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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today, as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017 in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating the rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Son Do↗
NASA NTRS2023

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While the InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model’s accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017, in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Urban flooding↗
DOE OSTI2022

Characterizing the Solvation Characteristics of Deep Eutectic Solvents Composed of Active Pharmaceutical Ingredients as a Hydrogen Bond Donor and/or Acceptor

The formation of eutectic mixtures from active pharmaceutical ingredients (APIs) has emerged as an effective technique to develop liquid pharmaceutical formulations. API-based deep eutectic solvents (DESs) enhance the water solubility of APIs and avoid their recrystallization. The physico-chemical properties of API-DESs can be easily modulated by judicious selection of a hydrogen bond acceptor (HBA) or hydrogen bond donor (HBD) excipient that is mixed with the API. Solvation properties of API-DESs can help in understanding the role of ionic and non-ionic excipients towards the overall solvation interactions of these DESs. In this study, the solvation characteristics of three subclasses of API-DESs containing API-HBAs, API-HBDs, or API-HBAs and API-HBDs are investigated. Inverse gas chromatography is employed to study solute-solvent interactions of a variety of probe molecules for twenty different API-DESs. Among the API-DESs comprised of API-HBAs and carboxylic acid or diol-based HBDs, DESs containing diol-based HBDs exhibited stronger dipolar, hydrogen bond basicity, and hydrogen bond acidity interactions compared to carboxylic acid-based HBDs. No significant differences were observed in the solvation properties of DESs comprised of API HBAs and API HBDs. API-DESs comprised of the choline chloride HBA and API-HBDs exhibited stronger hydrogen bond acidity and dipolarity and weaker dispersive-type interactions among the DESs evaluated in this study. An in-depth evaluation of solvation properties for these DESs is important to completely exploit their potential in separation science as well as the fields of biomaterial science, pharmaceutical chemistry, and biotechnology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

An Application Programming Interface for Synthetic Snowflake Particle Structure and Scattering Data

The work by Kuo and colleagues on growing synthetic snowflakes and calculating their single-scattering properties has demonstrated great potential to improve the retrievals of snowfall. To grant colleagues flexible and targeted access to their large collection of sizes and shapes at fifteen (15) microwave frequencies, we have developed a web-based Application Programming Interface (API) integrated with NASA Goddard's Precipitation Processing System (PPS) Group. It is our hope that the API will enable convenient programmatic utilization of the database. To help users better understand the API's capabilities, we have developed an interactive web interface called the OpenSSP API Query Builder, which implements an intuitive system of mechanisms for selecting shapes, sizes, and frequencies to generate queries, with which the API can then extract and return data from the database. The Query Builder also allows for the specification of normalized particle size distributions by setting pertinent parameters, with which the API can also return mean geometric and scattering properties for each size bin. Additionally, the Query Builder interface enables downloading of raw scattering and particle structure data packages. This presentation will describe some of the challenges and successes associated with developing such an API. Examples of its usage will be shown both through downloading output and pulling it into a spreadsheet, as well as querying the API programmatically and working with the output in code.

reflectivity properties of snowflakes↗
NASA NTRS2014

Using Social Media and Mobile Devices to Discover and Share Disaster Data Products Derived From Satellites

Data products derived from Earth observing satellites are difficult to find and share without specialized software and often times a highly paid and specialized staff. For our research effort, we endeavored to prototype a distributed architecture that depends on a standardized communication protocol and applications program interface (API) that makes it easy for anyone to discover and access disaster related data. Providers can easily supply the public with their disaster related products by building an adapter for our API. Users can use the API to browse and find products that relate to the disaster at hand, without a centralized catalogue, for example floods, and then are able to share that data via social media. Furthermore, a longerterm goal for this architecture is to enable other users who see the shared disaster product to be able to generate the same product for other areas of interest via simple point and click actions on the API on their mobile device. Furthermore, the user will be able to edit the data with on the ground local observations and return the updated information to the original repository of this information if configured for this function. This architecture leverages SensorWeb functionality [1] presented at previous IGARSS conferences. The architecture is divided into two pieces, the frontend, which is the GeoSocial API, and the backend, which is a standardized disaster node that knows how to talk to other disaster nodes, and also can communicate with the GeoSocial API. The GeoSocial API, along with the disaster node basic functionality enables crowdsourcing and thus can leverage insitu observations by people external to a group to perform tasks such as improving water reference maps, which are maps of existing water before floods. This can lower the cost of generating precision water maps. Keywords-Data Discovery, Disaster Decision Support, Disaster Management, Interoperability, CEOS WGISS Disaster Architecture

0↗
NASA NTRSDate not supplied

DIP for Service Providers

The second DIP workshop’s topic is DIP for Service Providers. The workshop presentation covers topics such as DIP onboarding process, service authentication, catalog service capabilities, API requirements, NASA services and their access points, and data requirements for service providers. The Onboarding Process describes the required documentation that enables to collaborate with NASA as well as other industry partners through DIP. Also, step by step approach for the partner to take to register services or consme services through the DIP platform. The Catalog Service describes the different ways for the users to search for services that are of interest as well as registering services to the platform, so other users can search and discover. The API Requirements and Service Specifications describes the benefits of DIP's API management approach via API Gateway where services are managed centrally but ownership is decentralized and partners completely own the administration of their own services. NASA Services and Data Access Points topic describes the services, both data access service and machine learning prediction service, that NASA provides via the DIP platform in near time and post operations via REST API endpoints defined on the API Gateway. Also, NASA's approach for data storage process is explained. The Data Requirements for service providers describes the requirements for storing and exposing to support the performance of services that are provided by the service providers. Lastly, NASA's technology development plan with preliminary scheduler will be presented. There will be a Q&A session at the end of each segment of the presentation to solicit feedback from the participants.

ATM-X↗
DOE OSTI2022

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numeric Behaviors

Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores, users have to use either legacy wmma APIs or current mma APIs. Legacy wmma APIs are more easy-to-use but can only exploit limited features and power of Tensor Cores. Specifically, wmma APIs support fewer operand shapes and can not leverage the new sparse matrix multiplication feature of the newest Ampere Tensor Cores. However, the performance of current programming interface has not been well explored. Furthermore, the computation numeric behaviors of low-precision floating points (TF32, BF16, and FP16) supported by the newest Ampere Tensor Cores are also mysterious. In this paper, we explore the throughput and latency of current programming APIs. Further, we intuitively study the numeric behaviors of Tensor Cores MMA and profile the intermediate operations including multiplication, addition of inner product, and accumulation. All codes used in this work can be found in https://github.com/sunlex0717/DissectingTensorCores.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Nanoparticle-Surfactant Stabilized Strong Foam for Enhanced Oil Recovery in High-Salinity Fractured Carbonate Reservoirs

Summary Foam flooding can minimize bypassing in gasfloods in fractured reservoirs. Finding a foam formulation effective in high-salinity brine is challenging, especially with divalent cations, e.g., American Petroleum Institute (API) brine (8% NaCl with 2% CaCl2). When formulating with nanoparticles, the colloidal dispersion stability is difficult due to the dramatic reduction in zeta potential and the Debye length at high salinity. The aim of this work was to develop a strong foam in API brine at the ambient temperature, using a nonionic surfactant and ethyl cellulose nanoparticles (ECNP), for gasflooding in fractured carbonate reservoirs. ECNPs was synthesized and dispersed in API brine using a nonionic surfactant (also denoted as SF). SF and SF/ECNP foams were generated, and their stability was studied at atmospheric pressure and 950 psi. Foam mobility was measured in a sandpack at high pressure. Foam flood experiments were conducted in oil-saturated fractured carbonate cores. The nonionic surfactant proved to be a good dispersion agent for ECNP in API brine. The SF/ECNP mixture stabilized foam in API brine, even in the presence of oil. Injecting a partially miscible gas (below its minimum miscibility pressure) as an SF foam into a fractured core more than doubles the oil recovery over injection of the gas alone. The injection of the strong foam (SF/ECNP) further improves the oil recovery over that of the SF foam, indicating the synergy between ECNP and surfactant. ECNP accumulates in the foam lamella and induces larger pressure gradients in the fracture to divert more gas into the matrix for oil displacement.

Engineering↗