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NASA NTRS1976

A spiral guidance approach concept for all-weather VTOL operations

A description is presented of the results of an investigation of the guidance and navigation requirements for VTOL spiral descents in the presence of winds. A spiral approach for all-weather operations for VTOL aircraft is discussed along with aspects of spiral approach guidance. Attention is given to the equations of motion, nominal spiral specification, the wind estimator, and the feedback guidance law. The performance of the spiral guidance algorithms is evaluated with the aid of a realistic example which was selected for the Kennedy International Airport in New York City.

Hoffman, W. C.↗

NASA NTRS1985

An Initial Analysis of LANDSAT-4 Thematic Mapper Data for the Discrimination of Agricultural, Forested Wetlands, and Urban Land Cover

The capabilities of TM data for discriminating land covers within three particular cultural and ecological realms was assessed. The agricultural investigation in Poinsett County, Arkansas illustrates that TM data can successfully be used to discriminate a variety of crop cover types within the study area. The single-date TM classification produced results that were significantly better than those developed from multitemporal MSS data. For the Reelfoot Lake area of Tennessee TM data, processed using unsupervised signature development techniques, produced a detailed classification of forested wetlands with excellent accuracy. Even in a small city of approximately 15,000 people (Union City, Tennessee). TM data can successfully be used to spectrally distinguish specific urban classes. Furthermore, the principal components analysis evaluation of the data shows that through photointerpretation, it is possible to distinguish individual buildings and roof responses with the TM.

Quattrochi, D. A.↗

NASA NTRS1993

Space Station redesign option A: Modular buildup concept

In early 1993, President Clinton mandated that NASA look at lower cost alternatives to Space Station Freedom. He also established an independent advisory committee - the Blue Ribbon Panel - to review the redesign work and evaluate alternatives. Daniel Goldin, NASA Administrator, established a Station Redesign Team that began operating in late March from Crystal City, Virginia. NASA intercenter teams - one each at Marshall Space Flight Center, Johnson Space Center, and Langley Research Center provided engineering and other support. The results of the Option A study done at Marshall Space Flight Center are summarized. Two configurations (A-1 and A-2) are covered. Additional data is provided in the briefing package MSFC SRT-001, Final System Review to SRT-002, Space Station Option A Modular Buildup Concept, Volumes 1-5, Revision B, June 10, 1993. In June 1993, President Clinton decided to proceed with a modular concept consistent with Option A, and asked NASA to provide an Implementation Plan by September. All data from the Option A redesign activity was provided to NASA's Transition Team for use in developing the Implementation Plan.

Source record↗

DOE OSTI2025

Prioritizing urban heat adaptation infrastructure based on multiple outcomes: Comfort, health, and energy

Globally, cities face increasing extreme heat, impacting comfort, health, and energy consumption. Infrastructure-based heat adaptation strategies can improve these outcomes, but each strategy has a unique mix of benefits and drawbacks. Here, we apply an urbanized meteorological model (WRF) with the newly integrated multilayer BEP-Tree street tree model to dynamically downscale Earth System Model projections and a 3-D microclimate model (TUF-Pedestrian) to simulate the street-scale radiation environment impacting pedestrians. We evaluate the performance of five heat adaptation strategies (street trees, cool roofs, green roofs, rooftop photovoltaics (PV), and reflective pavements) during extreme heat events in three cities with contrasting background climates (Toronto, Phoenix, and Miami), under contemporary and end-of-century projected climates, based on three metrics: outdoor heat stress, air conditioning (AC) energy use, and ventilation of vehicular air pollution. No single adaptation strategy improves all three outcomes. While street trees inhibit ventilation, they reduce outdoor heat stress four times more effectively than the next best strategy via shade provision, fully offsetting heat stress increases under a high-emissions end-of-century climate scenario in all cities studied. Cool roofs and green roofs moderately reduce heat stress and energy use. Alternatively, rooftop PV with energy storage can generate sufficient power for space cooling but have marginal effects on heat stress. Reflective pavements are the least effective across metrics. Where the ventilation of street-level emissions is of less concern, our results clearly support the combination of street trees and rooftop PV as a highly complementary and effective means of adaptive mitigation across different climates and neighborhood densities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1974

Energy impact of short-haul STOL systems

An analysis has been made to evaluate the impact on fuel requirements of a segregated short-haul system. A comparison has been made between two alternate scenarios: one with STOL, and the other without STOL. For the New York to Washington, D.C. city pair the results show: (1) the modal efficiency approach, while indicating the with STOL scenario requires more fuel than the without STOL scenario, is of questionable validity because it does not account for various effects which exist in realistic scenarios; (2) evaluation of fuel requirements based on more detailed modeling indicates that while a STOL scenario requires more fuel than without STOL when an idealized CTOL system is postulated, the STOL scenario requires less fuel than without STOL when the CTOL system has even moderate delays. These results are due to a combination of effects: the closer and more convenient locations of STOL ports to the traveler, congestion at the CTOL airports, and the impact of the through passenger traffic. Sensitivity of results to STOL aircraft block fuel and to CTOL congestion delays are considered.

Stewart, E. C.↗

NASA NTRS2016

Using Landsat, MODIS, and a Biophysical Model to Evaluate LST in Urban Centers

In this paper, we assessed and compared land surface temperature (LST) in urban centers using data from Landsat, MODIS, and the Simple Biosphere model (SiB2). We also evaluated the sensitivity of the models LST to different land cover types, fractions (percentages), and emissivities compared to reference points derived from Landsat thermal data. This was demonstrated in three climatologically- and morphologically-different cities of Atlanta, GA, New York, NY, and Washington, DC. Our results showed that in these cities SiB2 was sensitive to both the emissivity and the land cover type and fraction, but much more sensitive to the latter. The practical implications of these results are rather significant since they imply that the SiB2 model can be used to run different scenarios for evaluating urban heat island (UHI) mitigation strategies. This study also showed that using detailed emissivities per land cover type and fractions from Landsat-derived data caused a convergence of the model results towards the Landsat-derived LST for most of the studied cases. This study also showed that SiB2 LSTs are closer in magnitude to Landsat-derived LSTs than MODIS-derived LSTs. It is important, however, to emphasize that both Landsat and MODIS LSTs are not direct observations and, as such, do not represent a ground truth. More studies will be needed to compare these results to in situ LST data and provide further validation.

Al-Hamdan, Mohammad Z.↗

DOE OSTI2023

Urban Energy and Climate: Prospects for a Sustainable Transition

With the continuous migration of people towards metropolitan areas in search of employment, the demands for core services and energy, coupled with an increasing awareness of the impact of climate change, have placed the management and planning of global urban energy under a lot of pressure. Trends toward urban energy service transformations that offer greater affordability, reliability, efficiency and adaptability provide hope for a global sustainable future. At the same time, there are also limits to these transitions, as well as risks involved. For example, on one end of the spectrum, our urban energy future includes land use sprawl, high fossil fuel use, pollution, and unhealthy urban conditions. On the other side of this transition spectrum is more energy choices, and healthier, more livable cities, along with less energy use and fewer greenhouse gas emissions. What the future might hold for transforming the world's cities depends upon an understanding of the risks of current trajectories and the opportunities for and limitations to developing sustainable urban energy systems. This edited volume brings together leading experts on the prospects and challenges of urban energy innovation and on related-economic, social and environmental sustainability transitions. The focus of the volume is on multidisciplinary reviews, research informing technologies and policies for sustainability, and analytical insights addressing rapid urbanization and changes across a diverse typology of global cities. The volume will include an overview of the current state of urban energy systems. It will also document and evaluate urban energy prospects for a sustainable, resilient future.

drivers of change↗

DOE OSTI2021

Integration of Open-Source URBANopt and Dragonfly Energy Modeling Capabilities into Practitioner Workflows for District-Scale Planning and Design

High-performance districts and communities offer opportunities for reducing energy use, emissions, and costs, and can be instrumental in helping cities achieve their climate goals. The design of such communities requires identification of opportunities early on and their re-evaluation throughout the planning process. There is a need for energy modeling tools that connect 3D Computer-Aided Design (CAD) platforms to simulation engines, enabling detailed energy analysis of districts within the workflows and tools used by practitioners. This paper introduces the Dragonfly and URBANoptTM combined toolset that supports the creation of urban models from a range of geometry formats typically used by designers and planners, and provides an integrated pathway to simulate district-scale energy systems. The toolset is piloted by a global architecture and master planning firm to evaluate several key urban-scale technical questions for the design of a district in Chicago. The findings indicate that, while energy savings can be achieved through traditional architectural studies and enhancements to individual building efficiency, the modeling toolset helps identify additional savings and insights that can be achieved when considering district-scale energy systems. Finally, this study demonstrates how the Dragonfly/URBANopt toolset can integrate with master planning workflows, thereby enabling an iterative performance-based design process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1973

En route position and time control of aircraft using Kalman filtering of radio aid data

Fixed-time-of-arrival (FTA) guidance and navigation is investigated as a possible technique capable of operation within much more stringent en route separation standards and offering significant advantages in safety, higher traffic densities, and improved scheduling reliability, both en route and in the terminal areas. This study investigated the application of FTA guidance previously used in spacecraft guidance. These FTA guidance techniques have been modified and are employed to compute the velocity corrections necessary to return an aircraft to a specified great-circle reference path in order to exercise en route time and position control throughout the entire flight. The necessary position and velocity estimates to accomplish this task are provided by Kalman filtering of data from Loran-C, VORTAC/TACAN, Doppler radar, radio or barometric altitude,and altitude rate. The guidance and navigation system was evaluated using a digital simulation of the cruise phase of supersonic and subsonic flights between San Francisco and New York City, and between New York City and London.

Mcgee, L. A.↗

NASA NTRS1981

Estimating reforestation by means of remote sensing

LANDSAT imagery at the scale of 1:250.000 and obtained from bands 5 and 7 as well as computer compatible tapes were used to evaluate the effectiveness of remotely sensed orbital data in inventorying forests in a 462,100 area of Brazil emcompassing the cities of Ribeirao, Altinopolis Cravinhos, Serra Azul, Luis Antonio, Sao Simao, Santa Rita do Passa Quatro, and Santa Rosa do Viterbo. Visual interpretation of LANDSAT imagery shows that 37,766 hectares (1977) and 38,003.75 hectares (1979) were reforested areas of pine and eucalyptus species. An increment of 237.5 hectares was found during this two-year time lapse.

Dejesusparada, N.↗

NASA NTRS2016

Maps Suggest Transport and Source Processes of PM2.5 at 1 km x 1 km for the Whole San Joaquin Valley, Winter 2011 (Generalizations from DISCOVER-AQ)

We present interpreted data analysis using MAIAC (Multiangle implementation of Atmospheric Correction) retrievals and appropriate RAPid Update Cycle (RAP) meteorology to map respirable aerosol (PM2.5) for the period January and February, 2011. The San Joaquin Valley is one of the unhealthiest regions in the USA for PM2.5 and related morbidity. The methodology evaluated can be used for the entire moderate-resolution imaging spectrometer (MODIS, VIIRS) data record. Other difficult areas of the West: Riverside, CA, Salt Lake City, UT, and Doa Ana County, NM share similar difficulties and solutions. The maps of boundary layer depth for 1116 hr local time from RAP allows us to interpret aerosol optical thickness as a concentration of particles in a nearly well-mixed box capped by clean air. That mixing is demonstrated by DISCOVER-AQ data and afternoon samples from the airborne measurements, P3B (on-board) and B200 (HSRL2 lidar). This data and the PM2.5 gathered at the deployment sites allowed us to estimate and then evaluate consistency and daily variation of the AOT to PM2.5 relationship. Mixed-effects modeling allowed a refinement of that relation from day to day; RAP mixed layers explained the success of previous mixed-effects modeling. Compositional, size-distribution, and MODIS angle-of-regard effects seem to describe the need for residual daily correction beyond ML depth.We report on an extension method to the entire San Joaquin Valley for all days with MODIS imagery using the permanent PM2.5 stations, evaluated for representativeness. Resulting map movies show distinct sources, particularly Interstate-5 (at approx. 1km x 1km resolution) and the broader Bakersfield area. Accompanying winds suggest transport effects and variable pathways of pollution cleanout. Such estimates should allow morbiditymortality studies. They should be also useful for actual model assimilations, where composition and sources are uncertain. We conclude with a description of new work to extend these insights to similar regions, e.g. interior valleys of California, the Po Valley, the Mediterranean litoral, and the Ganges Plain.This work show generalizable use of remote sensing, a major goal of DISCOVER-AQ, Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality.

Chatfield, R.↗

DOE OSTI2026

Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures.

IoT↗

DOE OSTI2021

The Impact of Wall Control Performance on Connected Lighting Systems

Wall controls form the only touchpoint for individual room users and that room’s lighting control system. As such, these interfaces strongly influence user understanding and utilization of the system, as well as overall acceptance. The Next Generation Lighting Systems (NGLS) program began an evaluation of the wall controls in 14 connected lighting systems installed in various rooms of the NGLS Living Lab at Parsons School of Design in New York City. These systems demonstrate the wide range of approaches to wall control setup and functionality taken by manufacturers. Characterizing the 14 controls across six attributes, only four, or 29%, were identical. Requirements for wall control design were not specified explicitly by NGLS. Rather, wall controls were required to provide system performance according to three criteria: vacancy control (manual on/auto off) of two zones; manual continuous dimming of the same two zones; and an AV presentation mode in one of the two zones. This report examines the classification of entries, the assessment method used to evaluate the systems from installation through day-to-day use, and the system performance observed to date for each.

42 ENGINEERING↗

DOE OSTI2022

Conversion of food waste to renewable energy: A techno-economic and environmental assessment

Increasing quantities of food waste have become a concern due to high disposal costs in landfills and high greenhouse gas emissions. With this increase in food waste generation, there is also an increasing demand for renewable natural gas to supplement traditional fossil fuel combustion and offset the impacts of climate change. Collecting food waste from landfills and turning it into renewable natural gas using anaerobic digestion could be a win-win option for both food waste disposal and renewable energy production. While some literature exists on the energy potential, economic feasibility, and environmental benefit of food waste disposal via anaerobic digestion, no existing study simultaneously evaluates the energy, economic and environmental effect of food waste to renewable energy via anaerobic digestion, especially on a plant and city scale. Further, this study is focused on the techno-economic and environmental assessment of food waste to energy via anaerobic digestion in order to fill this gap. Four anaerobic digestion pathways are considered in this study: flare, pipeline natural gas, combined heat and power, and combined cycle for efficient power generation. Using a city of 1M people the results show that renewable natural gas from food waste could supply the natural gas usage for 1.9% of residential use, 2.7% of commercial use, 1.1% of industrial use, 167.5% of the compressed natural gas vehicle fleet, 0.7% of electric power generation, or 2.5% of industrial high-temperature heating processes. All pathways except pipeline natural gas will have a positive net present value in the baseline scenario, and the pipeline natural gas pathway will become economically viable with a net present value of 31 USD/t of food waste with renewable energy credits. Lastly, all of the pathways achieve negative greenhouse gas emissions, which indicates that anaerobic digestion is a more environmentally friendly method for the handling of food waste than landfills.

03 NATURAL GAS↗

NASA NTRSDate not supplied

Building a Real-Time Flood Prediction Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the local government of Howard County, Maryland, to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a statistical model capable of hindcasting the two severe flash flood events that devastated Ellicott City and transitioned to a ‘Long Short-Term Memory’ based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using Nash-Sutcliffe Efficiency. The final product, the Sequentially Trained Real-time EstimAted Model (STREAM) predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

NASA DEVELOP↗

DOE OSTI2025

Effectiveness of nature-based solutions to reduce flooding in Quad Cities Metro Area (QCMA) using SWMM-HEC based flood model

Nature-based solutions (NbS) have gained significant attention as strategies for addressing urban environmental challenges, particularly since the establishment of the UN Sustainable Development Goals (SDGs) for 2030. However, the current research on NbS for urban flood management lacks comprehensive methodological approaches for identifying suitable areas and evaluating their effectiveness across different urban settings. Here, this study attempts to fill this gap by proposing a methodological framework integrating multi-criteria analysis with a SWMM-HEC-based hydrologic and hydraulic (HH) model to assess the suitability of NbS for the Quad Cities Metro Area (QCMA), consisting of Davenport, Bettendorf, Moline, and Rock Island. Eight NbS options-green roofs, rain gardens, infiltration trenches, permeable pavements, vegetative swales, dry detention basins, retention ponds, and rain barrels/cisterns - were considered based on volumetric efficiency and runoff reduction efficiency. The study reveals that implementing the proposed NbS could have substantially reduced flood depths in key historical flood events by 21% in 1993, 15% in 2008, 16% in 2011, 23% in 2014, 40% in 2019, and 10% in 2023. The findings highlight a critical trade-off between peak runoff and NbS implementation: while NbS effectively reduce flood impacts, they also enhance volumetric efficiency by approximately 43%. In high-density areas of the QCMA, flood depth reductions of around 20% suggest that NbS are a viable solution for dense urban environments with limited space. This shows the potential for integrating NbS into existing infrastructure, offering a promising approach for cities facing increasing flooding risks. The proposed methodology provides a practical framework for incorporating NbS into urban stormwater management, addressing gaps in optimizing NbS performance, and offering a pathway to scale their application in other urban areas with various environmental and social contexts.

CMIP6↗

NASA NTRSDate not supplied

A Simulation Architecture for Air Traffic Over Urban Environments Supporting Autonomy Research in Advanced Air Mobility

NASA is conducting investigations into Advanced Air Mobility (AAM) concepts, aircraft, and operations. One of the most challenging scenarios for AAM will be enabling safe routine access into densely populated urban centers. To address challenges in the urban environment, a moderately high-fidelity simulation capability is needed to investigate AAM flight operations over a regional area for the development and evaluation of autonomy technologies. This paper describes a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (10's-100's of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to accurately simulate all phases of flight. Glass cockpit displays have been developed for monitoring aircraft operation over a detailed simulated urban environment. Simulation models have been integrated to simulate air and ground-based sensors, such as radar and LIDAR. The commercial X-Plane software package is used as a rendering engine to mimic vision-based sensors (such as onboard and ground-based cameras) at various times of day and in various weather conditions over a relatively detailed graphical model of the city. The paper presents a detailed illustration of the simulation and software architecture used for traffic over this urban region. This system is enabling the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward aircraft flight test evaluation.

Keerthana Kannan↗

DOE OSTI2024

DICER: Data Intensive Computing Environment and Runtime for Evaluating Unprecedented Scale of Geospatial-Temporal Human Mobility Data

With the significant increase in sources and volume of human mobility data through commercial data vendors as well as microsimulation of cities, the scale of geospatial-temporal data to analyze and assess for mobility characterization has grown to the level of Big Data. There are mobility related commercial organizations deploying scalable computing, but often the system architecture, workflow, and intermediate processing components are not fully disclosed in relevant scope. Current research literature has a notable lack of studies demonstrating architectures and workflows for human mobility analytics that are implemented on a TeraByte scale of geospatial-temporal data. In this context, this paper presents a hyperscale-level system solution named DICER (Data Intensive Computing Environment and Runtime) for processing and analytics of geospatial-temporal data at big data scale. Although the cluster computing architecture of DICER with Apache Spark job running on Kubernetes cluster is not new, there are innovations in the workflow, hierarchical processing logic, and a wide range of intermediate preprocessing and mobility metrics calculation. We have performed case studies to validate the effectiveness of DICER system solution by performing detailed analytics and assessment of human mobility microsimulation output at three different scopes and scale, including a usecase with 16.97 TeraByte and 259.2 Billion rows of data. In addition, we have presented another case study of utilizing DICER to perform the same mobility processing and comparative analytics on large-scale commercially available geospatial-temporal data. All these case studies validate the efficiency and usefulness of DICER in computing population mobility characteristics from geospatial-temporal trajectory data at an unprecedented scale (not only just data volume, but also combination of: number of user entities, temporal frequency, spatial resolution, data duration).

De, Debraj↗