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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 37 records · Page 2

How different power plant types contribute to electric grid reliability, resilience, and vulnerability: a comparative analytical framework

Abstract This work explores the dependability tradeoffs provided by the most common types of central power plants in the United States. Historically, the electricity sector has lacked consensus on how reliability , resilience , and vulnerability differ and how those metrics change depending on the power plant fleet composition. We propose distinct definitions for these metrics and an analytical framework to evaluate power plant fleet dependability. Using data analysis and literature review, we identify fifteen dependability attributes across which we rank eleven power plant types relative to natural gas combined-cycle (NGCC) plants. We use NGCC as the benchmark because it is common to many locations and is of relatively recent vintage. The framework shows that each power plant type has unique dependability benefits and drawbacks. We provide examples of how researchers may use the framework to evaluate grid dependability qualitatively under different scenarios. We find that assuming all attributes that contribute to grid dependability are equally important and additive, electric grid dependability is best supported when power plant fleets include a mixture of power generation technologies. Then, we discuss scenario characteristics that could alter the prioritization and relationships of attributes. We also find that if current capacity installation trends continue to favor low- and zero-carbon power plants, US power grids may benefit from increased resilience and reduced vulnerability at the cost of decreased reliability. We conclude by recommending methods for adapting the framework and quantifying relationships between attributes in individual scenarios.

Ramirez-Meyers, K. (ORCID:0000000291216952)↗

Techno-Economic Evaluation of Electrified Vehicle Options in Drayage Fleets

The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities. Parameter ranges reflect widely cited projections and observed drayage-duty-cycle variability. A surrogate-modeling method approximates economic performance across thousands of battery cost–electricity price combinations, enabling high-resolution identification of conditions that achieve TCO parity, acceptable PB thresholds, and target IRR levels. Additionally, the study estimates the evolving share of the fleet that can feasibly electrify over time under multiple economic metrics. This integrated framework offers a novel, data-driven approach to inform risk-aware decision-making for fleet electrification and supports investment planning under evolving cost and operational conditions.

Sun, Ruixiao [ORNL] (ORCID:0000000341768676)↗

MARAD maritime experiments using the NASA ATS-6 satellite

The objectives of the MARAD maritime experiments (conducted in the L-band fan beam mode) using the ATS-6 satellite are detailed. They include the following: (1) to evaluate the economic benefits of fleet operators through the use of satellite communications, (2) to evaluate performance criteria for shipboard terminal equipment needed to establish various grades of fleet operations services using commercial satellite systems, (3) to determine the effects of signal propagation, ship radio frequency noise, and ship antenna pointing on the maritime communications and navigation channel, and (4) to evaluate various modems for the transmission and reception of voice, data and position location signals via satellite systems.

Brandel, D. L.↗

Eaton High Impact Project (Cooperative Research and Development Final Report)

The NREL team will partner with Eaton to evaluate control strategies for managing Electric bus (E-bus) fleets for grid services. NREL will develop a replicable platform for evaluating EV fleet management and quantifying benefits from grid services. The proposal will cover the technology areas of solar, energy storage, mobility, and buildings.

33 ADVANCED PROPULSION SYSTEMS↗

Continued Evaluation of Gear Condition Indicator Performance on Rotorcraft Fleet

This paper details analyses of condition indicator performance for the helicopter nose gearbox within the U.S. Army's Condition-Based Maintenance Program. Ten nose gearbox data sets underwent two specific analyses. A mean condition indicator level analysis was performed where condition indicator performance was based on a 'batting average' measured before and after part replacement. Two specific condition indicators, Diagnostic Algorithm 1 and Sideband Index, were found to perform well for the data sets studied. A condition indicator versus gear wear analysis was also performed, where gear wear photographs and descriptions from Army tear-down analyses were categorized based on ANSI/AGMA 1010-E95 standards. Seven nose gearbox data sets were analyzed and correlated with condition indicators Diagnostic Algorithm 1 and Sideband Index. Both were found to be most responsive to gear wear cases of micropitting and spalling. Input pinion nose gear box condition indicators were found to be more responsive to part replacement during overhaul than their corresponding output gear nose gear box condition indicators.

Delgado, Irebert R.↗

EVs@Scale Next-Gen Profiles - Fleet Utilization 2023

As U.S. fleet operators begin transitioning to electric vehicles (EVs), critical questions arise regarding how to manage this shift without disrupting fleet operations or placing undue stress on the electric grid. A major challenge for fleets is maintaining effective operational schedules while accommodating charging requirements, particularly with high-power charging (HPC) infrastructure, which presents grid stability concerns for utilities. Proposed solutions such as charging substations, megawatt charging systems (MCS), and smart charge management systems (SCMS) offer potential pathways forward, but their effectiveness depends on alignment with real-world fleet behavior and operational constraints. This report investigates the charging and utilization behavior of EV and EVSE fleets actively employing HPC technologies by conducting detailed case study analyses based on telematics data. A suite of predefined metrics—covering charging, routing, and other operational behaviors—is developed to evaluate the impact of fleet activities on grid infrastructure and identify opportunities for optimization. Results highlight variations in charging behavior across fleets, such as weekday versus weekend usage, diurnal charging trends, and the role of operational predictability in enabling SCMS effectiveness. While SCMS can help lower costs and improve energy efficiency for fleets with stable schedules, they may be insufficient for fleets with highly variable or long-haul operations, which may require more robust solutions like MCS. Visualization of aggregated hourly energy metrics reveals that while fleet behaviors are diverse, there are common temporal patterns that could inform infrastructure planning and energy management. These insights emphasize the need for fleet-specific charging strategies that minimize grid impact while supporting reliable fleet operations. Additionally, the report underscores the broader economic stakes of electrification, particularly in high-value markets such as freight, where misaligned transitions could stall EV adoption. By examining current EV and EVSE fleet deployments using predetermined standardized metrics, this study offers a foundation for developing technologies and operational frameworks that support scalable, grid-compatible electrification across a variety of fleet types while establishing a baseline understanding of operational behaviors. In doing so, we aim to ensure that future charging solutions reflect actual fleet needs and grid constraints—an essential step toward maintaining operational continuity and achieving a successful transition to electric fleet operations.

Charging↗

Fleet retrofit report

Flight tests are evaluated of an avionics system which aids the pilot in making two-segment approaches for noise abatement. The implications are discussed of equipping United's fleet of Boeing 727-200 aircraft with two-segment avionics for use down to Category 2 weather operating minima. The experience is reported of incorporating two-segment approach avionics systems on two different aircraft. The cost of installing dual two-segment approach systems is estimated to be $37,015 per aircraft, including parts, labor, and spares. This is based on the assumption that incremental out-of-service and training costs could be minimized by incorporating the system at airframe overhaul cycle and including training in regular recurrent training. Accelerating the modification schedule could add up to 50 percent to the modification costs. Recurring costs of maintenance of the installation are estimated to be of about the same magnitude as the potential recurrent financial benefits due to fuel savings.

Source record↗

Development of a Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) Tool

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles. The main challenge is that light-duty tools do not estimate real-time vehicle mass, a factor that has a disproportionate impact on the energy consumption of large commercial vehicles. The Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) tool advances the state of the art in evaluating electrification potential and infrastructure requirements for fleets of commercial vehicles. In this work, the HEVII tool is demonstrated with non-uniformly sampled telematics data from an existing fleet to assess the suitability for electrification of each individual vehicle, determine optimal locations for charging infrastructure to support a fleet of EVs and analyze associated costs. Payload mass is predicted using sparse ground-truth data for all input drive cycles and an initial data analysis is conducted to assess the characteristics driving behaviors and energy consumption of the fleet using an adaptable vehicle model. Battery size requirements are determined by applying a novel charger placement algorithm to maximize routes that are viable for EVs and balance time delays with infrastructure development costs. This work details and demonstrates the different aspects of the HEVII tool, presenting preliminary results from an example use case.

ADVANCED PROPULSION SYSTEMS↗

PV Fleet Performance Data Initiative Program and Methodology

The US Department of Energy’s PV Fleet Performance Data Initiative has been launched in order to collect and evaluate production data across multiple PV fleet partners. Performance statistics are anonymized, aggregated and shared to represent a snapshot of the US commercial and utility-scale fleet. Production data have been collected from over 1500 systems representing more than 1.3 GWdc capacity. Preliminary analysis indicates median performance loss rates are in line with previous publications of system degradation, on the order of –0.6%/yr to –0.9%/yr (preliminary numbers subject to change). These values are higher than module-only degradation rates which are often used in pro-forma estimates of project performance and economics, potentially exposing owner/operators to increased risk if systems under-perform over time.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Medium- and Heavy-Duty Truck Duty Cycles

This dataset provides second-by-second duty cycle data for Class 6 and Class 8 diesel trucks in Texas, including key vehicle metrics, engine-related data, and GPS data (excluding GPS latitude and longitude to ensure confidentiality). The data were collected via tablets installed on the trucks and organized into daily datasets, each associated with a unique vehicle ID and date. There are 12 daily datasets for Class 6 diesel trucks (three unique vehicle IDs) and 43 daily datasets for Class 8 diesel trucks (six unique vehicle IDs). The units associated with each column are included in the name. The engine performance data include columns such as engine speed, engine percent torque, and engine fuel rate. Road grade (%/100) was estimated using the GPS altitude and wheel-based vehicle speed, which is used as an input for FASTSim. Cumulative distance was also calculated using the wheel-based vehicle speed. Additional columns include: - Engine Speed (RPM): Removed inaccurate readings and used to calculate angular velocity (radians/second). - Torque (N·m): Calculated using engine percent torque, nominal friction percent torque, and engine reference torque values (those columns were removed from dataset), then normalized to express as torque (%). - Flywheel Power (%): Calculated using the angular velocity and torque (in kW), then normalized as a percentage of the maximum value. - Engine Fuel Rate (%) and Torque (%): Both metrics were normalized by dividing by their respective maximum values within each dataset to express them as percentages. The datasets were analyzed to assess the energy impact of various driving behaviors, simulate energy efficiency, and recommend optimal routes for diesel trucks using NLR’s tool called RouteE. For driver coaching, factors like speed and acceleration limits were considered, and idle periods were reduced (assuming the engine was off during idling) to adjust each drive cycle. These adjusted drive cycles were then simulated in FASTSim to evaluate their effect on fleet energy consumption and estimate potential energy savings. The original cycles are available for download on this page ![image](CoVaR_Image_for_Data_Page_Kenworth_Truck.jpg)

1Hz↗

Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Missing Photovoltaic Data Imputation

The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and long-term reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis heavily depends on the quality of PV timeseries data. This paper proposes a novel Spatio-Temporal Denoising Graph Autoencoder (STD-GAE) framework to impute missing PV Power Data. STDGAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. It is empowered by two modules. (1) To cope with sparse yet various scenarios of missing data, STD-GAE incorporates a domain-knowledge aware data augmentation module that creates plausible variations of missing data patterns. This generalizes STD-GAE to robust imputation over different seasons and environment. (2) STD-GAE nontrivially integrates spatiotemporal graph convolution layers (to recover local missing data by observed “neighboring” PV plants) and denoising autoencoder (to recover corrupted data from augmented counterpart) to improve the accuracy of imputation accuracy at PV fleet level. We have evaluated our proposed model on two realworld PV datasets. Experimental results show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods such as MIDA and LRTC-TNN.

Fan, Yangxin↗

Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles

Due to the high upfront cost of electric vehicles, many public transit agencies can afford only mixed fleets of internal combustion and electric vehicles. Optimizing the operation of such mixed fleets is challenging because it requires accurate trip-level predictions of electricity and fuel use as well as efficient algorithms for assigning vehicles to transit routes. We present a novel framework for the data-driven prediction of trip-level energy use for mixed-vehicle transit fleets and for the optimization of vehicle assignments, which we evaluate using data collected from the bus fleet of CARTA, the public transit agency of Chattanooga, TN. We first introduce a data collection, storage, and processing framework for system-level and high-frequency vehicle-level transit data, including domain-specific data cleansing methods. We train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy. Based on these predictions, we formulate the problem of minimizing energy use through assigning vehicles to fixed-route transit trips. We propose an optimal integer program as well as efficient heuristic and meta-heuristic algorithms, demonstrating the scalability and performance of these algorithms numerically using the transit network of CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Smart Charging of Fleet and Personal Electric Vehicles through Joint Vehicle-to-Grid Optimization

As electric vehicle (EV) adoption accelerates, vehicle-to-grid (V2G) strategies offer advantages over unmanaged charging (V0G) by enhancing grid stability, reducing fleet operation costs, and supporting integration of variable generation resources. This research develops a day-ahead optimization framework linked with agent-based simulations to evaluate coordinated V2G participation by fleet and personal EVs under 5 energy-pricing settings in Austin, Texas. Three scenarios (V0G, fleet-only V2G, and joint-V2G) are examined, considering real-time price and grid profiles, health-damage costs, and operational constraints for both fleet and personal EVs. Results show how V2G scenarios shift fleet EV charging to mid-day while enabling strategic battery-discharge during evening peaks, mitigating grid stress and lowering EV energy costs. V2G delivers close to 80% energy-cost savings for a 2000-EV fleet in Austin on grid-stressed days, with 55% lower charging pollutant outputs. Joint-V2G amplifies system-level benefits by complementing fleet discharge, but smart-charging equipment costs can offset those benefits.

Electric vehicle↗

An Evaluation of The Dynamic Physical Security Risk Assessment Methodology for Fleet-Wide Applications

The requirements for U.S. nuclear power plants to maintain a large onsite physical security force contribute to their high operational costs. The cost of maintaining the current physical security posture is approximately 10% of the overall operation and maintenance budget for commercial nuclear power plants. The goal of the Light Water Reactor Sustainability (LWRS) program’s physical security pathway is to develop tools, methods, and technologies and provide the technical basis for an optimized physical security posture. The conservatisms built into current security postures may be analyzed and minimized to reduce security costs while still ensuring adequate security and operational safety. The research performed at Idaho National Laboratory within LWRS program’s physical security pathway has successfully developed a dynamic force-on-force modeling framework using various computer simulation tools and integrating them with the dynamic assessment Event Modeling Risk Assessment using Linked Diagrams (EMRALD) tool. This integrated process for physical security analysis is named Modeling and Analysis for Safety Security using Dynamic EMRALD Framework (MASS-DEF). This document provides an update on the progress in applying the MASS-DEF process to an operating commercial nuclear power plant as well as additional industry feedback regarding use of the tool for other physical security risk-informed topics. This report is only a summary of the progress and does not contain specific modeling results as those contain sensitive security information. Previous reports described how a user could integrate their plant-specific force-on-force models with the dynamic simulation tool EMRALD, model operator actions, and integrate with probabilistic risk assessment tools, such as CAFTA (Computer Aided Fault Tree Analysis System) or SAPHIRE (Systems Analysis Programs for Hands-on Integrated Reliability Evaluations), and with thermal-hydraulic tools, such as RELAP-5 or MAAP. Previous reports applied various combinations of available simulations codes with EMRALD using generic plant models to demonstrate how to perform the analysis. This report is an update the progress of applying the dynamic computational framework to an actual nuclear facility using their security scenarios and timelines. This report also provides an update to the procedural guidance for the MASS-DEF process and an overview of the generic models available for use by utilities. This report does not contain any plant’s sensitive information and/or safeguards information. This study’s purpose was to verify that the results achieved using generic models are similar to actual plant results and refine our guidance on the use of the framework. This assessment enables further analysis, such as what-if scenarios and staff-reduction evaluation, thereby optimizing physical security at plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Resume of the status and benefits of the two-segment approach and its applicability to the jet transport fleet

NASA, in cooperation with the FAA, is evaluating the two-segment approach as a routine procedure for reducing aircraft noise. The program calls for separate flight evaluations using a 727 and a DC-8, and an extrapolation of these results to determine the adaptability of the technique to the rest of the fleet. The total program is reviewed. The profile and procedures developed and the noise reduction achievable are presented. The vortex characteristics behind an aircraft on a two-segment path is covered. Finally, cost estimates for retrofitting aircraft with two-segment avionics are presented.

Denery, D. G.↗

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗

Effects of engine emissions from high-speed civil transport aircraft: A two-dimensional modeling study, part 1

The AER two-dimensional chemistry-transport model is used to study the effect on stratospheric ozone (O3) from operations of supersonic and subsonic aircraft. The study is based on six emission scenarios provided to AER. The study showed that: (1) the O3 response is dominated by the portion of the emitted nitrogen compounds that is entrained in the stratosphere; (2) the entrainment is a sensitive function of the altitude at which the material is injected; (3) the O3 removal efficiency of the emitted material depends on the concentrations of trace gases in the background atmosphere; and (4) evaluation of the impact of fleet operations in the future atmosphere must take into account the expected changes in trace gas concentrations from other activities. Areas for model improvements in future studies are also discussed.

Ko, Malcolm K. W.↗

Cargo Logistics Airlift Systems Study (CLASS). Volume 4: Future requirements of dedicated freighter aircraft to year 2008

The 1978 fleet operations are extended to the year 1992, thus providing an evaluation of current aircraft types in meeting the ensuing increased market demand. Possible changes in the fleet mix and the resulting economic situation are defined in terms of the number of units of each type aircraft and the resulting growth in operational frequency. Among the economic parameters considered are the associated investment required by the airline, the return on investment to the airline, and the accompanying levels of cash flow and operating income. Against this background the potential for a derivative aircraft to enter fleet operations in 1985 is defined as a function of payload size and as affected by 1980 technology. In a similar manner, the size and potential for a new dedicated 1990 technology, freighter aircraft to become operational in 1995 is established. The resulting aircraft and fleet operational and economic characteristics are evaluated over the period 1994 to 2008. The impacts of restricted growth in operational frequency, reduced market demand, variations in aircraft configurations, and military participation, are assessed.

Burby, R. J.↗