close collaboration with stakeholders from government and industry. Another important and influential aspect of these capabilities and architecture is the live, distributed flight tests that have been conducted across the Technical Capability Levels (TCLs) that culminated with a set of complex tests performed as part of TCL4 [Rios 2020]. The TCL4 flight test involved two FAA-designated UAS test sites building teams to collaborate with NASA’s UTM Project on the execution of several detailed, small UAS scenarios in urban environments.

conflict management↗
DOE OSTI2025

Bayesian Inferring Nucleon Gravitational Form Factors via Near-Threshold 𝐽/𝜓 Photoproduction

With Bayesian inference, we investigate the impact of recent near-threshold 𝐽/𝜓 production measurements by the 𝐽/𝜓⁢007 experiment and GlueX Collaboration on the extraction of proton gravitational form factors. We apply the generalized parton distribution framework at the next to leading order and demonstrate a stable expansion for the near-threshold kinematics. We find that the experimental constraints are in good agreement with the state-of-the-art lattice simulations, where negative 𝐶 𝑞 ⁡(𝑡) and 𝐶 𝑔 ⁡(𝑡) are strongly preferred. This highlights a great potential to extract them from future high-precision experiments.

QCD phenomenology↗
DOE OSTI2025

CAFE AU LAIT: Compute-Aware Federated Augmented Low-Rank AI Training

Federated finetuning is crucial for unlocking the knowledge embedded in pretrained Large Language Models (LLMs) when data are geographically distributed across clients. Unlike finetuning with data from a single institution, federated finetuning allows collaboration across multiple institutions, enabling the utilization of diverse and decentralized datasets while preserving data privacy. Given the high computing costs of LLM training and the emphasis on energy efficiency in Federated Learning (FL), Low-Rank Adaptation (LoRA) has emerged as a widely adopted algorithm due to its significantly reduced number of trainable parameters. However, this assumes that all data silos have the necessary computing resources to compute local updates of LLMs. Nevertheless, in practice, the computing resources across clients are highly heterogeneous: while some may have access to hundreds of GPUs, others might have limited or no GPU access. Recently, federated finetuning using synthetic data has been proposed, allowing clients to participate in a collaborative training run without training LLMs locally. However, our experimental results reveal a performance gap between models trained using synthetic data and those trained using local updates. Motivated by the observed heterogeneity in computing resources and the performance gap, we propose a novel two-stage algorithm that leverages the storage and computing capabilities of a strong server. In the first stage, under the coordination of the strong server, clients with limited computing resources collaborate to generate synthetic data, which is transferred to and stored on the strong server. In the second stage, the strong server uses this synthetic data on behalf of the resource-constrained clients to perform federated LoRA finetuning alongside clients with sufficient computing resources. This approach ensures that all clients can participate in the finetuning process. Experimental results demonstrate that incorporating local updates from even a small fraction of clients improves performance compared to using synthetic data for all clients. Furthermore, we incorporate the Gaussian mechanism in both stages to guarantee client-level differential privacy.

Wang, Jiayi [ORNL]↗
DOE OSTI2026

WHONDRS Surface Water Geochemistry and Organic Matter Characterization Data from Streams Distributed across Latin America

This dataset supports a broader study examining global transferability of stream biogeochemistry and was generated in collaboration with the MicroSudAqua (µSudAqua) network (https://microsudaqua.netlify.app/en/). The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen, cations) and organic matter characterization (FTICR-MS) from streams in Argentina, Brazil, Chile, and Colombia. Samples were collected across stream orders (1st to 6th order) within five basins. Related data were collected and will be published separately in collaboration with the µSudAqua network. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to this readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos; (2) a folder of surface water sample data, (3) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data; (4) file-level metadata; (5) data dictionary; (6) field metadata; (7) readme; (8) international generic sample number (IGSN) mapping file; and (9) field protocol. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) total dissolved nitrogen data and averages; (3) anions and averages; (4) methods codes; (5) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4.

Anions↗
DOE OSTI2024

Abstract for CRADA between NETL and West Virginia University Research Corporation

The National Energy Technology Lab (NETL) will collaborate with West Virginia University to develop and demonstrate a dynamically operated modular, microwave-enhanced catalytic process for the co-production of ammonia and olefins. Ammonia and olefins (e.g., ethylene) are the most widely produced chemicals in the U.S. by volume, but their processes are also the most carbon and energy intensive as they rely on fossil-based feedstocks and combustion of fossil fuels for heating. The project will leverage the unique ability of microwaves to intensify process heating by providing pulsed, targeted energy to active sites of a mixed catalyst bed to convert a mixture of CH 4 /N 2 into ethylene and ammonia. The collaboration will facilitate the development of microwave technology that has the potential to utilize distributed waste resources like shale gas or biogas as well as renewable electricity to produce these valuable chemicals. It is expected that the results will decarbonize chemical production by using renewable energy and waste feedstocks, improve efficiency by reducing unit operations (H 2 production), and de-risk microwave technology for chemical production by demonstrating long-term and dynamic operability. This project was funded by an award from the Department of Energy’s EERE IEDO.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1997

Virtual Collaborative Simulation Environment for Integrated Product and Process Development

Deneb Robotics is a leader in the development of commercially available, leading edge three- dimensional simulation software tools for virtual prototyping,, simulation-based design, manufacturing process simulation, and factory floor simulation and training applications. Deneb has developed and commercially released a preliminary Virtual Collaborative Engineering (VCE) capability for Integrated Product and Process Development (IPPD). This capability allows distributed, real-time visualization and evaluation of design concepts, manufacturing processes, and total factory and enterprises in one seamless simulation environment.

Gulli, Michael A.↗
NASA NTRS2020

Integrated Demand Management: Concepts and Procedures

This report provides a comprehensive description of the Integrated Demand Management concept. Motivation: NASA’s Integrated Demand Management (IDM) research explores the idea that, under certain conditions, time-based flow management (TBFM) arrival operations can benefit from the coordinated use of a strategic traffic management initiative (TMI) to “precondition” the inbound demand. The research was motivated by the observation that TBFM was usually turned off during convective weather, even in facilities where it was routinely used. Our hypothesis was that strategic adjustments to the inbound traffic so that it provided a better match to the off-nominal changes in capacity observed during these conditions could enable TBFM scheduling to continue to provide effective support for arrival traffic management. Concept: IDM proposes that a TMI (e.g., a Collaborative Trajectory Options Program, or CTOP) be used to adjust the rate and/or geographic distribution across flows of the traffic inbound to a high-demand, TBFM-managed airport before that traffic reaches the TBFM planning horizon. After this strategic preconditioning, TBFM can then tactically fine-tune the demand to deliver a well-managed, orderly feed to the destination airport. Coordinated use of these two flow management capabilities is intended to improve system performance in terms of: • Equity of ground delay assignment, avoiding excessive ground delay for TBFM-scheduled departures, without penalizing longer flights; • Throughput, by distributing traffic to maximize use of available capacity; • Predictability for operators, providing advance notice about the impact on individual flights; • Increased flexibility, supporting operator mitigation strategies such as slot swapping or trajectory options; • Efficiency of flight operations, using ground delay more effectively and reducing airborne delay. The operational description in this document highlights how the IDM concept builds upon already existing tools and procedures, and also indicates where tool enhancements could facilitate conduct of IDM operations. However, enhanced tools are not a requirement for concept introduction. In fact, initial deployment that focused on training procedures and rationale for coordinated use of TFMS and TBFM, without changes to existing tools, might be a simpler way to introduce and to familiarize traffic managers with the idea of preconditioning. The concept and procedures described in this document can hopefully provide useful guidance for introduction of IDM into field operations..

IDM↗