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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Modeling Programs Increase Aircraft Design Safety

Flutter may sound like a benign word when associated with a flag in a breeze, a butterfly, or seaweed in an ocean current. When used in the context of aerodynamics, however, it describes a highly dangerous, potentially deadly condition. Consider the case of the Lockheed L-188 Electra Turboprop, an airliner that first took to the skies in 1957. Two years later, an Electra plummeted to the ground en route from Houston to Dallas. Within another year, a second Electra crashed. In both cases, all crew and passengers died. Lockheed engineers were at a loss as to why the planes wings were tearing off in midair. For an answer, the company turned to NASA s Transonic Dynamics Tunnel (TDT) at Langley Research Center. At the time, the newly renovated wind tunnel offered engineers the capability of testing aeroelastic qualities in aircraft flying at transonic speeds near or just below the speed of sound. (Aeroelasticity is the interaction between aerodynamic forces and the structural dynamics of an aircraft or other structure.) Through round-the-clock testing in the TDT, NASA and industry researchers discovered the cause: flutter. Flutter occurs when aerodynamic forces acting on a wing cause it to vibrate. As the aircraft moves faster, certain conditions can cause that vibration to multiply and feed off itself, building to greater amplitudes until the flutter causes severe damage or even the destruction of the aircraft. Flutter can impact other structures as well. Famous film footage of the Tacoma Narrows Bridge in Washington in 1940 shows the main span of the bridge collapsing after strong winds generated powerful flutter forces. In the Electra s case, faulty engine mounts allowed a type of flutter known as whirl flutter, generated by the spinning propellers, to transfer to the wings, causing them to vibrate violently enough to tear off. Thanks to the NASA testing, Lockheed was able to correct the Electra s design flaws that led to the flutter conditions and return the aircraft to safe flight. Today, all aircraft must have a flutter boundary 15 percent beyond the aircraft s expected maximum speed to ensure that flutter conditions are not encountered in flight. NASA continues to support research in new aircraft designs to improve knowledge of aeroelasticity and flutter. Through platforms such as Dryden Flight Research Center s Active Aeroelastic Wing (AAW) research aircraft, the Agency researches methods for in-flight validation of predictions and for controlling and taking advantage of aeroelastic conditions to enhance aircraft performance.

Source record↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
NASA NTRS1994

Development of an expert system for power quality advisement using CLIPS 6.0

Proliferation of power electronic devices has brought in its wake both deterioration in and demand for quality power supply from the utilities. The power quality problems become apparent when the user's equipment or systems maloperate or fail. Since power quality concerns arise from a wide variety of sources and the problem fixes are better achieved from the expertise of field engineers, development of an expert system for power quality advisement seems to be a very attractive and cost-effective solution for utility applications. An expert system thus developed gives an understanding of the adverse effects of power quality related problems on the system and could help in finding remedial solutions. The paper reports the design of a power quality advisement expert system being developed using CLIPS 6.0. A brief outline of the power quality concerns is first presented. A description of the knowledge base is next given and details of actual implementation include screen output from the program.

Chandrasekaran, A.↗
DOE OSTI2022

INVESTIGATION OF TURBULENT JETS, SPRAYS, AND SUPERCRITICAL MIXING BY TIME RESOLVED RAINBOW SCHLIEREN DEFLECTOMETRY

As technological advancements have enabled higher efficiency compression ignition (CI) engines (diesel engines) by operating at higher pressures and temperatures, the mechanisms of fuel-air mixing are postulated to change. Thus, there is a greater need for experimental data to develop and verify new phenomena. However, the fast time scales and harsh environments associated with CI engines present many experimental difficulties and requires high quality non-intrusive diagnostics to obtain useful data. The diagnostics itself must be validated prior to its implementation in an engine environment. The purpose of this research is to advance rainbow schlieren deflectometry (RSD) to study turbulent fuel sprays including the effects of supercritical mixing in CI engine environments. This objective is met by developing and applying quantitative RSD, for the first time, to three separate aspects of fuel sprays: 1) turbulent mixing, 2) phase boundaries, and 3) non-ideal gas mixing. First, quantitative RSD is demonstrated for turbulent mixing in a canonical helium jet. Rayleigh scattering data in the literature is used to validate the concentration measurements obtained by RSD. The results also provide explanations for phenomena occurring in the helium jet and provide data not yet present in the literature. Second, RSD is applied to high pressure multi-phase fuel sprays and new methodologies are developed to distinguish the liquid region from the vapor region and to quantify the in-between region on a probabilistic basis. Isolating liquid and vapor regions generally requires two separate diagnostics, e.g., Mie scattering (liquid region) and Rayleigh scattering (vapor region). However, the results of this study demonstrate that RSD can be used as a single diagnostic to measure both liquid and vapor boundaries. Third, the optical-to-thermodynamic relations associated with refrac-tometry are investigated under non-ideal gas mixing conditions to develop a generalized relationship valid for both ideal or non-ideal gas mixing. The last part of this research develops the theoretical tools to understand and experimentally realize supercritical mixing at diesel conditions. Guided by the theoretical analysis, RSD is applied to compare supercritical versus transcritical mixing at CI conditions using a constant pressure test rig. It is found that while fully supercritical mixing ers benefits in diesel engines, i.e, faster mixing times (50% higher velocities), the absence of droplets which cause high levels of particulate emissions, etc., it would be difficult to achieve in practice because of the requirements of fuels with low critical temperature and/or substantial fuel preheating.

02 PETROLEUM↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2013

Shuttle Repair Tools Automate Vehicle Maintenance

Successfully building, flying, and maintaining the space shuttles was an immensely complex job that required a high level of detailed, precise engineering. After each shuttle landed, it entered a maintenance, repair, and overhaul (MRO) phase. Each system was thoroughly checked and tested, and worn or damaged parts replaced, before the shuttle was rolled out for its next mission. During the MRO period, workers needed to record exactly what needed replacing and why, as well as follow precise guidelines and procedures in making their repairs. That meant traceability, and with it lots of paperwork. In 2007, the number of reports generated during electrical system repairs was getting out of hand-placing among the top three systems in terms of paperwork volume. Repair specialists at Kennedy Space Center were unhappy spending so much time at a desk and so little time actually working on the shuttle. "Engineers weren't spending their time doing technical work," says Joseph Schuh, an electrical engineer at Kennedy. "Instead, they were busy with repetitive, time-consuming processes that, while important in their own right, provided a low return on time invested." The strain of such inefficiency was bad enough that slow electrical repairs jeopardized rollout on several occasions. Knowing there had to be a way to streamline operations, Kennedy asked Martin Belson, a project manager with 30 years experience as an aerospace contractor, to co-lead a team in developing software that would reduce the effort required to document shuttle repairs. The result was System Maintenance Automated Repair Tasks (SMART) software. SMART is a tool for aggregating and applying information on every aspect of repairs, from procedures and instructions to a vehicle s troubleshooting history. Drawing on that data, SMART largely automates the processes of generating repair instructions and post-repair paperwork. In the case of the space shuttle, this meant that SMART had 30 years worth of operations that it could apply to ongoing maintenance work. According to Schuh, "SMART standardized and streamlined many shuttle repair processes, saving time and money while increasing safety and the quality of repairs." Maintenance technicians and engineers now had a tool that kept them in the field, and because SMART is capable of continually evolving, each time an engineer put it to use, it would enrich the Agency-wide knowledge base. "If an engineer sees something in the work environment that they could improve, a repair process or a procedure, SMART can incorporate that data for use in future operations," says Belson.

Source record↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2007

NASA's Potential Contributions for Using Solar Ultraviolet Radiation in Conjunction with Photocatalysis for Urban Air Pollution Mitigation

More than 75 percent of the U.S. population lives in urban communities where people are exposed to levels of smog or pollution that exceed the EPA (U.S. Environmental Protection Agency) safety standards. Urban air quality presents a unique problem because of a number of complex variables, including traffic congestion, energy production, and energy consumption activities, all of which can contribute to and affect air pollution and air quality in this environment. In environmental engineering, photocatalysis is an area of research whose potential for environmental clean-up is rapidly developing popularity and success. Photocatalysis, a natural chemical process, is the acceleration of a photoreaction in the presence of a catalyst. Photocatalytic agents are activated when exposed to near UV (ultraviolet) light (320-400 nm) and water. In recent years, surfaces coated with photocatalytic materials have been extensively studied because pollutants on these surfaces will degrade when the surfaces are exposed to near UV light. Building materials, such as tiles, cement, glass, and aluminum sidings, can be coated with a thin film of a photocatalyst. These coated materials can then break down organic molecules, like air pollutants and smog precursors, into environmentally friendly compounds. These surfaces also exhibit a high affinity for water when exposed to UV light. Therefore, not only are the pollutants decomposed, but this superhydrophilic nature makes the surface self-cleaning, which helps to further increase the degradation rate by allowing rain and/or water to wash byproducts away. According to the Clean Air Act, each individual state is responsible for implementing prevention and regulatory programs to control air pollution. To operate an air quality program, states must adopt and/or develop a plan and obtain approval from the EPA. Federal approval provides a means for the EPA to maintain consistency among different state programs and ensures that they comply with the requirements of the Clean Air Act.

Ryan, robert E.↗
DOE OSTI2023

Extrusion parameter control optimization for DIW 3D printing using image analysis techniques

Material extrusion is a well-recognized facet of additive manufacturing that involves the fabrication of parts through the deposition of structural material from an extrusion head from a bulk supply. In the subdivision of Direct Ink Writing (DIW) additive manufacturing, challenges arise when the structural material is flowable, synchronous extrusion control and tool movement becomes critical for achieving high-quality parts with low defect populations. DIW techniques are most used in laboratory settings using expensive custom instruments and may require specialized 3D slicing software. Here, in this study, the fabrication of an inexpensive, consumer-friendly progressive cavity pump dispensing system is detailed, in which can create high-quality parts by executing G-code commands produced from a commercial slicing software. The precision and repeatability of the movement-synchronized material extrusion is demonstrated through a series of optimization schemes, entailing the alteration of various control parameters, which directly affect the extrusion properties demonstrated during a print. In situ diagnostics were implemented to evaluate the results of the established optimization experiment. Using a machine vision technique, images of the optimization prints are processed. Following this, a supervised machine learning model was trained to autonomously judge whether or not the extrusion parameters produced a passing or failing result. The machine learning scheme serves as a preliminary benchmark for future layer-by-layer evaluation of more complex DIW parts. The construction of the printer and development of in situ characterization capabilities demonstrates the ability for this printer to create high-fidelity DIW parts for a fraction of the price of other systems.

42 ENGINEERING↗
NASA NTRS1991

Space transportation main engine reliability and safety

Viewgraphs are used to illustrate the reliability engineering and aerospace safety of the Space Transportation Main Engine (STME). A technology developed is called Total Quality Management (TQM). The goal is to develop a robust design. Reducing process variability produces a product with improved reliability and safety. Some engine system design characteristics are identified which improves reliability.

Monk, Jan C.↗
NASA NTRS2021

Florida Transportation and Infrastructure: Monitoring Water Quality Along Southern Florida Seaports to Assess Impact on Coral Reef Tracts from Harbor Deepening Projects

The U.S. Army Corps of Engineers (USACE) and National Oceanic and Atmospheric Administration (NOAA) National Marine Fisheries Service (NMFS) will be supervising a harbor deepening project in Port Everglades, Florida. The project raises concerns about potential impacts on the nearby Florida reef tract through increased turbidity and sediment from the dredging. To better understand these potential impacts, the NASA DEVELOP team created an interactive Google Earth Engine tool to help establish a historical baseline of water quality parameters and assist monitoring these parameters more frequently than traditional sampling. This Seaport & Harbor Area Resource Quality (SHARQ) tool incorporates remotely sensed data from Sentinel-2 Multispectral Instrument, Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper+, Landsat 8 Operational Land Imager, and Aqua Moderate Resolution Imaging Spectroradiometer. It allows users to view true color images and calculate water quality parameters, like turbidity and chlorophyll-a, for any given study area and time period from 1984 onward. The SHARQ tool also generate time series charts, allowing users to interpret changes in water quality over a given time range. The accuracy of the remotely sensed water quality parameter algorithms was determined using in situ data to calculate percent difference and root mean square error values (RMSE), which ranged from 0.32 to 0.58 error between sites. Using the SHARQ tool’s time series analysis feature, a baseline average turbidity metric of ~6.8 FNUs provides a historical baseline average for turbidity between September 2000 and 2020 and can assist in future decision-making for determining thresholds for turbidity.

Benjamin Rocha↗
NASA NTRS2009

Evaluating the Environmental Performance of the U.S. Next Generation Air Transportation System

The environmental impacts of several possible U.S. Next Generation Air Transportation scenarios have been quantitatively evaluated for noise, air-quality, fuel-efficiency, and CO2 impacts. Three principal findings have emerged. (1) 2025 traffic levels about 30% higher than 2006 are obtained by increasing traffic according to FAA projections while also limiting traffic at each airport using reasonable ratios of demand to capacity. NextGen operational capabilities alone enable attainment of an additional 10-15% more flights beyond that 2025 baseline level with negligible additional noise, air-quality, and fuel-efficiency impacts. (2) The addition of advanced engine and airframe technologies provides substantial additional reductions in noise and air-quality impacts, and further improves fuel efficiency. 2025 environmental goals based on projected system-wide improvement rates of about 1% per year for noise and fuel-efficiency (an air-quality goal is not yet formulated) are achieved using this new vehicle technology. (3) Overall air-transport "product", as measured by total flown distance or total payload distance, increases by about 50% relative to 2006, but total fuel consumption and CO2 production increase by only about 40% using NextGen operational capabilities. With the addition of advanced engine/airframe technologies, the increase in total fuel consumption and CO2 production can be reduced to about 30%.

Environmental impact↗
NASA NTRS2012

Physics of Acoustic Radiation from Jet Engine Inlets

Numerical simulations of acoustic radiation from a jet engine inlet are performed using advanced computational aeroacoustics (CAA) algorithms and high-quality numerical boundary treatments. As a model of modern commercial jet engine inlets, the inlet geometry of the NASA Source Diagnostic Test (SDT) is used. Fan noise consists of tones and broadband sound. This investigation considers the radiation of tones associated with upstream propagating duct modes. The primary objective is to identify the dominant physical processes that determine the directivity of the radiated sound. Two such processes have been identified. They are acoustic diffraction and refraction. Diffraction is the natural tendency for an acoustic wave to follow a curved solid surface as it propagates. Refraction is the turning of the direction of propagation of sound waves by mean flow gradients. Parametric studies on the changes in the directivity of radiated sound due to variations in forward flight Mach number and duct mode frequency, azimuthal mode number, and radial mode number are carried out. It is found there is a significant difference in directivity for the radiation of the same duct mode from an engine inlet when operating in static condition and in forward flight. It will be shown that the large change in directivity is the result of the combined effects of diffraction and refraction.

Tam, Christopher K. W.↗
DOE OSTI2025

In situ probing of structure and deagglomeration of SnO 2 colloids via small-angle X-ray scattering

Transforming dry nanopowders into stable colloidal dispersions remains challenging due to the cohesive forces between the nanoparticles (NPs) which promote agglomeration. Effective dispersion and deagglomeration of these agglomerates is a critical process in the formulation and preparation of nanoparticle-based functional materials via the colloidal route. Understanding the deagglomeration dynamics provides information to improve microstructural quality in various applications by enabling engineering of agglomerate size, structure and morphology. However, the deagglomeration process dynamics with respect to the evolution of the fractal agglomerate structures, particularly for very small NPs, is still poorly understood and requires further investigation. This study employs in situ small-angle X-ray scattering (SAXS) to investigate the sonication-induced deagglomeration of SnO 2 NPs. Electrostatically stabilized SnO 2 colloids with varying primary particle size (6–21 nm) are investigated in a specifically designed in situ cell using synchrotron-based SAXS to study the influence of sonication time and intensity on the nanoscaled agglomerates. Complete structural analysis via SAXS reveals a direct correlation between changes in agglomerate size and structure, size-dependent deagglomeration behavior and a dependence on the overall energy introduced during sonication into the dispersion regardless of the actual power as well as ultrasonic process parameters in case of SnO 2 NPs. The results suggest that control of the dispersion process during ultrasonic deagglomeration results in tailoring agglomerates with respect to size and structure.

Deagglomeration↗
DOE OSTI2022

The field of human building interaction for convergent research and innovation for intelligent built environments

Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

42 ENGINEERING↗
DOE OSTI2021

CONVERSION OF COAL WASTES AND MUNICIPAL SOLIDS MIXTURES BY PYROLYSIS TORREFACTION AND ENTRAINED FLOW GASIFICATION

Hundreds of millions of tons of underutilized high-ash, low-energy-density bituminous and anthracite waste coal can be found in the U.S. These stockpiles contribute to water pollution from leaching that is harmful to waterways downstream of the piles. Conventional circulating fluidized combustion power plants use this coal to generate energy but emit toxic trace metals into groundwater (e.g., lead, mercury, arsenic). In addition, refuse-derived fuel (RDF) including municipal solid waste (MSW), biomass, waste plastics, and industrial waste are an enormous and untapped resource that is currently filling landfills or incinerated for low-quality energy with poor emissions. Mainstream Engineering has developed a combined pyrolysis torrefaction entrained flow gasification (PT-EFG) process that utilizes combined waste coal and RDF to create an alternative energy source, increasing energy independence and security and reducing the environmental burden from coal mines and processing facilities. Mainstream’s self-sustaining PT process converts RDF into feedstocks that can be pulverized and handled like conventional coal, enabling combined RDF-waste coal co-feeding into an EFG. The EFG operates at temperatures high enough to slag the ash completely, destroying any residual polycyclic aromatic hydrocarbons (PAHs), converting ash into nonporous vitrified slag or non-leachable ash, and generating high-hydrogen syngas for power generation or liquid fuels. During Phase II, Mainstream successfully demonstrated PT of biomass and MSW into a coal-like feedstock, which was co-gasified with waste coal in an EFG in a combined PT EFG process

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Translator Plan: A Coordinated Vision for Fiscal Years 2023-2025

Translators serve a unique role in the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility, offering scientific input through various leadership and service roles. The Translators direct the creation of value-added products (VAPs) and analysis tools that make ARM measurements more accessible to the scientific community. Translators also serve as liaisons between users and the ARM infrastructure, collecting information about priorities and communicating ARM data and services. A key group focus is supporting the DOE Atmospheric System Research (ASR) program scientists and ASR’s efforts towards a process-level understanding of cloud-aerosol interactions, and in reducing uncertainty in global climate model projections. The ARM Translator Group (Table 1) consists of the five Translators, a representative of software development, and one from the Data Quality Office (DQO). Additionally, the ARM Engineering and Process Manager participates in this group and provides input and direction from ARM and its programmatic priorities.

54 ENVIRONMENTAL SCIENCES↗