SEARCH · Engineering Papers

Results for “Vietnam”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRS2016

Spatial and Temporal Uncertainty of Crop Yield Aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Aggregation uncertainty↗

NASA NTRS2017

The South/Southeast Asia Research Initiative (SARI) Update and Meeting Objectives

Land Use/Cover Change (LU/CC) is one of the most important types of environmental change in South and Southeast Asian countries. Several studies suggest that LU/CC in these countries is in large part driven by population growth and economic development. In the region, changes that are most common include urban expansion, agricultural land loss, land abandonment, deforestation, logging, reforestation, etc. To address the research needs and priorities in the region, a regional initiative entitled South Southeast Asia Regional Initiative (SARI) has been developed involving US and regional scientists. The initiative is funded by NASA Land Cover, Land Use Change program. The goal of SARI is to integrate state-of-the-art remote sensing, natural sciences, engineering and social sciences to enrich LU/CC science in South Southeast Asian countries. In the presentation, LU/CC change research in SARI countries will be highlighted including the drivers of change. For example, in South Asia, forest cover has been increasing in countries like India, Nepal and Bhutan due to sustainable afforestation measures; whereas, large-scale deforestation in Southeast Asian countries is still continuing, due to oil palm plantation expansion driven by the international market demand in Malaysia and Indonesia. With respect to urbanization, South and Southeast Asian countries contain 23 megacities, each with more than 10 million people. Rapid urbanization is driving agricultural land loss and agricultural intensification has been increasing due to less availability of land for growing food crops such as in India, Vietnam, and Thailand. The drivers of LUCC vary widely in the region and include such factors as land tenure, local economic development, government policies, inappropriate land management, land speculation, improved road networks, etc. In addition, variability in the weather, climate, and socioeconomic factors also drive LU/CC resulting in disruptions of biogeochemical cycles, radiation and the surface energy balance of the atmosphere. The presentation will also highlight SARI collaborative activities with space agencies, universities and non-government organizations including data sharing mechanisms in the region.

Research↗

NASA NTRS2017

Flood Mapping in the Lower Mekong River Basin Using Daily MODIS Observations

In flat homogenous terrain such as in Cambodia and Vietnam, the monsoon season brings significant and consistent flooding between May and November. To monitor flooding in the Lower Mekong region, the near real-time NASA Flood Extent Product (NASA-FEP) was developed using seasonal normalized difference vegetation index (NDVI) differences from the 250 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) sensor compared to daily observations. The use of a percentage change interval classification relating to various stages of flooding reduces might be confusing to viewers or potential users, and therefore reducing the product usage. To increase the product usability through simplification, the classification intervals were compared with other commonly used change detection schemes to identify the change classification scheme that best delineates flooded areas. The percentage change method used in the NASA-FEP proved to be helpful in delineating flood boundaries compared to other change detection methods. The results of the accuracy assessments indicate that the −75% NDVI change interval can be reclassified to a descriptive 'flood' classification. A binary system was used to simplify the interpretation of the NASA-FEP by removing extraneous information from lower interval change classes.

Cambodia↗

NASA NTRS2018

Nguyen Xuan Vinh - A Life In Hypersonic Flight

Nguyễn Xuân Vinh (born January 1930 in Yên Bái, Vietnam) is a noted Vietnamese-American aerospace scientist and educator whose seminal work on the guidance, dynamics and optimal control of space vehicles and their interaction with the atmosphere has played a fundamental role in space exploration. Vinh is Professor Emeritus of Aerospace Engineering at the University of Michigan, where he taught for nearly thirty years. Among his many publications was "Hypersonic and Planetary Entry Flight Mechanics” (1980. Vinh, N. X.; Busemann, A.; Culp, R. D. University of Michigan Press) which contains equations for hypersonic flight that came to be known as “the Vinh equations”.

Lu, Ping↗

NASA NTRS2020

Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion

The increasing availability of very-high resolution (VHR; <2 m) imagery has the potential to enable agricultural monitoring at increased resolution and cadence, particularly when used in combination with widely available moderate-resolution imagery. However, scaling limitations exist at the regional level due to big data volumes and processing constraints. Here, we demonstrate the Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA), using a suite of open source software capable of efficiently characterizing time-series field-scale statistics across large geographical areas at VHR resolution. We provide distinct implementation examples in Vietnam and Senegal to demonstrate the approach using WorldView VHR optical, Sentinel-1 Synthetic Aperture Radar, and Sentinel-2 and Sentinel-3 optical imagery. This distributed software is open source and entirely scalable, enabling large area mapping even with modest computing power. FARMA provides the ability to extract and monitor sub-hectare fields with multisensor raster signals, which previously could only be achieved at scale with large computational resources. Implementing FARMA could enhance predictive yield models by delineating boundaries and tracking productivity of smallholder fields, enabling more precise food security observations in low and lower-middle income countries.

fusion↗

NASA NTRS2022

Diagnosing Challenges and Setting Priorities for Sustainable Water Resource Management Under Climate Change

Managing transboundary river basins requires balancing tradeoffs of sustainable water use and coping with climate uncertainty. We demonstrate an integrated approach to exploring these issues through the lens of a social-ecological system, combining remote and in-situ earth observations, hydrologic and climate models, and social surveys. Specifically, we examine how climate change and dam development could impact the Se Kong, Se San and Sre Pok rivers in the Mekong region. We find that climate change will lead to increased precipitation, necessitating a shift in dam operations, from maintaining low flows to reducing flood hazards. We also find that existing water governance systems in Laos, Vietnam, and Cambodia are ill-prepared to address the problem. We conclude that the solution space for addressing these complex issues will be highly constrained unless major deficiencies in transboundary water governance, strategic planning, financial capacity, information sharing, and law enforcement are remedied in the next decades.

Ibrahim Nourein Mohammed↗

NASA NTRS2022

Rainfall-induced Landslide Inventories for Lower Mekong Based on Planet Imagery and a Semi-Automatic Mapping Method

Fatal landslides occur every year during the rainy season (June–November) in the Lower Mekong Region (LMR). There is an urgent need to develop a landslide early warning system in the LMR. In collaboration with the Asian Disasters Preparedness Center and NASA’s SERVIR Programme, we are regionalizing the global Landslide Hazard Assessment System for Situational Awareness model for the LMR (LHASA-Mekong). A robust set of landslide inventories are needed to effectively train the machine learning-based LHASA-Mekong model. In this study, the Semi-Automatic Landslide Detection (SALaD) system was modified by incorporating a change detection module (SALaD-CD) to produce rainfall event-based landslide inventories using pre- and post-imagery from RapidEye and PlanetScope for various locations in the LMR that were identified based on media and government reports. These rainfall-induced landslides are published as initiation points for ease of use. In total, we created 22 inventories: 2 in Laos, 4 in Myanmar, 1 in Thailand and 15 in Vietnam. These inventories are being used to train the LHASA-Mekong model and quantify the effects of Land use/Land cover change on landslide susceptibility. These open data will be a valuable resource for advancing landslide studies in this region.

Pukar Amatya↗

NASA NTRS2022

Enhancement of a global lake and reservoir database to aid climate studies and resource monitoring utilizing satellite radar altimetry

Here, we describe the expansion and enhancement of a large (surface area ≥100 sq. km) lake and reservoir database (1.GREALD). These efforts have also resulted in the spin-off of two additional databases, one containing lakes and reservoirs (2.GREALD, area 10–99 sq. km), and the other containing ephemeral lakes (3.GELD, area ≥100 sq. km). These databases are unique sources for projects that utilize satellite radar altimeter data to monitor surface water levels. While 1.GREALD aims to be a complete catalog, 2.GREALD focuses on reservoirs in response to applied sciences programs that monitor water and energy resources. The creation of 3.GELD has climate change objectives as well as water resources and ecosystem conservation applications. The recording of information pertaining to the potential overpasses (waterbody crossings) of the current and archive satellite altimeters is a primary objective as is the need to highlight any form of controlled water level variation. The permanent water databases now contain 6282 entries, half experience some form of anthropogenic influence and ∼430 have been identified as potential climatically sensitive terminal lakes. The revised integral surface area distribution is a power law with exponent −1.016. Statistics reveal that with altimetric repeat visit times of 10-day to monthly, at least 80% of the permanent water bodies (≥10 sq. km) have been overflown at some period since the 1990s. Current information on water use and reservoir formation date show that the primary use of the reservoir class is hydroelectric power, and that China, Brazil, India, Turkey, and Vietnam dominate the dam building in recent decades.

Charon M. Birkett↗

NASA NTRS2021

Columnar Aerosol Types and Compositions Over Peninsular Southeast Asia Based on Long-Term AERONET Data

Aerosol chemical components such as black carbon (BC) and brown carbon (BrC) regulate aerosol optical properties, which in turn drive the atmospheric radiative forcing estimations due to aerosols. In this study, we used the long-term measurements from AERONET (Aerosol Robotic Network) to better understand the aerosol types and composition with respect to their seasonal and spatial variabilities in peninsular Southeast Asia (PSEA, here defined as Vietnam, Cambodia, Thailand, Laos, and Myanmar). Two methods (i.e., aerosol type cluster and aerosol component retrieval) were applied to determine the aerosol type and chemical composition during the biomass-burning (BB) season. AERONET sites in northern PSEA showed a higher AOD (aerosol optical depth) compared to that of southern PSEA. Differences in land use pattern, geographic location, and weather regime caused much of the aerosol variability over PSEA. Lower single-scattering albedo (SSA) and higher fine-mode fraction (FMF) values were observed in February and March, suggesting the predominance of BB type aerosols with finer and stronger absorbing particles during the dry season. However, we also found that the peak BB month (i.e., March) in northern PSEA may not coincide with the lowest SSA once dust particles have mixed with the other aerosols. Furthermore, we investigated two severe BB events in March of 2014 and 2015, revealing a significant BrC fraction during BB event days. On high AOD days, although the BC fraction was high, the BrC fraction remained low due to lack of aerosol aging. This study highlights the dominance of carbonaceous aerosols in the PSEA atmosphere during the BB season, while also revealing that transported dust particles and BrC aerosol aging may introduce uncertainties into the aerosol radiative forcing calculation.

Sheng-Hsiang Wang↗

NASA NTRS2022

Cause of a Lower-Tropospheric High-Ozone Layer in Spring Over Hanoi

The ozonesonde observations in Hanoi, Vietnam, over fourteen years since 2004 have confirmed the enhancement in lower tropospheric ozone concentration at about 3 km altitude in the spring season. We investigated the evolution of the ozone enhancement from analysis of meteorological data, backward trajectories, and model sensitivity experiments. In spring, air masses over Hanoi exhibit strong height dependence. At 3km, the high-ozone air masses originate from the land area to the west of Hanoi, while low-ozone air masses below about 1.5 km are from the oceanic area to the east. Above 4 km, the air masses are mostly traced back to the farther west area. The chemical transport model simulations revealed that precursor emissions from biomass burning in the inland Indochina Peninsula have the largest contribution to the lower tropospheric ozone enhancement, which is transported upward and eastward and overhangs the clean air intrusion from the ocean to the east of Hanoi. At this height level, the polluted air has the horizontal extent of about 20 degrees in longitude and latitude. The polluted air observed in Hanoi is transported further east and widely spread over the northern Pacific Ocean.

tropospheric ozone↗

NASA NTRS2022

Cause of a Lower-Tropospheric High-Ozone Layer in Spring Over Hanoi

Hanoi↗

NASA NTRS2024

Evaluation of Machine Learning and Deep Learning Algorithms for Fire Prediction in Southeast Asia

Vegetation fires are prevalent in South/Southeast Asian countries, making fire prediction crucial due to their potential environmental, economic, and social impacts. Accurate predictions of fires facilitate timely interventions, helping to mitigate uncontrolled fires that can lead to biodiversity loss and air quality issues. In this study, we utilize VIIRS satellite-derived fire data alongside six machine learning and deep learning models—Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and ConvLSTM—to determine the most effective fire prediction model, using Root Mean Square Error (RMSE) as the metric. Our results indicate that the CNN model is the most reliable in regions with spatial dependencies, such as Brunei, Indonesia, Malaysia, the Philippines, Timor-Leste, and Thailand. Conversely, the ConvLSTM model excels in countries with complex spatiotemporal dynamics like Laos, Myanmar, and Vietnam. The CNN-LSTM hybrid model also performed well in Cambodia, suggesting a need for a balanced approach in areas requiring both spatial and temporal feature extraction. Furthermore, simpler models like Persistence and MLP showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating models before implementing any decision support systems (DSS) in fire management. By tailoring models to specific regional fire data, we can enhance prediction accuracy and responsiveness, ultimately improving fire risk management in Southeast Asia and beyond.

Deep learning↗

DOE OSTI2023

Grid Interconnection and Renewables Deployment Related Air Quality and Human Health Benefits in Southeast Asia

The Association of Southeast Asian Nations (ASEAN) has increasingly focused on multilateral electricity trade, improved grid resiliency and modernization. These opportunities in the power sector have been studied through the ASEAN Interconnection Masterplan Studies, the most recent of which is the ASEAN Interconnection Masterplan Study III (AIMS III). AIMS III assessed four different scenarios of power system in the ASEAN countries that include a base scenario and three additional scenarios (called optimum RE, ASEAN RE, and High RE) considering different levels of deployment of renewable energy (RE) and cross-border generation and trade of electricity for three years (2025, 2030, 2040). In this work, we quantify the potential air quality and public health co-benefits of AIMS III scenarios. For a rapidly expanding, energy hungry region like SE Asia generation is power generation is expected to increase significantly. The AIMS III scenarios' capacity expansion modeling suggest that: 1) Generation nearly doubles from 2025 to 2040 in all four AIMS III scenarios; 2) Most of the increased generation is met by coal in all four AIMS III scenarios; 3) While renewables generation increases in all four AIMS III scenarios, the fraction of total generation (its share) generally decreases because of the much greater increase in non-renewable sources, mostly coal. Only in the High RE Target scenario do renewables represent a higher share in 2040 than in 2025, though even here it is at the expense of natural gas rather than coal; 4) As a result, emissions of gaseous and aerosol pollutants increase significantly in all scenarios. Because emissions increase in 2040 compared to 2025 for all four AIMS III scenarios, so do PM2.5 concentrations. Even for the High RE Target scenario (the scenario with highest share of renewable energy), compared to the Base scenario in 2025, in 2040 PM2.5 concentrations are higher. Compared to the Base scenario in the same years, the Optimum RE and ASEAN RE Target scenarios do not differ very much from the Base in terms of PM2.5 concentration. The High RE Target scenario, on the other hand, yields noticeably lower PM2.5 concentration. For example, in 2040 the population-weighted decrease in annual average PM2.5 concentration in the High RE Target scenario relative to the Base scenario is 0.5 ug m-3. Compared to the Base scenario in 2040, each of the alternative AIMS III scenarios is estimated to result in net reductions in power-sector air quality-related excess mortality in the ASEAN region. Yet there are a few countries for which the Optimum RE and ASEAN RE Target result in increases in PM2.5-related excess mortality (Thailand and Vietnam for the ASEAN RE Target scenario and Thailand for the Optimum RE scenario). However, for the High RE Target scenario, all countries benefit and find reductions in excess mortality resulting from the power sector scenarios modeled in AIMS III with regionwide mortality decreasing by 16,000 compared to the Base scenario in 2040. In summary, our analysis finds that changing power generation emissions is a crucial lever for improving public health in ASEAN member countries and provides a pathway for policymakers to make decision backed by a realistic power sector expansion and air quality analyses.

air quality↗

DOE OSTI2025

HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network

Machine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data. However, the use of ML requires sufficient programming knowledge due to a lack of a graphical user interface (GUI). In this study, we introduced a GUI package, named HydroEcoLSTM, with the long short-term memory network (LSTM) as the core model, that allows non-ML experts to utilize their domain knowledge to construct complex ML models. We demonstrated the functionalities of HydroEcoLSTM with two practical examples, including (1) predictions of streamflow in both gauged and ungauged catchments and (2) predictions of multiple outputs (i.e., streamflow and isotope transport from two catchments). The simulation results obtained in both case experiments are satisfactory. In the first example, the average Nash–Sutcliffe Efficiency (NSE) for streamflow simulation during the testing period is 0.79 while the application of the trained model in two assumed ungauged catchments also achieves the average NSE of 0.68. In the second example, the average NSE for streamflow and instream isotope simulation during the testing period is 0.71. Ultimately, applications of HydroEcoLSTM with real-world examples demonstrate its potential use for practical applications and research without requiring extensive coding skills.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Short‐Term Hourly Weather Forecasting Using PredRNN With Image Preprocessing

Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a short-term hourly weather forecast framework with a wavelet transform function for data preprocessing and a spatiotemporal DL model, PredRNN, for predicting five surface atmospheric variables, including wind speed and direction, mean sea level pressure (MSLP), temperature, and precipitation. The framework demonstrated promising results. It produces global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of 1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature. Although our model does not surpass state-of-the-art AI weather forecast models across all metrics, it outperforms these models in precipitation forecasting and wind prediction at short lead times and achieves comparable accuracy for MSLP. Its native hourly forecasting capability, together with training on widely accessible GPU hardware, contributes meaningfully to the advancement of accessible DL weather forecasting methods. Our work highlights the importance of integrating temporal components and data transformation techniques to improve the predictability and accuracy of weather forecasts.

Tran, Hoang [Pacific Northwest National Laboratory↗

DOE OSTI2025

Impact of Newly Measured 𝛽-Delayed Neutron Emitters around 78 Ni on Light Element Nucleosynthesis in the Neutrino Wind Following a Neutron Star Merger

Neutron emission probabilities and half-lives of 37 𝛽-delayed neutron emitters from 75 Ni to 92 Br were measured at the RIKEN Nishina Center in Japan, including 11 one-neutron and 13 two-neutron emission probabilities and six half-lives for the first time that supersede theoretical estimates. These nuclei lie in the path of the weak 𝑟 process occurring in neutrino-driven winds from the accretion disk formed after the merger of two neutron stars synthesizing elements in the 𝐴∼80 abundance peak. The presence of such elements dominates the accompanying kilonova emission over the first few days and have been identified in the AT2017gfo event, associated to the gravitational wave detection GW170817. Abundance calculations based on over 17,000 simulated trajectories describing the evolution of matter properties in the merger outflows show that the new data lead to an increase of 50%–70% in the abundance of Y, Zr, Nb, and Mo. This enhancement is large compared to the scatter of relative abundances observed in old very metal poor stars and thus is significant in the comparison with other possible astrophysical processes contributing to the light-element production. These results underline the importance of including experimental decay data for very neutron-rich 𝛽 -delayed neutron emitters into 𝑟 -process models.

59 ≤ A ≤ 89↗

DOE OSTI2024

Continuous Flow Photoelectrochemical Reactor with Gas Permeable Photocathode: Enhanced Photocurrent and Partial Current Density for CO 2 Reduction

Photoelectrochemical (PEC) CO 2 reduction using a photocathode is an attractive method for making valuable chemical products due to its simplicity and lower overpotential requirements. However, previous PEC processes have often been diffusion-limited leading to low production rates of the CO 2 reduction reaction, due to inefficient gas diffusion through the liquid electrolyte to the catalyst surface, particularly at high current densities. In this study, a gas-permeable photocathode in a continuous flow PEC reactor is incorporated, which facilitates the direct supply of CO 2 gas to the photocathode-electrolyte interface, unlike dark reaction-based flow reactors. This concept is demonstrated using Ag-TiO 2 on carbon paper, illuminated through a quartz window and flowing liquid electrolyte. CO 2 supply is managed via pressure and flow control on the non-illuminated side of the carbon paper. The photocurrent density is significantly influenced by the flow rates and pressure of CO 2 gas, and the electrolyte flow rates. Compared to the traditional H-cell, the continuous PEC flow reactor achieves ≈10-fold increase in CO faradaic efficiency, 30-fold increase in production rate and 16-fold increase in stability without catalyst modifications. This work provides essential insights into the design and application of continuous gas-liquid flow PEC reactor systems, highlighting their potential for other PEC reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Near-Real-Time Statistical Analysis and Visualization of Streamflow from a Deep-Learning Rainfall-Runoff Model

Near-real-time (NRT) streamflow data are critical importance for timely water resources management. Here, we developed an open-source tool, FlowStats, for NRT streamflow analysis and visualization in Germany, based on NRT meteorological data from the German Weather Service and simulated streamflow from a long short-term memory neural network (LSTM). The LSTM model achieved very good overall performance, median NSE of 0.80 for the test period across 1,479 catchments. FlowStats provides options for deriving various streamflow statistics, from normal and abnormal streamflow detection to drought and flood analyses. An example analysis from FlowStats revealed widespread below-normal to extreme low-flow conditions across Germany from March to May 2025, which weakened from June to September 2025. Drought analysis for September 2025 highlighted severe to extreme drought conditions in northwestern Germany, while flood classifications indicated that high-flow events occurred in southwestern Germany. FlowStats can be used for various hydrological assessments to support water resources management.

Hydrological modeling↗